RIVER RESEARCH AND APPLICATIONS

River. Res. Applic. 26: 469–486 (2010) Published online 8 September 2009 in Wiley InterScience (www.interscience.wiley.com) DOI: 10.1002/rra.1305

LINKING GEOMORPHIC CHANGES TO SALMONID HABITAT AT A SCALE RELEVANT TO FISH JOSEPH M. WHEATON,a* JAMES BRASINGTON,b STEPHEN E. DARBY,c JOSEPH MERZ,d,g ´ VERICAT f GREGORY B. PASTERNACK,e DAVID SEAR c and DAMIA a

f

Department of Watershed Sciences, Utah State University, 5210 Old Main Hill, NR 210, Logan, UT 84322-5210, USA b Institute of Geography & Earth Sciences, Aberystwyth University, Aberystwyth, SY23 3DB, UK c School of Geography, University of Southampton, Highfield, Southampton, SO17 1BJ, UK d Cramer Fish Sciences, 636 Hedburg Way, Suite 22, Oakdale, CA 95361, USA e Department of Land, Air and Water Resources, University of California at Davis, One Shields Avenue, Davis, CA 95616, USA Hydrology Group, Forest Technology Centre of Catalonia, Crta. Sant Llorenc¸ de Morunys, km 2 (direccio´ Port del Comte), E-25280 Solsona (Lleida), Catalunya, Spain g Institute of Marine Sciences, University of California Santa Cruz, Santa Cruz, CA 95064, USA

ABSTRACT The influence of geomorphic change on ecohydraulics has traditionally been difficult to quantify. With recent improvements in surveying technology, high-resolution, repeat and topographic surveys have become a common tool for estimating fluvial sediment budgets and documenting spatial patterns of net erosion and net deposition. Using a case study from a spawning habitat rehabilitation (SHR) project on California’s Mokelumne River, some new DEM-differencing analytical tools and ecohydraulic models were used to test whether hypotheses about pool-riffle maintenance mechanisms used in designing SHR projects were producing self-sustaining spawning habitat when subjected to competent flows. Following peak flows associated with the spring snow-melt, a total of 999.6 m3 of erosion and 810.1 m3 of deposition were recorded throughout the study area, with a net loss of 196.2 m3. Using an ecohydraulic spawning habitat suitability model to segregate the sediment budget, over 53% of the area in which gravel was placed in a 2005 SHR retained the same habitat quality characteristics, and 22% improved. The response to the flood was generically characterized by shallow deposition associated with areas of divergent flow over riffles and scour associated with areas of convergent flow in pools. Areas where habitat remained stable generally experienced only lowmagnitude elevation changes, and accounted for only 19.5% of the total volumetric change. Areas where habitat quality degraded (primarily pool exit slopes) were dominated by larger magnitude erosion and made up 46% of the total volumetric change. By contrast, areas where habitat quality improved (primarily constructed riffle) accounted for 34.5% the total volumetric change, and were dominated by shallow, low magnitude deposition. The results support hypotheses about pool-riffle maintenance mechanisms used to design the rehabilitation projects, while also highlighting some simple but powerful techniques for linking ecohydraulic and geomorphic field monitoring data at a salmon-relevant spatial scale. Copyright # 2009 John Wiley & Sons, Ltd. key words: DEM of difference (DoD); ecohydraulics; fluvial geomorphology; morphological method; Mokelumne River; CA Received 25 May 2009; Revised 26 June 2009; Accepted 9 July 2009

INTRODUCTION Repeat topographic surveys are useful for monitoring geomorphic changes in rivers and calculating morphological sediment budgets (Brasington et al., 2000; Brewer and Passmore, 2002). With improvements in surveying technology, the acquisition of topographic data is now cheaper, easier and becoming more common (Downs and Kondolf, 2002). Moreover, topographic data can now readily be acquired at spatial resolutions equal to or smaller than the size of fish themselves, and can therefore be used to characterize their physical habitat (i.e. degree of suitability of local depth, velocity, substrate and cover, in a stream to support a particular biological activity) at an ecologically relevant scale. *Correspondence to: Joseph M. Wheaton, Department of Watershed Sciences, Utah State University, 5210 Old Main Hill, NR 210, Logan, UT 84322-5210, USA. E-mail: [email protected]

