California Watersheds Research Articles

Western Message Petroglyphs indicate historic beaver presence in a San Francisco Bay Area watershed

Recent museum, archaeological, and observer record evidence suggests that North American beaver (Castor canadensis) were historically native to the watersheds of California’s coast, including San Francisco Bay. A wide variety of animals are abundantly represented in Native American petroglyphs and pictographs with their representations fulfilling intentions ranging from the mundane to ceremonial and mythological purposes. However, beaver symbols are poorly represented in California rock art and absent from the San Francisco Bay Area. A novel record, in the form of Western Message Petroglyphs, suggests that a beaver lodge was present in the late nineteenth century in the Alameda Creek watershed, potentially the last evidence of beaver prior to their extirpation in the region by the fur trade.

A Field-Based Experiment on the Influence of Stand Density Reduction on Watershed Processes at the Caspar Creek Experimental Watersheds in Northern California

Forests are integral to sustaining clean water resources and healthy watersheds. It is critical, therefore, that managers fully understand the potential impacts of their actions on myriad ecosystem services provided by forested watersheds. While forest hydrologists have long used paired-watershed experiments to elucidate the complex interactions between forest management and watershed biogeochemical and ecohydrological processes, there is still much to learn from these studies. Here, we present an overview of the process for designing a paired-watershed study using a large harvesting experiment at the Caspar Creek Experimental Watersheds in coastal California as an example. We detail many considerations when designing such an experiment and highlight the wide range of scientific investigations that are part of the larger experiment. Paired watershed studies are a great example of community engaged scholarship and offer the unique opportunity to work with land managers to solve applied problems while simultaneously discovering new fundamental knowledge about how watersheds function.

Improved agricultural Water management in data-scarce semi-arid watersheds: Value of integrating remotely sensed leaf area index in hydrological modeling

In watersheds located in semi-arid regions, vegetation dynamics, evapotranspiration (ET), and associated water and energy balances collectively play a major role in controlling hydrological regimes and crop yield. As such, it is challenging to predict the complex hydrological processes and biophysical dynamics. This challenge increases in areas with limited data availability. The key objective of this study was to evaluate the direct integration of remotely sensed Leaf Area Index (LAI) data into a hydrological model to improve streamflow, ET, and crop yield estimates. We also demonstrated how an improved model integrated with remotely sensed LAI data can inform water managers by predicting water productivity (WP) under different irrigation schemes. We took agricultural-dominated San Joaquin Watershed in California, United States, as our testbed and integrated the Moderate Resolution Imaging Spectroradiometer (MODIS) 500-m resolution 4-day total LAI data into the SWAT (Soil and Water Assessment Tool) model. Results showed that, compared to conventional SWAT model that relies on semi-empirical equations and user inputs for simulating biophysical processes, direct LAI integration into SWAT model (SWAT-LAI) notably captured the actual vegetation dynamics and improved ET and crop yield estimations. The WP simulated by the improved SWAT-LAI model for almond and grape yields varied within a range from 0.363 to 3.81 kg/m3 and 0.32 to 4.76 kg/m3 across different irrigation applications. The outcomes of this study showed that deficit irrigation application could be a viable option in water stressed regions, since it can save a substantial amount of irrigation water and maintain the higher water productivity required for both almond and grape yield production. This study shows an evidence of how remotely sensed data integrated into hydrological models can serve as a decision support tool by providing quantitative information on crop water use and crop production.

Modelling future changes to the hydrological and thermal regime of unaltered streams using projected changes in climate to support planning for sensitive species management

