Ben-Gurion University of the Negev Jacob Blaustein Institutes for Desert Research Albert Katz International School for Desert Studies
Watershed modeling as an assessment of pollutant transport from point and non-point sources to Wadi Alexander Thesis submitted in partial fulfillment of the requirements for the degree of "Master of Science" Ben Gurion University of the Negev, Jacob Blaustein Institute for Desert Research, Albert Katz International School for Desert Studies.
By: Mousa Diabat
2008
Ben-Gurion University of the Negev Jacob Blaustein Institutes for Desert Research Albert Katz International School for Desert Studies
Watershed modeling as an assessment of pollutant transport from point and non-point sources to Wadi Alexander Thesis submitted in partial fulfillment of the requirements for the degree of "Master of Science" Ben Gurion University of the Negev, Jacob Blaustein Institute for Desert Research, Albert Katz International School for Desert Studies. By: Mousa Diabat
Under the Supervision of: Prof. Eilon Adar1, prof. Alon Tal2, Dr. Lior Asaf3 1
The Zuckerberg Institute for Water Research, Ben Gurion University. The Department of Man in Dry Land, Ben Gurion University. 3 Arava Institute for Environmental Studies. 2
Author's Signature …………….…………………….. Date ……………. Approved by the Supervisor…………….…………… Date ……………. Approved by the Supervisor…………….…………… Date ……………. Approved by the Supervisor…………….…………… Date ……………. Approved by the Director of the School ……………. Date ……………..
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Acknowledgments I would like to emphasize the enormous role the Arava Institute for Environmental Studies (AIES) played in revealing new fields of study to my eyes and empowering me to act as member of great team of professional researchers. Thanks and appreciation to Prof. Alon Tal and Mr. David Lehrer for establishing AIES. It has been an honor to work with you all. Great thanks to Dr. Lior Asaf for his guidance, supervision, and field and technical assistance. I would like to express my gratitude to Prof. Eilon Adar, who encouraged and inspired me throughout the research in addition to his professional supervision. Also, I would like to thank, Dr. Ludmilla Katz, Natalia Bondarenko, and Arik Kaplan at the Zuckerberg Institute for Water Research for opening their doors for me to enroll amongst unique specialists in water research and lab training for the samples analysis. Particular gratitude goes to my team members Hila Ackerman and Adam Abramson for the help in field and sample collection during rain-storms and in the middle of the summer heat. Also, I thank our Palestinian co-researchers Dr. Nader Al-Khateeb and Alice Nassar from the Water and Environment Organization (WEDO), and Muath Abu Saada and Amjad Assi from the House of Water and Environment (HWE) for their part in the sample collection and modeling assistance. Thank to the only one of her kind, my wife Jehan, for encouraging, supporting and suffering my muddy clothes and my absence during dangerous weather. Finally, I would like to thank Dr. Yaakov Garb, Dr. Clive Lipchin, Dr. Jeffery Albert, Dr. Daniel Orenstein, Sharon Benheim, and Mazen Zoabi; you have all been a source of encouragement, inspiration and support. This work was supported through a scholarship from the MERC transboundary stream restoration research initiative, which supplied the main financial support together with the USAID Middle East Regional Cooperation (MERC).
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Table of contents Chapter 1 Introduction ...................................................................................................... 10 1.1 Preface-The influence of land use on water balance and water quality............ 10 1.2 The Alexander Watershed- location and environmental characteristics........... 11 1.2.1 Location .................................................................................................... 11 1.2.2 Climate...................................................................................................... 14 1.2.3 Geology..................................................................................................... 16 1.2.4 Soils........................................................................................................... 17 1.2.5 Hydrology ................................................................................................. 18 1.2.6 Land use .................................................................................................... 19 1.2.7 Runoff and floods ..................................................................................... 21 1.3 Pollution sources in the Alexander watershed .................................................. 22 1.4 Selecting a hydrological modeling system ....................................................... 29 1.4.1 BASINS Software......................................................................................... 31 1.4.2 HSPF- Hydrological Simulation Program- FORTRAN ............................... 32 1.5 Defining the problem to be addressed in this research ..................................... 33 1.5.1 Current Environmental Conditions in the Alexander Watershed ............. 33 1.5.2 Future plans for developing and restoring the watershed ......................... 35 Chapter 2 Hypothesis and Research Objectives ............................................................... 36 2.1 Research Hypothesis......................................................................................... 36 2.2 Research Objectives.......................................................................................... 37 Chapter 3 Methods............................................................................................................ 38 3.1 Streamflow manual measurements ................................................................... 38 3.2 Precipitation data .............................................................................................. 41 3.3 Automatic stations ............................................................................................ 42 3.4 Water sampling and chemical analysis ............................................................. 45 3.5 Spatial database................................................................................................. 47 3.5.1 DEM- Digital Elevation Model .................................................................... 48 3.5.2 Land-use........................................................................................................ 49 3.5.3 Delineation of the watershed to sub-basins .................................................. 50 3.5.4 Soil ................................................................................................................ 58 Chapter 4 Findings and Results ........................................................................................ 60 4.1 Baseflow monitoring......................................................................................... 60 4.2 Storm 1- Dec, 24th-26th, 2005 ......................................................................... 66 4.2.1 Rainfall, runoff, and water samples .............................................................. 66 4.2.2 Nutrient loadings and concentrations............................................................ 70 4.2.3 HSPF simulation ........................................................................................... 76 4.3 Storm 2- Dec 26th-28th, 2006 .......................................................................... 80 4.3.1 Rainfall, runoff, and samples ........................................................................ 80 4.3.2 Nutrient concentrations and loadings............................................................ 83 4.3.3 HSPF simulation ........................................................................................... 88 4.4 Nutrient loadings and transport during storm events........................................ 90 4.4.1 Total nitrogen................................................................................................ 90 4.4.2 Total Phosphorous ........................................................................................ 95 4.5 Annual mass balance of water discharge and nutrient loadings ....................... 99 4.5.1 Land-use effect on water quality and spatial transport of nutrients............ 102
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4.5.2 Simulation results........................................................................................ 106 Chapter 5 Discussion and summary................................................................................ 109 Chapter 6 References ...................................................................................................... 114 Chapter 7 Appendix ........................................................................................................ 125
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List of figures Figure 1.1: Daily discharge at Elyashiv station 2004-2005 (extracted from raw data of the Israeli Hydrological Service, 2006) ............................................................................ 22 Figure 1.2: Earthen dams in the town of Kalanswa preventing the flow of Taybe sewage. ..................................................................................................................................... 25 Figure 1.3: Effluents pipe snapping from Nitsani Shalom industrial area........................ 26 Figure 3.1: Direct streamflow measurement in shallow stream segment of the channel, electromagnetic meter is shown on the right-hand side.............................................. 39 Figure 3.2: Stream’s cross-section diagram of sampling site at Wadi Zomar. ................. 39 Figure 3.3 Average daily rain at the Alexander watershed calculated from eight spatially distributed rain gauges. ............................................................................................... 42 Figure 3.4: Cross section at sampling station, used to measure water head and streamflow, by M. Abu-Saada. ................................................................................... 43 Figure 3.5: Sigma automatic sampler at one of the monitoring stations, inside a metal box............................................................................................................................... 43 Figure 3.6: Land-use partition in the Alexander watershed, based on JNF database 2005. ..................................................................................................................................... 49 Figure 3.7: Model setup and sub-basins linking as it is shown in HSPF screen. ............. 50 Figure 4.1: Discharge measurements along Alexander stream’s main route (from up stream to down)........................................................................................................... 63 Figure 4.2: Average daily precipitation in the Alexander watershed. .............................. 66 Figure 4.3: Hourly flow recorded at Elyashiv, R57, and Zomar stations, 24-26 Dec 2005. ..................................................................................................................................... 68 Figure 4.4: Dec 2005 flood event: Elyashiv, Zomar, and R57 stations' records; and samples' time sets (m3/sec)......................................................................................... 69 Figure 4.5: Hourly flow at Zomar station, total N, and total P, 24-26 Dec 2005. ............ 71 Figure 4.6: Hourly flow at R57 total N, and total P, 24-26 Dec 2005.............................. 72 Figure 4.7: Hourly flow at Elyashiv total N, and total P, 24-26 Dec 2005....................... 73 Figure 4.8: concentration changes of total N during storm Dec 2005. ............................. 73 Figure 4.9: Concentration changes of total P during storm Dec 2005.............................. 75 Figure 4.10: Simulated discharge of each sub-basin's contribution to the total flow balance in the watershed, flood event Dec 2005......................................................... 78 Figure 4.11: Measured and simulated water discharge at R57 (A), Zomar (B), and Elyashiv (C) stations during the course of flood event Dec 2005. ............................. 79 Figure 4.12: Recorded hydrographs during storm Dec 2006............................................ 80 Figure 4.13: Dec 2006 flood event: Elyashiv, Zomar, and R57 stations' records; and samples' time sets........................................................................................................ 82 Figure 4.14: Hourly flow, total N, and total P during storm of Dec 2006 at Zomar station. ..................................................................................................................................... 83 Figure 4.15: Hourly flow, total N, and total P during storm of Dec 2006 at R57 station. 84 Figure 4.16: Hourly flow, total N, and total P during storm of Dec 2006 at Elyashiv station.......................................................................................................................... 85 Figure 4.17: Concentration of total N during the flood of Dec 26th-28th 2006............... 86 Figure 4.18: Change of total P concentrations during the flood of Dec 26th-28th 2006.. 87 Figure 4.19: Simulated discharge distribution of the flood event Dec 2006. ................... 90 Figure 4.20: Total nitrogen fluxes during flood event Dec 2005. .................................... 91
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Figure 4.3: Total nitrogen fluxes during flood event Dec 2006 ....................................... 92 Figure 4.22: total P fluxes during Dec 2005 flood event.................................................. 96 Figure 4.23: total P fluxes during Dec 2006 flood event................................................. 97 Figure 4.24: discharge of baseflow and flood at Elyashiv station. ................................... 99 Figure 4.25: Fluxes of total nitrogen in baseflow and flood........................................... 100 Figure 4.26: Fluxes of total phosphorous in baseflow and flood.................................... 100 Figure 4.27: Measured annual discharged flow, and calculated TN & TP loads ........... 103 Figure 4.28: Linear correlation between agricultural area and annual TN and TP loads. ................................................................................................................................... 105 Figure 4.29: Linear correlation between field crops and orchards’ area, and Dec 2005 event’s TN and TP loads........................................................................................... 105 Figure 4.30: Simulated annual discharge distribution in the Alexander watershed. ...... 107 Figure 7.1: Stabilized stream cross-section located at Deir Sharaf. ............................... 125 Figure 7.2: Sigma 900 Max Portable Sampler. A. Sampler inside the station’s box. B. Data logger. C. 24-bottle carousel. ........................................................................... 125 Figure 7.3: monthly runoff discharge. ............................................................................ 128 Figure 7.4: Monthly TN loads. ....................................................................................... 129 Figure 7.5: monthly TP loads.......................................................................................... 129 Figure 7.6: annual runoff discharge, TN, and TP. .......................................................... 130
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List of tables Table 1.1: the past five year's annual rainfall data (Ma'abarot records, 2006) ................. 15 Table 1.2: Land-use distribution in the Alexander watershed by major sub-basins......... 21 Table 3.1: Chemical analysis applied to water samples in lab ......................................... 47 Table 3.2: Sub-basins names and ID Numbers divided according to major tributaries. .. 52 Table 3.3: Areas of land-use types within Nablus City sub watershed ............................ 52 Table 3.4: Areas of land-use types within Wadi Deir-Sharaf sub-watershed................... 53 Table 3.5: Areas of land-use types within Anabta and Tul-Karem sub-basin .................. 53 Table 3.6: Areas of land-use types within Nablus TP sub-basin ...................................... 54 Table 3.7: Areas of land-use types within Upper-Alexander sub-basin ........................... 54 Table 3.8: Areas of land-use types within Amatz sub-basin ............................................ 55 Table 3.9: Areas of land-use types within Al Teen Upper sub-basin ............................... 55 Table 3.10: Areas of land-use types within Al-Teen middle sub-basin............................ 56 Table 3.11: Areas of land-use types within Al-Teen Lower sub-basin ............................ 56 Table 3.12: Areas of land-use types within Alexander-middle sub-basin ........................ 57 Table 3.13: Summary of the total land-use data of the Alexander modeled sub-basins... 57 Table 3.14: Sub groups of soil type, areas, and ratio within the modeled watershed, extracted from Israel Mapping Center, 2003. ............................................................. 59 Table 4.1: Range and average of Baseflow concentrations (mg\L) in the Alexander watershed. (2005-2006). ............................................................................................. 61 Table 4.2: Average daily baseflow discharge at the automatic stations (May 2005 – Apr 2006). .......................................................................................................................... 62 Table 4.3: Concentrations at different sampling sites along the Alexander main routes (June 11, 2006)............................................................................................................ 64 Table 4.4: Discharge rates, nutrients’ concentrations, and daily fluxes in the Alexander watershed (June 11th 2006). ....................................................................................... 65 Table 4.5: Average monthly precipitation and events percentages in the Alexander watershed (2005-2006). .............................................................................................. 67 Table 4.6: Calculated precipitation in the Alexander watershed per sub-basin................ 67 Table 4.7: Comparison between sub-basins contribution of volume discharges of Dec 2006 monthly and the rain event................................................................................. 68 Table 4.8: Dec 2005 flood event duration at all three stations and number of samples at each station.................................................................................................................. 69 Table 4.9: Concentration summary during the event........................................................ 75 Table 4.10: Measured and simulated discharge during Dec 2005 and storm 24-26 Dec 2005............................................................................................................................. 76 Table 4.11: Summary of simulation flow discharge results form sub-basins in the Alexander watershed for Dec 2005............................................................................. 77 Table 4.12: Comparison between sub-basin’s contribution of volume discharges of Dec 2006 monthly and rain event....................................................................................... 81 Table 4.13: Samples summary during Dec 2005 and storm Dec 2006............................. 82 Table 4.14: Concentrations summary during the flood event of Dec 26th-28th 2006. .... 87 Table 4.15: Summary and comparison between measured and simulated discharge. ...... 88 Table 4.16: Simulated discharge as it contributed from sub-basins in the watershed. ..... 89 Table 4.17: total flow volume in Dec 2005 and flood event 24-26 Dec 2005.................. 92 Table 4.18: total N loads in Dec 2005 and flood event 24-26 Dec 2005.......................... 93
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Table 4.19: total flow volume in Dec 2006 and storm event Dec 2006 ........................... 93 Table 4.20: total N loads in Dec 2006 and storm event Dec 2006. .................................. 93 Table 4.21: Land-use area and areas and percentages/ ..................................................... 94 Table 4.22: Average daily baseflow fluxes of nutrients (excluding flood input). ............ 95 Table 4.23: total P loads in Dec 2005 and storm event Dec 2005. ................................... 97 Table 4.24: total P loads in Dec 2006 and storm event Dec 2006. ................................... 98 Table 4.25: summary of flow volume, and total N and total P loads during Dec 2005 storm. .......................................................................................................................... 98 Table 4.26: summary of flow volume, and total N and total P loads during Dec 2006 storm. .......................................................................................................................... 98 Table 4.27: Measured discharged flow and calculated loads of TN, and TP for the year 2005-2006. ................................................................................................................ 101 Table 4.28: percentages of flood and baseflow contribution to the annual discharge and pollution fluxes. ........................................................................................................ 103 Table 4.29: comparison between Land-use area and annual pollution loads in the Zomar and, Upper Alexander and the total Alexander watershed (May 2005- April 2006).104 Table 4.30: simulated flow volumes and percentages of main sub-basins, Dec 2005.... 106 Table 4.31: simulated flow volumes and percentages of main sub-basins, Dec 2006.... 107 Table 7.1: Automatic sampling campaigns during seasons 2005-2006.......................... 126 Table 7.2: summary of major flood events and analysis of water chemistry. ................ 127 Table 7.3: major flood events' percentages of monthly loads......................................... 128 Table 7.4: summary of monthly data at Zomar sation. ................................................... 130 Table 7.5: summary of monthly data at R57 station....................................................... 131 Table 7.6: summary of monthly data at Elyashiv station................................................ 131 Table 7.7: Proposed maximum levels in effluents reuse for discharge to rivers-Inbar standard (Government of Israel, 2005) ..................................................................... 132
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List of maps Map 1.1: The Alexander (Zomar) watershed shared between Israel and the West Bank. 12 Map 1.2: Major streams (wadis) and cities in the Alexander watershed. ......................... 13 Map 1.3: Average rain distribution in Israel and the Palestinian West Bank (generated from raw data of the Israeli Hydrologic Service). ...................................................... 14 Map 1.4: Grid distribution of rainfall 2004-2005 (generated from raw data records by the clouds radar of Bet Dagan, delivered by the Israeli Soil Erosion Authority). ............ 15 Map 1.5: Geological map generated using data from the Jewish National Fund (JNF)... 16 Map 1.6: Soil type distribution (generated using data from the JNF) .............................. 18 Map 1.7: Major land-use types in the Alexander watershed area..................................... 20 Map 1.8: Land-use in the Alexander watershed. .............................................................. 21 Map 1.9: Pollution sources on the Israeli side of the Alexander watershed. .................... 28 Map 3.1: Spatial location of manual and automatic sampling in the Alexander watershed. ........................................................................................................................................... 40 Map 3.2: Locations of the rain gauges and metrological station, by Eng. M. Abu Saada.41 Map 3.3: Automatic sampling sites in the watershed. ...................................................... 46 Map 3.4: The watershed’s elevations (DEM). .................................................................. 48 Map 3.5: Land-use map of monitored parts of the Alexander watershed......................... 49 Map 3.6: Delineation of the watershed. ............................................................................ 51 Map 3.7: Soil-type distribution in the Alexander watershed, based on JNF database...... 58 Map 7.1:The Alexander watershed lays on both sides of the Green Line. ..................... 133 Map 7.2: Elevations of the Alexander watershed ........................................................... 133 Map 7.3: location of installed stations. ........................................................................... 134 Map 7.4: Land use map of Nablus City sub-watershed .................................................. 134 Map 7.5: Land-use map of Wadi Deir Sharaf sub-watershed......................................... 135 Map 7.6: Land-use map of Anabta-Tulkarem sub-watershed ........................................ 135 Map 7.7: Land-use map of Nablus TP sub-watershed.................................................... 135 Map 7.8: Land use map of Upper-Alexander sub-watershed ......................................... 136 Map 7.9: Land use map of Amatz sub-watershed........................................................... 136 Map 7.10:Land use map of At Teen Upper sub-watershed ............................................ 136 Map 7.11: Land use map of At Teen Middle sub-watershed.......................................... 137 Map 7.12: Land use map of At Teen Lower sub-watershed........................................... 137 Map 7.13: Land use map of Alexander Middle sub-watershed ...................................... 137 Map 7.14: Water reservoirs and sewage treatment plants in the Alexander watershed. 138
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Chapter 1 Introduction 1.1 Preface-The influence of land use on water balance and water quality. Watershed scale studies of surface water quality have been conducted for some time, but considerably less research has considered the correlation between land use distribution and the quality of surface watercourse ((Perry and Vanderklein, 1996). As water drains over the land’s surface, various contaminants and residues are washed off into nearby surface water bodies, reaching streams and lakes. Different Land-Use/Land Cover (LULC) patterns vary in their effect on water quality (Goulding et al., 1996). Agricultural fields have been found to be major sources of non-point source (NPS) pollutants affecting surface and ground water quality. (Puckett, 1995, Behera and Panda, 2006). In addition, urban and built-up areas are a leading cause of overwhelming loads of NPS pollutants generated during storm events which result in the contamination of surface water (Basnyat et al., 2000). As a result of sundry agricultural practices, runoff and leaching from fields contribute pollutants such as fertilizers, pesticides, salts, sediments, and other chemicals to surface water bodies, as well as groundwater (Zalidis et al., 2002; Thorburn et al., 2003). Moreover, enrichment with nutrient and sediment loads is a particularly salient feature of agricultural activities (Tong and Wenli, 2002). The greatest contributions of nitrogen, phosphorus, and fecal Coliform can be traced to agricultural lands as a result of using fertilizers and pesticides (Fisher et al., 2000) On average, growing urban areas combined with increasing impervious surfaces (due to buildings, road networks, and house roofs) generate critical changes to the aquatic ecosystems’ balance (Miltner et al., 2004; Carter and Jackson, 2006). Sub consequently, runoff rates of storm sewer networks are directly affected by these impervious surfaces, leading to discharges to nearby watercourses (such as stream routes and seashore). Stormwater runoff has its own specific hydro-chemical characteristics (Gordon et al., 2004). At the same time, high runoff rates and elevated water volumes increase stream bed and bank erosion (Wolman, 1954), speeding the transport of pollutants along streams (Olguin et al., 2001), while increasing the probability of flooding (Niezgoda and Johnson, 10
2005). Furthermore, urban lands that transform runoff in hydrological systems have been shown to alter flood patterns and change flowing water quality, as well as the quality of the receiving water bodies (Tong, 1990; Wu and Haith, 1993; Changnon and Demissie, 1996; Mander et al., 1998). Accordingly, the quantity and quality (chemical, biological and physical) of runoff water reaching streambeds and land-use/land-cover can affect ground water. Moreover, water quality and quantity have a strong relationship with landuse and land cover (Gburek and Folmer, 1999). Notwithstanding, studies on land-use’s impact on water quality and quantity, the essential relationship of land-use, runoff quantity to water quality along different spatial and temporal scales are not yet elucidated (Tong and Chen, 2002). Identifying and quantifying pollution sources within the watershed are a key component to improving restoration efforts of degraded watersheds. One method for quantifying the relationship involves dividing the watershed into smaller basic units or sub-basins. This method aims to identify critical units, from which irregular amounts of pollution reach the stream route (Mostaghimi et al., 1997). At the same time, assessment of pollution reduction effectiveness in best management practices (BMP) is possible at the specific sub-basin level (Sharpley et al., 1994; Rejesus and Hornbaker, 1999).
1.2 The Alexander Watershed- location and environmental characteristics 1.2.1 Location The Alexander watershed is one of twelve coastal watersheds draining to the Mediterranean Sea and located in Israel’s central coastal plane.
