Streamflow Depletion Modeling: Methods for an Adaptable and Conjunctive Water Management Decision Support Tool

Groundwater pumping depletes streamflow, which can have significant ecological impacts depending on the magnitude of depletion relative to environmental flow needs. Streamflow depletion estimates from groundwater pumping have been quantified using both analytical and numerical methods, but are not routinely compared to environmental flow needs or used in practical water management tools. Decision support tools that incorporate groundwater dynamics are becoming increasingly necessary for water managers as groundwater regulations become more important in environmental policy, particularly concerning the preservation of environmental flow needs. We develop and apply methods for a web‐based decision support tool for conjunctive groundwater and surface water management, demonstrated using a case study watershed in British Columbia, Canada. Open‐source data are analyzed with a combination of spatial algorithms and previously developed analytical models, such that the tool can be applied to other regions. Streamflow depletion estimates are calculated in four regions in the largely undeveloped Bulkley Valley, British Columbia. Our transparent methodology has geographic and data input flexibility which is a significant improvement on currently existing water management tool methods.     

Historical Groundwater Trends in Northern New England and Relations with Streamflow and Climatic Variables

Water‐level trends spanning 20, 30, 40, and 50 years were tested using month‐end groundwater levels in 26, 12, 10, and 3 wells in northern New England (Maine, New Hampshire, and Vermont), respectively. Groundwater levels for 77 wells were used in interannual correlations with meteorological and hydrologic variables related to groundwater. Trends in the contemporary groundwater record (20 and 30 years) indicate increases (rises) or no substantial change in groundwater levels in all months for most wells throughout northern New England. The highest percentage of increasing 20‐year trends was in February through March, May through August, and October through November. Forty‐year trend results were mixed, whereas 50‐year trends indicated increasing groundwater levels. Whereas most monthly groundwater levels correlate strongly with the previous month's level, monthly levels also correlate strongly with monthly streamflows in the same month; correlations of levels with monthly precipitation are less frequent and weaker than those with streamflow. Groundwater levels in May through August correlate strongly with annual (water year) streamflow. Correlations of groundwater levels with streamflow data and the relative richness of 50‐ to 100‐year historical streamflow data suggest useful proxies for quantifying historical groundwater levels in light of the relatively short and fragmented groundwater data records presently available.     

Use of Hydrologic Landscape Classification to Diagnose Streamflow Predictability in Oregon

We implement a spatially lumped hydrologic model to predict daily streamflow at 88 catchments within the state of Oregon and analyze its performance using the Oregon Hydrologic Landscape (OHL) classification. OHL is used to identify the physio‐climatic conditions that favor high (or low) streamflow predictability. High prediction catchments (Nash‐Sutcliffe efficiency of (NS) > 0.75) are mainly classified as rain dominated with very wet climate, low aquifer permeability, and low to medium soil permeability. Most of them are located west of the Cascade Mountain Range. Conversely, most low prediction catchments (NS < 0.6) are classified as snow‐dominated with high aquifer permeability and medium to high soil permeability. They are mainly located in the volcano‐influenced High Cascades region. Using a subset of 36 catchments, we further test if class‐specific model parameters can be developed to predict at ungauged catchments. In most catchments, OHL class‐specific parameters provide predictions that are on par with individually calibrated parameters (NS decline < 10%). However, large NS declines are observed in OHL classes where predictability is not high enough. Results suggest higher uncertainty in rain‐to‐snow transition of precipitation phase and external gains/losses of deep groundwater are major factors for low prediction in Oregon. Moreover, regionalized estimation of model parameters is more useful in regions where conditions favor good streamflow predictability.     

The Role of Ground Water in Generating Streamflow in Headwater Areas and in Maintaining Base Flow1

Abstract: The volume and sustainability of streamflow from headwaters to downstream reaches commonly depend on contributions from ground water. Streams that begin in extensive aquifers generally have a stable point of origin and substantial discharge in their headwaters. In contrast, streams that begin as discharge from rocks or sediments having low permeability have a point of origin that moves up and down the channel seasonally, have small incipient discharge, and commonly go dry. Nearly all streams need to have some contribution from ground water in order to provide reliable habitat for aquatic organisms. Natural processes and human activities can have a substantial effect on the flow of streams between their headwaters and downstream reaches. Streams lose water to ground water when and where their head is higher than the contiguous water table. Although very common in arid regions, loss of stream water to ground water also is relatively common in humid regions. Evaporation, as well as transpiration from riparian vegetation, causing ground‐water levels to decline also can cause loss of stream water. Human withdrawal of ground water commonly causes streamflow to decline, and in some regions has caused streams to cease flowing.     