AbstractClimate change will alter stream habitats through precipitation and air temperature changes and potentially threaten species that rely on contemporary streamflow and stream temperature regimes. Habitat projections are therefore critical to inform management decisions. Past and ongoing research has improved streamflow and temperature modelling in ungauged regions, but no studies merge these advancements with climate modelling for regional streamflow and stream temperatures predictions that describe stream habitat change. Here, we predict change in streamflow and stream temperature at the reach scale using projections from downscaled global climate models (GCMs) and the ‘business as usual’ carbon emission scenario. We focus on unaltered streams in six southern California watersheds using data from baseline (1982–2014) and projected end‐of‐century (2082–2100). Stream temperature is projected to increase regionally, with high‐elevation stream reaches increasing most rapidly. There is less consistency in the streamflow projections, but a spatial and temporal homogenization of stream flow characteristics was predicted, that is, flows become more similar across the region with less inter‐annual variation. Additionally, there is a regional trend towards larger high flow magnitudes and more storm events. Despite the increased frequency and magnitude of storm events, high‐elevation streams are predicted to become drier for a greater portion of the year. Conversely, low‐elevation streams are predicted to have longer hydroperiods. Mapping future streamflow and stream temperatures at the reach scale can direct conservation efforts to streams that remain suitable, restoration to areas that decrease in suitability for target species, and support water policies that consider future stream condition.

Assessing impacts of climate variability and changing cropping patterns on regional evapotranspiration, yield and water productivity in California’s San Joaquin watershed

Climate variability and changing cropping patterns are two main factors directly influencing catchment hydrological processes and agricultural productivity. Assessing the response of hydrological processes and agricultural production to the combined impacts is of great importance for enhancing water resources management, water productivity and agriculture sustainability. SWAT (Soil and Water Assessment Tools) models embedded with 2007 and 2018 land use were built for the San Joaquin watershed (SJWS) in California’s Central Valley. Global sensitivity analysis were conducted to select parameters that have the largest influence on evapotranspiration (ET) processes. The model was calibrated and validated produced good agreement for monthly ET from remote sensing and yield of 8 major crops using county level reported yields during the periods of 2001–2009 and 2010–2018. Based on the outputs of the calibrated SJWS SWAT model, regional ET showed a significant increase, which was caused by rises in temperature and changing cropping patterns associated with increase in area under nut crop production. Seven major crop yields increased excluding alfalfa, implying that the impacts of increasing temperature and small decreases in precipitation were not dominant factors in explaining observed changes in yield. From periods 2001–2009–2010–2018, total water productivity (WP) increased slightly from 2.11 to 2.15 kg/m3, associated with more water-intensive permanent crop production systems. Total economic water productivity (EWP) increased significantly by 111% from 0.55 to 1.16 US$/m3, which also indicated the substantial economic benefit brought to the SJWS by expanding the land area under high-EWP crops such as nut crops and vegetables. Nutritional water productivity (NWP) for energy, protein and fat were reduced from 1211 kcal/m3, 183 g/m3 and 105 g/m3 to 1141 kcal/m3, 111 g/m3 and 88 g/m3 during the period 2010–2018 mostly due to high water use. Total NWP for calcium was increased from 114 to 145 mg/m3, respectively. This study provides initial insights into the response of WP, EWP and NWP to the changing cropping patterns and climate variability for major crops in the SJWS, which could be used by a wide range of stakeholders including farmers, commodity boards, irrigation districts, groundwater sustainability basins, environmental justice groups and policy makers to optimize water resources planning and management. We recommend creating a comprehensive index that incorporates WP, EWP and NWP for making sustainable water resources planning and management decisions.

Assessing Stream-Aquifer Connectivity in a Coastal California Watershed

We report the results of field and laboratory investigations of stream-aquifer interactions in a watershed along the California coast to assess the impact of groundwater pumping for irrigation on stream flows. The methods used include subsurface sediment sampling using direct-push drilling, laboratory permeability and particle size analyses of sediment, piezometer installation and instrumentation, stream discharge and stage monitoring, pumping tests for aquifer characterization, resistivity surveys, and long-term passive monitoring of stream stage and groundwater levels. Spectral analysis of long-term water level data was used to assess correlation between stream and groundwater level time series data. The investigations revealed the presence of a thin low permeability silt-clay aquitard unit between the main aquifer and the stream. This suggested a three layer conceptual model of the subsurface comprising unconfined and confined aquifers separated by an aquitard layer. This was broadly confirmed by resistivity surveys and pumping tests, the latter of which indicated the occurrence of leakage across the aquitard. The aquitard was determined to be 2–3 orders of magnitude less permeable than the aquifer, which is indicative of weak stream-aquifer connectivity and was confirmed by spectral analysis of stream-aquifer water level time series. The results illustrate the importance of site-specific investigations and suggest that even in systems where the stream is not in direct hydraulic contact with the producing aquifer, long-term stream depletion can occur due to leakage across low permeability units. This has implications for management of stream flows, groundwater abstraction, and water resources management during prolonged periods of drought.

