Revealing the Diversity of Natural Hydrologic Regimes in California with Relevance for Environmental Flows Applications

Alterations to flow regimes for water management objectives have degraded river ecosystems worldwide. These alterations are particularly profound in Mediterranean climate regions such as California with strong climatic variability and riverine species highly adapted to the resulting flooding and drought disturbances. However, defining environmental flow targets for Mediterranean rivers is complicated by extreme hydrologic variability and often intensive water management legacies. Improved understanding of the diversity of natural streamflow patterns and their spatial arrangement across Mediterranean regions is needed to support the future development of effective flow targets at appropriate scales for management applications with minimal resource and data requirements. Our study addresses this need through the development of a spatially explicit reach‐scale hydrologic classification for California. Dominant hydrologic regimes and their physio‐climatic controls are revealed, using available unimpaired and naturalized streamflow time‐series and generally publicly available geospatial datasets. This methodology identifies eight natural flow classes representing distinct flow sources, hydrologic characteristics, and catchment controls over rainfall‐runoff response. The study provides a broad‐scale hydrologic framework upon which flow‐ecology relationships could subsequently be established towards reach‐scale environmental flows applications in a complex, highly altered Mediterranean region.     

Mid‐Range Streamflow Forecasts Based on Climate Modeling – Statistical Correction and Evaluation1

Abstract: Mid‐range streamflow predictions are extremely important for managing water resources. The ability to provide mid‐range (three to six months) streamflow forecasts enables considerable improvements in water resources system operations. The skill and economic value of such forecasts are of great interest. In this research, output from a general circulation model (GCM) is used to generate hydrologic input for mid‐range streamflow forecasts. Statistical procedures including: (1) transformation, (2) correction, (3) observation of ensemble average, (4) improvement of forecast, and (5) forecast skill test are conducted to minimize the error associated with different spatial resolution between the large‐scale GCM and the finer‐scale hydrologic model and to improve forecast skills. The accuracy of a streamflow forecast generated using a hydrologic model forced with GCM output for the basin was evaluated by forecast skill scores associated with the set of streamflow forecast values in a categorical forecast. Despite the generally low forecast skill score exhibited by the climate forecasting approach, precipitation forecast skill clearly improves when a conditional forecast is performed during the East Asia summer monsoon, June through August.     

Spatial Calibration and Temporal Validation of Flow for Regional Scale Hydrologic Modeling1

Abstract: Physically based regional scale hydrologic modeling is gaining importance for planning and management of water resources. Calibration and validation of such regional scale model is necessary before applying it for scenario assessment. However, in most regional scale hydrologic modeling, flow validation is performed at the river basin outlet without accounting for spatial variations in hydrological parameters within the subunits. In this study, we calibrated the model to capture the spatial variations in runoff at subwatershed level to assure local water balance, and validated the streamflow at key gaging stations along the river to assure temporal variability. Ohio and Arkansas‐White‐Red River Basins of the United States were modeled using Soil and Water Assessment Tool (SWAT) for the period from 1961 to 1990. R² values of average annual runoff at subwatersheds were 0.78 and 0.99 for the Ohio and Arkansas Basins. Observed and simulated annual and monthly streamflow from 1961 to 1990 is used for temporal validation at the gages. R² values estimated were greater than 0.6. In summary, spatially distributed calibration at subwatersheds and temporal validation at the stream gages accounted for the spatial and temporal hydrological patterns reasonably well in the two river basins. This study highlights the importance of spatially distributed calibration and validation in large river basins.     

Modeling the Spatially Varying Water Balance Processes in a Semiarid Mountainous Watershed of Idaho1

