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Uncertainty attribution in water supply forecasting is crucial to improve forecast skill and increase confidence in seasonal water management planning. We develop a framework to quantify fractional forecast uncertainty and partition it between (1) snowpack quantification methods, (2) variability in post‐forecast precipitation, and (3) runoff model errors. We demonstrate the uncertainty framework with statistical runoff models in the upper Tuolumne and Merced River basins (California, USA) using snow observations at two endmember spatial resolutions: a simple snow pillow index and full‐catchment snow water equivalent (SWE) maps at 50 m resolution from the Airborne Snow Observatories. Bayesian forecast simulations demonstrate a nonlinear decrease in the skill of statistical water supply forecasts during warm snow droughts, when a low fraction of winter precipitation remains as SWE. Forecast skill similarly decreases during dry snow droughts, when winter precipitation is low. During a shift away from snow‐dominance, the uncertainty of forecasts using snow pillow data increases about 1.9 times faster than analogous forecasts using full‐catchment SWE maps in the study area. Replacing the snow pillow index with full‐catchment SWE data reduces statistical forecast uncertainty by 39% on average across all tested climate conditions. Attributing water supply forecast uncertainty to reducible error sources reveals opportunities to improve forecast reliability in a warmer future climate. [ABSTRACT FROM AUTHOR]

Unexpected and large spring precipitation events in the Colorado River Basin (CRB) that significantly alleviated an otherwise severe water shortage have been observed for over a century, such as the "Miracle May" of 2015. Although these events are often termed as "drought‐busting" or "miracle events" by water managers and the media, they have not been extensively researched or characterized. In this collaborative study with water managers across the CRB, we propose a definition for these hard‐to‐predict, ultra‐high precipitation events occurring during the late‐snow or snowmelt season. This characterization provides a framework for quantifying the frequency and intensity of extreme dry‐to‐wet springtime transitions. Despite limitations of climate model simulations due to uncertainties and the inhomogeneous qualities, our findings suggest that such transitions may become less frequent and less intense in a warming climate. In view of the potentially wetter but less‐snowy climate in the basin, the need for future research to more quantitatively assess these "miracle events" is emphasized. [ABSTRACT FROM AUTHOR]

Climate change and rapid socioeconomic development have exacerbated the damage caused by hydrological droughts. To ensure effective drought defense and infrastructure development, it is essential to investigate variations in hydrological droughts. The primary objective of this study is to reconstruct the natural streamflow by using Soil and Water Assessment Tool (SWAT) hydrological modeling. The hydrological drought at different time scales (1, 3, 6, and 12 months) were measured using the streamflow drought index (SDI). The statistical parameters, including Nash–Sutcliffe Efficiency and the Coefficient of Determination, which yielded values of 0.84 and 0.82 during the calibration period and 0.78 and 0.76 during the validation period, respectively, showed a satisfactory SWAT model performance. Additionally, the Pettit test was used to identify a change point in streamflow within the 1991–2015 timeframe, leading to the division of the study period into two distinct phases: an undisturbed period (1991–1998) and a disturbed period (1999–2015). The SDI index‐based analysis revealed 9.39% moderate drought and 3.13% severe drought during the undisturbed period, while 11.76% moderate drought and 7.35% severe drought may happen due to the human influences that occurred in the disturbed period. These findings enhance the understanding of the hydrological drought variations in the Soan River basin for optimizing the water resources management system and effectively preventing and mitigating drought‐related damages. [ABSTRACT FROM AUTHOR]

In 2020, the Chesapeake Bay Program moved to offset impacts from climate change for the 30‐year period from 1995 through 2025 by having its seven watershed jurisdictions (Delaware, Maryland, New York, Pennsylvania, Virginia, West Virginia, and the District of Columbia) apply additional nutrient pollutant reduction practices. The climate change assessment was performed with integrated models of the Chesapeake watershed, airshed, and estuary. Scenarios run for the years 2025, 2035, 2045, and 2055 estimated effects from the different future climatic conditions. This article presents the results of that assessment and is intended to provide a guide to assist other modeling practitioners in assessing climate change impacts in coastal watersheds. Major influences of climate change that were quantified include increases in precipitation volume, potential evapotranspiration, watershed nutrient loads, tidal water temperature, and sea level. Minor influences quantified in the climate change analysis include changes in nutrient speciation and increases in wet deposition of nitrogen, CO2, rainfall intensity, tidal wetland loss, up‐estuary salt intrusion, and phytoplankton biomass. To offset climate change impacts from 1995 to 2025 on water quality, the scenarios indicate an additional 2.3 million and 0.3 million kg of nitrogen and phosphorus per annum, respectively, will need to be reduced beyond what is called for in the Chesapeake Total Maximum Daily Load. [ABSTRACT FROM AUTHOR]

This study proposes an approach to evaluate the efficiency of low impact development (LID) in reducing urban runoff using a rainfall generator to disaggregate daily rainfall into sub‐hourly rainfall data, which are used as input of a hydrological model at the urban watershed scale. Twelve scenarios are analyzed combining four percentages of impervious area retrofitted with LIDs (25%, 50%, 75% and 100%), and three LID combinations of green roofs (GRs) and rain gardens (RGs). The rainfall generator Rainsim V.3 is used to generate 500 years of rainfall data with a 15‐min time step to analyze the performance of LIDs in the long‐term with the LID module of the Soil and Water Assessment Tool hydrological model. An urban watershed of 3 km2 located in Florence (Italy) is selected as a case study. Results show the performances of GRs and RG on peak flow reduction, highlighting a maximum flow reduction of single facilities ranging between 15% and 60% that can improve in case of their combination. The hydrological performances of LID combinations are very sensitive to the intensity of rainfall events, as well as percentages of area treated underlining the importance of simulating multiple scenarios of intervention to determine the most efficient combination of LIDs for a given case study and support their proper design from a urban water hydrology perspective. [ABSTRACT FROM AUTHOR]

Baseflow was proven to be the most unpredictable component of streamflow through various research. However, the recent method for estimating baseflow is due to the development of theoretical and computational techniques. This paper attempted to develop a fully automated baseflow separation system based on a recursive digital filter with an optimization algorithm for the single separation. Most of the previous baseflow separation methods use a single set of a parameter and BFImax (the maximum value of baseflow index), which can underestimate or overestimate the baseflow; however, the system developed in this study estimates multiple optimized a parameters using seasonality and flow conditions and uses them for BFImax calculation and baseflow separation. This system derived baseflow results in better understanding of watershed and streamflow tendency characteristics. This study developed a Web‐based Hydrograph Analysis Tool 2020 with a user‐friendly interface and new separation method regarding the seasonality and flow conditions with a fully automated python module to optimize a parameters and BFImax. The application to the two area show diverse parameter sets and different baseflow according to seasonality and flow conditions representing the flow characteristics. This study could be a fundamental tool for detailed watershed management decisions regarding water security in the dry season or environmental water for aquatic ecosystems. [ABSTRACT FROM AUTHOR]

In this paper, we use remotely sensed imagery to identify the location and size of animal feeding operations in the Maumee River Watershed, a key drainage area to Lake Erie's Western Basin, which has recently experienced severe harmful algal blooms. We then estimate the relationship between the intensity of animal feeding operations in the watershed and surface water body concentrations of dissolved reactive phosphorus (DRP), the pollutant most responsible for algal growth. We find that stream reaches with relatively larger increases in upstream animal feeding exposure experience significantly higher increases in concentrations of DRP. The average marginal upstream animal feeding operation in the watershed increases downstream DRP concentrations by between 10% and 15%. In contrast, when restricting the analysis to include only permitted operations, coefficient estimates are practically zero and statistically insignificant. Our work presents evidence that the increasing intensity of animal feeding operations contributes to water quality problems. Permitting and identification of animal feeding operations is therefore important for managing runoff and correctly attributing the causes of excess nutrients in surface water bodies. [ABSTRACT FROM AUTHOR]