An analytical solution for rapidly predicting post‐fire peak streamflow for small watersheds in southern California

AbstractFollowing wildfires, the probability of flooding and debris flows increase, posing risks to human lives, downstream communities, infrastructure, and ecosystems. In southern California (USA), the Rowe, Countryman, and Storey (RCS) 1949 methodology is an empirical method that is used to rapidly estimate post‐fire peak streamflow. We re‐evaluated the accuracy of RCS for 33 watersheds under current conditions. Pre‐fire peak streamflow prediction performance was low, where the average R2was 0.29 and average RMSE was 1.10 cms/km2for the 2‐ and 10‐year recurrence interval events, respectively. Post‐fire, RCS performance was also low, with an average R2of 0.26 and RMSE of 15.77 cms/km2for the 2‐ and 10‐year events. We demonstrated that RCS overgeneralizes watershed processes and does not adequately represent the spatial and temporal variability in systems affected by wildfire and extreme weather events and often underpredicted peak streamflow without sediment bulking factors. A novel application of machine learning was used to identify critical watershed characteristics including local physiography, land cover, geology, slope, aspect, rainfall intensity, and soil burn severity, resulting in two random forest models with 45 and five parameters (RF‐45 and RF‐5, respectively) to predict post‐fire peak streamflow. RF‐45 and RF‐5 performed better than the RCS method; however, they demonstrated the importance and reliance on data availability. The important parameters identified by the machine learning techniques were used to create a three‐dimensional polynomial function to calculate post‐fire peak streamflow in small catchments in southern California during the first year after fire (R2= 0.82; RMSE = 6.59 cms/km2) which can be used as an interim tool by post‐fire risk assessment teams. We conclude that a significant increase in data collection of high temporal and spatial resolution rainfall intensity, streamflow, and sediment loading in channels will help to guide future model development to quantify post‐fire flood risk.

Patterns and Potential Drivers of Steelhead Smolt Migration in Southern California

AbstractDownstream migration of smolts is a critical aspect of the life history pathway for anadromous salmonids. Timing of downstream migration can vary along latitudinal and climatic gradients. Steelhead Oncorhynchus mykiss occur over a broad range of climate and hydrologic conditions, but relatively little is known about migration timing of smolts in the southern extent of the species’ range. Using a 19‐year data set (1994–2014) of smolt arrivals collected in a downstream migrant trap at the Vern Freeman Diversion facility on the Santa Clara River, one of the largest coastal watersheds in southern California, I report patterns of migration and potential environmental drivers determining migration timing. Large sections of the Santa Clara River and the confluences of its perennial tributaries are intermittent except during winter and spring stormflows, limiting migration opportunities. If tributaries were connected, smolts were regularly encountered in the downstream migrant trap between March and May, with rare observations of downstream migrants in January (0.1%) or February (0.3%). Although these migration data are limited by low smolt abundance and sampling efficiency during high‐flow events, potential environmental drivers were identified as cues for smolt migration timing in this region. Day length was a consistent predictor of smolt migration, while hydrology was both a constraint and a cue, with migrants only arriving after tributaries had reconnected to the main stem and with many arrivals occurring weeks or months after storm events had passed. Smolt migration was not consistently synchronized with periods when intermittent sections of the main stem were wetted and passable to the ocean. Between 0% and 70% of smolts arrived at the Vern Freeman Diversion after natural flows were likely insufficient for passage to the ocean. Smolt migration is a critical piece of the management puzzle for southern California steelhead, and these data will serve to inform effective management strategies and research needs for the successful recovery of the species.