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As a discipline, ecohydraulics has typically only relied on topographic datasets as boundary conditions for hydraulic and ecohydraulic models. Although temporal physical habitat dynamics are frequently explored by considering how different flows (i.e. hydrologic dynamics) influence habitat quality and availability, the topographic boundary is almost always treated as static. Repeat topographic surveys provide an opportunity to look at how physical habitat changes in response to geomorphic dynamics and river management activities. With this richer topographic data, demand is increasing for robust and useful methods of analysing and interpreting digital elevation model (DEM) of differences (DoDs) calculated from repeat topographic surveys (Golet et al., 2003; Lane and Chandler, 2003). Assuming actual geomorphic changes can be distinguished from noise (i.e. minimum levels of detection can be defined), the challenge of making meaningful interpretations of the identified changes remains (Brasington et al., 2003; Lane et al., 2003). A wealth of information on the kinematics and mechanisms of geomorphic change is recorded in DoDs. As the resulting morphologies are a large part of defining the template for physical habitat conditions for aquatic and riparian species (Brierley and Fryirs, 2000), it follows that DoDs might also be useful for considering the ecohydraulic ramifications of net geomorphic change. To explore this logical conjecture more thoroughly, we consider the example of spawning salmonids. The types of abiotic factors that define good conditions for spawning and incubation are well understood (Kondolf and Wolman, 1993; Geist and Dauble, 1998; Montgomery et al., 1999; Greig et al., 2007). Riverine salmonids typically spawn in cool, clear and well-oxygenated streams. The female constructs a nest or ‘redd’ in habitats typically comprised of clean gravels (i.e. substrate with a low percentage of fine grained < 2 mm material), with appropriate depths and velocities, and adequate inter-gravel flow for embryo survival. Spawning activity is often in close proximity to structural cover (e.g. pools, large woody debris, boulder clusters and overhanging vegetation) and hydrodynamic shear zones that provide important refuge from predation and resting zones for energy conservation. (Wheaton et al., 2004c). Whether in the context of a natural non-regulated river, a heavily regulated river or a restoration project, uncertainty exists regarding the ramifications of fluvial erosion and deposition on the quality of spawning habitat (Merz et al., 2006) and the likelihood of embryo survival (Lisle and Lewis, 1992). These issues are becoming increasingly prevalent as more money is spent on river restoration efforts (Bernhardt et al., 2005), more attention is given to restoration monitoring (Downs and Kondolf, 2002) and expectations grow as to what monitoring can say about the benefits (if any) that restoration efforts provide (Wheaton, 2008). The purpose of this paper is to explicitly explore the linkages between geomorphic change and ecohydraulic habitat at a scale relevant to fish. For example, we would like to know how high-flow dam releases (defined here as greater than bankfull flows) influence physical habitat quality (as estimated from ecohydraulic habitat models) for salmonids at restoration sites. More specifically, we would like to know whether design hypotheses about pool-riffle maintenance mechanisms (e.g. Wheaton et al., 2004a,c; Elkins et al., 2007) that are being used in designing spawning habitat rehabilitation (SHR) projects are producing predicted outcomes when subjected to competent flows. These questions are addressed with analyses of repeat topographic surveys that record changes following a wet winter and large dam releases to a SHR site on the heavily regulated Mokelumne River in California. The case study we use relies on some analytical DoD tools developed by Wheaton et al. (in press) and Wheaton (2008), which account for propagated reliability uncertainties in the original DEMs, and then allow a flexible spatial segregation of the DoD sediment budget through the use of masks. Determining the ecohydraulic implications of geomorphic change is a fundamental scientific question, which has not typically been investigated at a scale meaningful to fish or in a quantitative fashion.

DoD UNCERTAINTY AND MASKING METHODS Sediment budgets derived from high resolution (i.e. finer than 2 m) DoDs are known to provide useful insight into gross geomorphic changes at the reach-scale (Lane et al., 1994; McLean and Church, 1999). However, they can also be used to evaluate more detailed mechanistic inferences of finer scale processes occurring at the resolution of the DoD. To better exploit DoDs, a robust method to account for reliability uncertainties in the DoD is required. Those reliability uncertainties include a lack of measurements, inexactness of measurements, poor spatial coverage and/or interpolation errors. This is particularly important in fluvial environments, because a large portion of the elevation changes of interest can be of relatively low magnitudes (i.e. < 0.25 m). These changes are often similar to the Copyright # 2009 John Wiley & Sons, Ltd.

River. Res. Applic. 26: 469–486 (2010) DOI: 10.1002/rra

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magnitude of uncertainty in the elevation data itself. The uncertainty in DoD-calculated net elevation change is propagated from the uncertainties in the original DEM representations and the original survey data (Lane et al., 1994). This is fundamentally a change detection problem, and the most typical way of addressing this is through the estimation of a minimum level of detection (minLoD), below which calculated changes cannot be reliably distinguished from noise (i.e. changes due to uncertainties in elevation model). A single minLoD value tends to be estimated for an entire DoD and is assumed not to vary in space (e.g. Brasington et al., 2000; Fuller et al., 2003). Unfortunately, such an approach proves overly conservative in many areas (e.g. floodplains experiencing shallow deposition) and overly liberal in other areas (e.g. steep banks experiencing erosion magnitudes similar to the bank heights). Our technique used in this paper defines a minLoD on a cell-by-cell basis in the DoD, reflecting the spatial variability of DoD uncertainty. The method is reviewed briefly here and is described fully in Wheaton et al. (in press) and Wheaton (2008). A fuzzy inference system (FIS) is employed to consider how the various components contributing to DEM uncertainty (e.g. point density, slope and instrument point quality) combine to produce elevation uncertainties in individual DEMs. It is important to emphasize that this FIS is applied on a cellby-cell basis at the resolution of the DEM (typically 25–100 cm). This is arguably the habitat scale experienced by fish. Those spatially variable uncertainties are propagated into the DoD using standard error propagation, and a T-score is then used to represent those uncertainties probabilistically (e.g. Brasington et al., 2003; Lane et al., 2003). This enables minLoD thresholding on the basis of a user-defined confidence interval. Probability estimates can be improved upon using Bayes Theorem and a movingwindow analysis of the spatial coherence of individual erosion and deposition units. Essentially, the probability of change being real for cells with small magnitude changes, which might otherwise be beneath the estimated minLoD, are reassessed based on whether or not the small magnitude change is consistent with the trend of change in surrounding cells. For example, a cell that exhibits small amounts of erosion, surrounded entirely by cells that are also erosional would be assigned a higher probability of being real, whereas if the same cell was surrounded by entirely depositional cells it would be assigned a lower probability of being real. Once both analyses are performed, a spatially variable map of the probability of DoD changes being real is used to threshold the DoD by a user-defined confidence interval (95% in this paper). Those cells with probability above the confidence interval are then included in the volumetric sediment budget estimate from the DoD. Once the best estimate of uncertainty is acquired and a thresholded DoD calculated, a technique is needed to segregate the budget spatially to interpret what the changes mean. The estimate of surface representation uncertainty is based on the quantification of a reliability uncertainty, which culminates from a combination of measurement and interpolation errors. The more interesting question of what the changes we believe to be real actually mean is an example of a structural uncertainty. One easily overlooked attribute of a DoD is the explicit information about spatial patterns of geomorphic change inherent in the maps themselves. Whereas any individual DEM only represents a snap shot in time of the Earth’s surface, a DoD actually says something about the spatial and historical contingencies (Phillips, 2001) that have coalesced to produce the more recent morphology. Wheaton (2008) highlighted three types of DoD masking that can be used for budget segregation: 1. Standard classification (e.g. morphological units, facies, habitat types, hydraulic, etc.) of either the newer (typical) or older DEM. 2. Classification of difference (CoD, unique categories by differencing standard classifications of the older and newer DEMs each with n categories; always end up with n2 categories). 3. Geomorphic interpretation of DoD (expert-based classification of DoD itself in conjunction with field evidence, aerial photos and other layers). In the context of GIS, a mask is a sub-area of an entire dataset that will be included in an analysis (Jones, 1997). If the mask is defined with vector data it is a polygon, whereas if it is defined as a raster it is the collection of cells with the same integer value within that raster. For the purposes of this paper, the masks that will be used should have a specific geomorphic meaning—either relating to a specific style of change, an inferred geomorphic process or a particular morphological characteristic. The DoD data that fall within each mask are used to calculate areal and volumetric elevation change distributions (ECDs) and summary statistics. To segregate the DoD analysis by Copyright # 2009 John Wiley & Sons, Ltd.