Seasonal and Flood‐Induced Variations in Groundwater–Surface Water Exchange in a Northern Coldwater Fishery

Groundwater upwelling is important to coldwater fisheries survival. This study used stable isotopes to identify upwelling zones within a watershed, then combined isotope analyses with reach‐scale monitoring to measure surface water–groundwater exchange over time. Research focused on Amity Creek, Minnesota, a basin that exemplifies conditions limiting coldwater species survival along Lake Superior's North Shore where shallow bedrock limits groundwater capacity, lowering baseflows and increasing temperatures. Groundwater‐fed reaches were identified through synoptic isotope sampling, with results highlighting the importance of isolated shallow surficial aquifers (glacially derived sands and gravels) for providing cold baseflow waters. In an alluvial reach, monitoring well results show groundwater was stored in two reservoirs: one that reacts quickly to changes in stream levels, and one that remained isotopically isolated under most flow conditions, but which helps sustain summer baseflows for weeks to months. A 500‐year flood demonstrated the capacity of high‐flow events to alter surface water–groundwater connectivity. The previously isolated reservoir was exchanged or mixed during the flood pulse, while incision lowered the water table for years. The results here provide insight for streams that lack substantial groundwater inputs yet maintain coldwater species at risk in a warming climate and an approach for managers seeking to protect cold baseflow sources.     

A Simple Framework for Incorporating Seasonal Streamflow Forecasts into Existing Water Resource Management Practices1

Gong, Gavin, Lucien Wang, Laura Condon, Alastair Shearman, and Upmanu Lall, 2010. A Simple Framework for Incorporating Seasonal Streamflow Forecasts Into Existing Water Resource Management Practices. Journal of the American Water Resources Association (JAWRA) 46(3):574-585. DOI: 10.1111/j.1752-1688.2010.00435.x Abstract: Climate-based streamflow forecasting, coupled with an adaptive reservoir operation policy, can potentially improve decisions by water suppliers and watershed stakeholders. However, water suppliers are often wary of straying too far from their current management practices, and prefer forecasts that can be incorporated into existing system modeling tools. This paper presents a simple framework for utilizing streamflow forecasts that works within an existing management structure. Climate predictors are used to develop seasonal inflow forecasts. These are used to specify operating rules that connect to the probability of future (end of season) reservoir states, rather than to the current storage, as is done now. By considering both current storage and anticipated inflow, the likelihood of meeting management goals can be improved. The upper Delaware River Basin in the northeastern United States is used to demonstrate the basic idea. Physically plausible climate-based forecasts of March-April reservoir inflow are developed. Existing simulation tools and rule curves for the system are used to convert the inflow forecasts to reservoir level forecasts. Operating policies are revised during the forecast period to release less water during forecasts of low reservoir level. Hindcast simulations demonstrate reductions of 1.6% in the number of drought emergency days, which is a key performance measure. Forecasts with different levels of skill are examined to explore their utility.     

Incorporating Variable Source Area Hydrology into a Spatially Distributed Direct Runoff Model1

Buchanan, Brian, Zachary M. Easton, Rebecca Schneider, and M. Todd Walter, 2011. Incorporating Variable Source Area Hydrology Into a Spatially Distributed Direct Runoff Model. Journal of the American Water Resources Association (JAWRA) 48(1): 43‐60. DOI: 10.1111/j.1752‐1688.2011.00594.x Abstract: Few hydrologic models simulate both variable source area (VSA) hydrology, and runoff‐routing at high enough spatial resolutions to capture fine‐scale hydrologic pathways connecting VSA to the stream network. This paper describes a geographic information system‐based operational model that simulates the spatio‐temporal dynamics of VSA runoff generation and distributed runoff‐routing, including through complex artificial drainage networks. The model combines the Natural Resource Conservation Service’s Curve Number (CN) equation for estimating storm runoff with the topographic index concept for predicting the locations of VSA and a runoff‐routing algorithm into a new spatially distributed direct hydrograph (SDDH) model (SDDH‐VSA). Using a small agricultural watershed in central New York, SDDH‐VSA results were compared to those from a SDDH model using the traditional land use assumptions for the CN (SDDH‐CN). The SDDH‐VSA model generally agreed better with observed discharge than the SDDH‐CN model (average, Nash‐Sutcliffe efficiency of 0.69 vs. 0.58, respectively) and resulted in more realistic spatial patterns of runoff‐generating areas. The SDDH approach did not correctly capture the timing of runoff from small storms in dry periods. Despite this type of limitation, SDDH‐VSA extends the applicability of the SDDH technique to VSA conditions, providing a basis for new tools to help identify critical management areas and assess water quality risks due to landscape alterations.     