Hydrograph separation through multi objective optimization: Revealing the importance of a temporally and spatially constrained baseflow solute source

Quantifying baseflow is key to a reliable understanding of water quality, supply and habitat. This is especially valid for watersheds in California, USA, where changing precipitation, evapotranspiration and snowmelt patterns coupled with a land use/land cover transition towards urban/agricultural dominance imply drastic impacts on baseflow. This study presents a new approach to stream hydrograph separation using multi objective optimization. Applying the Mixed Integer Distributed Ant Colony Optimization (MIDACO) solver, baseflow is modeled at two stream sites in California by linking daily baseflow and surface runoff simulations from a recursive digital filter (RDF) with those obtained from chemical, i.e., specific conductance (SC), mass balance. Objective functions are based on (1) a modified Nash Sutcliffe Efficiency (mNSE) index that gages how well the model can reproduce measured stream SC, and (2) a mass balance indicator that limits the occurrences of stream SC exceeding the modeled baselow SC end member. This baseflow SC end member is derived from interpolation of stream SC on consecutive baseflow days predicted by the RDF in the optimization. The associated RDF parameter k, RDF pass number (1–5) and surface runoff SC end member are set as adjustable parameters determined via calibration. Optimum (i.e., trade off) solutions indicate good to very good model performance in terms of mNSE statistics despite the competing nature of applied objective functions. Results furthermore indicate a high sensitivity towards (1) site location, with best results for the more remote “upstream” site, and (2) RDF pass number, with best results obtained by the 2 and 3 pass applications. Significant correlations between baseflow and baseflow SC and annual drought indices reveal an immediate response of the system to precipitation deficit that should be considered carefully in future water resource projections.

Microbial Water Quality Conditions Associated with Livestock Grazing, Recreation, and Rural Residences in Mixed-Use Landscapes

Contamination of surface waters with microbial pollutants from fecal sources is a significant human health issue. Identification of relative fecal inputs from the mosaic of potential sources common in rural watersheds is essential to effectively develop and deploy mitigation strategies. We conducted a cross-sectional longitudinal survey of fecal indicator bacteria (FIB) concentrations associated with extensive livestock grazing, recreation, and rural residences in three rural, mountainous watersheds in California, USA during critical summer flow conditions. Overall, we found that 86% to 87% of 77 stream sample sites across the study area were below contemporary Escherichia coli-based microbial water quality standards. FIB concentrations were lowest at recreation sites, followed closely by extensive livestock grazing sites. Elevated concentrations and exceedance of water quality standards were highest at sites associated with rural residences, and at intermittently flowing stream sites. Compared to national and state recommended E. coli-based water quality standards, antiquated rural regional policies based on fecal coliform concentrations overestimated potential fecal contamination by as much as four orders of magnitude in this landscape, hindering the identification of the most likely fecal sources and thus the efficient targeting of mitigation practices to address them.

Using co-occurrence network topology in assessing ecological stress in benthic macroinvertebrate communities.

Ecological monitoring of streams has often focused on assessing the biotic integrity of individual benthic macroinvertebrate (BMI) communities through local measures of diversity, such as taxonomic or functional richness. However, as individual BMI communities are frequently linked by a variety of ecological processes at a regional scale, there is a need to assess biotic integrity of groups of communities at the scale of watersheds. Using 4,619 sampled communities of streambed BMIs, we investigate this question using co‐occurrence networks generated from groups of communities selected within California watersheds under different levels of stress due to upstream land use. Building on a number of arguments in theoretical ecology and network theory, we propose a framework for the assessment of the biotic integrity of watershed‐scale groupings of BMI communities using measures of their co‐occurrence network topology. We found significant correlations between stress, as described by a mean measure of upstream land use within a watershed, and topological measures of co‐occurrence networks such as network size (r = −.81, p < 10–4), connectance (r = .31, p < 10–4), mean co‐occurrence strength (r = .25, p < 10–4), degree heterogeneity (r = −.10, p < 10–4), and modularity (r = .11, p < 10–4). Using these five topological measures, we constructed a linear model of biotic integrity, here a composite of taxonomic and functional diversity known as the California Stream Condition Index, of groups of BMI communities within a watershed. This model can account for 66% of among‐watershed variation in the mean biotic integrity of communities. These observations imply a role for co‐occurrence networks in assessing the current status of biotic integrity for BMI communities, as well as their potential use in assessing other ecological communities.