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multiple masks is a simple matter of aggregating the discrete masks (polygons or unique integer values within a grid) into a single mutually exclusive classification. The geomorphic interpretation is an obvious motivation for the budget segregation, but there may be ecohydraulic interpretations as well. There are at least three types of ecologically relevant masks that might be used to explore the implications of geomorphic changes to salmonids. The first is a physical habitat classification mask (e.g. Newson et al., 1998; Raven et al., 1998; Brierley and Fryirs, 2000), and this can be applied either as a standard classification or a CoD (see bullets above for distinction). Secondly, a mask, constructed of redd surveys could be used to assess flood impact during the incubation period (as in this paper) or if surveys were detailed enough, how much sediment was moved by the red construction process itself (e.g. Gottesfeld et al., 2004). Finally, ecohydraulic habitat suitability models (e.g. Leclerc et al., 1995) might be used as masks to draw correlations between the quality of habitat and the types of change it experiences (i.e. a standard classification), or to assess how morphological changes relate to habitat quality changes (i.e. CoD). In a GIS, the masks (areas or polygons) are created in a similar fashion by the user with manual or automated classification regardless of the classification type (e.g. geomorphic or ecological).

MOKELUMNE STUDY SITE The case study site is on the heavily regulated lower Mokelumne River (LMR) of California, downstream of Camanche Dam (Figure 1). The dam has no fish-passage facility and is the uppermost barrier to upstream migration of adult salmon (Merz and Setka, 2004). The LMR’s unregulated flow regime is dominated by a spring snowmelt from the roughly 1497 km2 catchment. Today over 16 large dams exist upstream of Camanche Reservoir (the largest). The pre-dam Q100 was 1200 m3 s 1; it is now 300 m3 s 1 (Pasternack et al., 2004). Post-dam bankfull flows have been estimated at 34 m3 s 1. The reduction in peak flows and regulated flow regime is depicted in Figure 2. Although the LMR still supports a fall-run of up to 16 000 Chinook salmon (Oncorhynchus tshawytsch) spawners, average escapement following Camanche Dam’s construction (5500) is a fraction of the historic abundance and physical habitat degradation is one of the primary limiting factors (CDFG, 1991). A small steelhead (Oncorhynchus mykiss) population also exists.

Figure 1. Location map, catchment map, and context photo for Mokelumne River study site Copyright # 2009 John Wiley & Sons, Ltd.

River. Res. Applic. 26: 469–486 (2010) DOI: 10.1002/rra

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Figure 2. Hydrographs for Mokelumne River reflecting the 4-year study period and the preceding decade (inset box) for context. The thick black line represents the flows experienced at the study site with the Camanche Dam release. The gray line represents the hydrograph at the Mokelumne Hill gauge at Highway 49 upstream of Pardee reservoir in Figure 1 (reasonable proxy for natural flow regime). The seven analysis intervals are labelled as TS1 through TS7 (time step) and are defined by the dates of the topographic surveys. The shaded time steps extending to the top indicate the period when the SHR projects were constructed. The lighter shaded areas extending only to the controlled release line represent the spawning seasons

Starting in the mid 1990s, East Bay Municipal Utility District, owner and operator of Camanche Dam, began constructing one to two SHR projects each year for Chinook salmon. The projects each consisted of placing between 600 and 3000 m3 of clean, triple-washed spawning gravels in the channel with a rubber-tired front loader to enhance or create spawning habitat. Grain size frequency analysis from pebble counts before and after a project in this reach reported by Pasternack et al. (2004) indicated pre-project bed d50 values of 41  43 mm (1 std dev) and post project d50 values of 48  21 mm. The grain size distributions from pre to post project are consistently narrowed and contain a much smaller fraction of fines (< 2 mm), which can clog interstitial pore space. Up until 2000, these projects were constructed on an ad hoc basis at the direction of a fisheries biologist in the field; from 2001 onwards the projects were constructed from detailed designs developed using the Spawning Habitat Integrated Rehabilitation Approach (SHIRA) framework developed by Wheaton (2003) and Wheaton et al. (2004b). The study reach begins upstream at a fish guidance fence, which blocks fish migration upstream and is intended to divert migrating salmon into a fish hatchery. The reach extends virtually due west downstream, until it is diverted south by a prominent rock outcrop, roughly 150 m downstream of the Murphy Creek confluence. SHR began within this reach in 1997 and 1998, with ad hoc construction of two small riffles downstream of Murphy Creek. In 1999, a more substantial ad hoc project was constructed between 110 and 240 m downstream of the fish guidance fence. Detailed pre- and post-project monitoring and assessment were performed at this site and are reported in Merz and Setka (2004) and Pasternack et al. (2004), with other elements reported in Merz et al. (2004), Merz and Chan (2005) and Merz et al. (2006). Further downstream, other SHR efforts were undertaken using SHIRA. By 2003, the focus returned to the 510 m reach described here. As of 2008, six consecutive years of staged SHR projects had been Copyright # 2009 John Wiley & Sons, Ltd.