The watershed is
classified as a transboundary basin as its flow originates in the Palestinian West Bank and continues into Israel. The Alexander watershed covers an area of 556 km2 and crosses the Green Line, leaving about 55% of its area in the West Bank. Precipitation drains into five major tributaries (see fig. 1.1):
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Map 1.1: The Alexander (Zomar) watershed shared between Israel and the West Bank.
12
•
Wadi Zomar / Shchem/ Nablus- flows from the city Nablus in the Samarian Mountains in the east towards the west and joins the Alexander after crossing the Green Line;
•
Wadi Al-Teen / Te’enim- drains the south-east part of the watershed towards the west, also joining the Alexander after crossing the Green Line and its upstream stream bed is known as Wadi Zomar;
•
Wadi Hawattat / Alexander- drains the central-south part of the watershed and flows towards the Mediterranean Sea, towards the west;
•
Wadi Amatz- drains a northern section of the watershed and joins the Alexander at the north-west part; and
•
Avihayel Channel- flows from the southwest towards the north to meet the Alexander before it drains to the sea.
Map 1.2: Major streams (wadis) and cities in the Alexander watershed.
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1.2.2 Climate The climate of the Alexander watershed is influenced by its proximity to the Mediterranean Sea and by its altitude. Precipitation reflects a Mediterranean rainfall regime, with dry summers and wet winters (Goldreich, 2003). Mean annual rainfall ranges between 530 mm to 650 mm, at the costal side of the watershed in the west and in the Judean Mountains in the east, respectively (the Israeli Meteorological Service, map 1.3 and map 1.4). Table 1.1 shows the average precipitation of the past five years recorded at the Ma’abarot Meteorological station, which is located in the lower part of the watershed (the western part).
Map 1.3: Average rain distribution in Israel and the Palestinian West Bank (generated from raw data of the Israeli Hydrologic Service).
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Table 1.1: the past five year's annual rainfall data (Ma'abarot records, 2006)
Year 2001
Annual rainfall (mm) 536
2002
663
2003
670
2004
497
2005
553
2006
536
Map 1.4: Grid distribution of rainfall 2004-2005 (generated from raw data records by the clouds radar of Bet Dagan, delivered by the Israeli Soil Erosion Authority).
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1.2.3 Geology The geological structure and rock types in the watershed can be divided into six major sections from east to west (Vachtman, 2005 and Sneh et al., 1998) as shown in map 1.5: •
Limestone and Eocene chalk composed mountain ridge.
•
Leach chalk at the mountainous hill shades (Senonian).
•
Limestone and dolomite with breaks in directions east-west and northeastsouthwest (turonian).
•
Costal plain consisted of alluvial sediments, sand dunes, and red loam (Quatemary).
•
Gravel crests (Kurkar) close to the Mediterranean (Upper Senomanian); and
•
Sand dunes in the beach section (out of the map’s area to west).
Map 1.5: Geological map generated using data from the Jewish National Fund (JNF).
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1.2.4 Soils It is common to divide the Alexander watershed into four main longitudinal sections from east to west: •
Mountains- parent rocks made of chalk and dolomite, gradually replaced by kerton (Chalk): Rocky slope containing soils of terra-rossa and rendzina (dark and light) are found in between the rocks (Dan et al, 1977).
•
Lowland plains (down hill of the mountains) - parent rock made of cretonne are partially composed of chalk and dolomite; Dark and light rendzina are common in addition to alluvium and colluvium soils, which are found in rock sinks (Dan et al, 1977).
•
Costal plain- the majority of the soils close to the stream route is alluvial dark gromosol (the parent material is alluvial clay). At a lower section, the stream crosses a hydro-morphic flat soil strip, which has slow percolation features (Dan et al, 1977 and Alfi, 1971).
•
The shore link- consists of percolating sand soil on top of a kurkar sand ridge. The ridge is crossed by the stream routes or artificial channels. Moreover, sand from the sea helps in blocking the openings of the stream to the sea, which can be reopened by machine involvement (Pergament et al., 1986).
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Map 1.6: Soil type distribution (generated using data from the JNF)
1.2.5 Hydrology The total length of the Stream route is estimated to reach 45 km. Naturally, the upper stream sections of the watershed are ephemeral, while perennial in the lower segments, beginning about 10 kilometers before it reaches the sea. Water flows naturally in the upper parts only during particularly rainy winter periods, for 3-4 months during the year (Alfi, 1971). However, during the rest of the year (summer time), domestic effluents and agricultural back-flow from irrigation find their way to the streambed at various locations. Besides, seawater from the west penetrates and blends with the effluents during the last 5 km of the stream (Alfi, 1971). Furthermore, it is estimated that annual flow in the lower sections (at Elyashiv station) is 14.5 million cubic meters (MCM), 6 MCM of which is estimated to be generated by domestic wastewater (Brandeis, 2003). Historically, water from a number of springs in the eastern side of the watershed reached the Wadi route, as well as high ground water from wetlands which contributed to the base flow in the western parts of the watershed (Alfi, 1971). Over time, Friedler and Juanico (1996) found that due to the drawdown on the underlying aquifer, few springs
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still exist and discharge is less than 0.5 MCM/year in the Wadi (stream) route at the Hugla area. Sewage treatment plants discharge their effluents to specific estuaries in different qualities on the Israeli side (there is no fully operational sewage treatment plant on the Palestinian side). The Israeli Hydrological Service database shows annual discharge recorded for more than 50 years. It is important to note that even during the summer there is no constant flow rate due to the various changes of discharge from sewage treatment plants (Tal et al., 2006). Moreover, uncontrolled seepage from water retention facilities, such as reservoirs of treated sewage and fishponds, affects the watershed hydrology (Vachtman, 2005). During the wintertime, part of the baseflow originates in the rising ground water levels, either due to the rain or directly from sewage discharges (from urban collected systems or from treatment facilities). (Friedler and Juanico, 1996)
1.2.6 Land use Various agricultural operations with a myriad practices can be found in the watershed, covering a high portion of the watershed’s area. Geographical features and soil types shape the category of crop and the intensity of cultivation. Mountainous rocky areas (most of the Palestinian side of the Green Line) are used for grazing, while downhill areas (which contain more and better soils) are used for seasonal orchards (vine trees and olives) and, as in the lower valley areas, field crops (i.e. vegetables). Deep soils in the lowland plain are used for intensive orchards (citrus trees). Due to its soil type, the coastal plain is more appropriate for field crops (such as wheat). (Beker et al., 2006) Intensive animal husbandry is more developed on the Israeli side than the Palestinian side of the watershed, especially cowsheds and fishponds. The Israeli side also contains higher percentages of urban areas and cities than the Palestinian one. The urban building type and style vary on both sides. The watershed is located almost directly in the center of Israel, less than a one-hour drive from approximately two million residents in the cities of the center (figure 1.7). Map 1.7 and map 1.8 shows that on the one hand most of the Alexander watershed is situated in the West Bank and defined as open areas, with small portions of 19
urban developments and only traditional agricultural practices (terraces, olives). On the other hand, intensive irrigated agriculture (citrus, field crops), fishponds, and water reservoirs are located primarily in the lower part of the watershed (Israel). Urban development constitutes an additional land-use pressure along the stream banks on both sides of the Green Line, but is far greater on the Israeli side (Brandeis, 1995 and 2000).
Map 1.7: Major land-use types in the Alexander watershed area.
As the main stream routes of the watershed originate from the east, from the West Bank, and flow towards the Mediterranean Sea, other tributaries contribute to the water balance in the Alexander Stream. As a result, land-use variation and pollution sources affect the water quality in the stream. Five categories of land-use were used to explain stream water changes along the stream. Agricultural and urban areas dominate the landuse types with clear differences between sub-basins. (Table 1.2 and map 1.8).
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Table 1.2: Land-use distribution in the Alexander watershed by major sub-basins. Upper Middle AREA Zomar Al-Teen Amatz (Km2) Alexander Alexander Urban* 13.2% 19.55 8.8% 11.87 23.2% 20.92 21.6% 13.87 13.9% 6 Field crops 21.3% 31.45 24.1% 32.36 33.7% 30.34 53.5% 34.33 32.9% 14.26 Orchards 41.3% 61.07 39.6% 53.24 25.3% 22.79 23.6% 15.16 50.2% 21.71 Shrubs 23.3% 34.45 27.4% 36.79 16.1% 14.48 0.8% 0.49 2.1% 0.91 Forests 0.9% 1.4 0.1% 0.08 1.7% 1.49 0.4% 0.28 0.9% 0.41 TOTAL 147.92 * (including road network)
134.34
90.02
64.13
43.29
Total watershed 15.2% 72.9 30.1% 144.2 36.6% 175.4 18.3% 87.6 0.8% 3.7 479.7
Map 1.8: Land-use in the Alexander watershed.
1.2.7 Runoff and floods Out of its five tributaries, two in the Alexander drainage sub-basins occupy the largest area: Zomar (Nablus) tributary (about 27%) and Teen (Te’enim) tributary (over 25%). The average daily discharge during the summer ranges about 10,000 cubic meters, which mainly is contributed by Zomar tributary. However, based on hydrological measurements
21
of the Israeli Hydrological Service records from Elyashiv station, during a storm event daily discharge may reach three million cubic meters. Daily discharge at Elyashiv station 04-05
3,000,000.00
2,500,000.00
Q (m3/day)
2,000,000.00
1,500,000.00
1,000,000.00
500,000.00
0.00 /04 /04 /04 /04 /04 /04 /04 8/04 8/04 9/04 9/04 0/04 0/04 0/04 1/04 1/04 2/04 2/04 1/05 1/05 2/05 2/05 3/05 3/05 4/05 4/05 4/05 /0 /0 /0 /0 /1 /1 /0 /0 /0 /0 /0 /0 /1 /1 /1 /0 /1 /1 /05 /05 /05 /06 /06 /07 /07 /0 /0 01 15 29 12 26 10 24 07 21 04 18 02 16 30 13 27 11 25 08 22 05 19 05 19 02 16 30 Date
Figure 1.1: Daily discharge at Elyashiv station 2004-2005 (extracted from raw data of the Israeli Hydrological Service, 2006)
1.3 Pollution sources in the Alexander watershed Various pollution sources are located in the Alexander watershed: domestic, agricultural, and industrial. Yet, not all the pollution sources have been detected and even fewer are treated (Ministry of Environmental Protection, 2000). Domestic pollutants reach the stream channel as a point source from open sewage pipes discharging sewage which is either untreated and raw or partially treated. Raw sewage from the city of Nablus is discharged into the Nablus-stream (also known as Wadi-Zomar and NahalShchem) on the eastern side of the watershed. These effluents flow all the entire way, crossing the Green Line next to Tul-Karem city, beneath the separation wall. At this point, the effluents are pumped to the Yad-Hana treatment plant to be partially treated before being released back to Zomar Stream, which joins Alexander main channel downstream (Brandies, 2003).
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A field survey was performed during the research period (2005-2007) identified several potential pollution sources in the Israeli side of the Alexander watershed. Due to the fact that 55% of the watershed’s area is east of the Green Line, pollution sources in the Palestinian areas have a major effect on the pollution profile of the down-stream water. The Alexander watershed faces severe pollution from roughly 25 different sources that negatively affect the streamflow conditions. In many cases, the stream route acts as an “open sewage canal” for domestic and industrial effluents from the Israeli and Palestinian areas. Besides extensive utilization of natural fresh water sources in the watershed, raw sewage was discharged to the stream. Major pollution sources in the watershed can be listed as follows (Map 1.9): 1. Yad-Hana Waste Water Treatment Plant (Nablus tributary) - This WWTP was established in 2002 as an emergency solution for untreated effluents flowing from the Palestinian areas. However, this plant has been operating since 2002 providing two key functions (The Ministry of Environmental Protection, 2000; Brandeis, 2003): a. This plant treats (at a secondary treatment level) effluents from the city of Tul-Karem which have already undergone primary treatment on the Palestinian side of the border. After the secondary treatment, the Israeli station transfers the treated waters to agricultural reservoirs for use by farmers on the Israeli side. b. The plant captures water (of raw sewage quality) flowing in Zomar-Nablus tributary inside a single sedimentation pond. After this initial treatment, the effluents are released back to Zomar stream on the Israeli side. This function leads to a discharge ranging between 7000- 10000 m3/day of effluents into the Zomar stream throughout the year. Even though the water is treated, effluents discharged into the Zomar-Nablus tributary by Yad-Hana WWTP were found to be of low quality and do not meet the Israeli standards for effluents discharges to streams (Inbar standards, table 7.8 and table 4.1). Unfortunately, due to mechanical limitations, any discharge rates over 2,500 m3/hour into the Zomar tributary (such as those taking place during
23
flood events) exceed the plant’s carrying capacity and are by-passed into the stream with no treatment at all. 2. Tnouvot WWTP- This treatment plant is located close to the Upper Alexander tributary, downstream from Kalanswa. The facility is considered to be an advanced secondary-biological treatment plant (using activated sludge) with most of its high quality effluents directed to irrigation. Small amounts of treated water reach the stream, mostly during the winter, ranging from 300-1000 m3/day. During the summer almost all of the treated water is used for irrigation in the upper part of the Alexander stream. Relative to effluents produced by the Yad-Hana WWTP, discharged water to the stream from the Tnouvot WWTP is less polluted. However, levels of NH4 and TP are still higher than Israeli recommended standards. 3. Taybe sewage- Due to the absence of a sewage treatment solution, sewage from the city Taybe flows to the fields from the outlet of its sewage collecting system in a westerly direction at a couple of locations. Along the way, these untreated effluents cross Highway 6 (via the runoff tunnel beneath the highway) reaching the outskirts of Kalanswa and the upper-Alexander stream. Occasionally, earthen dams are used to divert or block the effluents from reaching the outer neighborhoods of Kalanswa (see fig. 1.2). Still, strong rain events can cause the demolition of these dams and lead to a massive discharge of raw sewage. Although all parameters show high pollution levels (table 4.1), some parameters are lower than expected in “normal” sewage. This fact can be attributed to samples that were taken during small rain events and at locations that were at some distance from the source where either mixing with ground water or biological processes appear to have taken place.
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Figure 1.2: Earthen dams in the town of Kalanswa preventing the flow of Taybe sewage.
4. Industrial areasa. The small Nitsani Shalom industrial area hosts, among other pollution sources, a paper factory and solid waste recycling facilities, located west of the city of TulKarem. Two pipes constantly discharge small amounts of a black fluid with a heavy odor to a canal that drains into the Al-Teen tributary. Although the quantities discharged are not strong enough to create any kind of flow that would reach the stream’s base flow (estimated at <5 l/sec (432 m3/day)), during rain events, this pollution is carried to a closer tributary and then to the main Alexander channel (see fig. 1.3). Discharges from this industrial area contain extremely high concentrations of nitrogen, mostly in the form of ammonia (51050 mg/lit), and high COD and BOD levels (618 and 512 mg/lit in average, respectively) reflecting very high organic loads and high levels of TSS (46-598 mg/lit).
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Figure 1.3: Effluents pipe snapping from Nitsani Shalom industrial area. b.
The Prigat factory processes fruit based solution as part of its production process, creating considerable organic waste effluents that need to be treated. However, several incidents caused it to discharge raw sewage into the “Zilfa” canal, which drains into the Alexander stream. Polluted waters in the canal are supposed to be pumped back short time after; once the factory has fixed this failure. (Such an incident was witnessed during sampling campaign on 24.5.05.)
5. Kalanswa landfill- The landfill is situated along the banks of the Upper-Alexander stream. During rain events, the area of the landfill drains directly into the stream. A sample taken from the drainage point upon its entrance to the stream revealed extremely high pollution concentrations of COD, BOD and TN (66.4, 16.6, and 16.3 mg/lit, respectively). 6. Accidental sewage flows: Technical failures in the municipal and industrial waste facilities lead to occasional discharges of sewage into the stream (such as the situation at the Tnouvot sludge treatment facility). An accidental sewage releases was witnessed in November 2006 due to the temporary deactivation of the YadHana WWTP for maintenance (according to the WWTP recorded data and its
26
administration) and in the Kobani canal next to Kibbutz Ha'ogen in May 2006. The results were an immediate increase in organic loadings into the stream and a drop in dissolved oxygen which resulted in a massive fish kill. 7.
Effluent leakage from fishponds and water reservoirs: Fishpond effluents are discharged to the stream occasionally and contribute to pollution concentrations. Water samples taken from stream segments contiguous to fishponds showed high levels of NO3 and PO4 (29.4 and 5.4 mg/lit respectively) due to leakage from the pond to the streamflow. In addition, treated sewage is often kept in water reservoirs, which are distributed throughout the watershed area, to be used for irrigation by farmers (such as Ha’ogen, East, north and south reservoirs, see in map 5.1). These reservoirs are located on the stream bank and may also leak to the stream route. In addition, all the West Bank towns lying in the watershed (except Tul-Karem)
discharge their domestic and industrial effluents, which eventually reach the stream. These include over seventy pollution sources, such as stone cutting and leather factories, to this open channel. The Israeli Ministry of Environmental Protection estimates that nearly 3 MCM of such effluents flow in the Zomar/Shchem route annually (The Ministry of the Environmental Protection, 2000). Moreover pollution also originates from water retention facilities (mainly treated sewage) and fishponds, that are located near the wadi banks, contributing seepage water to the overall pollution loadings (Brandeis, 1996).
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Map 1.9: Pollution sources on the Israeli side of the Alexander watershed.
Untreated domestic sewage reaches the wadi after it is discharged form urban collection systems that have no treatment facilities, as is the case in the Israeli city of Taybe. Taybe collects its sewage, transports it via pipeline to the western edge of the city and eventually releases it to open fields. Later on, this sewage reaches the main channel of Alexander stream. It is at this point, technically speaking; the point source of pollution becomes a non-point source. 28
In the event of failures at their treatment facilities, additional pollution may reach the Wadi from industrial areas such as Nitsani Oz and Emek Hefer or from individual factories (such as Prigat). Besides the point source pollution mentioned, runoff from urban, industrial, and agricultural areas contribute additional non-point pollutants.
1.4 Selecting a hydrological modeling system During recent years scientists found that non-point source pollution from agricultural areas form a significant source of surface water problems (Novotny and Olem, 1994). In contrast to point source pollution, where concentration, volume, and duration can be measured, pollution from non-point sources is hard to measure and estimate accurately. These pollutants are transported with runoff water as solution, suspended in water, or absorbed into soil particles. Now, several simulation models have been developed in order to better estimate pollution from non-point sources, with a focus on the diffusive (distributed) approach (Leon et al., 2001). Simulating hydro-chemical processes at a watershed scale by certain watershed models may assist in better understanding the relationship between land-use activities and streamflow quality within a given watershed and its temporal and spatial variation. Several programs are now commonly used around the world both in the academic and private sectors. One famous modeling program is the Chesapeake Bay project, which provides a main reference for regulation and guides the states surrounding the bay. Through it project it has become easier to simulate streamflow, sediment, and nutrient loading from watersheds. There are two main approaches for non-point source modeling, in which the field studied is divided to response units or cells. The earliest models used the 'lumped approach'; according to the lumped approach, the areas studied are divided into units with homogeneous hydro-geological features in order to calculate the Hydrologic Response of the Unit (HRU). However, simulating large areas/fields using this approach limited the size of the units. Later, the 'distributed approach' was developed taking into account the field’s heterogeneity in the study area. In turn, the Group Response Unit (GRU) concept
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based models addressed different, specific land-use types in the same field and offered a better simulation of non-point source pollution transport (Kouwen, 1999). The lumped approach model is widely used, while more complex models use the distributed concept. The most reviewed and relevant models for runoff-pollutants transport and water quality are HSPF- Hydrological Simulation Program- FORTRAN (Donigian and Davis, 1985), AGNPS- Agriculture Non-Point Source (Young et al., 1986), SWAT (Arnold et al, 1995), SWRRB (Williams et al., 1985). HRU based models simulate temporal parameters of water-soil processes such as interception, infiltration, surface storage, and surface flow in spatial distributed cells. Examples of some of the most widely used models are: •
AGNPS (Agricultural Nonpoint Source; Young et al., 1989)
•
GWLF (Generalized Watershed Loading Function Model; Haith and Shoenaker, 1987)
•
HSPF (Hydrologic Simulation Program-Fortran; Johansen et al., 1984; Bicknell et al., 1996), and
•
SWAT (Soil and Water Assessment Tool; Arnold et al., 1994). Several factors were considered in order to identify the most appropriate model
for the study needs. The single most important factor was the ability to simulate multiple flow patterns in different sections of the soil profile (such as: surface runoff, interflow, and deep percolation). Also, it was important to have a model that was capable of simulating water quality transport (chemistry and sediments) within spatial and temporal scales. Accordingly, the BASINS package and the HSPF interface were chosen to simulate water quality and pollutants transport in the Alexander watershed. The HSPF model is available online at the USEPA website. Furthermore, the HSPF model offers flexible and user-friendly interface characteristics which ease handling its intensive data input.
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1.4.1 BASINS Software For the past decade, the US Environmental Protection Agency (USEPA) has promoted a developed version of Better Assessment Science Integrating Point and Nonpoint Sources (BASINS) package. The BASINS package serves to assess water quality by using a system that integrates Geographical Information System (GIS), US national watershed data and environmental assessment and modeling tools into one package (USEPA, 2001). The BASINS package was originally released in September 1996 to assist in making decisions, such as the facilitation of inspection of environmental information or the study of point and non-point source management alternatives and Best Management Practices (BMP) (Saleh and Due, 2004). The BASINS package supports the development of total maximum daily loads (TMDLs) quantification. This requires a watershed-based approach that integrates both point and non-point sources. The passage of the Clean Water Act in the USA in the early 1970’s and the growing awareness of non-point source pollution as a subject increased the concern about developing process-based water quality models. Presently, two watershed models of nonpoint pollution sources are integrated within the current version of BASINS: HSPF and SWAT models. The main goal of developing such models is to provide guidance on best management practices that may well lead to moderate the non-point source’s (NPS's) effect on surface water systems at the watershed scale. While HSPF was originally developed by the USEPA, SWAT was developed by the Department of Agriculture’s (USDA) Agricultural Research Service (ARS). Only one model (HSPF) will be used in this study to simulate streamflow and nutrient loads in the Alexander watershed. The HSPF model was calibrated and validated using derived data generated by historical records from the hydrometric station- Elyashiv that is maintained by the Israel Hydrological Service (IHS) and several monitoring stations installed by the project’s team members.