Stratton, Benjamin T., Venakataramana Sridhar, Molly M. Gribb, James P. McNamara, and Balaji Narasimhan, 2009. Modeling the Spatially Varying Water Balance Processes in a Semiarid Mountainous Watershed of Idaho. Journal of the American Water Resources Association (JAWRA) 45(6):1390‐1408. Abstract: The distributed Soil Water Assessment Tool (SWAT) hydrologic model was applied to a research watershed, the Dry Creek Experimental Watershed, near Boise Idaho to investigate its water balance components both temporally and spatially. Calibrating and validating SWAT is necessary to enable our understanding of the water balance components in this semiarid watershed. Daily streamflow data from four streamflow gages were used for calibration and validation of the model. Monthly estimates of streamflow during the calibration phase by SWAT produced satisfactory results with a Nash Sutcliffe coefficient of model efficiency 0.79. Since it is a continuous simulation model, as opposed to an event‐based model, it demonstrated the limited ability in capturing both streamflow and soil moisture for selected rain‐on‐snow (ROS) events during the validation period between 2005 and 2007. Especially, soil moisture was generally underestimated compared with observations from two monitoring pits. However, our implementation of SWAT showed that seasonal and annual water balance partitioning of precipitation into evapotranspiration, streamflow, soil moisture, and drainage was not only possible but closely followed the trends of a typical semiarid watershed in the intermountain west. This study highlights the necessity for better techniques to precisely identify and drive the model with commonly observed climatic inversion‐related snowmelt or ROS weather events. Estimation of key parameters pertaining to soil (e.g., available water content and saturated hydraulic conductivity), snow (e.g., lapse rates, melting), and vegetation (e.g., leaf area index and maximum canopy index) using additional field observations in the watershed is critical for better prediction.     

APPLICATION OF THE RHESSys MODEL TO A CALIFORNIA SEMIARID SHRUBLAND WATERSHED1

ABSTRACT: Distributed hydrologic models which link seasonal streamflow and soil moisture patterns with spatial patterns of vegetation are important tools for understanding the sensitivity of Mediterranean type ecosystems to future climate and land use change. RHESSys (Regional Hydro‐Ecologic Simulation System) is a coupled spatially distributed hydroecological model that is designed to be able to represent these feedbacks between hydrologic and vegetation carbon and nutrient cycling processes. However, RHESSys has not previously been applied to semiarid shrubland watersheds. In this study, the hydrologic submodel of RHESSys is evaluated by comparing model predictions of monthly and annual streamflow to stream gage data and by comparing RHESSys behavior to that of another hydrologic model of similar complexity, MIKESHE, for a 34 km² watershed near Santa Barbara, California. In model intercomparison, the differences in predictions of temporal patterns in streamflow, sensitivity of model predictions to calibration parameters and landscape representation, and differences in model estimates of soil moisture patterns are explored. Results from this study show that both models adequately predict seasonal patterns of streamflow response relative to observed data, but differ significantly in terms of estimates of soil moisture patterns and sensitivity of those patterns to the scale of landscape tessellation used to derive spatially distributed elements. This sensitivity has implications for implementing RHESSys as a tool to investigate interactions between hydrology and ecosystem processes.     

Oregon Hydrologic Landscapes: A Classification Framework1

Wigington, Parker J., Jr., Scott G. Leibowitz, Randy L. Comeleo, and Joseph L. Ebersole, 2012. Oregon Hydrologic Landscapes: A Classification Framework. Journal of the American Water Resources Association (JAWRA) 1‐20. DOI: 10.1111/jawr.12009 Abstract: There is a growing need for hydrologic classification systems that can provide a basis for broad‐scale assessments of the hydrologic functions of landscapes and watersheds and their responses to stressors such as climate change. We developed a hydrologic landscape (HL) classification approach that describes factors of climate‐watershed systems that control the hydrologic characteristics of watersheds. Our assessment units are incremental watersheds (i.e., headwater watersheds or areas draining directly into stream reaches). Major components of the classification include indices of annual climate, climate seasonality, aquifer permeability, terrain, and soil permeability. To evaluate the usefulness of our approach, we identified 30 rivers with long‐term streamflow‐gauging records and without major diversions and impoundments. We used statistical clustering to group the streams based on the shapes of their annual hydrographs. Comparison of the streamflow clusters and HL distributions within river basin clusters shows that the Oregon HL approach has the ability to provide insights about the expected hydrologic behavior of HLs and larger river basins. The Oregon HL approach has potential to be a useful framework for comparing hydrologic attributes of streams and rivers in the Pacific Northwest.     

Does Wetland Location Matter When Managing Wetlands for Watershed‐Scale Flood and Drought Resilience?