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constructed in the reach, all relying on the Elkins et al. (2007) design concept of slope creation. Sawyer et al. (2009) used DoDs between design and as-built surfaces to evaluate the ability of SHR project construction methods to achieve detailed ecohydraulic design specifications. In 2005, the SHR project that was built aimed to continue rehabilitating the longitudinal profile of the river through slope creation, but it also included a set of site-specific goals. A technical report detailing all phases of the 2005 project is available upon request to co-author Pasternack. The 2005 site was located at an abnormally deep (i.e. > 3 m) pool in the river that was created by historic instream gravel mining. This large artificial hole acted as a sink for sediment in transport and prevented gravel eroded from upstream SHR projects from migrating downstream and building or expanding other bars. Such holes are common in the rivers draining to the Central Valley of California and pose the problem of whether to expend resources filling them in or ignore them and focus on riffles where per-unit gravel placement yields more immediate high-quality habitat. In this case, it was decided that the long-term geomorphic benefits of filling in the hole and creating a suite of geomorphic units appropriate to the modern hydrologic and geomorphic regimes outweighed the cost in terms of achieving a low amount of high quality habitat per unit of gravel added (a project evaluation metric termed ‘gravel efficiency’). Following standard practice on the Mokelumne, annual projects were designed to accommodate the available gravel and thus it took 2 years (2005 and 2006) to completely fill in the hole and establish the new suite of geomorphic units.

METHODS APPLIED AT MOKELUMNE High resolution topographic surveys (i.e. > 1pt m 2) were conducted with Leica TCRA 1205, Leica TPS 1200 and Topcon GTS-802A total stations each year prior to the projects being constructed and immediately following their construction as shown in Figure 2. Each of these surveys was analysed in Wheaton (2008), but here just 1 year is reported to illustrate the utility of the ecohydraulic masking methods. Of the 5 years of repeat topographic surveys, the 2005–2006 season is the best possible test of the response of the SHR projects to the maximum magnitude event that the study reach is capable of experiencing due to flow regulation. Specifically, looking at changes in 2005–2006 should illuminate the fate of 2017 m3 of gravel placed as part of a 2005 SHR project to create and improve spawning habitat. Camanche Dam cannot control a release greater than 141.6 m3 s 1 and uncontrolled flows bypass the rehabilitated reach via an emergency spillway that enters downstream of the site. As shown in Figure 2, the 141.6 m3 s 1 release was maintained for over a week as part of the spring snowmelt in 2006. Although smaller controlled ‘pulse flow’ releases were released in 2003 and 2005, these 2006 flows were the first geomorphic test in a high-flow setting of the Mokelumne SHIRA projects. DEMs of 25-cm resolution from after the 2005 SHR project placement (Figure 3A) and prior to the 2006 SHR project placement (Figure 3B) were constructed by building TINs from the raw survey data and converting the TINs to rasters using ESRI’s ArcGIS 3D Analyst. The DEMs were differenced to create a DoD (shown in Figure 3D) and a spatially variable uncertainty analysis was applied as described in ‘DoD Uncertainty and Masking Methods’ section and thresholded at a 95% confidence interval (i.e. only changes that have a 95% probability of being real are included in the budget). Areal and volumetric ECDs were derived from this DoD (Figures 3D and E, respectively). This DoD and its ECD were subsequently segregated, as described in ‘DoD Uncertainty and Masking Methods’ section, using three different masks to aid in interpreting the ecohydraulic significance of the geomorphic changes to spawning salmonids: 1. A simple geomorphic interpretation of the DoD. 2. A standard classification of physical habitat at the geomorphic unit (i.e. bar) scale. 3. A classification of difference between before and after ecohydraulic model estimates of spawning habitat quality. All these analyses were processed using a DoD uncertainty analysis program in Matlab produced by Wheaton et al. (in press; note that software is available to download with paper). For the third mask, an estimate of habitat quality was necessary and the methods used are described in the next subsection.

Copyright # 2009 John Wiley & Sons, Ltd.

River. Res. Applic. 26: 469–486 (2010) DOI: 10.1002/rra

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Figure 3. The geomorphic change recorded between 2005 and 2006 as a result of high winter and spring flows (see Figure 2). (A) Post 2005 SHR-project 25 cm DEM. (B) Pre 2006 SHR-project 25 cm DEM. (C) DEM of difference derived from 2006 to 2005 DEMs and thresholded at a 95% confidence interval after applying DoD uncertainty analysis. (D) Areal elevation change distribution from DoD. (E) Volumetric elevation change distribution from DoD. This figure is available in colour online at www.interscience.wiley.com/journal/rra

Copyright # 2009 John Wiley & Sons, Ltd.