Many cold water‐dependent aquatic organisms are experiencing habitat and population declines from increasing water temperatures. Identifying mechanisms which drive local and regional stream thermal regimes facilitates restoration at ecologically relevant scales. Stream temperatures vary spatially and temporally both within and among river basins. We developed a modeling process to identify statistical relationships between drivers of stream temperature and covariates representing landscape, climate, and management‐related processes. The modeling process was tested in three study areas of the Pacific Northwest United States during the growing season (May [start], August [warmest], September [end]). Across all months and study systems, covariates with the highest relative importance represented the physical landscape (elevation [1st], catchment area [3rd], main channel slope [5th]) and climate covariates (mean monthly air temperature [2nd] and discharge [4th]). Two management covariates (groundwater use [6th] and riparian shade [7th]) also had high relative importance. Across the growing season (for all basins), local reach slope had high relative importance in May, but transitioned to a regional main channel slope covariate in August and September. This modeling process identified regionally similar and locally unique relationships among drivers of stream temperature. High relative importance of management‐related covariates suggested potential restoration actions for each system. [ABSTRACT FROM AUTHOR]

In this study, a new remote sensing tool was used in conjunction with sampling of river bank sediments to map channel migration patterns and estimate the net contribution of bank erosion to the sediment and phosphorus (P) budget of the Nishnabotna River in southwestern Iowa. Between the years 2009 and 2018, we found that at least 1.81 ± 0.57 × 107 Mg of sediment and 8.26 ± 2.5 × 103 Mg of P entered the Nishnabotna River due to channel migration. This equates to 0.87 Mg of sediment per meter of channel per year and 0.40 kg of P per meter of channel per year. Barring additional deposition elsewhere in the river corridor, these values represent as much as 77% of annual suspended sediment and 46% of the annual P export from the watershed. Our results also indicate that the contribution of net sediment and P volume loss by stream order increases sharply from third to sixth order, even though the total channel length is much smaller in the higher orders. These results suggest that bank erosion is an important source of sediment and P within the watershed and that future attempts to decrease riparian exports of sediment and P should focus on high‐order reaches. [ABSTRACT FROM AUTHOR]

A better understanding of modeled streamflow errors related to basin reservoir storage is needed for large regions, which normally have many ungaged basins with reservoirs. We quantified the difference between modeled and observed streamflows for one process‐based and three statistical‐transfer hydrologic models, none of which explicitly accounted for reservoir storage. Streamflow statistics representing low to high flows, seasonality, annual variability, and daily autocorrelation were examined at 1082 study basins across the conterminous USA. All models increasingly overpredict (or decreasingly underpredict) observed annual maximum flows with increasing storage. Correlations between absolute values of errors for low‐flow statistics and storage are often larger in magnitude than those for signed errors—additional storage is associated with increases in model errors in both directions even when its overall effect in one direction is weak. The rate of increase in absolute values of model errors was nonlinear for most statistics. For low flows, model errors had a change point to larger errors at 48 days of reservoir storage (relative to long‐term mean daily flow); mean and high flows had change points at 147 to 176 days. We present predicted‐to‐observed errors for nine streamflow statistics over a large range of reservoir storage to help modelers and users of modeled streamflow understand the amount of storage for which explicit reservoir modeling is needed. [ABSTRACT FROM AUTHOR]

Flooding is a dominant physical process that drives the form and function of river‐floodplain ecosystems. Efficiently characterizing flooding dynamics can be challenging, especially over geographically broad areas or at spatial and temporal scales relevant for ecosystem management activities. Here, we empirically evaluated a low‐complexity geospatial model of floodplain inundation in six study segments of the Upper Mississippi River System (UMRS) by pairing spatially extensive, temporally limited and spatially limited, temporally extensive sampling designs. We found little evidence of systematic bias in model performance although discrepancies between model predictions and empirical data did occur locally. Assessments of model predictions revealed low segment‐wide discrepancies of wetted extent under contrasting flow conditions and agreement for inundation event detection and duration. Model performance for predicting event frequency and duration was similar among sites expected to exhibit contrasting patterns of hydrologic connectivity with the main channel. Our results suggest that low‐complexity models can efficiently characterize a critical physical process across geographically broad, complex river‐floodplain ecosystems. Such tools have the potential for advancing scientific understanding of landscape‐scale ecological patterns and for prioritizing management actions in large, complex river‐floodplain ecosystems like the UMRS. [ABSTRACT FROM AUTHOR]

There is growing interest in studying the impact of alternative agricultural management practices on runoff and soil loss under future climate change scenarios. In order to address this interest, it is important to demonstrate that runoff and soil loss can be accurately simulated under existing climates based on comparisons between modeled and experimental results. This study calibrates and validates the Water Erosion Prediction Project (WEPP) model to quantify the accuracy of predicting growing season runoff and soil erosion in agricultural hillslopes based on comparisons with experimental data from five Minnesota hydrologic unit code 12 watersheds. In order to accurately predict runoff and soil erosion in each watershed, the baseline effective hydraulic conductivity (Kbe), interrill and rill erodibility (EIR and ER), and monthly precipitation standard deviations (Pstdev) were calibrated in WEPP using observed runoff and total suspended solids data from five Minnesota Discovery Farms field sites. Before calibration, Nash–Sutcliffe model efficiency (NSE) and percent bias (PBIAS) values for predicted versus measured monthly average total runoff (Ravg‐T), runoff ratios (RRT), and total soil loss were generally not in acceptable ranges. After calibration, the NSE values showed very good fits between measured and predicted monthly Ravg‐T (0.64–0.98), RRT (0.66–0.93), and soil loss (0.58–0.80). PBIAS values were also within acceptable ranges for Ravg‐T and RRT (±25%) and soil loss (±55%), except for RRT at site BE1. NSE and PBIAS values during validation were within acceptable ranges, except for RRT at site BE1. These findings suggest that the WEPP hillslopes calibrated in this study are sufficiently robust to accurately predict monthly runoff and soil erosion in Minnesota agricultural fields during the growing season. [ABSTRACT FROM AUTHOR]

Operational streamflow forecasting is critically important to managers of river basins that supply water, hydropower, and flood protection. While seasonal water supply forecasts (WSFs) are important for long‐term water resources planning operations, shorter term (e.g., 1–5 weeks) streamflow forecasts are critical for balancing water conservation with flood risk during wet periods. In this study, we designed a streamflow forecasting system with the water resources group at the Salt River Project (SRP), a provider of water and power to millions of customers in central Arizona (AZ), to provide streamflow forecasts for a diverse and operationally important set of watersheds in AZ. The forecast system uses machine learning to make seasonal WSFs, a rainfall–runoff model driven by ensemble meteorological forecasts to make 35‐day streamflow forecasts, and an innovative approach to improve the WSFs based on the 35‐day streamflow forecasts. This model integration allows for an assessment of the impact of different meteorological forecasts on WSFs, helping SRP to balance water conservation goals with shorter term flood risks. In addition, seasonal WSFs are improved in the early winter when they incorporate the 35‐day streamflow predictions. Furthermore, these improvements are larger than when they incorporate 7‐day streamflow predictions, demonstrating the value of using subseasonal to seasonal (S2S, >1–2 weeks) forecasts to improve seasonal WSFs in these watersheds. [ABSTRACT FROM AUTHOR]