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1.4.2 HSPF- Hydrological Simulation Program- FORTRAN During the late 1970’s and the early 1980’s, the USEPA recognized the need for continuous simulation modeling systems to better analyze agricultural pollutants. As a result, the USEPA team developed various extensions extracted from other models providing a wide set of process algorithms for HSPF (Barnwell and Johansson, 1981). As one of the developed models, the HSPF provides a comprehensive, continuous watershed-scale. It includes two approaches, using distributed parameters of hydrology (Adams and Kurisu, 1976) and a lumped parameter model (Crawford and Donigian, 1973) that simulates changes in water quality, nutrients, pesticides, and sediments on pervious and impervious land surface (Bicknell et al., 2000). HSPF has its strength in implementing field scale and non-point source modeling as part of a watershed scale analysis framework, which includes non-point loading capabilities from land and transport within the stream channel and soil profile (Johansson et al, 1980). After the HSPF was developed by Hydro-Comp, Inc. for the USEPA during the 1970’s, it was first released as version 5.0 in 1980 (Johansson et al, 1980) by the USEPA Water Quality Modeling Center (now the Center for Exposure Assessment Modeling). The model maintained an excellent reputation as one of the most useful watershed-scale hydrology/water quality model publicly used. Nowadays, HSPF code has undergone a series of improvements resulting in the recent release of version No. 12, known as WinHSPF, designed to interact with BASINS 3.0 utilities (Bicknell et al., 2000). The HSPF watershed model is used to simulate surface runoff, point source pollution, nonpoint sources of pollutant loadings from several locations in the watershed, and performs hydrologic and water quality processes in streams (Bicknell et al, 1996). HSPF demands an extremely intensive database from different sites in the watershed and requires considerable preparation time. The information is driven by meteorological time series datasets (precipitation, temperature, solar radiation and evaporation, and wind speed), topography, geography, and land-use databases. Initially the watershed’s land-use database should be divided according to segments of pervious surfaces, impervious lands, and stream reaches in routine modules (PERLND, IMPLND, and RCHRES) and five utility modules. Consequently, each module is adjusted for
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specific features of land-use; the PERLND module performs hydrological and water quality processes suited for pervious land segments, the IMPLND module performs impervious surface area processes (due to the absence or only slight water infiltration), where the RCHRES module simulates a single stream or reach of an open channel (Saleh and Due, 2004). HSPF is capable of estimating the loadings of non-point source pollution from mixed land-use. It can also simulate transport processes in streams and one-dimensional water bodies within a single watershed or multiple watersheds in the hydrological system. In addition, the HSPF model is capable of simulating loadings of non-point source pollution that originated in various and mixed land-use scenarios.
1.5 Defining the problem to be addressed in this research Several studies already address the effect of land-use/land cover (LULC) on the quality of surface water and ground water. These studies consider the relationship between the type of crops and nutrients levels in streamflow (Mander et al., 1998; Changnon and Demissie, 1996) or the depth of vegetation roots and the decreased nutrients loads reaching the streambed (Sharpley et al., 1999). In turn, previous work assesses the influence of urbanized areas and impervious surfaces on storm flow patterns and quality. However, the correlation between spatial distribution of LULC and streamflow quality has been studied less due to numerous obstacles in field measurements (Tong and Wenli, 2002). 1.5.1
Current Environmental Conditions in the Alexander Watershed
It is important to note that nitrate loads in the streamflow originate from agricultural and industrial sources. Nitrate transport within the basin depends on several complex factors such as agricultural practices, industry management, the watershed’s morphology, meteorology, and hydrological features. Soil and surface water’s biogeochemical characteristics also have an effect on the transport process (Norton and Fisher, 2000).
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In Israel and the Palestinian West Bank, the law defines fresh water resources as public commodities managed by the state. Israel’s “Water Law” lists legitimate users of water as domestic, agriculture, industry, services and public services, while recently (2004) including the protection and restoration of natural habitats (Hacohen, 2004). Recommendations by several researchers and planners have urged an increase in the quantity of fresh water released in the Alexander watershed due to a shortage of high quality fresh water, a result of diverting spring water and over pumping of ground water. As a result, the quality of the Alexander streamflow does not reach international standards (Gafny and Bar-or, 1995, Brandies, 2005). Consequently, intensive restoration efforts have been made in the Alexander watershed, with over 12 million US dollars invested during the past decade under the rubric of the Alexander Restoration Project. Although the project won the River prize award in Brisbane, Australia, many of the restoration project’s objectives have not yet been met (Brandeis, 2003). Currently, the main source of water flowing in the stream is the semi-treated sewage discharged from Yad-Hana WWTP at the Green Line, as was mentioned in 1.3. On the Israeli side, due to the lack of an “end-of-the-pipe” solution for its wastewater, the city of Taybe discharges its sewage into adjacent open fields after it has been collected in the sewage system. It is worth noting that considerable progress has been made in the area of treated wastewater; the rest of the Israeli towns and cities lying in the watershed are connected to several WWTPs, and the processed water is largely used for agriculture irrigation. For example, the Tnouvot WWTP receives wastewater from the town of Kalnsawa and other nearby villages, and then releases treated waster at an average of 300-1,000 m3/day to the stream when there agricultural usage is exhausted (Tal et al., 2006). Furthermore, there is potential seepage from on-land aquatic agriculture, water reservoirs, treatment plants, and industrial areas and factories located in the western lower part of the watershed to find their way to the streambed causing extreme changes in water chemistry or quality (Ackerman, 2006). Most baseflow contains sewage and effluent discharges, but the loadings from nonpoint runoff are considerable. The Yad-Hana WWTP partially treats baseflow of the Zomar tributary and releases it back to the stream at a maximum rate of 20,000 m3/day.
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Yet, during flood events in times of heavy rain the flow rate exceeds the WWTP maximum capacity, and sewage often does not reach the treatment process at all. The sewage is also diluted by the surface flow reaching the stream. This research will show the correlation between water quality changes and land-use distribution during selected rain events. 1.5.2
Future plans for developing and restoring the watershed
Subsequent to the establishment of the Alexander River Administration in 1994, a restoration master plan for the watershed was prepared (the Israeli Ministry of the Environmental Protection, 2000). The proposed restoration efforts included removal of pollution sources and rehabilitation of natural life in the watershed, particularly in the lower segments of the watershed (Brandeis and Itskovitz, 2000). For its part, Israel’s Nature and Parks Authority requested an increase in the allocation of fresh water to the tributaries of the Alexander basin to overcome the low quality effluents being diverted into the stream from the WWTP’s. At present, reclaimed wastewater is diverted to streams, preventing them from drying out and maintaining a moderately green backdrop to the stream throughout the year. Policy makers appear to prefer this solution for the coming years (Juanico and Friedler, 1999; Kaplan, 2004).
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Chapter 2 Hypothesis and Research Objectives 2.1 Research Hypothesis Although the Alexander watershed is politically divided into two authorities, it is still connected through one hydro-chemical and ecological system, which includes complex and heterogeneous land-uses, topography, geography, hydrology, and pollution sources. There are various factors which affect the watershed’s features, such as geological structure, soil types, land-use and landscape, population, hydrological characteristics, surface flow, groundwater quality and quantity, and contribution of pollutants (Brandeis, 2005). Therefore, monitoring the hydrological system is highly important for better understanding the watershed’s characteristics. In turn, information gathered can assist in improving the quality of water in the stream. Pollutants carried and transported from agricultural, residential, or industrial nonpoint sources (NPS) are found to form a high percentage of the pollution in stream systems throughout the world. Furthermore, runoff from farmlands is assumed to be a key source of pollution (Doering et al., 1999; Choi et al., 1994). For some time, non-point source pollution has been recognized as a major source of natural and anthropogenic pollution contaminating surface waters affected by land-use practices (USEPA, 2005; Lopez-Flores et al., 2003). Sub-basins characterized by agricultural land-use contribute significant nitrogen and phosphorus loads to streambeds crossing this sub-basin (Puckett, 1995; Goulding et al., 1996; Pionke et al., 1996). Moreover, major pollution loads reach streamflow during flood events when pollutant residues are washed from urban and agricultural areas. During flood events caused by rain, water quality changes due to chemicals and pollutant-loads washed from point and non-point sources in the watershed. Originating from different land-uses, pollutants reach the streambed and are eventually transferred downstream to the Mediterranean Sea.
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2.2 Research Objectives This research aims to characterize nutrient levels in the water within the streamflow of the Alexander watershed (quality and quantity), and to estimate the correlation between land-use and water quality alterations. In due course, identifying polluting landuses can assist in the future restoration efforts of the Alexander watershed. This research addresses this issue by attempting to answer three main questions that are associated with the research objectives: •
What is the spatial and temporal alteration of pollution fluxes entering the Alexander streamflow?
•
What is the effect of flood events on pollution loads transferred in streamflow relative to base flow conditions during summer and winter?
•
Is there a correlation between land-use and the hydro-chemical characteristics of streamflow quality?
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Chapter 3 Methods 3.1 Streamflow manual measurements 1 Base flow and water chemistry (see section 3.4) during the summer were manually measured at several locations along the main tributaries of the Alexander watershed. At each location, the water discharge was measured using the velocity-area method (described below). Water height, channel cross-section, and flow velocity allow for precise determination of each tributary’s flow discharge and nutrient loadings. This method was used for measuring direct velocity of the water flowing in the Wadi (stream) routes assessed in calculating the baseflow discharge (Map 3.1). Summer and spring 2006 base flow was measured in various sites in the watershed. Most of the sites were set at the Alexander and Zomar-Nablus tributaries, where considerable base flow was found. In addition, manual samples were taken during storm events at locations different from the permanent monitoring sites in which the automatic samplers were located. These samples and measurements made it possible to better characterize summer and winter (2005-2006) stream flow.
Discharge calculation There are a number of methods to measure streamflow, but the most common method for direct base flow discharge (Q) is to develop a cross-section of the stream channel. In order to calculate the water volume that moves through the channel, the channel is divided to approximately known areas (width * depth- m*m). Then the flow within each area is measured (velocity- m/sec). When these values are multiplied, the result is flow (area*velocity= length^3/time= Q <==> m2*m/time = m3/time = Q). This method is also known as “velocity-area calculation”. Discharge was calculated using the equation (Figures 3.1 and 3.2):
Q=ΣAiUi Where: Q= Total Discharge (m3/sec), A= Area of each section (m2), U= Water velocity at each section (m/sec). 1
The work (flow measurements, sampling and chemical analysis) was performed by the research crew.
38
By using the electromagnetic current meter (of Marsh McBirney Inc. © flow-mate model 2002, ± 2% accuracy), flow velocity (ui) was measured in 0.6 depth of the water (Gordon, 2004) and multiplied with the wetted cross sectional area/segment (A), which is between two measuring points and determined by the topography of the wadis cross section. The method had one important assumption: the velocity at each vertical section represents the mean velocity in the segment. Measurements were repeated in each site to assure accurate mean flow discharge for the site. (Figure 3.2).
Figure 3.1: Direct streamflow measurement in shallow stream segment of the channel, electromagnetic meter is shown on the right-hand side.
Reach cross section at Zomar manual sampling site 0
Water depth (cm)
5
10
15
20
25 0
50
100
150
200
250
300
350
distance from bank (cm)
Figure 3.2: Stream’s cross-section diagram of sampling site at Wadi Zomar.
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400
450
500
Map 3.1: Spatial location of manual and automatic sampling in the Alexander watershed.
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3.2 Precipitation data Precipitation data was recorded by two major meteorological stations, Ma’abarot and Kochav-Yair, in resolution of five minutes and half hour intervals, respectively. In addition, daily precipitation data from seven more rain gauges were used to generate a precipitation database for each sub-basin. The additional rain gauges were spatially distributed in the watershed and its surroundings maintained by the Israeli Hydrological Authority (Figure 3.3). Amongst the rainfall records, data sets of two main meteorological stations and seven rain gauges were used as inputs for the modeling system. Also, radar-generated rainfall data sets for the main storms in the modeled period were collected. These rainfall data sets were analyzed and used to produce hourly time step rainfall distribution of the entire watershed, as well as the sub-watersheds. The potential evapo-transpiration was computed using the “WDMUtil” software (from the Hydrological Simulation ProgramFortran- HSPF package). WDMUtil uses the Hamon PET methodology to compute daily potential evapo-transpiration from minimum and maximum daily temperatures, monthly coefficients and latitude of the meteorological station, and then disaggregate the daily values of the PET to hourly values using the same software.
Map 3.2: Locations of the rain gauges and metrological station, by Eng. M. Abu Saada.
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60
Average daily rain (mm/day)
50
40
30
20
10
8/ 10 15 /05 /1 0 22 /05 /1 0 29 /05 /1 0/ 5/ 05 11 12 /05 /1 1 19 /05 /1 1/ 26 05 /1 1/ 3/ 05 12 10 /05 /1 2 17 /05 /1 2/ 24 05 /1 2 31 /05 /1 2/ 0 7/ 5 1/ 14 06 /1 / 21 06 /1 / 28 06 /1 /0 4/ 6 2/ 11 06 /2 / 18 06 /2 / 25 06 /2 /0 4/ 6 3/ 11 06 /3 / 18 06 /3 / 25 06 /3 /0 1/ 6 4/ 0 8/ 6 4/ 15 06 /4 /0 6
0
Date
Figure 3.3 Average daily rain at the Alexander watershed calculated from eight spatially distributed rain gauges.
3.3 Automatic stations Due to the non-constant timing of rain events and floods during the winter season, three automatic samplers (SIGMA 900 MAX ©, figure 3.4) were installed at key locations in the Israeli side (Zomar-Nablus, upper-Alexander, middle Alexander tributaries) and three more in the Palestinian side of the watershed (Tul-Karem, DeirSharaf, and Teen tributaries (map 3.1), installed by the Palestinian co-team). Rain events throughout the watershed lead to different flooding discharges and durations at different sites along the stream route. The samplers automatically functioned to grab water samples from the stream flow and store it in the sampler’s box as a function of streamflow level at all stations. At each station, the samplers were also programmed to record water level (H) and Electric Conductivity (EC) from the main route’s middle water column. These records were taken at two-minute time intervals. Apart from the automatically recorded data, surface water speed was manually measured during several storms by using floating object methods. Water bottles were simply thrown to the streamflow from specific location and picked up after known 42
distances while measuring the time it took to pass this distance. By knowing the time (t) and the distance (d), the surface water’s speed (v) was calculated. For better measurements, stream banks were stabilized using cement to create uniform stream cross-sections for calculating flow discharge, as is shown in figure 3.3 (Figure 1 in appendix).
Figure 3.4: Cross section at sampling station, used to measure water head and streamflow, by M. Abu-Saada.
In each monitoring station, automatic samplers were installed. Each of the samplers was equipped with: a. HASH manufacturer, Sigma 9000 Max Portable water sampler. b. Computerized data logger. c. Twenty-four bottles (one liter each) for
water
samples
located
in
carousel beneath the computerized part.
Figure 3.5: Sigma automatic sampler at one of the monitoring stations, inside a metal box.
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d. Water pump attached to pipe that reaches the stream bed used for flow samples. e. Pressure transducer (resolution up to 10 mm) reaches the stream bed, used for recording water’s height. f. An electric conductance (EC) probe (resolution up to ±10 µS/cm) reaches the stream bed, used for measuring water flow’s electrical conductivity (EC). g. Telemetry cellular communication system connecting remote computer to the data logger. This was used for following up and downloading records. h. All powered with two separate 20 Amp/h batteries. At 2-minutes intervals, EC and water level were measured and recorded in the data logger. The water level activates the sampling process as an indicator of flood events (Figure 3.5 below and figure 2 in appendix).
Discharge calculationSince the late 1950’s, the Elyashiv hydrometric station has been recording sufficient historical data of the water level and stream flow measurements at that location. The Elyashiv station continued the mission after Ma'abarot station, which was installed in 1938. At the other stations, our new records were used to calculate and produce hydrographs for several storm events at different locations. Recorded water height and station’s dimensions (slope, roughness, hydraulic radius) of the stream cross-section were used to calculate the flow discharge. All calculations are based on the Manning equation:
Where Q is the flow discharge (m3/sec), V is mean velocity (m/sec), A is cross section (area, m2), K is unit factor conversion (K=1 for metric values and 1.49 for English values), n is Manning surface roughness factor, P is wetted perimeter (m), and S is channel slope. A/P is known as the “Hydraulic Radius” (Munson et al, 1998 and Brater et al., 1996). The soil authorities workers helped to extract reliable flow values of the water levels recorded. 44
3.4 Water sampling and chemical analysis Samples (1 liter each) were manually collected from base flow and during flood events were stored at 4oc (in ice-filled thermally isolated cases) while transported to the analytical laboratory at the Sde Boker Campus of Ben Gurion University of the Negev, The samples' biological oxygen demand (BOD) was analyzed first to stand with the standard time limitation and the rest of the chemical analysis were completed within the days after. No other biological indicators were part of the monitoring program, so there was no time limitation for the chemical analysis. During flood events, automatic samples were kept in the storage tank after being pumped from the stream flow, to be collected and transferred to the laboratory. Bottles were used only after being soaked in phosphatefree detergent and rinsed with nitric acid and double distilled water, respectively.
Chemical Analysis In the field, before storing the samples, measured pH, Dissolved Oxygen (DO), and EC were measured by using a portable multi-parameter kit, (MultiCal®). Chemical analyses were performed in the library at the Zukerberg Institute for Water Research (ZIWR). Due to time limitations, Biological Oxygen Demand (BOD) analysis was performed first for most of the samples, according to standard method protocols (APHA, 2005). In addition, samples were analyzed for Chemical Oxygen Demand (COD), by the dichromate reflux method. As for the nutrient analysis, nitrogen compounds such as Ammonia (NH4+) were measured, -using a spectrophotometer following Nessler method, nitrate (NO3-) was evaluated by 2nd derivative UV/Visible spectroscopy methods (Michelle, 2001), and Nitrite (NO2-) by dionex column method. Total organic nitrogen (TN) and total phosphorous (TP) were measured according to Vanadomolybdate. After determining bicarbonate levels, each sample was filtered through a 0.45 µm filter in order to measure calcium, magnesium, potassium, and sodium using atomic adsorption (APHA, 2005). Values of total suspended solids were measured by the gravimetric method (APGA, 2005).
45
Map 3.3: Automatic sampling sites in the watershed.
46
Table 3.1: Chemical analysis applied to water samples in lab
Method
Parameter BOD
Dilution method (APHA, 2005)
COD
Closed reflux, titrimetric method (APHA, 2005)
NH4
Nesslerization method (APHA, 2005)
NO3 NO2
Second derivative UV spectroscopy technique (Ferree and Shannon, 2000) N- (1-naphthyl-ethylenediamine) (APHA, 2005)
PO4
Ascorbic Acid Method (APHA, 2005)
TN
Simultaneous Determination of Total Nitrogen and Total Phosphorus Vanadomolybdate (Gross, A. and Boyd, C. E. 1998)
TP TSS
Total suspended solids dried at 103-105 ºC (APHA, 2005)
Sampling and analysis were conducted during the period between February 2005 and December 2006. Since the inception of the project, 337 samples were collected, analyzed, and then divided into different categories: winter baseflow, summer baseflow, and storm events. These samples were used to evaluate the hydrological balance and the pollution loadings of different sub-watersheds, reflecting the Alexander basin’s baseflow and storm events.
3.5 Spatial database The modeled area in the present study extends from the upper stream of the Zomar Watershed (east of Nablus City) down to the coast of the Mediterranean Sea (chapter 1). The watershed is composed of three main streams. The Nablus-Shchem stream is generated from the eastern part of Nablus City, crossing the Green Line “Border” near Tul-Karem City. The second stream is Al-Teen, which is dry all year round, except during large storms in the rainy season. The third main stream is the Alexander, which starts from the West Bank Mountains and crosses Al-Taybe and AlTireh cities on the Israeli side. An additional tributary, Amatz, joins the Alexander in the north Israeli side of the watershed. The lower part of the watershed was excluded from
47
the model in order to simplify the modeling process. The modeled area (480 Km2, 73% of the watershed) represents most of the upper watershed section.
3.5.1 DEM- Digital Elevation Model Elevation distribution of the overall watershed was clipped from high resolution Digital Elevation Model (DEM, 25m by 25m). This DEM is considered the main input for the HSPF modeling software where the drainage area, the sub-watershed delineation, and the slope of the entire watershed have been developed. (Map 3.6). The smaller the sub-basins, the finer the modeling results, and the more manual work is needed. Therefore, the watershed was delineated into ten basins. Furthermore, it was considered better to define the watershed according to its five major tributaries: Zomar, Al-Teen, Upper-Alexander, Middle-Alexander, and Amatz (Figure 3.3 and 3.4).
Map 3.4: The watershed’s elevations (DEM).
48
3.5.2 Land-use Spatial distribution of land-use, soil and DEM are the most important datasets for building the computational model for the study area. The watershed can be represented in five main spatially distributed land-uses: urban, field crops, orchards, shrub-land, and forests (Figure 3.6 ). Map 3.4 shows that the upper part of the modeled area is mostly covered with trees (citrus, olives, vine, etc) and rocky stones, while the downstream section is dominated by urban and cultivated land. Land-use division in the Alexander watershed's over-all area
Shrubs 18.2%
Forests 0.8%
Urban (including road system) 15.1%
Field Crops 29.8%
Orchards 36.3%
Figure 3.6: Land-use partition in the Alexander watershed, based on JNF database 2005.
Map 3.5: Land-use map of monitored parts of the Alexander watershed.
49
3.5.3 Delineation of the watershed to sub-basins Based on the DEM data set and existing stream routes, the BASIN software computed the sub-basins. The watershed was divided into ten sub-basins (Map 3.6). The delineated watershed and its physical properties (land-use, length and slope of the stream) were then exported to the Hydrological Simulation Program –FORTRAN (HSPF) modeling software to form the base of the Zomar/Alexander model (Figure 3.7).