River. Res. Applic. 26: 469–486 (2010) DOI: 10.1002/rra

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Assessing habitat quality To address the question of spawning habitat quality, 2D ecohydraulic habitat suitability simulations at typical spawning flows (10 m3 s 1) were modelled using pre- and post-high-flow release morphologies. The habitat suitability model used is based on depth and velocity habitat suitability curves for fall-run Chinook from the LMR as reported in Elkins et al. (2007) and Wheaton (2003). Substrate is not included in the model because grain size distributions of the placed gravel in the study reach are optimal for spawning and do not limit habitat quality or help differentiate it (Pasternack et al., 2004). The only exceptions at this site are the pools, which can have sub-optimal grain size distributions, but they are already segregated out as poor or non-spawning habitat by the hydraulic habitat suitability criteria alone. In this study, each of those curves was modelled using a single fifth-order polynomial equation to calculate suitability based on velocity or depth separately. The two univariate suitability measures are then combined using a weighted sum (equal weighting of 0.5) to produce a global habitat suitability index (GHSI) that ranges from 0 to 1, with 1 being the highest quality. Habitat quality is calculated on a node-by-node basis using velocity and depth predictions from a 2D hydraulic model simulation at a given flow (Leclerc et al., 1995; Crowder and Diplas, 2000; Elkins et al., 2007). For the Mokelumne, with over 14 years of complete redd surveys and seven years of 2D hydraulic model simulations at spawning flows, there is a high degree of confidence in the predictive capability of the GHSI model. That is, the documented occurrence of spawning within the study reach is very well predicted by modelled high- and medium-quality habitat suitability classes in the GHSI, with only rare utilization of predicted low (< 13%) and poor quality (< 1%) habitat areas, and virtually no utilization of GHSI-predicted nonhabitat (Wheaton et al., 2004a; Elkins et al., 2007). Where spawning is correlated with lower quality classes, it has almost always been explained in terms of redd proximity to habitat heterogeneity elements such as shear zone refugia or structural cover (Wheaton et al., 2004c). These ecohydraulic model predictions of habitat quality are compared to the DoD changes that describe the geomorphic changes from the high flow dam releases. By using a CoD of the habitat suitability classes before and after as a mask for the DoD, ECDs were derived to address the question of impact of geomorphic changes on habitat quality directly. The two classifications employed are the GHSI spawning habitat suitability predictions from the 2005 post-project to the 2006 pre-project. In each, six categories were considered: 1. 2. 3. 4. 5. 6.

Outside 2005 SHR placement area, non-spawning habitat, very poor-quality spawning habitat, low-quality spawning habitat, medium-quality spawning habitat and high-quality spawning habitat

Thus the CoD had 36 categories. The six CoD categories for the areas outside the 2005 SHR placement area were discarded. The remaining 32 categories were simplified into three classes whereby habitat quality either remained the same, improved or degraded.

RESULTS AND INTERPRETATION Gross geomorphic changes from high-flow dam releases Figure 3 shows the best estimate for the DoD budget after applying the DoD Uncertainty Analysis. A total of 999.6 m3 of net erosion and 810.1 m3 of net deposition were recorded throughout the study area, so the reach as a whole experienced a net loss of 196.2 m3. The ECD in Figure 3E shows a bimodal distribution that roughly balances with only a 10% degradational tendency. The ECD signature of this water year is characteristic of a natural river: with a high depositional peak of low-magnitude deposition (i.e. broad sheets of deposition) and a more spread out and uniform erosional distribution, reflecting more spatially concentrated areas of erosion but spanning a greater range of scour depths. A separate DoD analysis of the as-built 2005 SHR placement, suggested that 2017 m3 of gravel were placed (Wheaton, 2008). From the gross net loss estimate in the following year, one might conclude that the gravel used to Copyright # 2009 John Wiley & Sons, Ltd.