The 2010 Chesapeake Bay Total Maximum Daily Load was established for the water quality and ecological restoration of the Chesapeake Bay. In 2017, the latest science, data, and modeling tools were used to develop revised Watershed Implementation Plans (WIPs). In this article, we examine the vulnerability of the Chesapeake Bay watershed to the combined pressures of climate change and growth in population, agricultural intensity, and economic activity for the 60‐year period 1995–2055. The results will be used to revise WIPs, as needed, to account for expected increases in loads. Assessing changes relative to 1995 for the years 2025, 2035, 2045, and 2055, mean annual precipitation increases of 3.11%, 4.21%, 5.34%, and 6.91%, respectively, air temperature increases of 1.12, 1.45, 1.84, and 2.12°C, respectively, and potential evapotranspiration increases of 3.36%, 4.43%, 5.54%, and 6.35%, respectively, are projected. Population in the watershed is expected to grow by 3.5 million between 2025 and 2055. Watershed model results show incremental increases in streamflow (2.3%–6.2%), nitrogen (2.6%–10.8%), phosphorus (4.5%–26.7%), and sediment (3.8%–18.8%) loads to the tidal Bay due to climate change. Growth in population, agricultural intensity, development, and economic activity resulted in relatively smaller increases in loads compared to climate change. [ABSTRACT FROM AUTHOR]

The rapid development of urbanization has led to changes in the morphology and structure of the river system, exacerbating flooding in the basin and seriously affecting the regulation and storage capacity of the river system. We propose the evaluation indexes of the regulation and storage capacity, establish a hydrological–hydrodynamic model to investigate the effects of the polder‐type flood control pattern on the regulation and storage capacity during urbanization. The results show that the river system's static regulation and storage capacity exhibits a decreasing trend during the urbanization process. The change in static regulation and storage capacity of low‐grade rivers has a more significant impact on the total regulation and static storage capacity. The construction of polders increases the flood pressure of the river system and reduces the dynamic regulation and storage capacity. The smaller the size of the flood, the more pronounced the effect. With the increase in flood‐protected areas, the dynamic regulation and storage capacity gradually decreases, and minor flooding is more sensitive to the number of polders. [ABSTRACT FROM AUTHOR]

Accurate projections of streamflow, which have implications for flooding, water resources, hydropower, and ecosystems, are critical to climate change adaptation and require an understanding of streamflow sensitivity to climate drivers. The northeastern United States has experienced a dramatic increase in extreme precipitation over the past 25 years; however, the effects of these changes, as well as changes in other drivers of streamflow, remain unclear. Here, we use a random forest model forced with a regional climate model to examine historical and future streamflow dynamics of four watersheds across the Northeast. We find that streamflow in the cold season (November–May) is primarily driven by 3‐day rainfall and antecedent wetness (Antecedent Precipitation Index) in three rainfall‐dominant watersheds, and 30‐day rainfall, antecedent wetness, and 30‐day snowmelt in the fourth, more snowmelt‐dominated watershed. In the warm season (June–October), streamflow is driven by antecedent wetness and rainfall in all watersheds. By the end of the century (2070–2099), cold season streamflow depends on the importance placed on snow in the machine learning model, with changes ranging from −7% (with snow) to +40% (without snow) in a single watershed. Simulated future warm season streamflow increases in two watersheds (56% and 193%) due to increased precipitation and antecedent soil wetness, but decreases in the other two watersheds (−6% and −27%) due to reduced precipitation. [ABSTRACT FROM AUTHOR]

The six mainstem reservoirs in the Missouri River basin (MRB) are managed mainly to prevent flooding from snowmelt and heavy rainfall, a goal for which the interannual variabilities of precipitation (P), evapotranspiration (ET), and surface air temperature (Tair) are vitally important. We tested the hypothesis that under the expected higher variability owing to global climate change, the months with the highest contributions to the interannual variability of P, ET, and Tair in the MRB will remain unchanged and quantified likely temporal trends in these quantities. Using high‐resolution, downscaled Coupled Model Intercomparison Project Phase 5 multi‐model ensemble data sets, we compared the multi‐year ratio of monthly and annual interannual variability and temporal trends in P, ET, and Tair during 2011–2020 with three future decades. Results showed that the 6 months with the highest interannual variability in P and ET (April–September) are the same in all four decades. However, for Tair, only 4 months (December–March) retain their status as highly variable throughout the four decades; September and October variability is exceeded by the variability in other months. This implies that, compared to P and ET, the cyclical change in the probabilities of Tair in the MRB is less stable under future global climate change. This finding can be used to consider the need to alter existing strategies for reservoir release while minimizing the likelihood of aggravating flooding below the reservoirs. [ABSTRACT FROM AUTHOR]

Eutrophication, harmful algal blooms, and human health impacts are critical environmental challenges resulting from excess nitrogen and phosphorus in surface waters. Yet we have limited information regarding how wetland characteristics mediate water quality across watershed scales. We developed a large, novel set of spatial variables characterizing hydrological flowpaths from wetlands to streams, that is, "wetland hydrological transport variables," to explore how wetlands statistically explain the variability in total nitrogen (TN) and total phosphorus (TP) concentrations across the Upper Mississippi River Basin (UMRB) in the United States. We found that wetland flowpath variables improved landscape‐to‐aquatic nutrient multilinear regression models (from R2 = 0.89 to 0.91 for TN; R2 = 0.53 to 0.84 for TP) and provided insights into potential processes governing how wetlands influence watershed‐scale TN and TP concentrations. Specifically, flowpath variables describing flow‐attenuating environments, for example, subsurface transport compared to overland flowpaths, were related to lower TN and TP concentrations. Frequent hydrological connections from wetlands to streams were also linked to low TP concentrations, which likely suggests a nutrient source limitation in some areas of the UMRB. Consideration of wetland flowpaths could inform management and conservation activities designed to reduce nutrient export to downstream waters. [ABSTRACT FROM AUTHOR]

In this study, we demonstrate a physically based semi‐Lagrangian water temperature model known as the River Basin Model (RBM) coupled with the Variable Infiltration Capacity (VIC) hydrological model and Weather Research & Forecasting Model in the Mississippi River Basin (MRB). The results of this coupling compare favorably with observed water temperature data available from six river gages located in the MRB. Further sensitivity analysis indicates that the mean water temperatures may increase by 1.3, 1.5, and 1.8°C in northern, central, and southern MRB zones under a hypothetical uniform air temperature increase of 3.0°C. If air temperatures increase uniformly by 6.0°C in this scenario, then water temperatures are projected to increase by 3.3, 3.5, and 4.0°C. Lastly, downscaled air temperatures from a global climate model are used to drive the coupled VIC and RBM model from 2020 to 2099. Average stream temperatures from 2020 to 2099 increase by 1.0 to 8.0°C above 1950 to 2010 average water temperatures, with non‐uniform increases along the river. In some portions of the MRB, stream temperatures could increase above survival thresholds for several native fish species, which are critical components of the stream ecosystem. In addition, increased water temperatures interact with nutrient loadings from sources throughout the MRB, which is expected to exacerbate harmful algal blooms and dead zones in the Gulf of Mexico. [ABSTRACT FROM AUTHOR]