Amatz Stream
Alexander Stream
Nablus Stream
At Teen Stream
Figure 3.7: Model setup and sub-basins linking as it is shown in HSPF screen.
Figure 3.7 shows the relationship between sub-basins shown in map 3.6 and were referred to according to the close major tributary as in Table 3.2:
50
Map 3.6: Delineation of the watershed.
51
Table 3.2: Sub-basins names and ID Numbers divided according to major tributaries. Major Sub-Basin Stream Name Nablus TP Anabta, Tul-Karem Zomar Nablus City Wadi Diersharaf Al-Teen Upper Al-Teen Al-Teen Middle Al-Teen Lower Alexander Alexander Middle Middle Amatz Amatz UpperUpperAlexander Alexander
Sub-basins within Zomar drainage area: Nablus City Sub-basin: The values of land-use dimensions and distribution over Nablus sub-basin (35.2 Km2) are shown in Table 3.3, where urban and orchards are the dominant land-uses, 27.2% and 27.8% respectively. The urban area includes the western part of Nablus City, which is characterized by high density residential areas and is therefore a potential source for generating runoff. On the other hand, orchards (mainly olive trees) may reduce the generation of runoff. In general, this type of cultivation is based on building terraces, which are considered a type of dam that stores or at least delays the generated flow from running towards the main stream. Table 3.3: Areas of land-use types within Nablus City sub watershed
Type Name Urban Field Crops Orchards Shrubs Forests
Area (km2) 9.59 6.73 9.78 8.69 0.41
52
% from Total Area 27.2% 19.1% 27.8% 24.7% 1.2%
Wadi Dier-Sharaf Sub-basin: According to Table 3.4, this sub-basin’s (52.3 Km2) land-use is mainly orchards and field crops (44.8% and 24.2%, respectively), especially olive trees. These areas include a unique length of ancient human-made terraces, which reduce runoff. In addition, the relatively small portion of urban areas was not found to generate significant runoff volumes. Table 3.4: Areas of land-use types within Wadi Deir-Sharaf sub-watershed
Type Name
Area (km2)
% from Total area
Urban
2.22
4.2%
Field Crops
12.63
24.2%
Orchards
23.43
44.8%
Shrubs
13.96
26.7%
Forests
0.05
<0.1%
Anabta and Tul-Karem Sub-Watershed: As the table 3.5 shows, this sub-basin (56.34 Km2) starts from Nablus and Wadi Deir-Sharaf and extends downstream until it reaches the Israeli side. This sub-basin is characterized by a smaller slope variation than the previous ones. In addition, around 49% of the land-use is orchards and there is a massive use of terraces. As a result, the area is expected to have a reduced amount of generated runoff. On the other hand, plastic greenhouses are commonly found in this sub-basin with a higher percentage of urban area (11.8%), which raises the amount of runoff. Table 3.5: Areas of land-use types within Anabta and Tul-Karem sub-basin Area (km2)
% from Total area
Urban
6.62
11.8%
Field Crops
10.07
17.9%
Orchards
26.95
47.8%
Shrubs
11.77
20.9%
Forests
0.93
1.7%
Type Name
53
Nablus treatment plant Sub-basin: It is the lowest and the smallest of Zomar’s sub-basin series (~4 km2). Therefore, it represents the outlet of Nablus –Tul-Karem stream to Middle-Alexander. This subbasin is important from a water quality point of view, due to the fact that within it YadHana WWTP has been working since 2002. At this WWTP streamflow from Nablus, the water is pumped to the plant for primary treatment and then discharged back to the stream. Because of the high percentages of urban areas (27%) and orchards (50%, mainly plastic greenhouses), it is assumed that this sub-basin would contribute greatly to the generated runoff (see Table 3.6). Table 3.6: Areas of land-use types within Nablus TP sub-basin
Type Name
Area (km2)
% from Total area
Urban
1.12
27.4%
Field Crops
2.02
49.4%
Orchards
0.91
22.3%
Shrubs
0.03
0.7%
Forests
0.01
0.2%
Upper-Alexander Sub-basin: This sub-basin is the largest amongst the delineated sub-basins (90 Km2), and extends between the mountains of the Palestinian West Bank and the foothills of the Israeli side of the Alexander watershed. Table 3.7 shows the high percentage of urban areas (23.2%) as well as the high percentage (33.7%) of field crops with a wide use of greenhouses. Consequently, the area is expected to experience quick reactions and to quickly generate runoff. Table 3.7: Areas of land-use types within Upper-Alexander sub-basin
Area (km2)
% from Total area
Urban
20.92
23.2%
Field Crops
30.34
33.7%
Orchards
22.79
25.3%
Shrubs
14.48
16.1%
Forests
1.49
1.7%
Type Name
54
Amatz Sub-basin: This sub-basin mostly drains from agricultural fields (Table 3.8); around 33% of the land is cultivated by field crops while more than 50% is cultivated by orchards. Out of the total area (43.3 Km2), only 6 Km2 are used for urban purposes. Table 3.8: Areas of land-use types within Amatz sub-basin
Area (km2)
% from Total area
Urban
6.00
13.9%
Field Crops
14.26
32.9%
Orchards
21.71
50.1%
Shrubs
0.91
2.1%
Forests
0.41
1.0%
Type Name
Al-Teen sub-basins Al Teen Upper Sub-basin In general, Al Teen stream is dry during the year, except runoff caused by strong storms. Different than Zomar sub-basins, the lack of urban areas which are disconnected from the stream, (less than 3% only) left the sub-basin (about 40 Km2) with no source of effluents except during rain events. A wide range of elevations and strong slopes, created areas which are difficult to cultivate (Table 3.9). Table 3.9: Areas of land-use types within Al Teen Upper sub-basin
Area (km2)
% from Total area
Urban
2.40
6.1%
Field Crops
12.18
31.0%
Orchards
16.12
41.1%
Shrubs
8.55
21.8%
Type Name
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Al-Teen Middle Sub-basin: Table 3.10 shows features similar to the previous sub-basin; it has a great variation of slopes and elevations, as well as land-use distribution, but over larger area (77.5 Km2), also Map 314. Table 3.10: Areas of land-use types within Al-Teen middle sub-basin
Area (km2)
% from Total area
Urban
4.58
5.9%
Field Crops
12.62
16.3%
Orchards
33.54
43.3%
Shrubs
26.71
34.5%
Type Name
Al-Teen Lower Sub-basin: Table 3.11 shows that this part of Al-Teen stream is different from the upper part, as it is characterized by more urban areas and more field crops, but a smaller area overall (17.7 Km2). Table 3.11: Areas of land-use types within Al-Teen Lower sub-basin
Area (km2)
% from Total area
Urban
4.89
27.7%
Field Crops
7.56
42.9%
Orchards
3.58
20.3%
Shrubs
1.53
8.7%
Forests
0.08
>0.5%
Type Name
Alexander Middle sub-basin: This is the downstream sub-watershed (64.13 Km2) in the modeled area; where the four main tributaries (Nablus-Tul-Karem, At Teen, Alexander and Amatz) mix together. It’s a coastal sub-basin with a very low slope. The land-use is dominated by
56
urban and agricultural activities, which are considered main factors for generating runoff Table 3.12. Table 3.12: Areas of land-use types within Alexander-middle sub-basin
Area (km2)
% from Total area
Urban
13.87
21.6%
Field Crops
34.33
53.5%
Orchards
15.16
23.6%
Shrubs
0.49
0.8%
Forests
0.28
0.5%
Type Name
To conclude, two main land-uses are found in nearly every sub-basin: urban and orchards. It was found that land-use percentages and area vary in different sub-basins. Therefore, it is assumed that variations between sub-basins’ features of land-use will have significant effect on their contribution of nutrient loading. Table 3.13: Summary of the total land-use data of the Alexander modeled sub-basins. AREA
Alexander Middle 1
Amatz 2
Nablus TP 3
Alexander Al-Teen Upper Middle 4 5
Anabta- Nablus Tul-Karem City 6 7
Wadi Diersharaf 8
Al-Teen Upper 9
Al-Teen Lower 10
Forests
13.87 34.33 15.16 0.49 0.28
6 14.26 21.71 0.91 0.41
1.12 2.02 0.91 0.03 0.01
20.92 30.34 22.79 14.48 1.49
4.58 12.62 33.54 26.71 0
6.62 10.07 26.95 11.77 0.93
9.59 6.73 9.78 8.69 0.41
2.22 12.63 23.43 13.96 0.05
2.4 12.18 16.12 8.55 0
4.89 7.56 3.58 1.53 0.08
TOTAL
64.13
43.29
4.09
90.02
77.45
56.34
35.2
52.29
39.25
17.64
Urban (including road network) Field crops Orchards Shrubs
Percentage
Alexander Middle 1
Urban (including road network) Field crops Orchards Shrubs Forests
21.6% 53.5% 23.6% 0.8% 0.4%
Amatz 2
13.9% 32.9% 50.2% 2.1% 0.9%
Nablus TP
Alexander Al-Teen Upper Middle
3
4
27.4% 49.4% 22.2% 0.7% 0.2%
57
23.2% 33.7% 25.3% 16.1% 1.7%
5
5.9% 16.3% 43.3% 34.5% 0.0%
Anabta- Nablus Tul-Karem City 6
11.8% 17.9% 47.8% 20.9% 1.7%
7
27.2% 19.1% 27.8% 24.7% 1.2%
Wadi Diersharaf 8
4.2% 24.2% 44.8% 26.7% 0.1%
Al-Teen Upper 9
6.1% 31.0% 41.1% 21.8% 0.0%
Al-Teen Lower 10
27.7% 42.9% 20.3% 8.7% 0.5%
3.5.4 Soil GIS layers from a geological survey prepared by the Hydrological Authority to determine the watershed’s soil types was used to generate the soil map (Map 3.7) and used for the HSPF model. The map shows the main soil groups, while Table 3.2 shows the sub groups of soil types and their areas within the model area. It should be noted that the upper part of the model area is mainly terra-rossa and dark rendzina soil groups, while the lower part is classified as dark grumosol and hamra soil groups.
Map 3.7: Soil-type distribution in the Alexander watershed, based on JNF database.
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Table 3.14: Sub groups of soil type, areas, and ratio within the modeled watershed, extracted from Israel Mapping Center, 2003.
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Chapter 4 Findings and Results 4.1 Baseflow monitoring In total, 337 water samples were taken between October 2005 and December 2006, especially during precipitation events in the Alexander watershed. Afterwards, all samples were divided according to the season and flow conditions, into summer and winter samples. Summer sampling (95 samples) focused on spatial distribution, while winter samples had more of a temporal dimension. Sampling campaigns during summer 2006 were conducted manually in several locations on the Israeli side of the watershed (Map 3.1), and analyzed for general chemistry and nutrient concentrations. Table 4.1 shows the maximum and minimum values for chemical analysis. The calculated averages enable us to categorize the water quality of summer baseflow. Monitored baseflow water during the summer leads to the identification of possible point sources of pollution found in the stream. It is important to mention that several samples which were taken from specific locations such as the Zomar tributary and close to the fishponds (Map 7.14 in appendix) showed an extremely low quality of water, even compared to Inbar's Standards (Table 7.8). While summer streamflow was generated mainly by the return flow from agricultural areas, reservoirs, fishponds, and small springs (map 7.14), winter streamflow included additional runoff from storm floods (Figures 4.24, 4.25, and 4.26). Moreover, this study found that most of the baseflow water discharge measured at Elyashiv station was contributed from Nablus-Zomar tributary all year round (Table 4.2). For example, the summer discharge at Elyashiv was about 16,000 m3/day, while at the upper Alexander station (R57) there was almost no flow, and at the Zomar Station the maximum summer discharge was 6,000-8,500 m3/day (Table 4.2).
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Table 4.1: Range and average of Baseflow concentrations (mg\L) 2006). Baseflow samples NO3 PO4 NH4 NO2 TSS Location (samples) mg/L mg/L mg/L mg/L mg/L Min 1.67 3.04 1.48 0.10 4.00 R57 (7) Max 13.50 55.60 30.40 4.56 54.00 Average 5.32 12.99 6.64 1.08 28.00 Min 0.50 1.00 0.08 0.02 20.00 16.26 37.38 72.40 14.36 2434.00 Zomar (20) Max Average 4.38 19.22 30.84 1.95 427.08 Min 11.50 3.42 1.80 0.16 4.00 30.25 15.20 35.80 11.80 116.00 Elyashiv (8) Max Average 19.49 7.59 9.66 4.77 68.63 Min 5.39 0.70 0.52 0.07 2.00 Agriculture Max 44.90 19.90 8.01 0.58 444.00 (7) Average 19.66 7.28 3.04 0.30 101.14 Min 5.75 1.26 0.85 0.13 2.00 Fish Ponds Max 53.30 11.96 18.00 2.46 130.00 (5) Average 29.43 5.44 9.11 0.83 38.11 Min 1.32 4.93 41.50 0.02 46.00 Industry (4) Max 39.10 1050.00 164.00 0.02 598.00 Average 14.29 372.01 96.44 0.02 197.00 Min 0.28 14.70 24.98 0.03 11.00 Taybe (4) Max 0.74 42.70 98.70 36.00 208.57 Average 0.51 27.10 50.93 12.02 103.96 Min 0.37 2.30 1.82 0.10 8.00 Kalanswa Max 4.41 19.40 24.90 5.10 45.00 Landfill (5) Average 2.39 11.00 16.56 1.26 23.09 Min 0.09 1.14 0.28 0.02 1.43 Others (35)* Max 55.81 45.00 95.47 20.00 570.00 Average 14.40 11.89 14.81 2.90 103.34
in the Alexander watershed. (2005-
COD BOD total N total P mgO2/L mgO2/L mg/L mg/L 15.68 4.40 2.13 2.12 79.80 23.20 8.51 15.93 40.47 14.57 4.55 5.55 22.70 1.70 3.39 3.68 441.92 58.90 85.10 17.44 172.56 29.03 34.22 9.26 26.20 3.60 3.51 1.55 75.40 36.70 23.84 5.82 56.57 19.13 14.06 4.29 6.72 0.96 2.02 1.35 302.00 99.80 11.50 4.70 84.55 30.28 6.26 3.08 27.00 4.39 4.44 0.69 60.80 34.70 25.10 9.62 38.38 14.27 14.55 5.16 104.00 22.60 28.80 2.65 1790.00 1800.00 45.30 2.83 618.00 512.10 37.05 2.74 54.40 15.80 25.60 8.06 436.80 321.00 68.70 16.90 259.16 124.83 46.63 11.58 10.90 10.50 6.90 3.03 94.40 25.10 23.40 9.69 66.38 16.60 16.32 5.69 8.96 1.00 0.75 0.21 737.60 408.00 125.00 44.50 91.47 34.69 20.91 7.07 * Spatially distributed samples that were taken from the stream’s route surrounding.
Concentrations of total nitrogen (TN) and total phosphates (TP) were found to be relatively higher in the upper parts of the watershed (such as in the Zomar or in the Taybe region), compared to downstream areas such as Elyashiv station (34.33 and 9.26 mg/lit in Zomar, 46.63 and 11.58 mg/lit in Taybe, and 14.06 and 4.29 mg/lit in Elyashiv, see Table 4.1). This can be attributed to the fact that the sources of the water upstream are from semi-treated effluents in Zomar stream and Taybeh’s raw sewage in the upper part of the Alexander sub-basin. On the other hand, downstream flow had additional water sources, from reservoirs and small springs, diluting nutrient concentrations (Table 4.3).
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Table 4.2: Average daily baseflow discharge at the automatic stations (May 2005 – Apr 2006).
Daily Discharge (m3/day) May-05 Jun-05 Jul-05 Aug-05 Sep-05 Oct-05 Nov-05 Dec-05 Jan-06 Feb-06 Mar-06 Apr-06
Elyashiv Zomar R57 19,508.5 7,387.4 860.2 16,416.8 7,004.0 319.9 16,416.7 6,336.8 0.0 16,416.7 6,336.8 0.0 16,416.8 5,659.8 0.0 15,880.0 6,078.7 860.2 19,812.2 8,440.7 17,831.1 27,828.2 15,043.8 9,297.4 28,418.3 13,337.0 13,497.0 26,023.0 14,842.3 14,839.9 19,800.8 10,154.9 10,599.9 30,203.4 7,416.0 8,958.7
Streamflow measurements and sampling: The summer season represents the stream’s most steady state because there are no sudden changes in streamflow discharges or dramatic changes in water structure from precipitation. Therefore, several measurements were performed during a short time period on any given day (6-10 hours). These samples defined streamflow discharge and pollution fluxes from different sub-basins represented by stream sections. However, samples were only taken from several locations in the stream because of measuring difficulties (such as unreliable cross sections). Typical summer spatial flow measurement, performed in June 2006, showed that both Zomar and Upper-Alexander (at Zomar station and R57 station, respectively) contribute significant portions of the flow volume. In addition, effluents from fish ponds, treated urban sewage, occasional effluent spills, and back flow from irrigation in agricultural areas may contribute to the streamflow’s volume difference (figure 4.1).
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Discharge (m3/day)
Daily discharge measured in June 2006
18000
16000
16000 12662
13123
downstream Aleander
Maabarot FishPonds
14000 12000 10000 8000 6000
4394
4000 2000
442
0 R57 Station
Zomar Station
Elyashiv
Location
Figure 4.1: Discharge measurements along Alexander stream’s main route (from up stream to down).
Chemical analysis of the streamflow in different locations along the stream revealed variations in pollutant concentrations. This fact can be attributed to additional water sources along the stream (figure 4.1). Moreover, variations in land-use and discharges from facilities in different sub-basins draining into the stream’s segments affect its water quality. It can be inferred that effluents from point sources, such as YadHana wastewater treatment plant (WWTP) and Taybeh's raw sewage in the upstream, and reservoirs and fish ponds in the downstream affect the quality of the water (Table 4.3).
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Table 4.3: Concentrations at different sampling sites along the Alexander main routes (June 11, 2006). COD BOD (mgO2/lit) (mgO2/lit) TN (mg/lit) TP (mg/lit) Location Zomar stream output from 86.7 12.0 65.1 9.09 wwtp Zomar 89.9 10.6 50.7 5.93 Station Taybe 436.8 321 65.6 16.90 sewage R57 Station 15.7 4.40 4.19 3.53 Joint Alexander61.8 19.4 40.3 6.93 Zomar Maabarot 35.2 22.0 24.1 5.62 FishPonds 54.7 15.9 23.8 5.82 Elyashiv
Bringing together the measured daily discharge rates with nutrient concentrations assists in estimating the daily nutrient fluxes that cross the stream’s different sections. Table 4.4 shows that TN is mostly contributed from Zomar sub-basin as treated effluents of Yad-Hana WWTP (222.6 Kg/day) with additional loads after the fish-ponds area (316222.6 = 93.4 Kg/day). The table also shows that a significant amount of TP is contributed from Zomar sub-basins after the WWTP (26 Kg/day) to form one third of TP loads at Ma’abarot sampling area. Bearing in mind that phosphorus is commonly found in agricultural runoff, these results emphasize the effect of land-use on water quality in the downstream segments, which receive discharges primarily from agricultural areas and fish-ponds. As a result, TP loading increases as the water flows down the stream. Table 4.4 also shows that a slight difference in the concentration along the stream has significant change to the loadings due to the variation in the streamflow’s discharge. For example TP concentration changed slightly from 5.9 mg/lit upstream to 5.8 mg/lit downstream, but the loadings increased from 222 Kg/day up to 381 Kg/day.
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Table 4.4: Discharge rates, nutrients’ concentrations, and daily fluxes in the Alexander watershed (June 11th 2006).
Location Zomar Station R57 Station Maabarot FishPonds Elyashiv
TN Discharge (m3/day) TN (mg/lit) TP (mg/lit) (Kg/day)
TP (Kg/day)
4394
50.7
5.93
222.6
26.04
442
4.19
3.53
1.85
1.56
13123 16000
24.1 23.8
5.62 5.82
316.0 381.4
73.71 93.05
It is noticeable that during baseflow measurements, upper-Alexander (represented by station R57) had only a minor effect on nutrients loadings due to low streamflow discharge rates. The next sections (4.2 and 4.3) introduce two major flood events caused by representative rainfall storms, emphasizing the change in total balance loads in the watershed.
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4.2 Storm 1- Dec, 24th-26th, 2005 From two major storms which occurred in Dec 2005, complete sets of samples and discharge records were collected and analyzed in the course of storm of Dec 24th-26th 2005. The following information explains and analyzes the storm’s features.
4.2.1 Rainfall, runoff, and water samples Figure 4.2 shows the average precipitation measured over the watershed during the simulated season of 2005-2006. Precipitation data show that the selected rain event of 24th-26th Dec 2005 was estimated to contribute almost 65% of the total monthly precipitation ( see table 4.5). As mentioned in chapter 3, precipitation in each sub-basin was calculated based on the records supplied by eight meteorological stations, spread throughout the Alexander drainage area. In addition, calculated precipitation on each subbasin (table 4.6) gave higher resolution of estimated rainfall, and assisted in understanding the measured flow from each station and the general water balance. 60
Average daily rain (mm/day)
50
40
30
20
10
8/ 10 15 /05 /1 0 22 /05 /1 0 29 /05 /1 0/ 5 / 05 11 12 /05 /1 1 19 /05 /1 1/ 26 05 /1 1/ 3 / 05 12 10 /05 /1 2 17 /05 /1 2 24 /05 /1 2 31 /05 /1 2/ 0 7/ 5 1/ 1 4 06 /1 / 2 1 06 /1 / 2 8 06 /1 /0 4/ 6 2/ 1 1 06 /2 / 1 8 06 /2 / 2 5 06 /2 /0 4/ 6 3/ 1 1 06 /3 / 1 8 06 /3 / 2 5 06 /3 /0 1/ 6 4/ 0 8/ 6 4/ 1 5 06 /4 /0 6
0
Date
Figure 4.2: Average daily precipitation in the Alexander watershed.