Streamflow Response to Climate as Influenced by Geology and Elevation1

Mayer, Timothy D. and Seth W. Naman, 2011. Streamflow Response to Climate as Influenced by Geology and Elevation. Journal of the American Water Resources Association (JAWRA) 47(4):724‐738. DOI: 10.1111/j.1752‐1688.2011.00537.x Abstract: This study examines the regional streamflow response in 25 predominately unregulated basins to warmer winter temperatures and snowpack reductions over the last half century in the Klamath Basin of California and Oregon. Geologic controls of streamflow in the region result in two general stream types: surface‐dominated and groundwater‐dominated basins. Surface‐dominated basins were further differentiated into rain basins and snowmelt basins on the basis of elevation and timing of winter runoff. Streamflow characteristics and response to climate vary with stream type, as discussed in the study. Warmer winter temperatures and snowpack reductions have caused significantly earlier runoff peaks in both snowmelt and groundwater basins in the region. In the groundwater basins, the streamflow response to changes in snowpack is smoothed and delayed and the effects are extended longer in the summer. Our results indicate that absolute decreases in July‐September base flows are significantly greater, by an order of magnitude, in groundwater basins compared to surface‐dominated basins. The declines are important because groundwater basins sustain Upper Klamath Lake inflows and mainstem river flows during the typically dry summers of the area. Upper Klamath Lake April‐September net inflows have decreased an estimated 16% or 84 thousand acre‐feet (103.6 Mm³) since 1961, with the summer months showing proportionately more decline. These changes will exacerbate water supply problems for agriculture and natural resources in the region.     

Transboundary Groundwater Policy: Developing Approaches in the Western and Southwestern United States1

Hathaway, Deborah L., 2011. Transboundary Groundwater Policy: Developing Approaches in the Western and Southwestern United States. Journal of the American Water Resources Association (JAWRA) 47(1):103‐113. DOI: 10.1111/j.1752‐1688.2010.00494.x Abstract: The western and southwestern United States include dozens of groundwater basins that cross political boundaries. Common among these shared groundwater basins is an overlay of differing legal structures and water development priorities, typically, with insufficient water supply for competing human uses, and often, a degraded ecosystem. Resolution of conflicts over ambiguously regulated groundwater has clarified transboundary groundwater policy in some interstate basins, while transboundary groundwater policy in international basins is less evolved. This paper identifies and contrasts approaches to transboundary groundwater policy, drawing from recent conflicts and cooperative efforts, including those associated with the interstate compacts on the Arkansas and Pecos Rivers; the Hueco and Lower Rio Grande Basins shared by New Mexico, Texas, and Mexico; and the Mexicali Basin in California and Mexico. Some efforts seek to fit groundwater policy into existing surface water allocation procedures; some strive for a better fit – incorporating scientific understanding of key differences between groundwater and surface water into policy frameworks. In some cases, neither policy nor precedent exists. The collective experience of these and other cases sets the stage for improved management of transboundary groundwater; as such, challenges and successes of these approaches, and those contemplated in several hypothetical model agreements, are examined.     

Water Balances of Two Piedmont Headwater Catchments: Implications for Regional Hydrologic Landscape Classification

In the Piedmont of North Carolina, a traditionally water‐rich region, reservoirs that serve over 1 million people are under increasing pressure due to naturally occurring droughts and increasing land development. Innovative development approaches aim to maintain hydrologic conditions of the undisturbed landscape, but are based on insufficient target information. This study uses the hydrologic landscape concept to evaluate reference hydrology in small headwater catchments surrounding Falls Lake, a reservoir serving Raleigh and the greater Triangle area. Researchers collected one year of detailed data on water balance components, including precipitation, evapotranspiration, streamflow, and shallow subsurface storage from two headwater catchments representative of two hydrologic landscapes defined by differences in soils and topographic characteristics. The two catchments are similar in size and lie within the same physiographic region, and during the study period they showed similar water balances of 26‐30% Q, ?4 to 5% ΔS, 59‐65% evapotranspiration, and 9‐10% G. However, the steeper, more elevated catchment exhibited perennial streamflow and nongrowing season runoff ratios (Q/P) of 33%, whereas the flat, low‐lying stream was drier during the growing season and exhibited Q/P ratios of 52% during the nongrowing season. A hydrologic landscape defined by topography and soil characteristics helps characterize local‐scale reference hydrology and may contribute to better land management decisions.     