A wastewater model was applied to the Potomac River watershed to provide (i) a means to identify streams with a high likelihood of carrying elevated effluent‐derived contaminants and (ii) risk assessments to aquatic life and drinking water. The model linked effluent discharges along stream networks, accumulated wastewater, and predicted contaminant loads of municipal wastewater constituents while accounting for instream dilution and attenuation. Simulations using 2016 data suggested that nearly 30% (8281 km) of streams were wastewater impacted. Low‐ to medium‐order streams had the largest range of accumulated wastewater (ACCWW%) values. ACCWW% exceeded a 1% threshold at >39% of drinking‐water intakes (varied by temporal condition). Risk assessments of municipal wastewater‐contaminant mixtures indicated that 22% (1479 km) of streams impacted by municipal wastewater (5.5% of all reaches modeled) may pose high risk to aquatic organisms under mean‐annual conditions, with fish more susceptible to chronic‐exposure effects relative to other taxa. Risk varied temporally and by stream order, with the greatest risk occurring in the summer in small streams. These findings suggest that wastewater may be an important factor contributing to environmental degradation in the Potomac River watershed. [ABSTRACT FROM AUTHOR]

Wild salmon stocks in the Pacific Northwest are imperiled by a variety of declining habitat factors, including riparian shade and in‐channel large wood. In this paper, a relatively simple lidar model of the riparian canopy was used along anadromous streams in the Skagit River watershed in western Washington State, United States, to delineate where riparian trees were most lacking, and where restoration efforts would have the greatest benefit in terms of shade and large wood recruitment potential. Within a 45‐m riparian buffer, 61% of riparian zones were currently incapable of delivering large wood to the stream. Current potential for large wood recruitment is greatest adjacent to stream edges and falls off rapidly with distance from the channel. Approximately 99% of large wood recruitment potential lies within 45 m of the channel edge, and 50% of the wood potential is within 9 m. A hypothetical canopy model in which all trees mature to a 100‐year height would provide 18% more shade distributed over the entire watershed, and 90% more shade in the tributaries. Most of the potential gains in improved shade and large wood contributions are in agricultural areas, as opposed to forestry or urban land uses. The shade and large wood models were constructed from widely available geographic information system tools and are readily transferable to other watersheds with similar characteristics. Model outputs are intended for use in planning restoration projects, as an input to stream temperature models, and to inform policy on restoration priorities and regulatory buffer widths. [ABSTRACT FROM AUTHOR]

Polder is usually used for flood control in the river delta area. With the rapid development of urbanization, the dikes or pumps cut the original stream network system, and the stream network connectivity (SNC) and the river system pattern have changed. The dikes or pumps generally force up the river's water level, and regional flood formation mechanisms and processes have changed. In order to quantitatively describe the characteristics of polder‐type flood control measure (PFCM) and the change law of SNC, firstly, the streams inside polders were generalized as virtual streams, a hydrological‐hydrodynamic model was constructed by connecting Hydrologic Engineering Center‐Hydrologic Modeling System and MIKE11 model. Secondly, an SNC evaluation model was constructed based on flow resistance and hydrological process. Finally, the SNC under different scenarios was simulated and evaluated to reveal the influence of the PFCM on SNC. And the dominance analysis method obtained the main control factors of SNC changes. The results showed that the pumps as the main drainage facility under the PFCM, SNC after the opening of the pumps were increased by 0.060, 0.103, and 0.311 for 50%, 30%, and 3% frequency flood scales compared with the pumps closed, respectively. However, compared with the natural stream (without the PFCM), the SNC decreased by 0.391, 0.456, and 0.487, respectively, at the same time of the same flood scale. The PFCM negatively impacted the SNC, and the number of pumps was the main control factor of the SNC. [ABSTRACT FROM AUTHOR]

A nutrient loss reduction strategy is necessary to guide the efforts of improving water quality downstream of an agricultural watershed. In this study, the effectiveness of two winter cover crops, namely cereal rye and annual ryegrass, is explored as a loss reduction strategy in a watershed that ultimately drains into a water supply reservoir. Using a coupled optimization‐watershed model, optimal placements of the cover crops were identified that would result in the tradeoffs between nitrate‐N losses reduction and adoption levels. Analysis of the 10%, 25%, 50%, and 75% adoption levels extracted from the optimal tradeoffs showed that the cover crop placements would provide annual nitrate‐N loss reductions of 3.0%–3.7%, 7.8%–8.8%, 15%–17.5%, and 20.9%–24.3%, respectively. In addition, for the same adoption levels (i.e., 10%–75%), sediment (1.8%–17.7%), and total phosphorus losses (0.8%–8.6%) could be achieved. Results also indicate that implementing each cover crop on all croplands of the watershed could cause annual water yield reduction of at least 4.8%, with greater than 28% in the months of October and November. This could potentially be detrimental to the storage volume of the downstream reservoir, especially in drought years, if cover crops are adopted in most of the reservoir's drainage area. Evaluating water yield impacts, particularly in periods of low flows, is thus critical if cover crops are to be considered as best management practices in water supply watersheds. [ABSTRACT FROM AUTHOR]

Ensuring an adequate, reliable, clean, and affordable water supply for citizens and industries requires informed, long‐range water supply planning, which is critically important for water security. A balance between water supply and demand must be considered for a long‐term plan. However, water demand projections are often highly uncertain. Climate change could impact the hydrologic processes, and consequently, threaten water supply. Thus, understanding the uncertainties in future water demand and climate is critical for developing a sound water supply plan. In Illinois, regional water supply planning attempts to explore the impacts of future water demand and climate on water supply using scenario analyses and hydrologic modeling. This study is aimed at developing a water supply planning framework that considers both future water demand and climate change impacts. This framework is based on the Soil and Water Assessment Tool to simulate the watershed hydrology and conduct scenario analyses that consider the uncertainties in both future water demand and climate as well as their impacts on water supply. The framework was applied to water supply planning efforts in the Kankakee River watershed. The Kankakee River watershed model was calibrated and validated to observed streamflow records at four long‐term United States Geological Survey streamflow gages. Because of the many model parameters involved, the calibration process was automated and was followed by a manual refinement, resulting in good model performance. Long‐range water demand projections were prepared by the Illinois State Water Survey. Six future water demand scenarios were established based on a suite of assumptions. Climate scenarios were obtained from the Coupled Model Intercomparison Projection Phase 5 datasets. Three representative concentration pathways (RCPs), RCP2.6, RCP4.5, and RCP8.5, are used in the study. The scenario simulation results demonstrated that climate change appears to have a greater impact on water availability in the study area than water demand. The framework developed in this study can also be used to explore the impacts of uncertainties of water demand and climate on water supply and can be extended to other regions and watersheds. [ABSTRACT FROM AUTHOR]

Data‐driven techniques are used extensively for hydrologic time‐series prediction. We created various data‐driven models (DDMs) based on machine learning: long short‐term memory (LSTM), support vector regression (SVR), extreme learning machines, and an artificial neural network with backpropagation, to define the optimal approach to predicting streamflow time series in the Carson River (California, USA) and Montmorency (Canada) catchments. The moderate resolution imaging spectroradiometer (MODIS) snow‐coverage dataset was applied to improve the streamflow estimate. In addition to the DDMs, the conceptual snowmelt runoff model was applied to simulate and forecast daily streamflow. The four main predictor variables, namely snow‐coverage (S‐C), precipitation (P), maximum temperature (Tmax), and minimum temperature (Tmin), and their corresponding values for each river basin, were obtained from National Climatic Data Center and National Snow and Ice Data Center to develop the model. The most relevant predictor variable was chosen using the support vector machine‐recursive feature elimination feature selection approach. The results show that incorporating the MODIS snow‐coverage dataset improves the models' prediction accuracies in the snowmelt‐dominated basin. SVR and LSTM exhibited the best performances (root mean square error = 8.63 and 9.80) using monthly and daily snowmelt time series, respectively. In summary, machine learning is a reliable method to forecast runoff as it can be employed in global climate forecasts that require high‐volume data processing. [ABSTRACT FROM AUTHOR]