PFHydro: A New Watershed-Scale Model for Post-Fire Runoff Simulation

Runoff increases after wildfires that burn vegetation and create a condition of soil-water repellence (SWR). A new post-fire watershed hydrological model, PFHydro, was created to explicitly simulate vegetation interception and SWR effects for four burn severity categories: high, medium, low severity and unburned. The model was applied to simulate post-fire runoff from the Upper Cache Creek Watershed in California, USA. Nash–Sutcliffe modeling efficiency (NSE) was used to assess model performance. The NSE was 0.80 and 0.88 for pre-fire water years (WY) 2000 and 2015, respectively. NSE was 0.88 and 0.93 for WYs 2016 (first year post-fire) and 2017 respectively. The simulated percentage of surface runoff in total runoff of WY 2016 was about six times that of pre-fire WY 2000 and three times that of WY 2015. The modeling results suggest that SWR is an important factor for post-fire runoff generation. The model was successful at simulating SWR behavior.

A unified approach to evaluating precipitation frequency estimates with uncertainty quantification: Application to Florida and California watersheds

Although a variety of climate datasets are now available, including observational and model products, there remains a pressing need for the development of a unified, automated and application-focused approach for evaluating and comparing climate extremes within and across these datasets. Precipitation frequency (PF) estimates for extreme precipitation events are useful decision-relevant quantities among water managers. Our objective and quantitative framework for computing PF estimates of any duration uses known techniques in a novel combination. Our framework consists of three steps: (1) use of a regionalization-based statistical method for precipitation frequency estimation at observed sites; this involves hierarchical clustering (here we used Ward’s method) for delineating homogeneous regions, and regional frequency analysis using L-moments; (2) use of kriging for spatial mapping because of its advantages over other conventional interpolation methods; (3) use of z-scores accounts for estimation uncertainties when comparing PF estimates between datasets. The 24-h PF estimates in the Kissimmee-Southern Florida watershed (flat topography) and the Sacramento-San Joaquin watershed (complex topography) demonstrate the applicability of this approach in regions of any geographical complexity. Our results are shown to be reasonably calibrated using the probability integral transform (PIT) histogram of the best fitted distribution. Our results are compared with that of the NOAA Atlas 14 reports and show that for most of the stations the two estimates are statistically indistinguishable; and, the confidence intervals of our estimates are narrower than those of the NOAA estimates. Our approach is applicable to a variety of datasets and provides a baseline for assessing performance of climate models in historical simulations – a necessary first step towards analyzing future projections.

Environmental predictors of stream flow in semi-arid watersheds for biological assessments

Abstract The aim of this study is to test a spatially explicit statistical model to identify indicators of natural stream flow using readily available stream, climate and landscape data. Understanding flow behavior of unmonitored streams at different temporal scales using environmental indicators is of great interest considering the logistic constraints of providing comprehensive flow instrumentation for all stream reaches in a region. Our results have applications to assess human impact in watersheds, to study environmental changes in fresh water resources, and in the management of local ecosystem. This study uses classification and regression tree analysis to identify significant explanatory variables for a predictive model of stream flow in semi-arid watersheds of southern California, USA. The study collected 77 variables with 30 years record, for a set of 48 sites, interpolated to create raster files at 30 m spatial resolution. After applying Pearson correlation analyses to eliminate redundant variables, nine variables were found to have strong positive predictive value for estimating stream flow at ungauged sites. Nine prediction rasters portraying spatial variation of stream flow for the study region at three key index months during wet, dry, and average rainfall conditions. The predictive power of the variables was tested and cross validated over a subset of data not included when building the model. Model validation by site at monthly temporal resolution showed mixed results. While some sites where accurately predicted others did not. The comparison of observed vs predicted values by month suggest that this statistically based approach is able to predict the general patterns of stream flow at the regional scale, however it may be inaccurate in estimating actual flow values by month since the models tends to under-predict monthly discharge.