River. Res. Applic. 26: 469–486 (2010) DOI: 10.1002/rra

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create the project was washing away and therefore a loss of created spawning habitat must be taking place. Indeed, Merz et al. (2006) documented the systematic net loss (between 3 and 20%) of gravel from three other SHR projects also on the LMR. They attributed the losses to a combination of fluvial erosion, gravitational sloughing, mobilization from spawning fish (i.e. faunal pedoturbation) settling and measurement uncertainties. Here, the measurement and DEM uncertainties have been accounted for robustly. Thus, the question remains to what extent is that net loss in the study reach the result of settling and compaction of SHR placed gravels versus fluvial erosion of bed material? Moreover, to what extent are these changes actually changing spawning habitat quality and/or degrading it? A wealth of spatially explicit information from the DoDs can be exploited with masks to segregate the budget and investigate such questions. Geomorphic interpretation of change Figure 4A shows a simple geomorphological interpretation of the observed net changes that took place to the study reach. In this segregation of the DoD, four masks help discriminate between changes due to natural fluvial net erosion and net deposition outside the area (pink in online version) where SHR gravel was placed. The segregated budget is shown in Figure 5 and tabulated in Table I. First, the questionable changes are separated out, these account for about 8% of the total volume of change. These changes exhibit an ECD typical of potentially erroneous changes with a normal distribution centered around no change and dominated by low magnitude (and difficult to detect) elevation change. Outside the SHR placement area, net fluvial scour (primarily in pools) outpaces net fluvial deposition at 25 versus 14% of the total volume of change (i.e. pool maintenance is occurring), respectively. Over 47% of the total volume of erosion is taking place outside the SHR placement boundaries (44% inside the SHR boundaries). Within the SHR boundaries, 51% of the total volumetric changes in the DoD coverage are taking place. Interestingly deposition slightly outpaced erosion (476.0 vs. 445.3 m3). It should be noted that with the SHR boundaries, actual fluvial erosion is difficult to distinguish from elevations being lowered due to settling and/or compaction (Merz et al., 2006). Although the reach experienced net degradation, the SHR project area actually experienced net aggradation as a result of a major flood. Thus, the net loss for the entire reach is more a result of fluvial erosion (e.g. in the pools) outside the SHR project area rather than due to the loss of placed gravels. Such a conclusion could not have been drawn without segregating the budget spatially with masks. However, it would be helpful to look more closely within these coarse masks to determine how these geomorphic changes have changed physical habitat. Changes to morphological units Figure 6 shows the segregation of the DoD budget (Figure 6A) by the morphological units defined in Figure 4B. The riffles, riffle crests and lateral bars form the bulk of the area where gravel was placed as part of the SHR project and bulk of spawning habitat for salmonids. Their ECDs (Figures 6B–D) are all bimodal, exhibiting some net erosion and some net deposition, but each had a stronger depositional signal with a low magnitude peak around 10–25 cm of deposition. Their collective net aggradational signal explains the net aggradation shown within the SHR site above in Figure 5C. The pools and pool-exit-slopes (Figures 5E and F) also each show some erosion and some deposition but the erosional signature overwhelms the depositional one. The pool exit-slope has a more pronounced peak centered at about 50–60 cm of erosion, reflecting the downstream adjustment of the poolboundary eroding into the riffle. The pool, by contrast, has a broader distribution with some substantial higher magnitude erosion in the 100–150 cm range, indicating the lateral erosion and widening of the pool into the lateral bars. The majority of the scour (c. 60%) was in the form of deepening or accentuation of pool boundaries in areas with convergent flow, whereas nearly half of the deposition (c. 48%) was over wide riffles with divergent flows over them. From a physical habitat perspective, what are the implications of shallow net deposition over bars and riffles and net scour of pools? Both sediment transport processes lead to a rejuvenation and turnover of substrates, which is thought to promote higher rates of hypohrheic exchange and therefore higher survival of incubating salmonid embryos in the gravels (Montgomery et al., 1996; Merz et al., 2004). Such sediment transport processes could be problematic if they coincided with the embryo incubation period and scour depths exceeded the burial depth of eggs (see discussion). However, such processes accentuate meso-scale habitat patch differences into zones suitable for Copyright # 2009 John Wiley & Sons, Ltd.

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Figure 4. Two types of masks used to segregate DoD budget from Figure 3E. (A) A simple geomorphic interpretation divides the DoD (C) into fluvial erosion and deposition outside the SHR gravel placement area, versus changes within it (see also Figure 5). The orange areas are zones where little to no field evidence of geomorphic change was present. (B) A standard classification of the earlier DEM into ecologically relevant geomorphic units will provide masks of the DoD (C), which can be used to highlight specifically what changes took place to those units (see also Figure 6). This figure is available in colour online at www.interscience.wiley.com/journal/rra

different salmonid freshwater life stages (i.e. riffle building supporting spawning and pool maintenance supporting rearing). In the context of the SHR efforts, although the habitat is changing the riffles and bars are growing and persisting as places of net deposition and the pools are being maintained (as opposed to reverting to a plane-bed or run morphology). This pattern of change was explicitly encouraged in the rehabilitation project design to promote Copyright # 2009 John Wiley & Sons, Ltd.

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Figure 5. DoD elevation change distributions from geomorphic interpretation masks (Figure 4A) of overall DoD budget in Figures 3E. (A, B) Show fluvial deposition and scour respectively that took place without SHR gravel placement boundaries. (C) Shows changes that took place within the SHR gravel placement boundaries. (D) Questionable change

riffle-pool self-sustainability, relying on the flow convergence routing mechanism suggested by MacWilliams et al. (2006). This is the first real test of the pool maintenance design hypotheses proposed in Wheaton et al. (2004d) at high flows. While these results are encouraging, within the SHR area it would be useful to know how these changes in physical habitat influenced habitat quality. Table I. Segregation of the DoD budget by geomorphic interpretation

Fluvial Deposition Fluvial scour Changes to SHR placed gravel Questionable change Placed boulder Total

Copyright # 2009 John Wiley & Sons, Ltd.

Erosion volume (m3)

Deposition volume (m3)

Total volume (m3)

2.4 464.8 445.3 81.9 5.1 999.5

257.6 3.0 476.0 66.2 0.4 803.3

260.1 467.8 921.4 148.0 5.5 1802.8

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Figure 6. DoD elevation change distributions from geomorphic unit masks (Figure 4B). (B–F) Show the segregation of the overall DoD budget in A. The elevation change distributions for point bars, chutes, central bars and runs were negligible (i.e. < 35 m3) and are not shown. Collectively, the total volume of change from all these masks sums to produce the totals in A

What impact did measured changes have on habitat quality? Figure 7 shows the results of running the 2D ecohydraulic spawning habitat model at spawning flows for 2005 (Figure 7A) and 2006 (Figure 7B). Although there is a shift in habitat, the majority of habitat remains the same quality, 22% experienced some improvement and 25% decreased in quality (Table II). These habitat quality changes provide a good masking template to segregate the DoD. Copyright # 2009 John Wiley & Sons, Ltd.