Data limitations often challenge the reliability of water quality models, especially in intensively managed watersheds. While numerous studies report successful hydrological model setup and calibration, few have addressed in detail the data challenges for multisite and multivariable model calibration to an intensively managed watershed. In this study, we address some of these challenges based on our reflective experience calibrating the Soil and Water Assessment Tool (SWAT) to the Upper Sangamon River Watershed in central Illinois based on daily flow, annual crop yield, and monthly sediment, nitrate, and total phosphorus loads. We highlight some challenges in SWAT calibration processes due to data errors and inconsistencies, and insufficient precipitation and water quality observations. Following, we demonstrate the merits of additional weather and water quality observations that could help reduce input uncertainties, and we provide suggestions for selecting appropriate observations for the model calibration. After dealing with the data issues, we show that the SWAT model could be calibrated with acceptable results for the case study watershed. [ABSTRACT FROM AUTHOR]

In the Upper Colorado River Basin (UCRB), there is a deep reliance on seasonal snowpack for maintenance of water resources. The term "snow drought" has recently emerged to describe periods of anomalously low snowpack. Unique seasonal patterns in precipitation and temperature that drive snow drought can have distinct hydrologic signatures, and these relationships have not been carefully studied in the UCRB. Here we examine snow drought with a new classification scheme using peak snow water equivalent (SWE) and the ratio of basin‐wide modeled peak SWE to accumulated (onset to peak) precipitation (SWE/P) that clusters snow drought years into three distinct groups—"warm," "dry," and "warm & dry"—that minimize within‐group variance. Over the period 1916–2018, we identify 14 warm years (P¯ = 160 mm; SWE/P¯ = 0.24), 24 dry years (P¯ = 117 mm; SWE/P¯ = 0.35), and 21 warm & dry years (P¯ = 94 mm; SWE/P¯ = 0.23). An elevation‐based analysis reveals two distinct patterns: warm snow droughts see severe SWE reductions primarily at lower (<2600 m) elevations (65% at lower elevations, 37% overall), whereas "dry" scenarios exhibit a consistent reduction across all elevations (39% overall). Using naturalized streamflow data, we also differentiate snow droughts by their earlier streamflow timing and decreased peakedness (warm: 7 days, 2%; dry: 7 days, 2%; warm & dry: 13 days, 5%). This research provides new insights into snow drought patterns relevant for regional water management. [ABSTRACT FROM AUTHOR]

Excess loading of nitrogen and phosphorus to river networks causes environmental harm, but reducing loads from large river basins is difficult and expensive. We developed a new tool, the River Basin Export Reduction Optimization Support Tool (RBEROST) to identify the least‐cost combinations of management practices that will reduce nutrient loading to target levels in downstream and mid‐network waterbodies. We demonstrate the utility of the tool in a case study in the Upper Connecticut River Basin in New England, USA. The total project cost of optimized lowest‐cost plans ranged from $18.0 million to $41.0 million per year over 15 years depending on user specifications. Plans include both point source and non‐point source management practices, and most costs are associated with urban stormwater practices. Adding a 2% margin of safety to loading targets improved the estimated probability of success from 37.5% to 99%. The large spatial scale of RBEROST, and the consideration of both point and non‐point source contributions of nutrients, make it well suited as an initial screening tool in watershed planning. [ABSTRACT FROM AUTHOR]

We present four reconstruction estimates of Arkansas River baseflow and streamflow using a total of 78 tree‐ring chronologies for three streamflow gages, geographically spanning the headwaters in Colorado to near the confluence of the Arkansas‐Mississippi rivers. The estimates represent different seasonal windows, which are dictated by the shared limiting forcing of precipitation on seasonal tree growth and soil moisture—and subsequently on the variability of Arkansas River discharge. Flow extremes that were higher and lower than what has been observed in the instrumental era are recorded in each of the four reconstructions. Years of concurrent, cross‐basin (all sites) low flow appear more frequently during the 20th and 21st Centuries compared to any period since 1600 A.D., however, no significant trend in cross‐basin low flow is observed. As the most downstream major tributary of the Mississippi River, the Arkansas River directly influences flood risk in the Lower Mississippi River Valley. Estimates of extreme high flow in downstream reconstructions coincide with specific years of historic flooding documented in New Orleans, Louisiana, just upstream of the Mississippi River Delta. By deduction, Mississippi River flooding in years of low Arkansas River flow imply exceptional flooding contributions from the Upper Mississippi River catchments. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In all, 13 stream water‐quality parameters, including specific conductance (SC), pH, dissolved oxygen (DO), dissolved organic carbon (DOC), three nutrients, and six major ions were compared between the northern bedrock and southern coastal plain regions of New Jersey, USA and related to watershed‐disturbance gradients characterized by the percentage of urban land, impervious surface (IS), agriculture, and altered land (sum of urban land and agriculture) in the watersheds. SC, DO, calcium, magnesium, sodium, and chloride concentrations were greater in the north. DOC was higher and pH was lower in the south. Nutrient, potassium, and sulfate concentrations did not differ between regions. Regional water‐quality differences are attributed to geologic setting and land use. Except for DO in southern streams, all water‐quality parameters were related to urban land, agriculture, or both. Significant correlations between urban land and IS and water‐quality variables were similar in both regions with differences in unitless correlation coefficients ranging from 0.00 to 0.06. Compared to urban land and agriculture, relationships between most water‐quality variables and altered land were stronger in the south. The extent of urban and agricultural lands in the watersheds did not differ by region. Altered land was correlated with urban land in both regions and with agriculture only in the south. Although focused on New Jersey, this study has broader implications for watershed planning. [ABSTRACT FROM AUTHOR]

River flooding impacts human life and infrastructure, yet provides habitat and ecosystem services. Traditional flood control (e.g., levees, dams) reduces habitat and ecosystem services, and exacerbates flooding elsewhere. Floodplain restoration (i.e., bankfull floodplain reconnection and Stage 0) can also provide flood management, but has not been sufficiently evaluated for small frequent storms. We used 1D unsteady Hydrologic Engineering Center's River Analysis System to simulate small storms in a 5 km‐long, second‐order generic stream from the Chesapeake Bay watershed, and varied % channel restored (starting at the upstream end), restoration location, restoration bank height (distinguishes bankfull from Stage 0 restoration), and floodplain width/Manning's n. Stream restoration decreased (attenuated) peak flow up to 37% and increased floodplain exchange by up to 46%. Floodplain width and % channel restored had the largest impact on flood attenuation. The incremental effects of new restoration projects on flood attenuation were greatest when little prior restoration had occurred. By contrast, incremental effects on floodplain exchange were greatest in the presence of substantial prior restoration, setting up a tradeoff. A similar tradeoff was revealed between attenuation and exchange for project location, but not bank height or floodplain width. In particular, attenuation and exchange were always greater for Stage 0 than for bankfull floodplain restoration. Stage 0 thus may counteract human impacts such as urbanization. [ABSTRACT FROM AUTHOR]