Wetland protection and restoration strategies that are designed to promote hydrologic resilience do not incorporate the location of wetlands relative to the main stream network. This is primarily attributed to the lack of knowledge on the effects of wetland location on wetland hydrologic function (e.g., flood and drought mitigation). Here, we combined a watershed‐scale, surface–subsurface, fully distributed, physically based hydrologic model with historical, existing, and lost (drained) wetland maps in the Nose Creek watershed in the Prairie Pothole Region of North America to (1) estimate the hydrologic functions of lost wetlands and (2) estimate the hydrologic functions of wetlands located at different distances from the main stream network. Modeling results showed wetland loss altered streamflow, decreasing baseflow and increasing stream peakflow during the period of the precipitation events that led to major flooding in the watershed and downstream cities. In addition, we found that wetlands closer to the main stream network played a disproportionately important role in attenuating peakflow, while wetland location was not important for regulating baseflow. The findings of this study provide information for watershed managers that can help to prioritize wetland restoration efforts for flood or drought risk mitigation.     

Spatiotemporal Evaluation of Simulated Evapotranspiration and Streamflow over Texas Using the WRF‐Hydro‐RAPID Modeling Framework

This study assesses a large‐scale hydrologic modeling framework (WRF‐Hydro‐RAPID) in terms of its high‐resolution simulation of evapotranspiration (ET) and streamflow over Texas (drainage area: 464,135 km²). The reference observations used include eight‐day ET data from MODIS and FLUXNET, and daily river discharge data from 271 U.S. Geological Survey gauges located across a climate gradient. A recursive digital filter is applied to decompose the river discharge into surface runoff and base flow for comparison with the model counterparts. While the routing component of the model is pre‐calibrated, the land component is uncalibrated. Results show the model performance for ET and runoff is aridity‐dependent. ET is better predicted in a wet year than in a dry year. Streamflow is better predicted in wet regions with the highest efficiency ~0.7. In comparison, streamflow is most poorly predicted in dry regions with a large positive bias. Modeled ET bias is more strongly correlated with the base flow bias than surface runoff bias. These results complement previous evaluations by incorporating more spatial details. They also help identify potential processes for future model improvements. Indeed, improving the dry region streamflow simulation would require synergistic enhancements of ET, soil moisture and groundwater parameterizations in the current model configuration. Our assessments are important preliminary steps towards accurate large‐scale hydrologic forecasts.     

Global Daily Reference Evapotranspiration Modeling and Evaluation1

Abstract: Accurate and reliable evapotranspiration (ET) datasets are crucial in regional water and energy balance studies. Due to the complex instrumentation requirements, actual ET values are generally estimated from reference ET values by adjustment factors using coefficients for water stress and vegetation conditions, commonly referred to as crop coefficients. Until recently, the modeling of reference ET has been solely based on important weather variables collected from weather stations that are generally located in selected agro‐climatic locations. Since 2001, the National Oceanic and Atmospheric Administration’s Global Data Assimilation System (GDAS) has been producing six‐hourly climate parameter datasets that are used to calculate daily reference ET for the whole globe at 1‐degree spatial resolution. The U.S. Geological Survey Center for Earth Resources Observation and Science has been producing daily reference ET (ETo) since 2001, and it has been used on a variety of operational hydrological models for drought and streamflow monitoring all over the world. With the increasing availability of local station‐based reference ET estimates, we evaluated the GDAS‐based reference ET estimates using data from the California Irrigation Management Information System (CIMIS). Daily CIMIS reference ET estimates from 85 stations were compared with GDAS‐based reference ET at different spatial and temporal scales using five‐year daily data from 2002 through 2006. Despite the large difference in spatial scale (point vs. ～100 km grid cell) between the two datasets, the correlations between station‐based ET and GDAS‐ET were very high, exceeding 0.97 on a daily basis to more than 0.99 on time scales of more than 10 days. Both the temporal and spatial correspondences in trend/pattern and magnitudes between the two datasets were satisfactory, suggesting the reliability of using GDAS parameter‐based reference ET for regional water and energy balance studies in many parts of the world. While the study revealed the potential of GDAS ETo for large‐scale hydrological applications, site‐specific use of GDAS ETo in complex hydro‐climatic regions such as coastal areas and rugged terrain may require the application of bias correction and/or disaggregation of the GDAS ETo using downscaling techniques.     