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Figure 7. The derivation of the habitat suitability classification of difference for 2005–2006. Habitat quality was compared on a cell-by-cell basis from the beginning of the time step (fall 2005: sub-figure A) to the end of the time step (summer 2006: subfigure B) to calculate where habitat quality remained stable, improved or degraded in sub-figure C. The changes in habitat are due to the geomorphic changes. The greyed out areas reflect those areas outside the SHR placement zone and outside the analysis extent. This figure is available in colour online at www.interscience.wiley.com/journal/rra

Figure 8A–C shows the primary results of the DoD budget segregation based on a 2D ecohydraulic habitat suitability modelling analysis with three ECDs for the primary habitat change categories. The top portion of Table II tabulates the same results. Over 53% of the area in which gravel was placed in 2005 retained the same habitat quality characteristics, as predicted by GHSI. Interestingly, this stable habitat class shows the most balanced ECD (although it is depositionally biased; Figure 8A) and only accounts for 19.5% of the total volumetric change to the SHR area. By contrast, the improved and degraded habitat quality class masks account for 34.5 and 46.0% of the total volumetric change, respectively (Figures 8B and C). The improved and degraded classes also make an interesting contrast geomorphically through their ECDs. In general, habitat degradation was associated with net erosion whereas habitat improvement was associated with net deposition. Both the stable and improved class ECDs have their most pronounced peak in areas of shallow deposition (10–25 cm), with the stable class favouring shallower deposition. The habitat degradation class ECD has its erosional peak at about 75 cm. This is primarily due to the erosion and re-sculpting of the pool-exit slope. While such a change does result in habitat quality reduction by simple hydraulic suitability criteria, pool-exit slopes tend to be hot-spots of spawning activity due to their proximity to deep pool refugia and increased hyporheic downwelling (Geist and Dauble, 1998; Geist, 2000; Wheaton et al., 2004c). Such erosion is a reflection of the translation of the pool-exit slope downstream. Copyright # 2009 John Wiley & Sons, Ltd.

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Table II. Summary of classification of difference between 2005 Post-Project and 2006 Pre-Project GHSI predicted habitat suitability

Overall Habitat improvement Habitat degradation Habitat stable Improvement details Very poor quality improvement Low-quality improvement Medium-quality improvement High-quality improvement Degradation details Very poor quality degradation Low-quality degradation Medium-quality degradation High-quality degradation Stable details Remained very poor quality Remained low quality Remained medium quality Remained high quality

Erosion volume (m3)

Deposition volume (m3)

Percentage of SHR area (%)

Percentage of habitat class (%)

19.0 185.1 58.3

69.6 24.0 98.3

22 25 53

NA NA NA

0.4 3.6 14.9 NA

1.1 25.2 42.5 NA

0 6 15 NA

30 44 38 NA

0.5 15.4 58.3 110.9

0.4 1.0 10.6 12.1

0 2 8 14

20 14 21 33

0.9 6.9 17.6 31.9

1.5 19.3 31.1 43.2

0 6 16 29

50 43 40 67

The percentage of SHR area is calculated by comparing the area of the said CoD class to the total area within the SHR placement boundary mask. The percentage of habitat class is calculated by comparing the area of the said CoD category (e.g. improved, degraded or remained) to the total area in the quality class (e.g. low, medium or high quality).

To further differentiate these results, the specifics of habitat stability, degradation and improvement CoDs are shown in Table II. From the fourth column, it is encouraging to note that the highest recorded percentage (at 29%) of the SHR area remained high quality. As these areal percentages are largely a reflection of the distribution of habitat qualities, it can be helpful to normalize the percentages by calculating them with respect to their specific habitat quality class (e.g. very poor, low, medium or high) as done in column 5 of Table II. From this, the majority of high-quality habitat remained high quality habitat (67%). Additionally, across the habitat quality classes there are consistently higher percentages of habitat improvement than habitat degradation.

DISCUSSION Our results indicate that changes associated with a wet water year and the largest possible flow releases from Camanche Dam resulted in a net improvement to constructed spawning habitat quality. Furthermore, the pattern of changes observed are consistent with the design hypotheses regarding the central role of channel nonuniformity and flow convergence routing in maintenance of morphological units (MacWilliams et al., 2006) being used in the SHIRA framework for SHR (e.g. Wheaton et al., 2004b). If these underlying mechanisms are responsible for morphological unit maintenance and if they have been properly applied in the Mokelumne River long-term SHR effort, then it is likely that the results observed in this study are transferable through time. On the contrary, it is also possible that the 2005 results represent a natural initial adjustment following a restoration intervention (i.e. addition of a large supply of spawning gravels) with no larger meaning and that matching with design hypotheses was purely coincidental. It is interesting to note that the patterns of habitat degradation were consistently more closely related with higher magnitude scour (generally above typical egg burial depths); habitat improvement was associated more with shallow deposition and that habitat stability (not surprisingly) was associated with lower magnitude changes altogether. These correlations as revealed by the ECD masks are probably more generally transferable. It is also encouraging that these geomorphic changes are occurring spatially where they were designed to (i.e. degradation at Copyright # 2009 John Wiley & Sons, Ltd.

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Figure 8. The elevation change distributions (A–C) from the masks based on the habitat quality classification of difference (E—see also Figure 7) derived from the thresholded DoD for 2005–2006 (D). The ECDs correspond to the three different classification of difference categories: habitat quality stable (beige in E), habitat quality improved (green in E) and habitat quality degraded (orange in E). This figure is available in colour online at www.interscience.wiley.com/journal/rra

flow with constrictions and aggradation at flow width expansions) even at the highest possible flows for the LMR (Wheaton et al., 2004a). The conclusion of a net improvement in habitat quality following these large releases is not what would have been logically concluded from just a gross reach-scale DoD analysis. Given the net gravel loss, the logical conclusion would have been an overall loss of total available spawning habitat in this area. The CoD masks here, revealed coherent patterns of geomorphic change that were consistently correlated with specific changes and/or stability of habitat quality. This type of analysis is straightforward to apply (particularly with the semi-automated DoD Analysis Software), and makes much better use of topographic monitoring data from both a geomorphic and ecohydraulic perspective. Given the increasing calls for thorough monitoring of river restoration projects (e.g. Downs and Kondolf, 2002) and the increased price tag associated with such monitoring, the expectations about what monitoring can usefully say will grow too. These simple but interpretively powerful DoD tools provide a more robust means of getting more from topographic monitoring data. The examples of DoD segregation masks presented here are indicative of the types of analyses one can undertake and are by no means the only useful masks available for more meaningful interpretations of DoDs. In general, any interpretation mask that falls under the three generic categories of (a) standard classification, (b) CoD or Copyright # 2009 John Wiley & Sons, Ltd.