They note that while it is common to study effects of drought on water flows, drought effects on water quality and, subsequently, on ecosystems are often ignored. Featured Collection introduction: Severe sustained drought revisited managing the Colorado River system in times of water shortage 25 years later - Part II The call for papers for the I JAWRA i Featured Collection: Severe Sustained Drought Revisited: Managing the Colorado River System in Times of Water Shortage 25 Years Later led to more insights than could fit into a single issue. The article by Salehabadi et al. ([11]) quantifies the impacts of three drought scenarios based on three past sustained droughts. [Extracted from the article]

The Los Angeles region contains a number of heavily altered watersheds, resulting in the degradation of both water and habitat quality along numerous streams. Assessing the impacts of these anthropogenic stressors on biological communities has primarily focused on the California Stream Condition Index (CSCI), a measure of the biotic integrity of benthic macroinvertebrate assemblages. To complement the CSCI an Algal Stream Condition Index (ASCI) was developed to assess the biotic integrity of both soft‐bodied and diatomaceous algal assemblages. Using random forest modeling, we evaluated the performances of the CSCI, the ASCI for diatom assemblages (D_ASCI), and the ASCI for hybrid assemblages containing both diatoms and soft‐bodied algae (H_ASCI). We found that our models of the D_ASCI and H_ASCI could account for approximately 77% and 78% of their observation variation across the watersheds of the Los Angeles region, nearly as high as the 82% accounted for by the CSCI. This indicates the future potential of using indices of biotic integrity based on, or in part, diatom assemblages for streams in this region as additional forms of bioassessment. [ABSTRACT FROM AUTHOR]

Much has been learned about Colorado River hydrology since the severe sustained drought study in 1995. We summarize our updated understanding of plausible future drought conditions by considering historical flows, tree‐ring reconstructions, and climate change. We focus on natural streamflow at Lees Ferry, the primary metric used to quantify the runoff in the Colorado River Basin. We identify drought periods using historical records and tree‐ring reconstructed streamflow at Lees Ferry, which we then use to characterize potential future droughts. Resampling from past drought periods generates plausible future conditions to consider during planning. We produced three drought scenarios, each comprising 100 streamflow sequences to be used as input to systems operation and management models. We used analysis of the duration‐severity and cumulative deficit relative to the mean natural flow to evaluate droughts and drought simulations and show that the current millennium drought that started in 2000 has an average flow far less than the historical record. However, the flows reconstructed from tree rings or future flows projected from climate models indicate that even more severe droughts are possible. When used as input to the Colorado River Simulation System the drought scenarios developed indicate considerable periods when Lake Powell falls below its hydropower penstocks, indicating a need to rethink management and operation of these reservoirs during these critical conditions. [ABSTRACT FROM AUTHOR]

This study investigates the combined impacts of climate change and agricultural conservation on the magnitude and uncertainty of nutrient loadings in the Maumee River Watershed, the second‐largest watershed of the Laurentian Great Lakes. Two scenarios — baseline agricultural management and increased agricultural conservation — were assessed using an ensemble of five Soil and Water Assessment Tools driven by six climate models. The increased conservation scenario included raising conservation adoption rates from a baseline of existing conservation practices to feasible rates in the near future based on farmer surveys. This increased adoption of winter cover crops on 6%–10% to 60% of cultivated cropland; subsurface placement of phosphorus fertilizers on 35%–60% to 68% of cultivated cropland; and buffer strips intercepting runoff from 29%–34% to 50% of cultivated cropland. Increased conservation resulted in statistically significant (p ≤ 0.05) reductions in annual loads of total phosphorus (41%), dissolved reactive phosphorus (18%), and total nitrogen (14%) under the highest emission climate scenario (RCP 8.5). While nutrient loads decreased with increased conservation relative to baseline management for all watershed models, different conclusions on the true effectiveness of conservation under climate change may be drawn if only one watershed model was used. [ABSTRACT FROM AUTHOR]

Extremes in temperature and precipitation are associated with damaging floods, prolonged drought, destructive wildfires, agricultural challenges, compromised human health, vulnerable infrastructure, and threatened ecosystems and species. Often, the steady and progressive trends (or presses) of rising global temperature are the central focus in how climate impacts are described. However, observations of extreme weather events (or pulses) increasingly show that the intensity, duration and/or frequency of acute events are also changing, resulting in greater impacts on communities and the environment. Describing how the influence of extreme events may shape water management in the Colorado River Basin in clear terms is critical to sound future planning and efforts to manage risk. Three scenario planning workshops in 2019 and 2020 were held as part of a Colorado River Conversations series, identifying potential impacts from multiple intersecting extreme events. Water managers identified climate‐related events of concern in the Colorado River Basin that necessitate greater attention and adaptive responses. To support efforts to include consideration of climate‐change‐driven extremes in water management and planning, we explore the current state of knowledge at the confluence of long‐term climate shifts and extreme weather in the Colorado River Basin related to the events of concern that were identified by scenario planning participants. [ABSTRACT FROM AUTHOR]

This study determines the most cost‐effective selection and location of Best Management Practice (BMP) scenarios for farmlands to reduce soil erosion and the delivery of sediment and phosphorus to the Fort Cobb Reservoir watershed under current and future climate scenarios. Using the Soil and Water Assessment Tool, detailed conservation practices (conservation tillage, contour farming, no‐tillage, and terracing) are simulated to determine crop yields, sediment, nitrogen, and phosphorus loss of three crops (winter wheat, irrigated cotton, and pasture) by each land use unit (hydrologic response units) and location in the watershed. An economic model is used to determine the cost‐minimizing choice of BMP scenarios for each land‐use unit that meets 50%, 75%, and 90% reduction of sediment, nitrogen, and phosphorus loads and the impact of climate change on the net return to farmers. Results indicate that the effective spatial placement of BMP scenarios is not the same under different climatic conditions. The average optimal net return to farmers ranges from 170.39 to 316.32 $/acre for all the scenarios. The shadow prices vary from 2.36 to 17.78 $/ton/acre of sediment abated. Although individual BMP scenario is effective in reducing sediment loads, combined BMP scenarios achieve higher performance. This work will help guide policymakers and farmers in decision‐making for the future regarding water quantity used and water quality downstream in the recreation area of the Fort Cobb Reservoir. [ABSTRACT FROM AUTHOR]

Runoff from the Upper Colorado River Basin (UCRB) is an important water resource in the western United States. The majority of annual runoff is derived from spring snowmelt, and therefore April–July water supply volume (WSV) forecasts produced by the Colorado Basin River Forecast Center are critical to basin water management. The primary objective of this study was to evaluate the impact of snow data assimilation (DA) in a distributed hydrologic model on WSV forecasting error and skill in headwater catchments of the UCRB. To do this, a framework was built to use the Localized Ensemble transform Kalman filter to update modeled snow water equivalent (SWE) states in the Hydrology Laboratory‐Research Distributed Hydrologic Model with SNOTEL SWE observations and spatially and temporally complete MODIS Snow Covered‐Area and Grain size data. The DA approach was assessed by evaluating ensemble streamflow prediction forecasts over a 20‐year period for 23 catchments in the UCRB. Overall, the DA approach improved water supply forecast skill in 80% of pilot basins with an average improvement in the median continuous rank probability skill score of 4%. A research‐to‐operations transition was facilitated by automating the implementation of the DA approach. This work demonstrates the capacity for gridded and point snow products to be used to objectively update model states in an operational forecasting setting. [ABSTRACT FROM AUTHOR]