66
Table 4.5: Average monthly precipitation and events percentages in the Alexander watershed (20052006). Percentage Percentage Average from the from the event 2 monthly Event 1 month month (mm) (mm) (mm) Month September 0 0 0 0 October 18 10.08 56.0% 7.53 41.8% November 65 44.51 68.9% 22.35 34.6% December 142 42.46 29.8% 93.63 65.8% January 165 60.58 36.7% 38.08 23.0% February 116 77.47 66.9% 30.74 26.6% March 16 12.87 80.4% 2.80 17.5% April 94 47.32 50.4% 20.44 21.8% Table 4.6: Calculated precipitation in the Alexander watershed per sub-basin. Alex Mid Amatz Alex Up Al-Teen Zomar Day 22/12/2005 0.00 0.00 0.02 0.21 2.97 23/12/2005 4.30 4.45 3.40 7.68 8.47 24/12/2005 16.00 16.00 12.42 10.37 33.98 25/12/2005 68.20 68.10 62.21 58.09 45.01 26/12/2005 7.15 7.35 9.05 14.87 8.72 27/12/2005 0.91 1.05 0.78 1.90 0.71
Runoff results Based on past data, the storm event that occurred between Dec 24th morning (8:00) and Dec 26th night (23:00) was chosen for analysis. Calculations showed that about 50% of the total monthly volume flowed at the Elyashiv station during this event alone. Moreover, the upstream station's discharges showed percentages of 32% and 55%, respectively, at Zomar and R57 (table 4.7). To conclude, storm events generate runoff that forms high portions of the total monthly discharged volume during the rainy season. However, each sub-basin has a different contribution depending on its area and land-use features (table 4.7). Moreover, the hourly hydro-graph in figure 4.3 shows the temporal and spatial correlation between all three stations. The graph clearly shows the variation in time of water discharge contribution from Zomar and R57 (upper-Alexander) stations, which were located upstream, to the discharge measured at Elyashiv station (middle-Alexander).
67
Table 4.7: Comparison between sub-basins contribution of volume discharges of Dec 2006 monthly and the rain event.
Total monthly Event Percentage
Elyashiv Zomar R57 2,146,620 647,273 1,033,758 1,053,325 208,822 572,213 49% 32% 55%
55,000
45,000 Elyashiv
Hourly Discharge Q (m3/hr)
Zomar R57
35,000
25,000
15,000
5,000
24/12/2005 6:14 -5,000
24/12/2005 14:24
24/12/2005 22:33
25/12/2005 6:43
25/12/2005 14:52
25/12/2005 23:02
26/12/2005 7:12
26/12/2005 15:21
Time
Figure 4.3: Hourly flow recorded at Elyashiv, R57, and Zomar stations, 24-26 Dec 2005.
Sampling results Forty eight samples were captured by the automatic samplers during the noticeable wave of runoff generated in most of the Alexander watershed sub-basins during rainfall events (table 4.8) and then analyzed. At that time, only three of the six samplers were installed: Elyashiv, R57, and Zomar (all on the Israeli side of the watershed). Figure 4.4 shows the installed samplers’ response to runoff generated by the storm event. Despite the lack of in-storm flow records from the Palestinian side, performing a simulation with the HSPF model has resulted in an estimation of streamflow contributed by the sub-basins on the eastern side of the watershed.
68
16 Elyashiv Sample ELY
14
Zomar Sample ZMR
12 Water disharge Q (m3/sec)
Sample R57 R57 running Average (Ave. per 10)
10
8
6
4
2
0 24/12/2005 24/12/2005 24/12/2005 24/12/2005 25/12/2005 25/12/2005 25/12/2005 25/12/2005 26/12/2005 26/12/2005 26/12/2005 0:00 6:00 12:00 18:00 0:00 6:00 12:00 18:00 0:00 6:00 12:00
Time
Figure 4.4: Dec 2005 flood event: Elyashiv, Zomar, and R57 stations' records; and samples' time sets (m3/sec).
Figure 4.4 above, shows a temporal relationship between all three station’s hydrographs representing the sub-basins (Zomar, Upper-Alexander, and MiddleAlexander). A clear relationship is found between discharge changes at R57 and Elyashiv stations, Upper-Alexander and Middle-Alexander respectively. In addition, the increase of Zomar’s discharge affects the total flow at Elyashiv. Moreover, Al-Teen and Amatz sub-basins are drained to the main route of the Alexander, also affecting the flow at. Flows at the last two stations were not measured, although it can be estimated from the amount of total flow volume that crossed Elyashiv. Chemical analysis of manual samples taken from Al-Teen and Zomar enable an estimate of the nutrient fluxes. Table 4.8: Dec 2005 flood event duration at all three stations and number of samples at each station.
Zomar R57 Elyashiv Event Start 24/12/2005 8:00 24/12/2005 8:02 24/12/2005 9:30 First Sample 25/12/2005 1:26 24/12/2005 11:04 24/12/2005 13:00 Last Sample 25/12/2005 9:26 25/12/2005 11:08 25/12/2005 11:00 Event End 26/12/2005 23:00 25/12/2005 19:00 12/26/2005 3:00 no. of samples 12 18 18
69
4.2.2 Nutrient loadings and concentrations Water samples from the stream flow were analyzed for water chemistry at the hydrology department laboratory and set on the hydrographs. Nitrogen and phosphorous concentrations were studied and their results presented as follows:
Total Nitrogen During the flood event, changes in total nitrogen concentrations varied in patterns specific to each of the stations. On the one hand, at the upstream stations, Zomar and R57, concentrations of TN increased (~6 up to ~16 mg/lit and ~2 up to ~6 mg/lit, respectively) during the beginning of the flood wave, but decreased during the rest of the wave (see table below). Thus, these findings confirm a "first flush" effect (Skipworth et al., 2000; Lee et al., 2002). This in demonstrated in figures 4.5, 4.6, and 4.7On the other hand, at Elyashiv, the down stream station, there was an anticipated pattern of concentration changes with regards to the sample timing during the flood. The flood formed two major waves with a clear increase of TN concentrations during the rising of the first wave, but it dropped during the second wave of flow. When compared to the upstream stations, at Elyashiv there is narrow range of change in the concentration that can only be attributed to the dilution of the heavily polluted base flow from the upperAlexander’s and Zomar’s flow by "cleaner water" from Amatz and Al-Teen sub-basins. Figure 4.5, 4.6, and 4.7 present a clear depiction of the storm event: 1. Increasing TN levels at all stations during the first 5-7 hours of the flood event. (Each station showed a different magnitude of change, for example, TN measured concentrations at the Zomar station displayed a range of changes over 10 mg/lit (5.7-16.2 mg/lit), as is shown in Figure 4.6. 2. At all stations, TN levels dramatically dropped after the initial peak in TN, but remained above the original concentrations until the end of the flood event. TP trends are in figure 4.7. 3. Increases in TN concentration was shown to be repeated in correlation with the second major discharge wave, clearly noticed at R57 station.
70
In order to explain the spatial relationship between all three stations, figure 4.8 shows a correlation between upstream (Zomar and R57) and down stream concentrations. This spatial-temporal association has an important role in defining the area from which pollutants contribute to the general stream flow. For example, area 1 in figure 4.8 shows that increasing levels of TN at the R57 and Zomar stations have a chronological correlation with the increase at Elyashiv down stream. In addition, trend and area 3 in the same figure shows that during the course of the flood event all three stations showed similar TN concentrations; TN concentrations measured at Zomar and R57 station had affected the Elyashiv station. This fact proves that Elyashiv is highly affected by the upper stations. Moreover, TN's steep increase at Zomar station directly affected TN concentrations down stream at Elyashiv during the decay in concentrations at the time of the flood (see area 3 in figure 4.8 and table 4.9).
18
14,000 Zomar
Water discharge at Zomar (m3/hr)
12,000
total N
16
total P
14
10,000 12 8,000
6,000
10 8.30
8
7.35 6 5.71
4,000
4 2,000
2.37
2
1.63
0 24/12/2005 0:00
0.96
24/12/2005 12:00
25/12/2005 0:00
Concentration of total nitrogen and total phosphates (mg/lit)
16.2
1.09
25/12/2005 12:00
26/12/2005 0:00
26/12/2005 12:00
Time
Figure 4.5: Hourly flow at Zomar station, total N, and total P, 24-26 Dec 2005.
71
0 27/12/2005 0:00
7
40,000 R57
y = -4.3887x + 169893 R2 = 0.602
total N
6.042
6 total P
30,000 5
y = -4.742x + 183573 R2 = 0.3082
4.658
25,000
4 20,000 3.26
2.91
15,000
2.323
3
y = -4.012x + 155312 R2 = 0.7271
2.26
2
10,000 1.34
1.31
1
5,000
Concentrations of total nitrogen and total phosphates (mg/lit)
Water discharge at R57 station (m3/hr)
35,000
y = -0.9717x + 37619 R2 = 0.1202 0 24/12/2005 0:00
24/12/2005 12:00
25/12/2005 0:00
25/12/2005 12:00
26/12/2005 0:00
Time
Figure 4.6: Hourly flow at R57 total N, and total P, 24-26 Dec 2005.
72
26/12/2005 12:00
0 27/12/2005 0:00
Elyashiv
14
Water discharge at Elyashiv station (m3/hr)
total N 11.60
50,000
total P
12
10 40,000 8.31
8
6.99
30,000 6
5.56 4.73
20,000
4.67
4.07
y = -1.5533x + 60132 R2 = 0.3168
10,000
4
2 1.53
1.00
0 24/12/2005 0:00
24/12/2005 12:00
25/12/2005 0:00
25/12/2005 12:00
26/12/2005 0:00
26/12/2005 12:00
0 27/12/2005 0:00
Time
Figure 4.7: Hourly flow at Elyashiv total N, and total P, 24-26 Dec 2005.
Concentration changes of total nitrogen (mg/lit)
18
Elyashiv
16
R57
2
14
Zomar
12
3 10 8
1
6 4 2
0 24/12/2005 0:00
24/12/2005 6:00
24/12/2005 12:00
24/12/2005 18:00
25/12/2005 0:00
25/12/2005 6:00
25/12/2005 12:00
Time
Figure 4.8: concentration changes of total N during storm Dec 2005.
Total Phosphorous
73
25/12/2005 18:00
Concentration of total nitrogen and total phosphates (mg/lit)
60,000
26/12/2005 0:00
Relative to the TN concentrations, Figures 4.5 - 4.7 and Table 4.9 show that TP concentration had variable patterns of change at each station during flood events: 1. TP levels decreased during the first phase of the flood event with respect to the first discharge wave. For example TP concentration dramatically dropped from 11.6 to 1 mg/lit at Elyashiv station (Figure 4.5), while it decreased from 2.26 to 1.34 mg/lit at R57 station (Figure 4.6). 2. The second discharge flood had an extreme effect on TP concentrations at all stations. In a short period of time, TP concentrations noticeably increased before decreasing once again in time intervals with similar levels to previous values (Figure 4.6). 3. Elyashiv station showed significantly higher TP concentration values than the upstream stations. This is represented by the wider range and higher average (111.6 and 2.8 mg/lit, respectively). 4. TP values at Zomar and R57 stations were found to have a smaller range than at Elyashiv (1-2.4 mg/lit at Zomar and 1.2-3.1 mg/lit in R57, while 0.1-11.6 mg/lit in Elyashiv). Similar to changes of TN values, the decreasing TP values at all stations is attributed to the "first flush" effect. Moreover, the flood event’s first phase had higher values of TP at R57 and Elyashiv stations, while at Zomar the values were lower (range of 1.3-3.1 mg/lit compared to 2.4-4.3 mg/lit). In addition, streamflow at the Zomar station drains runoff with lower TP concentrations, which had an effect during the last phase of the flood. Still TP values at the Zomar station were similar to the other station due to the dilution occurring after the first flush effect at R57 and Elyashiv. Previous results assist in better understanding the spatial relationship between upstream and down stream sections in terms of pollution sources (figure 4.9 and table 4.9).
74
Concentrations changes of Total phosphorous (mg/lit)
5
Elyashiv
4.5
R57 4
Zomar
3.5 3 2.5 2 1.5 1 0.5 0 24/12/2005 0:00
24/12/2005 6:00
24/12/2005 12:00
24/12/2005 18:00
25/12/2005 0:00
25/12/2005 6:00
25/12/2005 12:00
25/12/2005 18:00
26/12/2005 0:00
Time
Figure 4.9: Concentration changes of total P during storm Dec 2005. Table 4.9: Concentration summary during the event. NO3 PO4 NH4 NO2 TSS COD BOD total N total P Location mg/L mg/L mg/L mg/L mg/L mgO2/L mgO2/L mg/L mg/L Min 5.0 0.0 3.9 0.1 4.0 5.1 7.0 4.7 0.1 Elyashiv Max 22.2 0.0 13.8 0.5 80.0 30.1 26.1 8.3 11.6 Avg 13.7 0.0 5.6 0.3 38.6 18.7 15.3 6.2 2.7 Min 7.6 0.0 7.4 0.4 10.0 19.2 14.7 5.7 1.0 Zomar Max 26.5 0.0 23.4 1.7 38.0 169.9 55.3 16.2 2.4 Avg 14.2 0.0 13.0 0.8 20.2 136.7 31.7 9.4 1.5 Min 1.0 0.0 2.7 0.1 2.0 9.0 3.6 2.1 1.2 R57 Max 10.0 0.0 8.7 0.4 64.0 163.5 27.5 6.0 3.1 5.8 0.0 4.0 0.3 27.9 98.5 11.0 3.3 1.8 Avg
75
4.2.3 HSPF simulation The Alexander sub-basins delineation was performed using an HSPF simulation model, established using the basic layers of hydro-geology, litho-logy, soils type, landuse, and land cover. Simulation system development, calibration, and validation were done together with team member Eng. Muath Abu-Saada. The system was calibrated and validated against the historical records (supplied by the Israeli Hydrological service) of Elyashiv station for the period of two years (2004-2005). At this stage, the HSPF simulation model is used to estimate flood discharge volumes from sub-basins in the watershed. In addition to the simulation, installed samplers assisted in estimating nutrient fluxes in the Alexander watershed. The system enables the user to identify nutrients’ transport between sub-basins in the watershed. The simulation results matched the daily discharge records about 90%. Table 4.10 shows measured and simulated results of discharged volumes at each station on a monthly and event basis. It shows a high match between measured and simulated records at Elyashiv and Zomar stations, but lower matching was found at R57 station. Lower matching is attributed to the difference of resolution in which the sampler’s recorded water levels (every two minutes) compared to the simulation output (hourly time steps). As result, matching at R57 station reached 61% and 83% in monthly measurements and for the event, respectively, Table 4.10. Table 4.10: Measured and simulated discharge during Dec 2005 and storm 24-26 Dec 2005. Total month (CM) Event (CM) percentage* R57 measured 1,033,758 572,213 55.4% station simulated 637,124 479,752 75.3% Upper matching** 61.6% 83.8% measured 647,273 208,822 32.3% Zomar simulated 717,856 225,964 31.5% station 110.9% 108.2% matching** Elyashiv measured 2,146,620 1,053,325 49.1% station simulated 2,312,809 1,037,625 44.9% Middle 107.7% 98.5% Alexander matching**
* Percentage of the events discharges volume from the total monthly discharge. ** Level of matching between simulation results to measured volume in monthly and event comparison.
76
Table 4.11 shows both the measured and the simulated discharge volumes over the course of a month and during the flood event. In addition, the table shows the percentage of discharge volume during flood events from the total monthly volume. Moreover, it is noticeable that the simulated results at Elyashiv and Zomar deviate by 10% from the measured values. Simulated values at R57 had a deviation of 30%-40%. In total, the event was simulated with only a 9% difference from the concentrations that were measured during the actual storm event. In turn, the similarities between measured and simulated sub-basins enable the estimation of nutrient contribution from the simulated sub-basins not sampled during the flood event. Although Zomar and Upper-Alexander sub-basins form most of the watershed’s area, Al-Teen and Amatz also contributed to the streamflow. The HSPF results assist in defining the source of the volume of the rest of the flow crossing Elyashiv. The simulation results in table 4.11 show each sub-basin’s discharge based on the total monthly contribution and the single storm event.
Table 4.11: Summary of simulation flow discharge results form sub-basins in the Alexander watershed for Dec 2005.
Dec 2005 sub-basins Alexander Middle Amatz Zomar UpperAlexander
Total month volume (m3)
sub-basin's % of monthly discharge
2,312,809 145,212 717,856
Event flow volume
subbasin's % of event discharge
27.1% 1,037,625 6.3% 105,473 31.0% 225,964
12.3% 10.2% 21.8%
637,124
27.5%
479,752
46.2%
186,934 excluding MiddleAlexander
8.1%
98,391
9.5%
Al-Teen
72.9%
87.7%
The HPSF modeling system calculates two types of runoff: runoff from precipitation in each sub-basin and runoff transferred from sub-basins upstream. The following diagram shows measured and simulated runoff discharge flow at Upper Alexander (R57 station) and Zomar sub-basin (Zomar station), and their combined contribution to the fluxes at the Middle-Alexander sub-basin (Elyashiv station).Figure
77
4.10, in which there are three hydrographs of the stations, shows measured and simulated results of hourly discharge. Different than Figures 4.11A and 4.11C that show excellent representation of results, Figure 4.11B offers a poor comparison between simulated and measured discharge. The disparity between the modeled and the measured can be explained by the difference between the time steps at the hydrometric (2 min) and the modeled values simulated in hourly rates (A, C). In addition, the gap between the measured and the modeled results is probably due to the difference between the total volumes which was estimated. (Matching is mainly based on total volume rather than hourly discharge rates. improving the resolution of the model will improve the graph matching to 108% matching.) The simulation also produced quantitative analysis of each sub-basin's contribution to the general flow balance in the watershed (Figure 4.11). The simulation’s results show that upper sub-basins (Zomar and upper-Alexander) contributed the majority of the discharge event's volume (21.8% and 46.2 %, respectively).
Upper-Alexander, 46.2%
Al-Teen, 9.5% Zomar, 21.8% Amatz, 10.2%
Alexander Middle, 12.3%
Figure 4.10: Simulated discharge of each sub-basin's contribution to the total flow balance in the watershed, flood event Dec 2005.
78
A
Road 57 14,000
35,000
B
Zomar Modelled Zomar
Modelled R57
10,000 Q (m3/hr)
Q (m3/hr)
25,000
20,000
15,000
8,000
6,000
10,000
4,000
5,000
2,000
0 24/12/2005 0:00
Measured Zomar
12,000
Measured R57
30,000
24/12/2005 12:00
25/12/2005 0:00
25/12/2005 12:00
26/12/2005 0:00
26/12/2005 12:00
27/12/2005 0:00
0 24/12/2005 0:00
24/12/2005 12:00
25/12/2005 0:00
60,000
25/12/2005 12:00
26/12/2005 0:00
Time
Time
C
Elyasiv Modelled Eyashiv Measured Elyashiv
50,000
Q (m3/hr)
40,000
30,000
20,000
10,000
0 24/12/2005 0:00
24/12/2005 12:00
25/12/2005 0:00
25/12/2005 12:00
26/12/2005 0:00
26/12/2005 12:00
27/12/2005 0:00
Time
Figure 4.11: Measured and simulated water discharge at R57 (A), Zomar (B), and Elyashiv (C) stations during the course of flood event Dec 2005.
79
26/12/2005 12:00
27/12/2005 0:00
4.3 Storm 2- Dec 26th-28th, 2006 4.3.1 Rainfall, runoff, and samples Almost in parallel to the previous year, the first major rain event was during the last week of December, from Dec. 26th-28th 2006. Unfortunately, Ma'abarot meteorological station was shut down at the beginning of the season meaning there were no rain records of the specific event. However, it was defined as the major event during the month based on flow records at Elyashiv station.
Runoff results Based on the measurements response of all three samplers installed in the Israeli side of the watershed, the flood started on 26th Dec at 20:00. Figure 4.12 indicates that the flood event started in the Upper-Alexander sub-basins (at R57 station) and included two major runoff waves. Elyashiv station also recorded larger responses to the two major runoff waves, which lasted until 43 hours later. On the other hand, Zomar station recorded shorter flood waves, which lasted for only 23 hours.
50,000 Elyashiv Zomar R57
Flow Discharge Q (m3/hr)
40,000
30,000
20,000
10,000
0 26/12/2006 12:00
27/12/2006 0:00
27/12/2006 12:00 Time
Figure 4.12: Recorded hydrographs during storm Dec 2006.
80
28/12/2006 0:00
28/12/2006 12:00
Calculations for this event (which can represent every flood event) showed that flood volume form high percentages of total monthly flow volume in the stream. It showed that during this event, about 50% of the total monthly volume flowed at Elyashiv station, more than 90% of the monthly flow of R57, and 25% of the volume at Zomar. In addition, records show that each sub-basin has a different contribution to the general discharge balance in the watershed. Two of the runoff's waves were recorded in the upper and middle Alexander sub-basins, while only the second runoff wave was recorded to be contributed by Zomar sub-basin (Figure 4.12 and Table 4.12). Table 4.12: Comparison between sub-basin’s contribution of volume discharges of Dec 2006 monthly and rain event.
Elyashiv Zomar R57 Total monthly 1,880,679 449,291 399,109 Event 984,607 113,261 374,277 Percentage 52% 25% 94%
Moreover, the first runoff wave’s peak at Elyashiv occurred in a delay of about five hours from R57 station, similar to the time delay between Elyashiv and Zomar station in the second wave. Once Zomar and R57 flow discharge increased during the second wave, it dramatically raised the flow at Elyashiv. As was already explained, in addition to Zomar and Upper-Alexander sub-basins, Elyashiv (Middle-Alexander subbasin) receives runoff flow from two more sub-basins: Al-Teen, with its three sub-basins, and Amatz. Both sub-basins had no samplers at the time of rain, preventing the measurement of flow during flood events. In turn, spatial correlation between sub-basins has an important role in defining the source of runoff and pollution (as will be explained in section 4.4).
Samples Records of all three stations showing the flood event in most of the watershed are presented in Table 4.14 and Figure 4.13. All thirty one samples taken along the flood event from all three stations were chemically analyzed in the lab. The hydrograph shows that other than Zomar station, samples were taken along the flood over time. However, only two samples were taken to represent the flood features at Zomar station.