Climatic Fluctuations and Forecasting of Streamflow in the Lower Colorado River Basin1

Abstract:  Water‐resource managers need to forecast streamflow in the Lower Colorado River Basin to plan for water‐resource projects and to operate reservoirs for water supply. Statistical forecasts of streamflow based on historical records of streamflow can be useful, but statistical assumptions, such as stationarity of flows, need to be evaluated. This study evaluated the relation between climatic fluctuations and stationarity and developed regression equations to forecast streamflow by using climatic fluctuations as explanatory variables. Climatic fluctuations were represented by the Atlantic Multidecadal Oscillation (AMO), Pacific Decadal Oscillation (PDO), and Southern Oscillation Index (SOI). Historical streamflow within the 25‐ to 30‐year positive or negative phases of AMO or PDO was generally stationary. Monotonic trends in annual mean flows were tested at the 21 sites evaluated in this study; 76% of the sites had no significant trends within phases of AMO and 86% of the sites had no significant trends within phases of PDO. As climatic phases shifted in signs, however, many sites had nonstationary flows; 67% of the sites had significant changes in annual mean flow as AMO shifted in signs. The regression equations developed in this study to forecast streamflow incorporate these shifts in climate and streamflow, thus that source of nonstationarity is accounted for. The R² value of regression equations that forecast individual years of annual flow for the central part of the study area ranged from 0.28 to 0.49 and averaged 0.39. AMO was the most significant variable, and a combination of indices from both the Atlantic and Pacific Oceans explained much more variation in flows than only the Pacific Ocean indices. The average R² value for equations with PDO and SOI was 0.15.     

Generation of Ensemble Streamflow Forecasts Using an Enhanced Version of the Snowmelt Runoff Model1

Harshburger, Brian J., Von P. Walden, Karen S. Humes, Brandon C. Moore, Troy R. Blandford, and Albert Rango, 2012. Generation of Ensemble Streamflow Forecasts Using an Enhanced Version of the Snowmelt Runoff Model. Journal of the American Water Resources Association (JAWRA) 48(4): 643‐655. DOI: 10.1111/j.1752‐1688.2012.00642.x Abstract: As water demand increases in the western United States, so does the need for accurate streamflow forecasts. We describe a method for generating ensemble streamflow forecasts (1‐15 days) using an enhanced version of the snowmelt runoff model (SRM). Forecasts are produced for three snowmelt‐dominated basins in Idaho. Model inputs are derived from meteorological forecasts, snow cover imagery, and surface observations from Snowpack Telemetry stations. The model performed well at lead times up to 7 days, but has significant predictability out to 15 days. The timing of peak flow and the streamflow volume are captured well by the model, but the peak‐flow value is typically low. The model performance was assessed by computing the coefficient of determination (R²), percentage of volume difference (Dv%), and a skill score that quantifies the usefulness of the forecasts relative to climatology. The average R² value for the mean ensemble is >0.8 for all three basins for lead times up to seven days. The Dv% is fairly unbiased (within ±10%) out to seven days in two of the basins, but the model underpredicts Dv% in the third. The average skill scores for all basins are >0.6 for lead times up to seven days, indicating that the ensemble model outperforms climatology. These results validate the usefulness of the ensemble forecasting approach for basins of this type, suggesting that the ensemble version of SRM might be applied successfully to other basins in the Intermountain West.     