In water‐limited regions, it is important to understand the response of vegetation to hydrologic predictors for fire management and water resource planning. We present a novel spatially distributed analysis of ecohydrological interactions in the semi‐arid Colorado River Basin (CRB) over 18 years, 2001–2019. The hydrologic predictors used include precipitation from the Integrated Multi‐satellitE Retrievals for GPM, 0‐ to 10‐cm‐depth surface soil moisture (SSM) estimations from the National Land Data Assimilation System, and newly available 0‐ to 40‐cm depth Soil MERGE root zone soil moisture (RZSM) estimations. These are evaluated using time‐lagged correlations with the Enhanced Vegetation Index (EVI) from MODerate Resolution Imaging Spectroradiometer (MODIS). We demonstrate that EVI response is strongest and most immediate to the hydrologic predictors in the hot and dry southwestern CRB, with lag times of 0–32 days. RZSM was expected to provide the best predictor of EVI, but we found SSM to be a superior predictor of EVI with interquartile range correlations of 0.20–0.37 across the CRB. RZSM had slightly lower interquartile range correlations of 0.15–0.35, and precipitation was the least effective predictor of EVI with interquartile range correlations of 0.11–0.26. Plotting these cross‐correlations provides a spatially explicit overview of temporal dependence between SSM, RZSM, precipitation, and EVI with publicly available datasets. [ABSTRACT FROM AUTHOR]

Regulated rivers worldwide are managed to meet human demands and reduce flood hazards but the resulting flow regime often fails to support ecological and hydrogeomorphic processes. Designing environmental flows (e‐flows) for regulated rivers has been the focus of recent studies, but in the absence of site‐specific monitoring data, it can be difficult to assign flow regimes. Given the recent development of dams throughout China, and the desire to minimize adverse downstream ecological effects, we classified river basins to identify characteristics for improved basin management. The purpose of the classification is to minimize the need for localized data when developing initial evaluations for planning e‐flow prioritization plans. Building on the functional flow framework of managing rivers to deliver environmental water in the right place, time, and amount, we use k‐means consensus clustering to group 185 subbasins of China's Yangtze River into seven hydroclasses (consensus = 0.80). Similar hydrogeomorphic and ecological characteristics are used for the purposes of identifying flow regime components indexed against threatened aquatic species and anthropogenic pressures. This case study can inform water resource management and e‐flow requirements broadly. As hydropower dams are expected to more than double over the next 15 years globally, flexible management strategies that balance ecosystem needs with human demands can support efforts for sustainable development. [ABSTRACT FROM AUTHOR]

This study is to establish a new approach to estimate river salinity of semi‐arid agricultural watershed and identify drivers by using hydrologic modeling and machine learning. We augmented the limitations of the Soil and Water Assessment Tool (SWAT) to model salinity by coupling with eXtreme Gradient Boosting (XGBoost), a decision‐tree‐based ensemble machine learning algorithm. Streamflow, precipitation, elevation, main reach length, and dominant soil texture of the top two layers were used along with NO3, NO2, and total phosphorus (TP) output from a calibrated SWAT model are used as predictors to Total Dissolved Solids (TDS) in the XGBoost algorithm. Then, the SWAT model simulations of streamflow, NO3+NO2, and TP from 2000 to 2015 are used as inputs of the XGBoost model to predict monthly water TDS distribution along the river. The predicted river water TDS showed a higher concentration as going downstream from El Paso (inlet) through the Hudspeth canal to Fort Quitman (outlet). Finally, this study carried out cause analysis focusing on soil physical characteristics. The soil salinity level is directly affected by the soil permeability and irrigation water. As a result, the highest TDS is shown in sites with silt loam, whereas the lowest TDS was shown in sites with very cobbly soil. Silt soils can hold more water and are slower to drain than soils of a sand type. These analyses can be used to better understand the mitigation of water salinity. [ABSTRACT FROM AUTHOR]

Excessive nitrogen (N) inputs to coastal waters can lead to severe eutrophication and different chemical forms of N exhibit varying levels of effectiveness in fueling primary production. Efforts to mitigate N fluxes from coastal watersheds are often guided by models that predict changes in N loads as a function of changes in land use, management practices, and climate. However, relatively little is known on the impacts of such changes on the relative fractions of different N forms. We leveraged a long‐term dataset of N loads from over 100 river stations to investigate how the NO3‐ fraction, that is, the ratio of NO3‐ to total N (NO3‐/TN), changes as a function of spatio‐temporal changes in TN loads in the Chesapeake Bay watershed. We built a hierarchical model that separates the response of NO3‐ to changes in TN load occurring at different scales: Across river stations, where differences in TN loads are largely driven by spatial differences in anthropogenic inputs, and within stations, where inter‐annual variability in hydrology is a key driver of changes in TN loads. Results suggest that while increases in TN loads resulting from changes in anthropogenic inputs lead to an increase in the NO3‐ fraction, a decrease in the NO3‐ fraction may occur when increases in TN loads are driven by increased streamflow. These results are especially relevant in watersheds that may experience changes in N loads due to both management decisions and climate‐driven changes in hydrology. [ABSTRACT FROM AUTHOR]

The Bureau of Reclamation (Reclamation) plays a central management role in the Colorado River Basin (CRB), with an increasing focus on meeting the needs of stakeholders during the current drought. One aspect of this role involves generating five‐year projections of reservoir operating conditions in the federal multi‐reservoir system. These projections are the basis for estimating the probability of shortage conditions, which are relied on by stakeholders, and are particularly important during drought. Currently, Ensemble Streamflow Prediction (ESP) forecasts drive Reclamation's Colorado River Mid‐term Modeling System to produce probabilistic reservoir projections to be used in risk‐based analysis and decision support for the first two years of the outlook period. The lack of significant forecast skill beyond the first year motivates interest in alternative forecasting approaches. The CRB Operational Prediction Testbed was created to provide a quantitative and consistent framework for assessing the skill of streamflow forecasts and their impact on associated reservoir system projections. Reservoir system projections are evaluated by analyzing Lakes Powell and Mead operations, including projected pool elevation and operating tiers. In an initial application of this testbed, ESP forecasts were compared to experimental streamflow forecasts to assess their skill impact on two‐year reservoir projections, which are critical information for managing drought. [ABSTRACT FROM AUTHOR]

"Deep uncertainty" is a term that describes planning contexts in which it is impossible to determine the likelihood of any given set of future conditions, there are conflicting performance objectives and priorities, and decision outcomes are unpredictable. Evidence of the relevance of this term in the Colorado River Basin can be found in the unexpected severity and length of the ongoing drought, the existence of a wide range of future streamflow and demand projections and anticipated system requirements, as well as in the diverse range of stakeholders, viewpoints, and priorities that must be considered when developing and analyzing future operating policies. Decision Making under Deep Uncertainty (DMDU) is a branch of decision science devoted to developing planning approaches that can address these challenges. Reclamation began exploring DMDU techniques in 2012 in the Colorado River Basin Water Supply and Demand Study when it used the concepts of adaptation, vulnerability, and robustness in the design and evaluation of portfolios of options and strategies to address a potential supply‐demand imbalance in coming decades. This article presents the basis for continuing to explore DMDU techniques and briefly describes how Reclamation's recent and ongoing DMDU studies could contribute to future planning efforts in the Colorado River Basin. [ABSTRACT FROM AUTHOR]