81
16.00 Elyashiv Elyashiv samples
Flow discharge and Samples Q (m3/sec)
14.00
Zomar Zomar samples
12.00
R57 samples R57 average tredline
10.00
8.00
6.00
4.00
2.00
0.00 26/12/2006 0:00
26/12/2006 12:00
27/12/2006 0:00
27/12/2006 12:00
28/12/2006 0:00
28/12/2006 12:00
29/12/2006 0:00
29/12/2006 12:00
Time
Figure 4.13: Dec 2006 flood event: Elyashiv, Zomar, and R57 stations' records; and samples' time sets.
Calculations showed that during this flood event, Zomar and upper-Alexander contributed 25% and 94%, respectively, of the total monthly water discharge from each sub-basin. The flood event contributed 52% of the monthly total discharged water volume from the whole watershed (table 4.12). Table 4.13: Samples summary during Dec 2005 and storm Dec 2006.
Event Start First Sample Last Sample Event End number of samples
Zomar 26/12/2006 10:00 27/12/2006 10:00 27/12/2006 12:00 27/12/2006 19:00 2
R57 26/12/2006 16:00 26/12/2006 19:18 27/12/2006 14:18 28/12/2006 11:00 16
82
Elyashiv 26/12/2006 19:00 26/12/2006 22:00 27/12/2006 14:44 28/12/2006 11:00 13
4.3.2 Nutrient concentrations and loadings Total Nitrogen Related to nitrogen concentrations, during the flood event, changes in total nitrogen concentrations were found to vary in patterns and magnitude according to the sample's location (the station). Sample results at R57 and Elyashiv show noticeable decrease (with time) in TN values and dilution during the event after the "first flush" stage that has the higher concentrations. For example: TN concentration values decreased at R57 station from ~12 down to ~7 mg/lit, at Elyashiv station TN values decreased from ~36 down to 15 mg/lit, Figures 4.15, 4.16, and 4.17.
16,000
120 Zomar 102.85
total N 100 total P
12,000
80 10,000
8,000
60
6,000 40 4,000 19.10
20
2,000 10.40 0 0 26/12/2006 26/12/2006 27/12/2006 27/12/2006 27/12/2006 27/12/2006 28/12/2006 28/12/2006 28/12/2006 12:00 18:00 0:00 6:00 12:00 18:00 0:00 6:00 12:00 Time
Figure 4.14: Hourly flow, total N, and total P during storm of Dec 2006 at Zomar station.
83
C (mg/lit)
Hourly discharge at Zomar station Q (m3/hr)
14,000
y = -11.868x + 463795 R2 = 0.3225 14
35,000
30,000
total N
12.20
12
total P 9.48
25,000
10
y = -9.2221x + 360392 R2 = 0.7168 8
20,000 6.77
7.14 15,000
6
5.58
C (mg/lit)
Houlry discharge at R57 station Q (m3/hr)
R57
4
10,000 2.98 5,000
y = -5.8542x + 228776 R2 = 0.8916
2
1.58
0 0 26/12/2006 26/12/2006 27/12/2006 27/12/2006 27/12/2006 27/12/2006 28/12/2006 28/12/2006 28/12/2006 12:00 18:00 0:00 6:00 12:00 18:00 0:00 6:00 12:00 Time
Figure 4.15: Hourly flow, total N, and total P during storm of Dec 2006 at R57 station.
60,000
Elayshiv total N 33.18
35
total P 30
40,000
y = -50.496x + 2E+06 R2 = 0.5578
y = -99.351x + 4E+06 R2 = 0.8914
30,000
25 C (mg/lit)
Hourly discharge at Elyashiv station Q (m3/hr)
35.81 50,000
40
20 16.62 13.65
15
14.82 13.65
20,000 10 10,000
y = -13.494x + 527345 R2 = 0.4066
7.42
5.15
y = -51.011x + 2E+06 R2 = 0.9543
5
0 0 26/12/2006 26/12/2006 27/12/2006 27/12/2006 27/12/2006 27/12/2006 28/12/2006 28/12/2006 28/12/2006 12:00 18:00 0:00 6:00 12:00 18:00 0:00 6:00 12:00 Time
84
Figure 4.16: Hourly flow, total N, and total P during storm of Dec 2006 at Elyashiv station.
Flood's records at R57 and Elyashiv stations showed two major runoff waves with high TN concentrations during the first phase of the flood event, which decreased as more runoff volume was discharged in the stream. A spatial effect can be identified in the second wave, while the nitrogen concentration increased as a result of the influence of Zomar sub-basins. A correlation between upstream (Zomar and R57) and down stream concentrations is shown in figure 4.18. Trends in the figure points out the following correlations: 1. With the beginning of the runoff, TN values at R57 were in the highest range (7.3-11.8 mg/lit), while with the runoff decays, TN values subsequently decreased (6.7-9.5 mg/lit). 2. At the Zomar station, samples showed remarkable values of TN concentration close to the peak time of the wave (more than 100 mg/lit) followed by a significant drop (19.1 mg/lit). 3. TN values at Elyashiv station had much higher values than at R57 during the beginning segment of the event - 35 mg/lit. Then the concentrations decreased to 14 mg/lit as more water flowed in the stream, which can be explained by the “first flush effect” mentioned earlier. 4. Higher concentrations at the lower parts of the watershed indicate additional sources of Nitrogen contributed from that part. 5. There was a chronological correlation between TN values of samples taken at the Zomar station and the ones at Elyashiv, emphasizing nutrient transport between sub-basins.
85
40
120 35.81
35
Elyashiv 100
R57
30
Zomar 80
25
20
60
19.16
C (mg/lit) *Zomar*
Total nitrogen concentrations (mg/lit)
102.85
33.18
16.62 15
14.82
11.78 10
40
9.48 6.77
7.33
0 26/12/2006 12:00
26/12/2006 18:00
20
19.10
5
27/12/2006 0:00 27/12/2006 6:00
27/12/2006 12:00
27/12/2006 18:00
0 28/12/2006 0:00
Time th
th
Figure 4.17: Concentration of total N during the flood of Dec 26 -28 2006.
Total Phosphorous In general, Figure 4.18 shows that total phosphorous (TP) concentrations at R57 and Elyashiv stations continuously decreased during the course of the flood event as more water was discharged in the stream. However, values at Elyashiv station also showed an increase correlated to the second runoff wave of discharge that was contributed by Zomar sub-basin (Figures 4.12 and 4.14). In addition, the previous increase was chronologically correlated to the increase at Zomar station up stream. Once loads are calculated (section 4.4), a linear correlation was proved between sub-basins.
86
Concentrations changes of Total phosphorous during storm 20 Elyashiv 18 R57
Total P Concentration (mg/lit)
16
Zomar 13.65
14
13.65
12 10
A
8 7.42 5.58
6
5.15 4 2
B
0 26/12/2006 12:00
1.58
26/12/2006 18:00
27/12/2006 0:00 27/12/2006 6:00
27/12/2006 12:00
27/12/2006 18:00
28/12/2006 0:00
Time
Figure 4.18: Change of total P concentrations during the flood of Dec 26th-28th 2006.
Furthermore, Figure 4.18 and Table 4.14 show a higher and wider range of TP concentrations (A) at Elyashiv than at R57 station (B), whose range was 13.7-5.2 mg/lit and 5.6-1.6 mg/lit, respectively. In turn, previous values indicate that additional sources of water with higher TP concentrations had overcome any dilution effect at the Elyashiv station. TP at Zomar station was noticeably high in both samples that we took. Table 4.14: Concentrations summary during the flood event of Dec 26th-28th 2006. NO3 PO4 NH4 NO2 TSS COD BOD total N total P Location mg/L mg/L mg/L mg/L mg/L mgO2/L mgO2/L mg/L mg/L Min 3.0 1.6 2.1 0.1 652.0 134.4 40.3 14.8 5.2 Elyashiv Max 66.5 9.9 16.0 8.3 6598.0 731.2 149.0 35.8 13.7 27.7 5.9 7.9 1.1 4005.2 369.2 100.3 23.0 10.2 Avg Min 4.8 3.9 0.9 0.1 636.0 29.4 4.5 6.8 1.3 R57 Max 18.5 9.2 4.3 0.6 1896.0 239.4 39.5 12.2 5.6 Avg 9.9 6.4 2.2 0.2 1340.9 140.9 27.7 9.5 3.3 Min 89.4 18.0 24.9 0.0 16044.0 4713.6 -102.8 10.4 Zomar Max 103.1 21.6 29.8 0.0 18204.0 5107.2 -114.8 19.1 96.2 19.8 27.4 0.0 17124.0 4910.4 -108.8 14.8 Avg
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Results of samples collected during the summer and over the course of flood events of the years 2005 and 2006 assisted in better understanding the spatial relationship between upstream and down stream parts with regards to phosphorous sources. In addition, the HSPF simulation helped in estimating the contribution of additional runoff sources to the overall TP load.
4.3.3 HSPF simulation Following the previous storm modeling, a continuous simulation was performed including the storm event of Dec 2006. Simulated flow can assist in estimating nutrient sources in the watershed. Once again, simulated results were compared to the measured data and matching results found (Table 4.15). The simulation resulted in an overwhelming match with the measured discharge at Elyashiv and R57 station in a monthly comparison, as well as per rain event. However, the simulation predicted different discharge at Zomar station than the actually measured. A great deal of this deviation is attributed to the fact that the Yad-Hana waste water treatment plant is located at the outlet of the sub-basin. In turn, the WWTP diverted some of the runoff generated at the beginning of the storm to its treatment pool. As a result, a lower volume of runoff than the simulated result was measured during the month, and specifically during the event. The diversion had a larger magnitude due to the small volume generated as result of the rainfall. Table 4.15: Summary and comparison between measured and simulated discharge. Total month (CM) Event (CM) percentage* R57 measured 399,109 374,277 93.8% station simulated 445,987 383,802 86.1% Upper matching** 111.7% 102.5% measured 449,291 113,261 25.2% Zomar simulated 574,284 180,771 31.5% station matching** 127.8% 159.6% Elyashiv measured 1,880,679 984,607 52.4% station simulated 1,965,888 1,037,625 52.8% Middle matching** 104.5% 105.4%
* Percentage of the event’s discharge volume from the total monthly discharge. ** Level of matching between simulation results to measured volume in monthly and event comparison.
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Vast deviation was found on a monthly basis and during events at R57 and Elyashiv stations. On the whole, the system overestimated the discharge from the Zomar sub-basin. Nonetheless, simulated volume discharged at the major three sub-basins can assist in estimating nutrients’ fluxes in the rest of the watershed. Moreover, the simulation results show each sub-basin’s discharge during the month and for the chosen flood event (Table 4.16). The simulation shows that 12.2% and 15.2 % of the discharged volume was contributed from Amatz and Al-Teen sub-basins. In turn these volumes will indicate the magnitude of their specific nutrients loadings (to be discussed in chapter 5).
Table 4.16: Simulated discharge as it contributed from sub-basins in the watershed. % of % of discharge Total month discharge at Event flow at volume (m3) Elyashiv volume Elyashiv
Alexander Middle Amatz Zomar Alexander Upper Al-Teen excluding Alexander middle
1,965,888 145,212 574,284
30.3% 1,037,625 7.4% 126,568 29.2% 180,771
18.2% 12.2% 17.4%
445,987
22.7%
383,802
37.0%
205,628
10.5%
157,425
15.2%
69.7%
81.8%
As was explained in the previous simulation, the HPSF modeling system calculates two types of runoff: runoff resulted from precipitation on each sub-basin and runoff transferred from sub-basins upstream. Previous tables showed simulation's runoff results in monthly and event's volumes attributed to each of the ten sub-basins created in the simulation. For comparison, the end of the table also contains measured flow.
89
Zomar, 29.2% Alexander Upper, 22.7%
Al-Teen, 10.5% Amatz, 7.4%
Alexander Middle, 30.3%
Figure 4.19: Simulated discharge distribution of the flood event Dec 2006.
4.4 Nutrient loadings and transport during storm events Nutrient loads were calculated based on analyzed samples during the course of the flood event. Flow discharge was recorded as two-minute time-steps throughout each event at all stations (Zomar, R57, and Elyashiv), while samplers were programmed to grab samples depending on the stream route's features. The linear correlation between nutrients’ values of samples taken was multiplied by the flow discharge during the flood event, which resulted in nutrient fluxes at all three stations (Figure 4.20). By using this method, flux results can be represented in better resolution for further analysis.
4.4.1 Total nitrogen Figure 4.20 shows the temporal correlation between up-stream stations and the down-stream one (Elyashiv) during the event in Dec 2005. It shows that flood discharge increased TN loadings at Elyashiv (~35 up to ~240 TN Kg/hour) in large amounts, even before the up-stream sub-basins contributed. Later during the flood event, up-stream subbasins had their effect on the Elyashiv’s records down-stream (up to ~300 TN Kg/hour). The figure also shows a temporal correlation between stations representing the sub-basins
90
from which TN is contributed. Zomar and upper-Alexander sub-basins added to the second flux wave 12 hours after the flood started.
Elyashiv
300
Zomar
Total nitrogen fluxes (Kg/hr)
R57
250
200
150
100
50
0 24/12/2005 0:00
24/12/2005 6:00
24/12/2005 12:00
24/12/2005 18:00
25/12/2005 0:00
25/12/2005 6:00
25/12/2005 12:00
25/12/2005 18:00
26/12/2005 0:00
Time
Figure 4.20: Total nitrogen fluxes during flood event Dec 2005.
Changes in nutrient fluxes over time crossing each station were found to express the spatial and temporal correlation between sub-basins. This shows the second nitrogen flux at Elyashiv was chronologically related to the fluxes measured at Zomar and R57 stations. The case was repeated once again during the flood event in Dec 2006, as the spatial correlation is proved in figure 4.21. The figure shows that Zomar sub-basin directly changed TN loads at Elyashiv station down-stream, while R57 played a minor role. Tables 4.19 and 4.21 repeatedly show that similar discharged water volumes from Zomar and upper-Alexander sub-basins during the month contributed extremely different nitrogen loads (Zomar had discharged almost four time the loads from upper-Alexander). Moreover, in comparison to upper-Alexander sub-basin, Zomar had contributed similar nitrogen loads and less than one third of the discharged water volume.
91
Elyashiv
1,400
Zomar R57
Totla nitrogen fluxes (Kg/hr)
1,200 1,000 800 600 400 200 0 26/12/2006 12:00
26/12/2006 18:00
27/12/2006 0:00
27/12/2006 6:00
27/12/2006 12:00
27/12/2006 18:00
28/12/2006 0:00
Time
Figure 4.21: Total nitrogen fluxes during flood event Dec 2006
Tables 4.17 and 4.18 summarize total discharged volume and loads in Dec 2005 and as a result of the event. The tables show that the amount of water discharged does not determine the discharged nitrogen loads. On the contrary, lower monthly discharge instead led to the discharge of higher nitrogen loads. The case of results from R57 and Zomar prove that fact: over 1,000,000 m3 was measured to be discharged at R57 and almost 650,000 m3, however, only 6,725 Kg of TN was discharge from the first station, while Zomar sub-basin contributed almost 16,000.
Table 4.17: total flow volume in Dec 2005 and flood event 24-26 Dec 2005. Monthly Event Percentage* Station Cubic meter Cubic meter Zomar 647,273 210,685 33% R57 1,033,758 590,190 57% Elyashiv 2,146,620 1,060,439 49%
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Table 4.18: total N loads in Dec 2005 and flood event 24-26 Dec 2005. Monthly Event Total Nitrogen Kg Kg Zomar 15,907 2,983 R57 6,725 2,945 Elyashiv 21,896 7,417
Percentage*
19% 44% 34%
* Percentage of the event's contribution from the monthly amount. The tables also show that results of the flood event had analogous features to the monthly measurements. It was found that R57 (representing upper-Alexander sub-basin) had contributed similar amounts of nitrogen loads as Zomar sub-basin had contributed, but almost triple the water discharge. Although the amount of discharged runoff from Upper Alexander was almost three times higher than Zomar’s, their contribution to nitrogen load was similar in both events (Tables 4.19 and 4.20). This can be explained by the fact that there are more urban effluents released into the stream route from Palestinian towns along the drainage area of Zomar sub-basin. Raw sewage effluents start draining from the eastern side of the subbasin in the streambed and are discharged into the main Alexander stream. At the same time, raw sewage from Taybe is released into the fields all year around and is flushed through to the stream during flood events (chapter 3).
Table 4.19: total flow volume in Dec 2006 and storm event Dec 2006
Flow Station Zomar R57 Elyashiv
Monthly Cubic meter 449,291 399,109 1,880,679
Event Cubic meter 113,261 374,277 984,607
Table 4.20: total N loads in Dec 2006 and storm event Dec 2006. Total Nitrogen Monthly Event Kg Kg Zomar 26,648 11,787 R57 3,421 3,275 Elyashiv 35,577 19,778
Percentage* 25% 94% 52%
Percentage* 44% 96% 56%
* Percentage of the event's contribution from the monthly amount. The flood event of Dec 2005 was longer in time than the one in Dec 2006. Still, similar runoff was discharged at the Elyashiv station. Therefore, the second event reached
93
a higher peak (1250 against 300 Kg/hour only). While in the Dec 2005 event, Zomar and Upper-Alexander sub-basins discharged most of the TN reaching Elyashiv station (5.9 of 7.4 Ton); only 30% was discharged in Dec 2006 (2.4 of 8 Ton). Thus, less pollution was drained in the upper sub-basins, but more was contributed from the lower parts of the watershed. Unlike the Upper-Alexander sub-basin, much of Zomar sub-basin is urbanized with no treatment solution to its sewage collection networks (Table 4.21). Eventually, these effluents (or raw sewage discharges) find their way to the stream itself. This sewage is considered a major point-source of nitrogen compounds. Lacking suitable wastewater treatment such as secondary and tertiary plants, the sewage is deposited onto open areas, and subsequently joins the non-point flow of pollution. At the Upper-Alexander subbasin, most of the sewage systems arrive at the Tnouvot Treatment Plant where it is treated and used for agriculture.
Table 4.21: Land-use area and areas and percentages/ Zomar AREA (Km2) Urban (including road network) Field crops Orchards Shrubs Forests TOTAL
Percentage (%) Urban (including road network) Field crops Orchards Shrubs Forests
19.55 31.45 61.07 34.45 1.4 147.92 Zomar
Total AlUpper Middle Amatz watershed Teen Alexander Alexander (Km2) 11.87 32.36 53.24 36.79 0.08 134.3
20.92 30.34 22.79 14.48 1.49 90.02
13.87 34.33 15.16 0.49 0.28 64.13
6 14.26 21.71 0.91 0.41 43.29
Middle AlUpper Amatz Teen Alexander Alexander
13.2% 8.8% 21.3% 24.1% 41.3% 39.6% 23.3% 27.4% 0.9% 0.1%
23.2% 33.7% 25.3% 16.1% 1.7%
21.6% 13.9% 53.5% 32.9% 23.6% 50.2% 0.8% 2.1% 0.4% 0.9%
72.2 142.7 174.0 87.1 3.7 479.7 Total watershed 15.1% 29.8% 36.3% 18.2% 0.8%
Based on water chemistry and flow measurements, each sub-basin was estimated to contribute different TN loads. Also, TN loads during storm-floods appear to be much higher in comparison with the TN transferred in the base flow during the same range of time (table 4.22). The table shows that hourly TN fluxes during the flood event are equal 94
to daily baseflow flux. Furthermore, the data show that the larger the discharge of the storm, the larger the overall pollution loads (Tables 4.16 to 4.20).
Table 4.22: Average daily baseflow fluxes of nutrients (excluding flood input).
Average Daily Flux (Ton/day) May-05 Jun-05 Jul-05 Aug-05 Sep-05 Oct-05 Nov-05 Dec-05 Jan-06 Feb-06 Mar-06 Apr-06
Elyashiv Zomar R57 TN TP TN TP TN TP 343.9 100.8 218.7 53.6 5.1 5.0 289.4 84.8 207.3 50.8 1.9 1.9 289.4 84.8 187.6 45.9 0.0 0.0 289.4 84.8 280.3 52.6 0.0 0.0 289.4 84.8 250.3 47.0 0.0 0.0 280.0 82.0 179.9 44.1 10.3 7.9 349.3 102.4 348.6 65.4 0.9 0.9 472.1 138.7 445.3 109.1 110.8 53.4 402.5 127.1 371.5 90.7 75.4 73.5 360.6 115.1 436.9 106.5 78.9 77.8 349.1 102.3 300.6 73.6 56.4 55.6 532.5 156.0 328.0 61.6 44.9 44.3
4.4.2 Total Phosphorous The previous method used to calculate nitrogen fluxes was used for phosphorous loads. Phosphorous loads, which are closely related to fertilizer use in agriculture, were compared at each station. Both analyzed flood events had different TP temporal and spatial concentration changes. Based on water chemistry samples and flow calculations, each sub-basin was estimated to contribute different TP loading, which could be attributed to the generated runoff from the sub-basin. For example, the flood of Dec 2006 reached a peak of double the flux-rate measured in the Dec 2005 flood event (Figures 4.22 and 4.23).
95
250
Elyashiv Zomar R57
Total P flux (Kg/hr)
200
150
100
50
0 24/12/2005 0:00
24/12/2005 6:00
24/12/2005 12:00
24/12/2005 18:00
25/12/2005 0:00
25/12/2005 6:00
25/12/2005 12:00
25/12/2005 18:00
26/12/2005 0:00
Time
Figure 4.22: total P fluxes during Dec 2005 flood event.
Two major flux waves and peaks occurred during this flood event (Figure 4.23), in addition to the temporal and spatial correlation between upper stations and Elyashiv, especially for the second flux wave. Due to insignificant changes in Zomar's contribution of TP, the figure shows that Elyashiv is dominantly affected by R57 station and the loads contributed by upper-Alexander sub-basin. On the other hand, the flood event in Dec 2006 (figure 4.22) shows that the majority of TP measured at Elyashiv was not the result of Zomar and upper-Alexander sub-basins. Instead, additional sources of water with higher pollution levels appear to have overcome any such dilution effect at Elyashiv.