Hydrologic Landscape Classification to Estimate Bristol Bay,Alaska Watershed Hydrology

Hydrologic landscapes (HLs) have proven to be a useful tool for broad scale assessment and classification of landscapes across the United States as they help organize larger geographical areas into areas of similar hydrologic characteristics. We developed a HL classification for the Bristol Bay watershed of southwest Alaska that incorporates indices of annual climate and seasonality, terrain, geology, and the influences of large lakes and glaciers. A HL classification is particularly useful in this large watershed because of its hydrologic and landscape variability, important salmon fishery, variety of environmental and potential anthropogenic stressors, and lack of widespread hydrologic data. Following creation of Bristol Bay basin‐wide HL classes, we compared the HL distributions within watersheds grouped by two calculated runoff parameters derived from available long‐term streamflow records and found HL distributions within these groups provided predictive insight on hydrologic behavior. Using these developed runoff groups, we estimated expected hydrologic behavior in watersheds across the larger Bristol Bay watershed that lacked gauged streamflow records. The HL approach provides a scientific basis for estimating the first‐order hydrologic behavior of watersheds and landscapes that lack detailed hydrologic information.     

Hydrological Variability and Uncertainty of Lower Missouri River Basin Under Changing Climate

The lower Missouri River Basin has experienced increasing streamflow and flooding events, with higher risk of extreme hydrologic impacts under changing climate. The newly available North American Regional Climate Change Assessment Program (NARCCAP) climate projections were used as atmospheric forcing for Soil and Water Assessment Tool (SWAT) model which runs with varying potential evapotranspiration (PET) methods to assess the hydrological change and uncertainty of 2040‐2069 over 1968‐1997. The NARCCAP temperature and precipitation predictions were refined using a bias correction method. The results show that, following the seasonal variability of precipitation, various water fluxes would increase in most seasons except the summer. Expected precipitation tends to increase in intensity with little change in frequency, triggering faster surface water concentration to form floods. The greatest streamflow increase would occur from November to February, increasing by around 10% on average. An increase of 3% occurs in the other months except for July and August in which river discharge decreases by around 2%. The climate predictions contribute more uncertainty annually, but PET algorithms gain more influence in winter or when other weather factors such as wind play a relatively more important role on evapotranspiration flux. This study predicts an even wetter environment compared to the historically very wet period, with the possibility of more flooding.     

HYDROLOGICAL MODELING OF THE IROQUOIS RIVER WATERSHED USING HSPF AND SWAT1

ABSTRACT: The performance of two popular watershed scale simulation models — HSPF and SWAT — were evaluated for simulating the hydrology of the 5,568 km² Iroquois River watershed in Illinois and Indiana. This large, tile drained agricultural watershed provides distinctly different conditions for model comparison in contrast to previous studies. Both models were calibrated for a nine‐year period (1987 through 1995) and verified using an independent 15‐year period (1972 through 1986) by comparing simulated and observed daily, monthly, and annual streamflow. The characteristics of simulated flows from both models are mostly similar to each other and to observed flows, particularly for the calibration results. SWAT predicts flows slightly better than HSPF for the verification period, with the primary advantage being better simulation of low flows. A noticeable difference in the models' hydrologic simulation relates to the estimation of potential evapotranspiration (PET). Comparatively low PET values provided as input to HSPF from the BASINS 3.0 database may be a factor in HSPF's overestimation of low flows. Another factor affecting baseflow simulation is the presence of tile drains in the watershed. HSPF parameters can be adjusted to indirectly account for the faster subsurface flow associated with tile drains, but there is no specific tile drainage component in HSPF as there is in SWAT. Continued comparative studies such as this, under a variety of hydrologic conditions and watershed scales, provide needed guidance to potential users in model selection and application.     