Three generations of global climate models (GCMs), Coupled Model Intercomparison Project version 3 (CMIP3), CMIP5, and CMIP6, are evaluated for performance simulating seasonal mean and annual‐to‐decadal variability of temperature and precipitation in the Upper Colorado River Basin. Low‐frequency precipitation variability associated with drought is a particular focus and found to be a significant model shortcoming. The evaluation includes remote teleconnected atmospheric responses to the Pacific Ocean, including the El Niño/Southern Oscillation and Pacific Decadal Oscillation. GCMs have improved their simulation of the Upper Basin over model generations, but primarily in atmospheric circulation metrics. Persistent winter precipitation biases have changed little, including in multiyear precipitation variability. Users generally bias‐corrected GCM data before use; evaluation using a simple spatially and temporally averaged bias correction shows that the CMIP6 models outperform earlier generations after the bias correction, although more complex precipitation biases remain even after the simple bias correction. These model rankings will be useful when selecting GCMs for a variety of hydrological and ecological climate studies in the Upper Basin. [ABSTRACT FROM AUTHOR]

Understanding effects of climate change on nitrogen fate and transport in the environment is critical to nutrient management. We used climate projections within a previously calibrated spatially referenced regression (SPARROW) model to predict effects of expected climate change over 1995 through 2025 on total nitrogen fluxes to Chesapeake Bay and in watershed streams. Assuming nitrogen inputs and other watershed conditions remain at 2012 levels, effects of increasing temperature, runoff, streamflow, and stream velocity expected between 1995 and 2025 will include an estimated net 6.5% decline in annual nitrogen delivery to the bay from its watershed. This predicted decline is attributable to declines in the delivery of nitrogen from upland nonpoint sources to streams due to predicted warmer temperatures. Such temperature‐driven declines in the delivery of nitrogen to streams more than offset predicted increased delivery to and within streams due to increased runoff and streamflow and may be attributable to increasing rates of denitrification or ammonia volatilization or to changes in plant phenology. Predicted climate‐driven declines in nitrogen flux are generally similar across the watershed but vary slightly among major nonpoint source sectors and tributary watersheds. Nitrogen contributions to the bay from point sources are not affected by temperature‐driven changes in delivery from uplands and are therefore predicted to increase slightly between 1995 and 2025. Research Impact Statement: Assuming nitrogen inputs and other watershed conditions remain constant, near‐term effects of climate change may include decreased delivery of nitrogen to Chesapeake Bay from its watershed. [ABSTRACT FROM AUTHOR]

Under‐representations of headwater channels in digital stream networks can result in uncertainty in the magnitude of headwater habitat loss, stream burial, and watershed function. Increased availability of high‐resolution (<2 m) elevation data makes the delineation of headwater channels more attainable. In this study, elevation data derived from light detection and ranging was used to predict ephemeral stream networks across a forested and urban watershed in the Maryland Piedmont USA. A method was developed using topographic openness (TO) and wetness index to remotely predict the extent of stream networks. Predicted networks were compared against a comprehensive field survey of the ephemeral network in each watershed to evaluate performance. Comparisons were also made to the U.S. Geological Survey National Hydrography Dataset (NHD) and a flow accumulation approach where a single drainage area threshold defined channel initiation. Although the NHD and flow accumulation methods resulted in low commission errors, omission errors were highest in these networks. The TO‐based networks detected a larger number of ephemeral channels, but with higher commission error. Small ephemeral channels with less defined banks or originating at groundwater seeps were difficult to detect in all methods. Comparisons between forested and urban watersheds also highlight the difficulty of identifying headwater channels using topographic attributes in human‐modified landscapes. Research Impact Statement: We describe a new approach using lidar‐derived elevation data to remotely map headwater streams in forested and urban landscapes, and address ongoing challenges with mapping small streams. [ABSTRACT FROM AUTHOR]

Understanding the temporal dynamics and drivers of dissolved inorganic nitrogen (DIN) and dissolved inorganic phosphorus (DIP) provides a critical link to better management of nutrient‐related impacts such as eutrophication and harmful algal blooms (HABs). DIN and DIP are a primary control on persistent eutrophication and HABs in the Pacific Northwest (PNW). An understudied phenomenon, this paper examines multi‐decadal trends in DIN and DIP concentrations and loads, and their relationships to climatic and hydrologic factors (e.g., stream and air temperature, discharge, precipitation) in the PNW. Dissolved constituents act as a broad sentinel of linkages between watershed and in‐stream mechanisms such as nitrification, denitrification, nutrient use efficiency, evapotranspiration, hydrologic connectivity, groundwater extraction, irrigation, and land uses. As opposed to the total N and P often used in individual, autochthonous, lentic systems, DIN and DIP are used here as measures of multiscaled processes in allochthonous lotic systems with diverse flow paths. Time‐series data from public agencies were used for up to 20 years in river outlets from the Willamette, Salmon, Spokane, and Yakima watersheds. Seasonal Mann Kendall (SMK) tests suggest significant decreasing multi‐decadal trends in DIN and DIP loads for three out of four watersheds (for DIN, SMK = −0.104; for DIP, SMK = −0.081, −0.181, and −0.213), significant decreasing trends in DIN concentrations for one of the four watersheds (SMK = −0.144), and significant decreasing trends in DIP concentrations in three of the four watersheds (SMK = −0.120, −0.135, and −0.157). Multivariate regressions found significant relationships for concentrations, loads, and ratios when regressed against stream and air temperatures, precipitation, and discharge (16 significant regressions, with adjusted R2 values between 0.016 and 0.65). Highlights of these regression results are as follows: (1) precipitation, discharge, and water and air temperatures help to explain DIN and DIP concentrations and loads, (2) changes in DIN concentrations are sensitive to more hydroclimatic variables than DIP concentrations, and (3) DIP concentrations are positively correlated with stream temperature while DIP loads are negatively correlated with stream temperature. Furthermore, seasonal changes in nutrients — and their potential to alter aquatic productivity during a year — has received little attention in the literature. Regressions established significant seasonality or monthly variation of DIN and DIP concentrations and loads in all four watersheds (20 significant regressions, with adjusted R2 values between 0.038–0.65). Nutrient thresholds of DIN (0.3–0.5 mg/L) and DIP (0.05–0.005 mg/L) concentrations were used to analyze N‐ and P‐limitation. P‐limitation is known to occur in lakes, and N‐limitation is known to occur in rivers. Surprisingly, except for one watershed (Salmon), nutrient concentrations for both DIN and DIP in all watersheds were shown to be above the limitation thresholds across multiple seasons. In certain situations, such as where significant decreasing trends continue, the DIN:DIP ratio suggests seasonal switching between N‐ and P‐limited could create ideal conditions for HABs. The findings of this study have important implications for water resource management issues such as agriculture, land use development, fish populations, timber harvests, water quality, and public health. [ABSTRACT FROM AUTHOR]

This study updates analysis of western water markets with data from thirteen river basins in eight states based on analysis of over 7,000 transactions from 2002 to 2019. We take a new approach to characterizing the time trend in prices across basins by estimating hedonic price indices. In doing so, we do not attempt to explain how prices are determined but rather to characterize general price movements across the region. There have been nine widely cited analyses of trends in western water rights prices and trading. Since 2010, when Stratecon ceased publishing the Water Strategist, no one has extended this time series until now (Stratecon Inc. Accessed January 25, 2021). Researchers, planners, and investors remain keenly interested in water markets, but limited historic data are available on general water right price trends and volatility. Understanding general price movements over time help to fill this gap. Our analysis confirms the findings of earlier studies that urban buyers systematically pay premium prices relative to agricultural or environmental buyers, and that prices vary significantly within basins, across basins and states, and over time. Results from the pooled‐period method indicate an annual rate of appreciation of around 2.8% and results from the adjacent‐period method indicate a rate of 5.1%. Both methods suggest that water right prices from 2002 to 2019 have increased at a rate similar to the general price level. [ABSTRACT FROM AUTHOR]