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(c) geomorphic interpretation can be used to glean more information out of DoDs. The choice is up to the investigator. Moreover masking helps break up the net change in storage terms of a sediment budget into physically and spatially important patterns at scales of relevance to organisms; in this case salmon. The masking analysis is easy to apply for large datasets as it has been semi-automated in a wizard-assisted DoD Analysis software package available in Wheaton et al. (submitted). Although not presented here, Wheaton (2008) reported another example of DoD masking that is relevant to salmonid fish—redds. What makes the redds potentially useful DoD masks is that they allow assessment of local impacts at each redd and thus make a more precise assessment of overall impact of single or multiple events (depending on when the topographic surveys were performed). A redd mask is primarily appropriate where topographic surveys exist before and after a change event (e.g. flood) that coincides with the incubation period for salmon embryos. Using redds as masks, impacts of net geomorphic changes can be inferred. For example, net scour over redds can be compared with known or typical burial depth of embryos. Similarly, net deposition over redds effectively increases the burial depth of eggs, and may do so to the point that fry cannot emerge and/or inter-gravel flow is no longer adequate. Unfortunately, the morphological method takes no account of the calibre and composition of sediment or mechanisms like infiltration of fine sediment into interstitial spaces. Thus, using redd surveys as a mask for DoDs derived from the incubation period will likely give a conservative estimate of the potential impact to incubating salmonids from deposition. If the topographic surveys were performed prior to and following redd construction, the redd masks could be used to estimate the volume of material liberated by salmon during redd construction (e.g. Gottesfeld et al., 2004; Merz et al., 2006). This analysis only looks at how availability of different quality spawning habitats changed in response to net geomorphic changes. Habitat utilization data might provide a useful proxy to see if spawning salmonids actually use the habitat. Redd surveys have been conducted on the Mokelumne on a weekly basis during the spawning season each year since 1994 (Elkins et al., 2007). In the autumn following the 2005, SHR project and preceding the high flow releases of the spring of 2006, there were 245 redds recorded in the project area. The fall-run in the following year, after the high flows, by contrast only had 87 redds. The redds were still utilizing the high quality habitat, but the lower numbers had more to do with the total run size. The highest recorded number of redds (2157) since monitoring began was recorded in 2005; whereas 2006 was one of the lowest (755). However, when normalized to run size, 11.3% of the total run used the SHR project area in 2005 and 11.5% in 2006. Thus, the minor net improvement in habitat quality seems to be matched by minor gains in relative habitat utilization. Embryonic survival and or physical conditions influencing survival (e.g. intragravel flow velocity, porosity, dissolved oxygen, temperature, etc.) were not monitored in this study. However, previous work by Merz et al. (2004) on the Mokelumne established that SHR projects do generally lead to increased embryo survival.

CONCLUSION Repeat topographic river surveying is becoming more affordable and tractable and recent developments in accounting for DEM uncertainty allow greater confidence levels in making detailed DoDs interpretations. As geomorphic changes to rivers result in changes to physical habitat at a scale that fish experience, DoDs can be exploited to make ecohydraulic interpretations from topographic monitoring data. In this paper, some simple masking tools were introduced to demonstrate how the ecohydraulic implications of geomorphic change can be elucidated from DoDs. We illustrated these concepts with the example of high flow dam releases on physical spawning habitat quality for salmonids in a heavily regulated California River. Spawning habitat quality did not change in over half the areas experiencing scour and deposition, but these areas also experienced very low magnitude elevation changes. In general, high flow net deposition led to expansion of riffle habitat and spawning habitat quality improvement; whereas erosion led to net scour (primarily along the pool-exit slope) that lead to spawning habitat degradation but preservation of the pool habitat. This case study acts as a proof of concept that DoDs can be used to make more sophisticated interpretations of topographic monitoring data, which explicitly highlight the ecohydraulic significance of geomorphic change. More significantly, these ecohydraulically useful interpretations revealed by these analyses would not have been possible from more traditional, reach-scale interpretation of the DoDs. Copyright # 2009 John Wiley & Sons, Ltd.

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ACKNOWLEDGEMENTS

This research was primarily funded by a PhD studentship for the lead author at the University of Southampton paid for jointly by the School of Geography and the NERC Centre for Ecology and Hydrology, with international fees covered by an Overseas Research Studentship from Universities UK. Financial support for the Mokelumne data collection was provided by the US Fish and Wildlife Service (contracting entity for CALFED Bay-Delta Ecosystem Restoration Program: Cooperative Agreement DCN# 113322G003). Construction projects were funded by EBMUD and CVPIA. EBMUD and UC Davis staff and students assisted with topographic surveys. The authors are grateful to the numerous individuals who assisted in the data collection on the Mokelumne River) and want to specifically acknowledge Marisa Escobar who was the lead scientist for the SHIRA-based project design in 2005 and 2006. Administrative support was provided by staff at the Center for Watershed Sciences at UC Davis. The authors thank two anonymous reviewers and the special issue editors, whose feedback helped greatly to improve the clarity of the manuscript.

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