96
Elyashiv
500
Zomar R57
Total P flux (Kg/hr)
400
300
200
100
0 26/12/2006 12:00
26/12/2006 18:00
27/12/2006 0:00
27/12/2006 6:00
27/12/2006 12:00
27/12/2006 18:00
28/12/2006 0:00
28/12/2006 6:00
28/12/2006 12:00
Time
Figure 4.23: total P fluxes during Dec 2006 flood event.
Tables 4.23 and 4.24 show the relationship between runoff discharged in both events and TP loads from each sub-basin. The tables show that majority of TP loading occurred during the flood event (coherently shown at R57 station 41% and 89% in Dec 2006 and 2006, respectively) was discharged during the flood event. It is important to point out that, similar to the case of TN, short periods of floods contributed to large amounts of TP transports. That might be attributed to the fact that various non-point sources of water could find their way to the stream route when they were absent from the baseflow. Table 4.23: total P loads in Dec 2005 and storm event Dec 2005.
Monthly Kg Zomar R57 Elyashiv
Event Total Phosphorous Kg 3,763 597 3,583 1,460 7,232 2,613
97
Percentage*
16% 41% 36%
Table 4.24: total P loads in Dec 2006 and storm event Dec 2006. Total Phosphorous Monthly Event Kg Kg Zomar 4,087 1,298 R57 1,275 1,131 Elyashiv 12,655 8,025
Percentage* 32% 89% 63%
Tables 4.26 and 4.27 summarize the monthly and event’s calculated nutrient fluxes. Measured TN was found to be generally consistent in both flood events (76% and 80%), while TP was found to more than double in Dec 2006, as compare to Dec 2005 (30% to 79%). Table 4.25: summary of flow volume, and total N and total P loads during Dec 2005 storm. measured flow and loads at R57 & Zomar of Elyashiv Dec-05 Q (m3) TN (Kg) TP (Kg) Zomar 210,685 2,983 597 R57 590,190 2,945 1,460 Sum 800,875 5,928 2,057 Elyashiv 1,060,439 7,417 2,613 Zomar 19.9% 40.2% 22.9% R57 55.7% 39.7% 55.9% Sum 75.5% 79.9% 78.7% Table 4.26: summary of flow volume, and total N and total P loads during Dec 2006 storm. measured flow and loads at R57 & Zomar of Elyashiv Dec-06 Q TN TP Zomar 113,261 11,787 1,298 R57 374,277 3,275 1,131 Sum 487,538 15,062 2,428 Elyashiv 984,607 19,778 8,025 Zomar 11.5% 59.6% 16.2% R57 38.0% 16.6% 14.1% Sum 49.5% 76.2% 30.3%
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4.5 Annual mass balance of water discharge and nutrient loadings Total annual loads of nutrients were calculated by separating the flow into two stages: dry weather (baseflow) and storm events. Measured stream flow head was converted to hourly flow along the year. Baseflow was calculated to be under 2,000 m3/hr, 500 m3/hr, and 15 m3/hr at Elyashiv, Zomar, and R57 respectively. At baseflow condition, hourly volume was multiplied by the average concentration of samples taken in each station. During the winter, several storm events were calculated according to the records from automatic samples (Figures 4.24, 4.25, and 4.26).
Elyashiv discharge (CM/month)
4,000,000 3,500,000 Flood 3,000,000
Baseflo
2,500,000 2,000,000 1,500,000 1,000,000 500,000 0 May- Jun05 05
Jul05
Aug- Sep- Oct- Nov- Dec- Jan- Feb- Mar- Apr05 05 05 05 05 06 06 06 06 Month
Figure 4.24: discharge of baseflow and flood at Elyashiv station.
99
70 Elyashiv TN monthly load (Ton/month)
Flood Addition Base Flow
60 50 40 30 20 10 0
May- Jun- Jul-05 Aug- Sep05 05 05 05
Oct05
Nov- Dec05 05
Jan06
Feb06
Mar06
Apr06
Month
Elyashiv TP monthly load (Ton/month)
Figure 4.25: Fluxes of total nitrogen in baseflow and flood.
25 Flood Addition Base Flow
20 15 10 5 0 May05
Jun- Jul-05 Aug05 05
Sep05
Oct05
Nov05
Dec05
Jan06
Feb06
Mar06
Apr06
Month
Figure 4.26: Fluxes of total phosphorous in baseflow and flood.
However, not all storm events were captured and monitored comprehensively, so the contributions of these unmonitored events were calculated according to the measured storm's average concentration at the same station, multiplied by total discharge volume (Table 4.27).
100
Table 4.27: Measured discharged flow and calculated loads of TN, and TP for the year 2005-2006.
R57 Zomar MiddleAlexander, AlTeen, & Amatz Elyashiv
Discharge TN load TP load MCM % Ton/year % Ton/year % 6.7 48.5% 33.9 16.8% 26.4 42.5% 4.0 29.1% 118.2 58.5% 26.0 41.7%
3.1 22.4%
50.0 24.7%
13.8
202.1
9.9 15.8% 62.3
Some water quality parameters changed dramatically between the flood events and the "normal" baseflow conditions. In addition, addressing the urban effluent sources lead to the identification of a major source of water pollution: untreated sewage discharges. Among the monitored sub-basins, the Zomar tributary contributed most of the baseflow water volume, which contained a great portion of raw sewage partially treated at Yad-Hana emergency waste water treatment plant before being released back to the stream route in the Israeli section (Table 4.27 and Figure 4.27). In addition, there are several ephemeral point sources of water pollution in the watershed; many of these sources may not be sufficiently characterized in model or storm event loading estimates. Examples of these effluent sources include the fish ponds in the lower parts of the watershed which occasionally discharge the ponds’ contents and excess treated effluents not used by farmers (Map 7.2). Hydro-chemical analyses of baseflow data for the stream listed in table 4.2 reveal a broad range of conditions in different locations along the stream. This spatial variation reflects diverse processes occurring along the stream such as: treated and untreated sewage discharges, dilution levels, natural purification processes, and agricultural effect. However, total P concentrations deviated little during the course of the stream’s flow (from 1.5 to 44.5 mg/lit). Rainfall events were found to cause sequential shifts in pollution concentrations and in some cases caused a noticeable drop in the concentration average (due to washing and first flush effects). For example, the average total P concentration decreased from 5.5 to 2 mg/lit at R57 station and in other cases, average concentration increased, and even doubled (such as Elyashiv station in which concentration climbed from 5 to 10.5 mg/lit in average). Moreover, extreme events were
101
found to exceed the lowest concentration edges due to the strong dilution effect that caused a drop in concentration averages that were as low as 1 mg/lit of total P and N (Table 4.14).
4.5.1 Land-use effect on water quality and spatial transport of nutrients The monitoring efforts through flow-measurement and sample-analysis over the year revealed a broad range of discharges and nutrient concentrations. In turn, the data were used to calculate the pollutant loadings and fluxes contributed by each of the monitored sub-basins. However, several areas of the watershed remained without sufficient data for calculation, such as the Amatz and Al-Teen segments. Different than R57 and Zomar stations, which measured the flow contributed by Upper-Alexander and Zomar sub-basins, Elyashiv station measured the flow from all 5 sub-basins. Table 4.27 shows the annual flow of discharge contributed by the Upper-Alexander and the Zomar. In addition it shows the volume discharge contributed by Amatz, Al-teen and Middle Alexander as extracted from measurements at the Elyashiv station. Table 4.28 shows that Zomar watershed contributed high portions of the total nitrogen and phosphorous loads (58.5% and 41.7%, respectively) measured at Elyashiv station, down stream, despite the lower portion of the annual flow discharge (29.1%). This phenomenon is attributed to the fact that flow in Zomar sub-basins is mostly generated by sewage effluents from towns in the Palestinian West Bank. On the other hand, the upper Alexander (R57) contributes less than 20% of TN but more than 40% of TP, while it provides 48.5% of the total runoff. These numbers point to the fact that there are fewer sources of sewage drained in the upper-Alexander sub-basin than in the lower ones.
102
Table 4.28: percentages of flood and baseflow contribution to the annual discharge and pollution fluxes.
Flood contribution Discharge R57 74.9% Zomar 23.4% Elyahiv 44.3% basflow R57 25.1% Zomar 76.6% Elyahiv 55.7%
NO3 PO4 NH4 NO2 TSS COD BOD total N total P 72.2% 57.2% 61.8% 56.8% 97.8% 70.4% 64.7%
65.7%
63.5%
15.1%
7.4% 48.9% 29.6% 13.4%
13.3%
11.0%
40.3% 35.2% 35.9% 32.6% 41.8% 37.8% 42.9%
36.1%
38.3%
27.8% 42.8% 38.2% 43.2%
2.2% 29.6% 35.3%
34.3%
36.5%
84.9% 91.1% 87.3% 92.6% 51.1% 70.4% 86.6%
86.7%
89.0%
59.7% 64.8% 64.1% 67.4% 58.2% 62.2% 57.1%
63.9%
61.7%
8.9% 12.7%
Figure 4.27 shows the annual discharge and nutrients loads contributed by Amatz, Al-Teen, and Middle Alexander. All three sub-basins contributed 22.4% of the total discharged water volume, as well as 24.7% and 15.8% of annual TN and TP, respectively, measured downstream at Elyashiv. It is important to emphasize, that geographically, they form 50.4% of the watershed’s area (9%, 28%, and 13.4, respectively as sub-basins). Measured annual discharge MCM/year 250 13.8
14
Discharge 202.1
TP load
12
200
10 150
8 118.2
6.7
6
100 4.0 62.3
4
33.9
3.1
50.0 50
26.4
26.0
2
9.9
0
0
R57
Zomar
Station
Middle-Alexander, AlTeen, & Amatz
Elyashiv
Figure 4.27: Measured annual discharged flow, and calculated TN & TP loads
103
calculated TN & TP loads Ton/year
TN load
As a result of runoff inputs during the winter season, discharge volumes, TN, and TP fluxes significantly increased (Figures 4.25, 4.26, and 4.27). Table 4.28 emphasizes the fact that runoff during floods (excluding baseflow contents) contributed over one third of the annual loads of TN and TP during the winter season. It is important to point out that almost two-thirds of the annual TN and TP drained in the Upper-Alexander subbasin (represented by R57 station) is drained during flood events. On the other hand, Zomar sub-basin’s flood drained only 13.3% and 11% of the annual TN and TP fluxes, respectively. In turn, concentrations can be attributed to the stabilizing factor Yad-Hana WWTP plays in the watershed’s mass balance. A combination between land-use, discharged runoff, total Nitrogen, and total Phosphorous (Table 4.29) shows a linear correlation between agricultural area and nutrient loads. In addition, pollution sources were addressed according to their effect on the sub-basin in which they were located. Figure 4.28 shows that as agricultural area in the watershed increases, more TN and TP are released to the stream route as part of the annual loading balance. Results (figure 4.29) also show that total loads of TN and TP from discharged runoff is correlated to the land-use in the sub-basin from which it is contributed.
Table 4.29: comparison between Land-use area and annual pollution loads in the Zomar and, Upper Alexander and the total Alexander watershed (May 2005- April 2006). Measured Zomar upper-Alexander Aelxander (overall)
Annual discharge (m^3) flood discharge (m^3) total area (Km^2) Urban (Km^2) Field crops (Km^2) Orchards (Km^2) Shrubs (Km^2) Forests (Km^2) annual TN (ton) annual TP (ton) TN (flood) (ton) TP (flood) (ton) Dec 05 TN (ton) Dec 05 TP (ton)
4,019,827 942,341 147.92 19.55 31.45 61.07 34.45 1.4 118.20 25.95 15.7 2.8 15.9 3.8
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6,689,280 5,013,407 90.02 20.92 30.34 22.79 14.48 1.49 33.88 26.45 22.2 16.8 6.7 3.6
13,803,974 6,117,512 479.7 72.21 142.74 173.97 87.12 3.66 202.05 62.26 72.9 23.9 21.9 7.2
Annual TN & TP (Ton/year)
250 y = 0.5479x + 33.606 R2 = 0.8581
200
annual TN annual TP Linear (annual TN) Linear (annual TP)
150
100 y = 0.1448x + 15.908 R2 = 0.9774 50
0 0
50
100
150
200
250
300
350
Field crops and orchards area (Km2)
Figure 4.28: Linear correlation between agricultural area and annual TN and TP loads.
Dec 05 flood's TN & TP loads (Ton)
25.0 y = 0.047x + 7.5957 R2 = 0.7655 20.0 Dec 05 TN Dec 05 TP Linear (Dec 05 TN) Linear (Dec 05 TP)
15.0
10.0 y = 0.0144x + 2.6398 2 R = 0.9909 5.0
0.0 0
50
100
150
200
250
300
350
Field crops and orchards area (Km2)Km2
Figure 4.29: Linear correlation between field crops and orchards’ area, and Dec 2005 event’s TN and TP loads
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4.5.2 Simulation results Results of the HSPF simulation assisted in configuring each sub-basin flow’s annual pollution contribution (May 2005 - April 2006) in hourly time steps. Table 4.30 shows the simulation results of the sub-basins contribution during the Dec 2005 flood event. The simulation results match about 88% of the discharge measured at the lower point of Middle Alexander sub-basin (Elyashiv station). According to the simulation, Upper-Alexander and Zomar sub-basins contributed 46.2% and 21.8% (sum of 68%), respectively, while samplers recorded 55.7% and 19.9% (sum of 75.6%), respectively, at the parallel stations. In addition, there was a match of 89%, between annual simulation and measured flow, which offers a satisfying level of flow assessment in the un-measured sub-basins, Amatz and Al-Teen (which contributed 10.2% and 9.5% of the flood volume).
Table 4.30: simulated flow volumes and percentages of main sub-basins, Dec 2005.
Dec 2005 sub-basin Alexander Middle Amatz Zomar UpperAlexander Al-Teen
Total month volume (m3) 2,312,809 145,212 717,856
subsub-basin % basins % of monthly Event flow of event discharge volume discharge 27.1% 1,037,625 6.3% 105,473 31.0% 225,964
12.3% 10.2% 21.8%
637,124
27.5%
479,752
46.2%
186,934
8.1%
98,391
9.5%
In turn, the sub-basins’ contribution of runoff during the flood event was consistent with previous figures, with slight distribution changes between sub-basins. In both events, for example, the upper-Alexander was found to generate a greater runoff portion of the event than simulated (46% and 37% during Dec 2005 and Dec 2006, respectively, Table 4.32).
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Table 4.31: simulated flow volumes and percentages of main sub-basins, Dec 2006.
Total month volume (m3) Dec-06 Alexander 1,965,888 Middle 145,212 Amatz 574,284 Zomar Alexander 445,987 Upper Al-Teen
205,628
sub-basin % of monthly discharge
subbasins % of event discharge
Event flow volume
30.3% 1,037,625 7.4% 126,568 29.2% 180,771
18.2% 12.2% 17.4%
22.7%
383,802
37.0%
10.5%
157,425
15.2%
Simulation results show the total annual runoff discharge contributed by all subbasins in the Alexander watershed. Results of the simulation had also shown that Zomar contributed the majority of the discharged runoff in the watershed (43%, figure 4.30). The runoff contributed by Zomar is due to continuous sewage flow that is generated from towns in the sub-basin, while the runoff generated in upper-Alexander, Al-teen and Amatz sub-basins is mainly due to flood events caused by rainfall.
Zomar 43% Al-teen 7%
middle Alexander 25%
upper-Alexander 22%
Amatz 3%
Figure 4.30: Simulated annual discharge distribution in the Alexander watershed.
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At a monthly level, Amatz, Middle-Alexander, and Al-Teen were found to contribute 20% and 24% of TN transported during the flood event of Dec 2005 and 2006, respectively. In contrast, TP had a wider range between flood events (79% and 30% during Dec 2005 and 2006 flood events, respectively), as shown in tables 4.30 and 4.31. Along with the sampling campaigns, representative samples were analyzed for TN and TP concentrations. Field sampling showed that 20.1% and 22.3% of TN and TP, respectively, were found to be generated in from Zomar and Upper-Alexander subbasins.
Due to the fact that Amatz sub-basin contains more intensively utilized
agricultural land than the Al-Teen, it tends to contribute more phosphorous to the streamflow during flood events, (see tables 4.3 and 4.4).
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Chapter 5 Discussion and summary This chapter discusses the findings of the study and answers its goals, with relation to the sources of pollution loads contributed to the streamflow. The study’s survey revealed that there were several point-sources of pollution, distributed throughout the watershed’s sub-basins, which contributed nutrients to the stream-flow. Pollutants were released to the base-flow and were washed to the stream during flood events. Although rainfall normally causes the dilution of pollution in streamflow, runoff increased the total contamination loads due to large runoff discharges. Sub-basins were found to contribute different volumes of runoff during the summer season than the winter season, as a result of flood events and effluent discharges. Unlike Al-Teen sub-basin in which the stream flows only during times of heavy precipitation, effluents were discharged to Zomar sub-basin throughout the year. Agricultural land-use is the main land-use practice in Al-Teen sub-basin, while Zomar had larger urban practices. As a result, Zomar’s runoff features were found to be similar to urban effluents (sewage, high TN, TP, and BOD concentrations), while Al-Teen’s was found to be regular agricultural runoff (low TP and TN concentration). Similar to Al-Teen, Amatz and upper-Alexander had high percentages of agricultural land-use. This produced insignificant amounts of return flow during the summer. Instead, flood events formed their major stream-flow during the winter season. The lowest part of the study area, the middle-Alexander sub-basin, collects water from all the previous sub-basins. Therefore, this route flows all year round, accumulating the pollution loads transferred from up-stream areas. The correlation between land-use, discharged runoff, total nitrogen, and total phosphorous is a linear pattern between agricultural area and nutrient loads. Based on water chemistry and flow measurements, each sub-basin was estimated to contribute different TN loading which can be attributed to the sub-basin from which the runoff was generated. Moreover, pollution sources were addressed according to their affect on the sub-basin in which they were located. Although the amount of discharged runoff from Upper Alexander during the winter season was almost three times higher than the Zomar, the nitrogen load was similar in both analyzed events. This is explained by the fact that there are more urban effluents
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released into the stream route from Palestinian towns along the drainage area of Zomar sub-basin. Raw sewage effluents start draining for the eastern side of the sub-basin in the streambed and are discharged into the main Alexander stream. At the same time, raw sewage from Taybe is released into the fields throughout the year, and then flushed through to the stream during flood events. Unlike the Upper-Alexander sub-basin, much of the Zomar sub-basin is urban with no treatment solution for sewage collection networks. Eventually these effluents (or raw sewage discharges) find their way into the stream itself. This sewage is considered a major point-source of nitrogen compounds. This lack of suitable wastewater treatment, such as secondary and tertiary plants, means that the sewage is deposited onto open areas, and subsequently joins the nonpoint source flow of pollution. In the Upper-Alexander sub-basin, most of the sewage is taken to the Tnouvot Treatment Plant, where it is treated and used for agriculture. Discharged total pollution loads (such as TN and TP) during storm-floods had much higher pollution loads, compared to the base flow during the same range of time. Furthermore, the data show that the larger the discharge of the storm, the larger the overall pollution loads, despite the low concentration of pollution. Calculations show that the sum total of 62.3 tons of total phosphorous (TP) were generated in the watershed between May 2005 and April 2006, from which over 50 tons were from the Zomar and Upper-Alexander sub-basins. Due to discharged flood-waters, Zomar’s average base flow concentration was lower than R57, although both contributed similar annual loads (25.9 and 25.5, respectively). The high average of TP concentration in discharged flood runoff, relative to the Zomar sub-basin, is attributed to the large amount of agriculture around the upper-Alexander. Looking at these results along with land-use data showed a lower contribution from agricultural areas in the Zomar and Al-Teen sub-basins, compared to the upper and middle Alexander and Amatz segments. Both analyzed flood events had different changes in TP temporal and spatial concentration. The flood of December 2005 had a broader range of concentration at the downstream Elyashiv measuring station than in the upper parts. In turn, additional sources of water with higher pollution levels appear to have overcome any such dilution effect at Elyashiv.