Spatial and Temporal Patterns of Low Streamflow and Precipitation Changes in the Chesapeake Bay Watershed

Spatial and temporal patterns in low streamflows were investigated for 183 streamgages located in the Chesapeake Bay Watershed for the period 1939–2013. Metrics that represent different aspects of the frequency and magnitude of low streamflows were examined for trends: (1) the annual time series of seven‐day average minimum streamflow, (2) the scaled average deficit at or below the 2% mean daily streamflow value relative to a base period between 1939 and 1970, and (3) the annual number of days below the 2% threshold. Trends in these statistics showed spatial cohesion, with increasing low streamflow volume at streamgages located in the northern uplands of the Chesapeake Bay Watershed and decreasing low streamflow volume at streamgages in the southern part of the watershed. For a small subset of streamgages (12%), conflicting trend patterns were observed between the seven‐day average minimum streamflow and the below‐threshold time series and these appear to be related to upstream diversions or the influence of reservoir‐influenced streamflows in their contributing watersheds. Using multivariate classification techniques, mean annual precipitation and fraction of precipitation falling as snow appear to be broad controls of increasing and decreasing low‐flow trends. Further investigation of seasonal precipitation patterns shows summer rainfall patterns, driven by the Atlantic Multidecadal Oscillation, as the main driver of low streamflows in the Chesapeake Bay Watershed.     

MODIS Biophysical States and NEXRAD Precipitation in a Statistical Evaluation of Antecedent Moisture Condition and Streamflow1

Abstract: The potential of remotely sensed time series of biophysical states of landscape to characterize soil moisture condition antecedent to radar estimates of precipitation is assessed in a statistical prediction model of streamflow in a 1,420 km² watershed in south‐central Texas, Moderate Resolution Imaging Spectroradiometer (MODIS) time series biophysical products offer significant opportunities to characterize and quantify hydrologic state variables such as land surface temperature (LST) and vegetation state and status. Together with Next Generation Weather Radar (NEXRAD) precipitation estimates for the period 2002 through 2005, 16 raw and deseasoned time series of LST (day and night), vegetation indices, infrared reflectances, and water stress indices were linearly regressed against observed watershed streamflow on an eight‐day aggregated time period. Time offsets of 0 (synchronous with streamflow event), 8, and 16 days (leading streamflow event) were assessed for each of the 16 parameters to evaluate antecedent effects. The model results indicated a reasonable correlation (r² = 0.67) when precipitation, daytime LST advanced 16 days, and a deseasoned moisture stress index were regressed against log‐transformed streamflow. The estimation model was applied to a validation period from January 2006 through March 2007, a period of 12 months of regional drought and base‐flow conditions followed by three months of above normal rainfall and a flood event. The model resulted in a Nash‐Sutcliffe estimation efficiency (E) of 0.45 for flow series (in log‐space) for the full 15‐month period, ?0.03 for the 2006 drought condition period, and 0.87 for the 2007 wet condition period. The overall model had a relative volume error of ?32%. The contribution of parameter uncertainties to model discrepancy was evaluated.     

Streamflow Hydrology Estimate Using Machine Learning (SHEM)

Continuity and accuracy of near real‐time streamflow gauge (streamgage) data are critical for flood forecasting, assessing imminent risk, and implementing flood mitigation activities. Without these data, decision makers and first responders are limited in their ability to effectively allocate resources, implement evacuations to save lives, and reduce property losses. The Streamflow Hydrology Estimate using Machine Learning (SHEM) is a new predictive model for providing accurate and timely proxy streamflow data for inoperative streamgages. SHEM relies on machine learning (“training”) to process and interpret large volumes (“big data”) of historic complex hydrologic information. Continually updated with real‐time streamflow data, the model constructs a virtual dataset index of correlations and groups (clusters) of relationship correlations between selected streamgages in a watershed and under differing flow conditions. Using these datasets, SHEM interpolates estimated discharge and time data for any indexed streamgage that stops transmitting data. These estimates are continuously tested, scored, and revised using multiple regression analysis processes and methodologies. The SHEM model was tested in Idaho and Washington in four diverse watersheds, and the model's estimates were then compared to the actual recorded data for the same time period. Results from all watersheds revealed a high correlation, validating both the degree of accuracy and reliability of the model.     