POTENTIAL CLIMATE CHANGE IMPACTS ON MOUNTAIN WATERSHEDS IN THE PACIFIC NORTHWEST1

ABSTRACT: Global climate change due to the buildup of greenhouse gases in the atmosphere has serious potential impacts on water resources in the Pacific Northwest. Climate scenarios produced by general circulation models (GCMs) do not provide enough spatial specificity for studying water resources in mountain watersheds. This study uses dynamical downscaling with a regional climate model (RCM) driven by a GCM to simulate climate change scenarios. The RCM uses a subgrid parameterization of orographic precipitation and land surface cover to simulate surface climate at the spatial scale suitable for the representation of topographic effects over mountainous regions. Numerical experiments have been performed to simulate the present-day climatology and the climate conditions corresponding to a doubling of atmospheric CO₂ concentration. The RCM results indicate an average warming of about 2.5°C, and precipitation generally increases over the Pacific Northwest and decreases over California. These simulations were used to drive a distributed hydrology model of two snow dominated watersheds, the American River and Middle Fork Flathead, in the Pacific Northwest to obtain more detailed estimates of the sensitivity of water resources to climate change. Results show that as more precipitation falls as rain rather than snow in the warmer climate, there is a 60 percent reduction in snowpack and a significant shift in the seasonal pattern of streamflow in the American River. Much less drastic changes are found in the Middle Fork Flathead where snowpack is only reduced by 18 percent and the seasonal pattern of streamflow remains intact. This study shows that the impacts of climate change on water resources are highly region specific. Furthermore, under the specific climate change scenario, the impacts are largely driven by the warming trend rather than the precipitation trend, which is small.     

GIS BASED LONG TERM HYDROLOGIC IMPACT EVALUATION FOR WATERSHED URBANIZATION1

ABSTRACT: To adequately manage impacts of ongoing or future land use changes in a watershed, the magnitude of their hydrologic impacts needs to be assessed. A grid based daily streamflow model was calibrated with two years of observed streamflow data, using time periods when land use data are available and verified by comparison of model predictions with observed streamflow data. Streamflow data were separated into direct runoff and baseflow to estimate the impacts of urbanization on each hydrologic component. Analysis of the ratio between direct runoff and total runoff from 30 years of simulation results and the change in these ratios with urbanization shows that estimated annual direct runoff increased from 49.2 percent (1973) to 63.1 percent (1984) and 65.0 percent (1991), indicating the effects of urbanization are greater on direct runoff than on total runoff. The direct runoff ratio also varies with annual rainfall, with dry year ratios larger than those for wet years. This suggests that the impact of urbanization on areas that are sensitive to runoff ratios, such as stream ecosystems, might be more serious during drier years than in wetter years in terms of water quality and water yield. This indicates that sustainable base‐flow is important to maintaining sound stream ecosystems.     

Constraining SWAT Calibration with Remotely Sensed Evapotranspiration Data

Historically, many watershed studies have been based on using the streamflow flux, typically from a single gauge at the basin's outlet, to support calibration. In this setting, there is great potential for equifinality of parameters during the optimization process, especially for parameters that are not directly related to streamflow. Therefore, some of the optimal parameter values achieved during the autocalibration process may be physically unrealistic. In recent decades a vast array of data from land surface models and remote sensing platforms can help to constrain hydrologic fluxes such as evapotranspiration (ET). While the spatial resolution of these ancillary datasets varies, the continuous spatial coverage of these gridded datasets provides flux measurements across the entire basin, in stark contrast to point‐based streamflow data. This study uses Global Land Evaporation: the Amsterdam Model data to constrain Soil and Water Assessment Tool parameter values associated with ET to a more physically realistic range. The study area is the Little Washita River Experimental Watershed, in southern Oklahoma. Traditional objective metrics such as the Nash‐Sutcliffe coefficients record no performance improvement after application of this method. However, there is a dramatic increase in the number of days with receding flow where simulations match observed streamflow.     

MEAN AREAL PRECIPITATION FOR DAILY HYDROLOGIC MODELING IN MOUNTAINOUS REGIONS1

ABSTRACT: A procedure using detrended kriging has been developed to calculate daily values of mean areal precipitation (MAP) for input to hydrologic models. The important features of this procedure that overcome weaknesses in existing MAP procedures are: (1) specific precipitation-elevation relationships are determined for each time period as opposed to using relationships based on climatological averages, (2) spatial variability is incorporated by estimating precipitation for each grid cell over a watershed, (3) the spatial correlation structure of precipitation is explicitly modeled, and (4) station weights for precipitation estimates are determined objectively and optimally. Detailed cross-validation testing of the procedure was done for the Reynolds Creek research watershed in southwestern Idaho. The procedure is suitable for use in operational streamflow forecasting.     