Occurrence, fate, and confounding influence of ghost passive integrated transponder tags in an intensively monitored watershed

Over the last three decades, passive integrated transponder (PIT) tags have been widely used to study fish populations. Interpretation of PIT tag detections, however, can be confounded by the presence of ghost tags, tags liberated when a fish dies. We used a combination of mobile antenna surveys, stationary antenna detections, and multistate mark–recapture modeling to assess the abundance and fate of ghost tags in a coastal California watershed. Accumulation of ghost tags from released hatchery-origin coho salmon (Oncorhynchus kisutch) smolts was substantial during California’s recent drought, with 2224 ghost tags identified during mobile reader surveys. Between surveys, PIT tags moved downstream a median distance of 346 m and a maximum distance of 1982 m. Stationary antenna array detections indicated that these movements occurred during high-flow events, concurrent with live fish movement. The multistate model estimated that, during winter, approximately 40% of tags were buried in the substrate beyond the read range of mobile readers. Failure to account for transport and burial dynamics of ghost tags can lead to biased estimates of fish abundance, survival, and movement.

Propagation of future climate conditions into hydrologic response from coastal southern California watersheds

As a biodiverse region under a Mediterranean climate with a mix of highly developed and natural watersheds, coastal Santa Barbara County (SB), located in southern California, is susceptible to the hydrologic impacts of climate change. This study investigates the potential changes in hydro-meteorological variables in this region as well as their societal and ecological implications for projected climate conditions during the twenty-first century. Daily streamflow ensembles from 135 coastal watersheds for the period 2021–2100 are developed using the Hillslope River Routing (HRR) model forced with downscaled precipitation and temperature projections derived from 10 climate models in the Coupled Model Inter-Comparison Project, Phase 5, and two emission scenarios (Representative Concentration Pathways, RCP, 4.5 and 8.5). Analysis of the projected ensemble precipitation and streamflow series relative to historical conditions (1961–2000) shows (i) minimal change in annual precipitation (median change within ±3%); (ii) an altered seasonal rainfall distribution with a decrease in rainfall at the beginning of the rainy season (Oct–Dec), an increase during the Jan–Mar period, and a decrease at the end of the season (Apr–Jun); (iii) increases in the magnitude and frequency of large storms (> 36 mm/day) which combined with a shorter rainy season, lead to increases in annual peak flows; and (iv) the propagation of the altered precipitation characteristics resulting in nonlinear changes in the magnitude and variability of annual maximum discharges (i.e., mean, standard deviation, skew) impacting estimated return period discharges (e.g., estimated 100-year flood discharges for the period 2061–2100 under 8.5 increase by up to 185%). While these results are specific to southern coastal California, the nature of nonlinear hydrologic response to altered precipitation characteristics underscores the value of regional studies investigating potential impacts of climate projections on streamflow dynamics.

Water residence time (age) and flow path exert synchronous effects on annual characteristics of dissolved organic carbon in terrestrial runoff

Catchment hydro-physical controls on the interannual variability of dissolved organic carbon (DOC) in terrestrial watershed runoff, important for water quality, ecosystem structure, and foodweb dynamics, are not well understood. To address this, we simulated water residence time (“age”) and flow path of terrestrial runoff and analyzed their mediating effect on relationships between annual runoff volume, DOC concentration, and DOC age. We applied this analysis to a snow-influenced watershed in California's Sierra Nevada (USA) across a range of soil types, elevations (90–4210 m), and years (1950–1999). Simulated increases in annual runoff volume were accompanied by younger ages (r2 = 0.53–0.63) of DOC in quickflow, comprised of surface runoff and lateral flow through soil. Increases in annual runoff volume were also accompanied by gentler relationships between intra-annual (weekly) values of DOC concentration and runoff volume, regression-slopes of which followed a power-law relationship to annual runoff (r2 = 0.12–0.92) for approximately 70% of the watershed. Simulations including dynamics of water age and soil temperature produced annual ages of quickflow DOC ranging from 1 to 70 days over all soil types and water years. Similarity of this range to an observed, 1–69 day range in half-lives of relatively labile DOC in previous studies suggests substantial interannual and spatial variability in the biodegradability of DOC in terrestrial runoff. Simulations excluding dynamics of water age and soil temperature predicted order-of-magnitude less interannual variability in age of quickflow DOC, demonstrating the important effect of interannual variability in soil-water interaction times. These findings suggest that the distribution of DOC bioprocessing along transitions between terrestrial and aquatic systems may be strongly influenced by year-to-year variability in age of water.