Hydrograph Separation by Incorporating Climatological Factors: Application to Small Experimental Watersheds 1

Abstract: Evaluating the relative amounts of water moving through the different components of the hydrological cycle is required for precise management and planning of water resources. An important aspect of this evaluation is the partitioning of streamflow into surface (quick flow) and base‐flow components. A prior study evaluated 40 different approaches for hydrograph‐partitioning on a field scale watershed in the Coastal Plain of the Southeastern United States and concluded that the Boughton’s method produced the most consistent and accurate results. However, its accuracy depends upon the proper estimation of: (1) the end of surface runoff, and (2) the fraction factor (α) that is function of many physical and hydrologic characteristics of a watershed. Proper identification of the end of surface runoff was accomplished by using a second derivative approach. Applying this approach to 12 years of separately measured surface and subsurface flow data from a field scale watershed (study area) proved to be accurate for 87% of the time. Estimation of the α value was accomplished in this study using two steps: (1) alpha was fitted to individual hydrographs: and, (2) a regression equation that determines these alpha values based on climatological factors (e.g., rainfall, evapotranspiration) was developed. Using these strategies improved the streamflow partitioning method’s performance significantly.     

Finding Common Ground: How Advocacy Coalitions Succeed in Protecting Environmental Flows1

Abstract: This paper reviews several recent case studies in which states or countries have strengthened their protection of environmental flows to explore the key policy, stakeholder, and scientific elements that contributed to these advances in water management. A conceptual framework is developed to describe the actions of interest groups and individuals, how environmental flow issues get onto the formal agenda of decision makers, the events and conditions which precipitate this attention, the role of science and scientific uncertainty, and how interactions and dialog among individuals and groups with different interests lead to changes in state and national statutes. In general, the review found that changing policies is a result of actions of informed groups of interested parties using science and information to inform both the public and decision makers about the need for action and about the specific action needed. In almost all cases, environmental flow issues make it onto the formal agenda of institutions through one or more precipitating events, often legal challenges that call into question the existing legal framework for water management. Significantly, in almost all cases the engagement between advocacy coalitions with different and often opposing views results in reframing the issues to provide a common approach or solution upon which the competing coalitions can agree.     

Impacts of Variable Climate and Effluent Flows on the Transboundary Santa Cruz Aquifer

Assessing groundwater resources in the arid and semiarid borderlands of the United States and Mexico represents a challenge for land and water managers, particularly in the Transboundary Santa Cruz Aquifer (TSCA). Population growth, residential construction, and industrial activities have increased groundwater demand in the TSCA, in addition to wastewater treatment and sanitation demands. These activities, coupled with climate variability, influence the hydrology of the TSCA and emphasize the need for groundwater assessment tools for decision‐making purposes. This study assesses the impacts of changes in groundwater demand, effluent discharge, and climate uncertainties within the TSCA from downstream of the Nogales International Wastewater Treatment Plant to the northern boundary of the Santa Cruz Active Management Area. We use a conceptual water budget model to analyze the long‐term impact of the different components of potential recharge and water losses within the aquifer. Modeling results project a future that ranges from severe long‐term drying to positive wetting. This research improves the understanding of the impact of natural and anthropogenic variables on water sustainability, with an accessible methodology that can be globally applied.     

A Regional‐Scale Habitat Suitability Model to Assess the Effects of Flow Reduction on Fish Assemblages in Michigan Streams1

Zorn, Troy G., Paul W. Seelbach, and Edward S. Rutherford, 2012. A Regional‐Scale Habitat Suitability Model to Assess the Effects of Flow Reduction on Fish Assemblages in Michigan Streams. Journal of the American Water Resources Association (JAWRA) 48(5): 871‐895. DOI: 10.1111/j.1752‐1688.2012.00656.x Abstract: In response to concerns over increased use and potential diversion of Michigan’s freshwater resources, and the resulting state legislative mandate, an advisory council created an integrated assessment model to determine the potential for water withdrawals to cause an adverse resource impact to fish assemblages in Michigan’s streams. As part of this effort, we developed a model to predict how fish assemblages characteristic of different stream types would change in response to decreased stream base flows. We describe model development and use in this case study. The model uses habitat suitability information (i.e., catchment size, base‐flow yield, and July mean water temperature) for over 40 fish species to predict assemblage structure in an individual river segment under a range of base‐flow reductions. By synthesizing model runs for individual fish species at representative segments for each of Michigan’s 11 ecological stream types, we developed curves describing how typical fish assemblages in each type respond to flow reduction. Each stream type‐specific, fish response curve was used to identify streamflow reduction levels resulting in adverse resource impacts to characteristic fish populations, the regulatory standard. Used together with a statewide map of stream types, our model provided a spatially comprehensive framework for evaluating impacts of flow withdrawals on biotic communities across a diverse regional landscape.