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It is important to point out that short periods of flooding contributed to large amounts of TP and TN being transported between sub-basins. In this way, various nonpoint sources of water could find their way to the stream route while being absent from the base flow. Rainfall enables the "washing" of a watershed's nutrients, and drains them into the stream route from upper sub-basins to the lower ones, and eventually transferred to the sea. Urban areas also had an affect on discharged TN loads in Zomar sub-basin. A larger volume of discharged runoff from Upper-Alexander during flood events than the Zomar resulted in similar TN loads discharged to the stream flow. This is attributed to the fact that during the course of a flood event, there are more urban effluents released into the stream route from the Palestinian towns along the drainage area of Zomar sub-basin. Raw sewage effluents start draining from the eastern side of the sub-basin in the streambed and are discharged into the main Alexander stream. At the same time, raw sewage from the city of Taybe is released into the fields all year round and is flushed to the stream during flood events. The variation in the water chemistry of the stream flow is attributed to different sources of pollution in each sub-basin. For example, most of the middle-Alexander sub-basin is cultivated as an agricultural area in which farmers use more fertilizers, which eventually lead to larger loads of phosphorous than transferred from Zomar or Al-teen sub-basins. In general, the simulation produced an overwhelmingly high match with measured flow and the predicted runoff from the monitored sub-basins. Results show that about 50% of December's runoff volume flowed at Elyashiv during the course of the flood, 25%-31% of the month's runoff drained in Zomar sub-basins crossing the Zomar station, and 86%-94% crossed the R57 station. Also, the simulation predicted that during the storm event, flow at Zomar and R57 would constitute 17.4% and 37% of the flow at Elyashiv, respectively (actual measured records were 11.5% and 38%, respectively). Moreover, the simulation showed an overall runoff generated from upper sub-basins that was more than 81% of the flow crossing the Elyashiv station. In turn, consistency between measured and simulated runoff from sub-basins enabled an approximation of the nutrient contribution from each sub-basin that was not sampled during the flood event. The simulation results show that about 82% of runoff reaching the lower point of middle-
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Alexander sub-basin (at Elyashiv station) was generated from upper sub-basins, while the rest was 18%. According to the simulated event, upper-Alexander and Zomar sub-basins contributed 37% and 17.4% of the event's runoff (sum of 54.4%), respectively, while the samplers recorded 38% and 11.5% (sum of 49.5%) at the parallel stations. This is a good enough match (90%) to determine that the contribution of the other simulated unmeasured sub-basins, Amatz and Al-Teen, contributed an additional 12.2% and 15.2% of the event’s flood volume. Comparing base flow loads and storm event loads, calculations show greater load fluxes being transferred during storm events than the usual daily fluxes. In most cases, 30-80% of the monthly total N and P load were transferred during flood events. This leads to the attribution of the increase of pollution loads as a result of flood events to non-point sources of pollutants in the watershed. Furthermore, the data show that the higher the storm flow volume, the greater the pollution loads. Hence, several storms, such as 24th to 26th Dec 2005, caused large amounts of pollution loads discharged into the stream flow. Similar storm events were found to have high discharge peaks and pollution fluxes due to nutrients already contained in the stream flow. Still, different storms were found to contribute diverse quantities of pollutants. Even though concentrations were lower in storm events than regular base flow, the “cumulative effect” causes an increase in total pollution loads and overall mass balance. Results suggest that storm events cause the transport of 40%-80% and 70%-90% of TN and TP, respectively, from non-point sources. Nonetheless, these figures do not take into account chronic sewage discharged to the stream. It is likely, however, that over time, the point-source sewage contribution will continue to drop as the sanitation infrastructure improves and the percentage of wastewater utilized for irrigation increases. In addition, total suspended solids (TSS) and sediments found in stream flow tend to positively affect TN and TP mass balances. TSS was found to be lower during the summer dry-season and far below in the average values recorded during the winter season. The fact that higher loads and dissolved constituency were found during the course of flood rather than in base flow suggests an impact of non-point sources (such as cultivated lands) on the total load in the stream. The base flow’s pollution primarily
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comes from point sources (such as WWTPs and fishponds), as opposed to the washed out fertilizers, pesticides, and herbicides which play a major role during floods. Furthermore, the stream flow carries with it various pollutants, of which total P and total N were found to be distributed differently than the water volume distribution. It was found that Zomar contributed 73% and 45% of annual loads to total N and total P, respectively. On the other hand, the Upper Alexander, that supplied almost 50% of the annual flow volume, contributed only 22% and 36% of the annual loads of TN and TP, respectively. Still, a small portion of the annual loads (5 and 18 of TN and TP loads, respectively) was generated from the further sub-basins. See figures 5.2 and 5.3. In shorter time sets, the study shows more precise results with regards to the flow origin in the stream. Both the best monitored and simulated storms offer a better picture of pollution loading's dynamics.
Conclusions This study found that the stream flow in the Alexander watershed is polluted because of two main categories of pollution: point and non-point sources of pollution. The base flow was mainly contaminated by the point source pollution in the major stream routes. Pollution loads from point sources were measured and monitored in several key locations. On the other hand, non-point sources of pollution gained greater magnitude during flood events because they were washed into the watershed with runoff. The study’s findings assisted in calculating quantitative estimations of pollution loads and identified the area from which they were generated. The study also found that base flow pollution is mainly contributed from lower parts of the watershed (middle-Alexander and Amatz sub-basins) and treated effluents from Yad-Hana. Upper parts of the watershed (Zomar, Al-Teen, and upper-Alexander sub-basins) were found to contribute most of the total flood pollution loads. Due to the variation in pollution patterns between point and non-point pollution sources, it is suggested to address both sources as two separate categories of pollution once a restoration plan is established.
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124
Chapter 7 Appendix
Figure 7.1: Stabilized stream cross-section located at Deir Sharaf.
A
B
C
Figure 7.2: Sigma 900 Max Portable Sampler. A. Sampler inside the station’s box. B. Data logger. C. 24-bottle carousel.
125
Table 7.1: Automatic sampling campaigns during seasons 2005-2006. Event Zomar R57 Event Start NA 16/12/2005 14:38 16-17 Dec First Sample NA 16/12/2005 14:38 05 event Last Sample NA 17/12/2005 15:24 Event End NA 17/12/2005 0:00 number of samples 0 17 Zomar R57 Event Start 24/12/2005 8:00 24/12/2005 8:02 24-26 Dec First Sample 25/12/2005 1:26 24/12/2005 11:04 05 event Last Sample 25/12/2005 9:26 25/12/2005 11:08 Event End 26/12/2005 23:00 25/12/2005 19:00 number of samples 12 18 Zomar R57 Event Start 13/1/2006 23:00 13/1/2006 23:00 13-15 Jan First Sample NA NA 06 event Last Sample NA NA Event End 17/1/2006 1:00 17/1/2006 1:00 number of samples 0 0 Zomar R57 Event Start 26/1/2006 19:00 25/1/2006 23:00 25-26 Jan First Sample 27/1/2006 1:14 NA 06 event Last Sample 27/1/2006 14:00 NA Event End 28/1/2006 23:00 29/1/2006 23:00 number of samples 11 0 Zomar R57 Event Start 9/2/2006 1:00 9/2/2006 4:00 9-10 Feb First Sample 9/2/2006 10:26 9/2/2006 9:44 06 event Last Sample 10/2/2006 11:10 10/2/2006 23:44 Event End 10/2/2006 23:00 13/2/2006 6:00 number of samples 17 20 Zomar R57 Event Start 26/12/2006 10:00 26/12/2006 16:00 26-28 Dec First Sample 27/12/2006 10:00 26/12/2006 19:18 06 event Last Sample 27/12/2006 12:00 27/12/2006 14:18 Event End 27/12/2006 19:00 28/12/2006 11:00 number of samples 2 16
126
Elyashiv 16/12/2005 19:00 17/12/2005 10:00 17/12/2005 16:46 19/12/2005 0:00 7 Elyashiv 24/12/2005 9:30 24/12/2005 13:00 25/12/2005 11:00 12/26/2005 3:00 18 Elyashiv 14/1/2006 4:00 15/1/2006 0:28 15/1/2006 13:30 1/17/2006 6:00 8 Elyashiv 26/1/2006 4:00 27/1/2006 0:46 27/1/2006 15:16 29/1/2006 4:00 13 Elyashiv 9/2/2006 4:00 NA NA 11/2/2006 18:00 0 Elyashiv 26/12/2006 19:00 26/12/2006 22:00 27/12/2006 14:44 28/12/2006 11:00 13
Table 7.2: summary of major flood events and analysis of water chemistry. % of month Discharge NO3 PO4 NH4 NO2 TSS COD BOD total N total P Elyashiv 428,504 739 289 2,765 216 88,270 18,169 6,540 2,884 1,221 16-17 Dec 05 event R57 201,791 1,020 4,592 418 354 28,503 7,776 1,579 874 721 Zomar 50,260 702 1,045 2,077 294 6,048 4,596 1,343 1,488 364 Elyashiv 2,146,620 28,708 5,132 18,332 4,168 197,658 86,070 30,051 21,896 7,232 monthly R57 1,033,758 5,833 12,392 7,204 1,090 49,205 82,828 10,482 6,725 3,583 Zomar 647,273 9,303 10,112 22,249 2,983 60,928 64,359 18,972 15,907 3,763 Elyashiv 1,080,550 16,742 451 6,460 663 50,860 23,371 12,426 7,772 2,717 24-26 Dec 05 event R57 591,355 3,597 2,324 3,281 313 13,725 60,077 6,110 2,959 1,467 Zomar 225,474 3,410 1,339 4,822 519 10,172 25,790 7,702 3,421 705 Elyashiv 2,146,620 28,708 5,132 18,332 4,168 197,658 86,070 30,051 21,896 7,232 monthly R57 1,033,758 5,833 12,392 7,204 1,090 49,205 82,828 10,482 6,725 3,583 Zomar 647,273 9,303 10,112 22,249 2,983 60,928 64,359 18,972 15,907 3,763 Elyashiv 413,504.39 4,776.76 1,396.37 632.67 51.32 27,884.47 13,529.44 6,822.86 1,354.03 972.74 13-15 Jan 06 event R57 329,504.83 1,679.21 1,170.79 777.05 57.51 9,555.64 9,556.71 4,951.37 997.07 780.40 Zomar 38,837.92 542.62 807.83 1,604.65 226.94 4,673.50 3,551.34 1,037.75 1,717.70 322.35 Elyashiv 2,202,043 28,731 10,407 15,091 4,716 116,462 103,648 39,047 20,424 7,729 monthly R57 1,527,618 7,749 21,584 13,876 1,597 44,300 72,471 20,078 7,024 6,504 Zomar 439,797 5,824 8,553 17,060 2,382 49,000 38,149 11,997 18,235 3,415 Elyashiv 555,183 3,784 1,833 1,670 115 6,780 20,618 9,801 2,515 1,687 25-26 Jan 06 event R57 354,395 1,806 1,213 806 58 10,277 10,198 5,334 1,065 831 Zomar 34,919 167 131 332 16 280 1,127 1,178 329 55 Elyashiv 2,202,043 28,731 10,407 15,091 4,716 116,462 103,648 39,047 20,424 7,729 monthly R57 1,527,618 7,749 21,584 13,876 1,597 44,300 72,471 20,078 7,024 6,504 Zomar 439,797 5,824 8,553 17,060 2,382 49,000 38,149 11,997 18,235 3,415 Elyashiv 3,096,347 54,444 21,281 44,057 15,902 283,029 215,657 53,883 54,392 15,947 1,854,133 5,935 3,341 8,246 126 2,030,147 53,931 0 6,379 3,560 9-10 Feb 06 event R57 Zomar 548,941 2,278 1,901 7,620 90 336,252 78,781 99 7,098 1,031 Elyashiv 3,989,693 67,016 25,696 50,822 18,160 350,163 260,973 69,177 63,376 19,059 monthly R57 3,458,968 13,637 37,791 31,652 2,772 2,074,320 150,656 17,433 16,032 12,392 Zomar 951,803 7,907 10,280 24,265 2,444 384,730 115,619 10,864 19,023 3,952 Elyashiv 979,457 31,099 5,961 8,386 681 4,654,905 263,713 96,868 19,688 7,999 374,956 3,479 2,547 1,112 125 473,104 47,560 7,929 3,279 1,134 26-28 Dec 06 event R57 Zomar 120,395 11,547 2,544 3,609 47 14,488 537,867 22,764 12,102 1,357 Elyashiv 1,880,679 46,970 12,169 21,260 5,330 4,737,642 326,661 112,538 35,577 12,655 monthly R57 399,109 3,601 3,097 1,464 167 473,804 49,070 8,209 3,421 1,275 Zomar 449,291 16,142 9,385 17,198 1,969 301,862 567,941 31,553 26,648 4,087
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Table 7.3: major flood events' percentages of monthly loads. Event/Month (%) 16-17 Dec 05 event
24-26 Dec 05 event
13-15 Jan 06 event
25-26 Jan 06 event
9-10 Feb 06 event
26-28 Dec 06 event
Elyashiv R57 Zomar Elyashiv R57 Zomar Elyashiv R57 Zomar Elyashiv R57 Zomar Elyashiv R57 Zomar Elyashiv R57 Zomar
Discharge NO3 PO4 NH4 NO2 TSS COD BOD total N total P 20% 3% 6% 15% 5% 45% 21% 22% 13% 17% 20% 17% 37% 6% 33% 58% 9% 15% 13% 20% 8% 8% 10% 9% 10% 10% 7% 7% 9% 10% 50% 58% 9% 35% 16% 26% 27% 41% 35% 38% 57% 62% 19% 46% 29% 28% 73% 58% 44% 41% 35% 37% 13% 22% 17% 17% 40% 41% 22% 19% 19% 17% 13% 4% 1% 24% 13% 17% 7% 13% 22% 22% 5% 6% 4% 22% 13% 25% 14% 12% 9% 9% 9% 9% 10% 10% 9% 9% 9% 9% 25% 13% 18% 11% 2% 6% 20% 25% 12% 22% 23% 23% 6% 6% 4% 23% 14% 27% 15% 13% 8% 3% 2% 2% 1% 1% 3% 10% 2% 2% 78% 81% 83% 87% 88% 81% 83% 78% 86% 84% 54% 44% 9% 26% 5% 98% 36% 0% 40% 29% 58% 29% 18% 31% 4% 87% 68% 1% 37% 26% 52% 66% 49% 39% 13% 98% 81% 86% 55% 63% 94% 97% 82% 76% 75% 100% 97% 97% 96% 89% 27% 72% 27% 21% 2% 5% 95% 72% 45% 33%
4,500,000
4,000,000
Monthly Discharge (m3/month)
3,500,000
3,000,000
2,500,000
Elyashiv R57 Zomar
2,000,000
1,500,000
1,000,000
500,000
0 May- Jun-05 Jul-05 Aug-05 Sep-05 Oct-05 Nov-05 Dec-05 Jan-06 Feb-06 Mar-06 Apr-06 05 Month
Figure 7.3: monthly runoff discharge.
128
Monthly TN loads (Ton) 70.000
60.000
Ton/month
50.000
40.000 R57 Elyashiv Zomar 30.000
20.000
10.000
0.000 May-05
Jun-05
Jul-05
Aug-05
Sep-05
Oct-05
Nov-05
Dec-05
Jan-06
Feb-06
Mar-06
Apr-06
Month
Figure 7.4: Monthly TN loads. Monthly TP Loads (Ton) 20 18 16 14
Ton/month
12 R57
10
Elyashiv Zomar
8 6 4 2 0 May-05 Jun-05
Jul-05
Aug-05 Sep-05
Oct-05 Nov-05 Dec-05 Jan-06 Month
Figure 7.5: monthly TP loads.
129
Feb-06 Mar-06
Apr-06
Annual TN & TP load (Ton) Ton 16 202.1 200
TN load (Ton) 13.8
14
TP load (Ton) Discharge (MCM)
12
146.7
150 10
8
6.7
100
6 62.3 4.0 45.1
4
50 28.0
23.0
2
0
0 R57
Zomar
Elyashiv
Station
Figure 7.6: annual runoff discharge, TN, and TP.
Table 7.4: summary of monthly data at Zomar sation. R57 Month May-05 Jun-05 Jul-05 Aug-05 Sep-05 Oct-05 Nov-05 Dec-05 Jan-06 Feb-06 Mar-06 Apr-06
Discharge (Cubic meter) NO3 PO4 NH4 NO2 TSS COD 26,669 134.8 606.9 388.6 46.8 773.4 1,666.3 9,598 48.5 218.4 139.8 16.9 278.3 599.7 0 0.0 0.0 0.0 0.0 0.0 0.0 0 0.0 0.0 0.0 0.0 0.0 0.0 0 0.0 0.0 0.0 0.0 0.0 0.0 26,669 134.8 606.9 388.6 46.8 773.4 1,666.3 8,644 43.7 196.7 125.9 15.2 250.7 540.1 1,033,758 4,813.5 7,799.5 7,204.3 735.6 49,205.0 82,827.9 1,527,618 7,749.4 21,584.4 13,875.7 1,596.9 44,300.2 72,470.5 3,458,968 13,636.8 37,791.3 31,652.1 2,772.1 2,074,320.0 150,656.3 328,597 1.7 7.5 4.8 0.6 9.5 20.5 268,761 1.4 6.1 3.9 0.5 7.8 16.8
BOD total N total P 309.6 320.6 154.7 111.4 115.4 55.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 309.6 320.6 244.7 100.3 103.9 50.1 10,482.2 6,725.1 3,583.3 20,078.2 12,219.7 6,504.5 17,433.4 25,257.6 12,392.5 3.8 4.0 3.0 3.1 3.2 2.5
6,689,280 26,564.5 68,817.8 53,783.7 5,231.3 2,169,918.3 310,464.6 48,831.6 45,070.1 22,990.9 (Ton) (Ton) (Ton) (Ton) (Ton) (Ton) (Ton) (Ton) (Ton)
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Table 7.5: summary of monthly data at R57 station. Discharge Zomar (Cubic Month meter) NO3 PO4 NH4 NO2 TSS COD 229,008 3,199.5 4,763.4 9,461.8 1,338.1 27,557.3 20,940.5 May-05 Jun-05 210,120 2,935.7 4,370.5 8,681.5 1,227.8 25,284.4 19,213.4 196,440 2,744.5 4,086.0 8,116.2 1,147.8 23,638.3 17,962.5 Jul-05 196,440 2,744.5 4,086.0 8,116.2 1,147.8 23,638.3 17,962.5 Aug-05 Sep-05 170,001 2,375.1 3,536.0 7,023.9 993.3 20,456.8 15,544.9 188,441 2,632.8 3,919.6 7,785.7 1,101.1 22,675.7 17,231.0 Oct-05 253,572 3,542.7 5,274.3 10,476.7 1,481.7 30,513.1 23,186.6 Nov-05 Dec-05 647,273 9,302.7 10,112.3 22,249.2 2,983.3 60,928.3 64,359.2 439,797 5,824.0 8,552.5 17,060.0 2,382.3 49,000.3 38,148.9 Jan-06 951,803 7,906.7 10,280.3 24,265.0 2,444.4 384,729.6 115,618.7 Feb-06 Mar-06 314,802 4,398.2 6,547.9 13,006.6 1,839.4 37,881.2 28,785.5 Apr-06 222,480 3,108.3 4,627.6 9,192.1 1,300.0 26,771.8 20,343.6
BOD total N total P 6,119.1 10,128.4 1,900.7 5,614.4 9,293.1 1,744.0 5,248.9 8,688.0 1,630.4 5,248.9 8,688.0 1,630.4 4,542.4 7,518.7 1,411.0 5,035.1 8,334.2 1,564.0 6,775.4 11,214.8 2,104.6 18,972.2 15,906.9 3,762.5 11,996.7 18,235.3 3,415.5 10,863.7 24,969.4 4,379.0 8,411.5 13,922.9 2,612.8 5,944.7 9,839.7 1,846.5
4,020,177 50,714.9 70,156.2 145,435.1 19,387.0 733,075.1 399,297.1 94,773.0 146,739.5 28,001.4 (Ton) (Ton) (Ton) (Ton) (Ton) (Ton) (Ton) (Ton) (Ton)
Table 7.6: summary of monthly data at Elyashiv station. Elyashiv Discharge Month (Cubic meter) NO3 PO4 NH4 NO2 TSS COD BOD total N total P 604,763 10,649.4 4,165.8 8,638.3 3,119.2 55,514.9 42,237.3 10,514.0 10,661.2 3,124.4 May-05 492,503 8,672.6 3,392.5 7,034.8 2,540.2 45,209.7 34,396.8 8,562.3 8,682.2 2,544.4 Jun-05 508,919 8,961.7 3,505.6 7,269.3 2,624.9 46,716.7 35,543.3 8,847.7 8,971.6 2,629.2 Jul-05 508,919 8,961.7 3,505.6 7,269.3 2,624.9 46,716.7 35,543.3 8,847.7 8,971.6 2,629.2 Aug-05 492,503 8,673.0 3,392.7 7,035.1 2,540.3 45,211.8 34,398.4 8,562.7 8,682.6 2,544.5 Sep-05 492,279 8,669.0 3,391.1 7,031.9 2,539.2 45,191.2 34,382.7 8,558.8 8,678.6 2,543.3 Oct-05 725,925 12,783.5 5,000.6 10,369.4 3,744.3 66,639.9 50,701.5 12,620.9 12,797.7 3,750.5 Nov-05 2,146,620 28,708.3 5,131.9 18,332.4 4,168.3 197,658.0 86,070.4 30,050.7 21,896.1 7,232.1 Dec-05 2,202,043 28,731.0 10,407.0 15,091.3 4,716.0 116,462.2 103,647.8 39,046.7 20,424.2 7,728.8 Jan-06 3,989,693 67,015.7 25,696.2 50,822.0 18,159.7 350,162.6 260,973.2 69,177.0 63,376.1 19,058.6 Feb-06 613,825 10,809.5 4,228.4 8,768.1 3,166.1 56,349.2 42,872.0 10,672.0 10,821.4 3,171.3 Mar-06 1,025,984 18,067.6 7,067.6 14,655.6 5,292.0 94,185.3 71,658.8 17,837.7 18,087.6 5,300.7 Apr-06 13,803,974 220,702.9 78,884.9 162,317.4 55,235.1 1,166,018.0 832,425.6 233,298.0 202,050.8 62,257.0 (Ton) (Ton) (Ton) (Ton) (Ton) (Ton) (Ton) (Ton) (Ton)
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Table 7.7: Proposed maximum levels in effluents reuse for discharge to rivers-Inbar standard (Government of Israel, 2005)
Parameter Conductivity BOD TSS COD Ammonia Total nitrogen Total phosphorous Chloride Fluoride Sodium Fecal coliform Dissolve Oxygen pH Hydrocarbons Residual chlorine Anionic detergent Total oil SAR Boron
Rivers (mg/lit) 10 10 70 1.5 10 1.0 400 200 200/100 ml <3 7.0-8.5 1 0.05 0.5 1 -
Parameter Arsenic Barium Mercury Chromium Nickel Selenium Lead Cadmium Zinc Iron Copper Manganese Aluminum Molybdenum Vanadium Beryllium Cobalt Lithium Cyanide
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Rivers (mg/lit) 0.1 50 0.0005 0.05 0.05 0.008 0.005 0.2 .002 0.005
Maps:
Map 7.1:The Alexander watershed lays on both sides of the Green Line.
Map 7.2: Elevations of the Alexander watershed
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Map 7.3: location of installed stations.
Map 7.4: Land use map of Nablus City sub-watershed
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Map 7.5: Land-use map of Wadi Deir Sharaf sub-watershed
Map 7.6: Land-use map of Anabta-Tulkarem sub-watershed
Map 7.7: Land-use map of Nablus TP sub-watershed
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Map 7.8: Land use map of Upper-Alexander sub-watershed
Map 7.9: Land use map of Amatz sub-watershed
Map 7.10:Land use map of At Teen Upper sub-watershed
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Map 7.11: Land use map of At Teen Middle sub-watershed
Map 7.12: Land use map of At Teen Lower sub-watershed
Map 7.13: Land use map of Alexander Middle sub-watershed
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Map 7.14: Water reservoirs and sewage treatment plants in the Alexander watershed.
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