Sensitivity and uncertainty analysis for streamflow prediction using multiple optimization algorithms and objective functions: San Joaquin Watershed, California

Uncertainty analysis prior to the model calibration is key to the effective implementation of the hydrologic models. The major application of sensitivity analysis is to indicate the uncertainties in the input parameters of the model, which affects the model performance. There are different optimization algorithms developed and applied in the hydrologic model, which can be performed with different objective functions to calibrate and quantify the uncertainties in the system. The purpose of this study was to evaluate the model calibration performance and sensitivity of parameters using three optimization algorithms and five objective functions for predicting monthly streamflow. Sequential Uncertainty Fitting (SUFI-2), Generalized Likelihood Uncertainty Estimation (GLUE), and Parameter Solution (ParaSol) were used to calibrate the monthly streamflow for the semi-arid San Joaquin Watershed in California by using Soil and Water Assessment Tool (SWAT) model. The best performance metrics (R2, NSE, PBIAS, P-factor, and R-factor) were obtained by SUFI-2 while using NSE as the objective function. The coefficient of determination (R2), Nash–Sutcliffe Efficiency (NSE), the percentage of bias (PBIAS), Kling-Gupta efficiency (KGE) and Ratio of the standard deviation of observations to root mean square error (RSR) were used as an objective function to assess the monthly calibration performance. KGE was found to be a suitable objective function to calibrate this complex and snowmelt-dominated watershed. The findings from this study will serve as a guideline for hydro-ecological researchers to achieve further watershed management goals.

A Snowpack Forecasting Model for the Eastern Sierra Nevada Based on Cointegration With the North Pacific High Sea-Level Pressure Anomaly

A cointegrated relationship has been identified between the January sea level pressure anomaly at the climatological location of the North Pacific High (NPH) and seasonal precipitation throughout California (Costa-Cabral et al., 2016). This cointegration can be used for forecasting precipitation or snowpack indices at California locations. Here we develop a cointegration model, termed Vector Error Correcting Model (VECM), for issuing a forecast, in early February, for April 1 snow water content at snow stations in the Eastern Sierra Nevada mountain range of California. Snowmelt from this region flows into the Owens River and serves as a major source of freshwater for the Los Angeles metropolitan area. The VECM relies on the cointegration between three variables: the January NPH sea level pressure, the February 1 snow water content, and the April 1 snow water content. Forecasts based on this VECM model have higher measures of skill compared to linear correlation methods. The statistical tool presented can be applied to other California watersheds and may provide reservoir operators the needed insight for making storage decisions in early February.

Effects of Flow‐Related Variables on Oversummer Survival of Juvenile Coho Salmon in Intermittent Streams

AbstractWhile many studies have established the importance of streamflow as a driver of fish population dynamics, few have examined relationships between survival of juvenile salmonids and flow‐related variables in intermittent streams. With predictions for a higher frequency of drought conditions due to climate change and the associated increasing human demand for water during the dry season, understanding fish–flow relationships is becoming increasingly important for the protection of sensitive aquatic species. To examine the effects of low streamflow on juvenile salmonids rearing in small intermittent streams, we estimated survival and collected environmental data in four coastal California watersheds from 2011 to 2013. We used an individual‐based mark–recapture modeling approach to evaluate the influence of flow‐related variables on oversummer survival of PIT‐tagged juvenile Coho Salmon Oncorhynchus kisutch stocked into eight stream reaches. Survival was positively associated with streamflow magnitude, wetted volume, and dissolved oxygen and negatively associated with days of disconnected surface flow (days of disconnection) and temperature. Days of disconnection best explained survival, though the relationship varied by geomorphic reach type. Survival was lower in alluvial reaches than in bedrock and clay reaches and showed a faster rate of decline with increasing days of disconnection and drought condition. In all reaches, the onset of pool disconnection represented a turning point at which water quality, water quantity, and survival declined. For this reason, we suggest that days of disconnection (or the flow magnitude at which pools become disconnected) is a useful metric for identifying flow‐impaired reaches, informing streamflow protection strategies, and prioritizing streamflow enhancement efforts designed to benefit sensitive salmonid populations in intermittent streams.