GEOLOGICAL AND CLIMATIC CONTROLS ON STREAMFLOWS IN THE NEBRASKA SAND HILLS1

ABSTRACT: This paper presents hydrological characteristics of the streamflow of the Dismal, Middle Loup, North Loup, and Cedar Rivers in the Nebraska Sand Hills and their relation to climate and ground water variation. Time series of streamflow, precipitation, temperature, and ground water levels from 1976 to 1998 were used to analyze trends and fluctuations of streamflow and to determine relationships among streamflow, climate, and the ground water system. An increase of precipitation and a decrease of maximum temperature over the period resulted in higher ground water levels and increased streamflow in the region. The high permeability of the soil and the thick unsaturated zone enhance precipitation recharge, limit surface runoff, and prevent ground water losses through evapotranspiration. Thus, an abundance of ground water is stored, supplying more than 86 percent of streamflow in the four rivers. Streamflow is generally more constant in the Sand Hills than elsewhere in the region. The four rivers present different hydrologic characteristics because of the spatial heterogeneity in hydrogeologic conditions. Streamflow of the Dismal and Middle Rivers, which are less sensitive to climatic variation, is much steadier than that of the North Loup and Cedar Rivers.     

AN EVALUATION OF TWO HYDROLOGIC MODELS FOR CLIMATE CHANGE SCENARIOS1

ABSTRACT: Understanding the effects of climate change on water resources requires coupling atmospheric and hydrologic models. With the wide array of hydrologic models, from simple empirical to complex physically based, it is not clear which is preferable to simulate hydrologic variations over long time scales. To address this issue, a black-box artificial neural network (ANN) model was compared to a distributed parameter conceptual Geographic Information System based Hydrologic Modeling System (GIS-HMS). Both models computed daily direct surface runoff in four sub-basins of the West Branch of the Susquehanna River Basin, Pennsylvania and were evaluated with five objective functions. Overall, results were comparable between models. However, the ANN was favored in the larger sub-basins, while GIS-HMS was more accurate in the smaller catchments. Both models were impaired by the poor spatial and temporal resolution of precipitation data and the simplified representation of antecedent soil-moisture conditions. In the context of climate change, where simulations are limited by computing power, results suggest that both models are appropriate. When detailed simulations are essential, GIS-HMS is a preferable model to use. On the other hand, the ANN model is more suitable when multiple scenarios require immediate analysis and the distributed qualities of runoff are not required.     

SIMULATION OF THE EFFECTS OF URBANIZATION ON BASIN STREAMFLOW1

ABSTRACT: The hydrologic modeling of streamflow in the Waterford River Basin has been conducted as part of comprehensive investigations of the effects of urbanization on water resources in the basin. Using a detailed input data base, continuous simulation of streamflow in the study area has been done by means of the HSPF model, which has been calibrated for the existing conditions and then applied to several future land use scenarios. The basin climate and geology contribute to high conversion of precipitation into streamflow under the existing conditions. Consequently, future urban development in the study basin should not increase the annual streamflow, but would contribute to increases in peak flows and the incidence of flooding because of the increased speed of runoff. If the impervious area in the basin is doubled, the peak flows may increase by about 20 percent.     

Process-based calibration of WRF-Hydro in a mountainous basin in southwestern U.S.

The National Water Model (NWM) will provide the next generation of operational streamflow forecasts across the United States (U.S.) using the WRF-Hydro hydrologic model. In this study, we propose a strategy to calibrate 10 parameters of WRF-Hydro that control runoff generation during floods and snowmelt seasons, and due to baseflow. We focus on the Oak Creek Basin (820 km²), an unregulated mountainous sub-watershed of the Salt and Verde River Basins in Arizona, which are the largest source of water supply for the Phoenix Metropolitan area. We calibrate the model against discharge observations at the outlet in 2008–2011, and validate it at two stream gauging stations in 2012–2016. After bias correcting the precipitation forcings, we sequentially modify the model parameters controlling distinct runoff generation processes in the basin. We find that capturing the deep drainage to the aquifer is crucial to improve the simulation of all processes and that this flux is mainly controlled by the SLOPE parameter. Performance metrics indicate that snowmelt, baseflow, and floods due to winter storms are simulated fairly well, while flood peaks caused by summer thunderstorms are severely underestimated. We suggest the use of spatially variable soil depth to enhance the simulation of these processes. This work supports the ongoing calibration effort of the NWM by testing WRF-Hydro in a watershed with a large variety of runoff mechanisms that are representative of several basins in the southwestern U.S.