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Tree rings have been central to the understanding of variability of flow of the Colorado River. Spurred by steadily declining flows after the 1920s, early tree‐ring research drew attention to the importance of climate variability to water supply by identifying episodes in the past that were even drier. Application of modern statistical methods to tree‐ring data later yielded a reconstruction of annual flows at Lees Ferry back to the early 1500s that highlighted the unprecedented wetness of the base period for the 1922 Colorado River Compact. That reconstruction served as the framework for a collection of papers in a 1995 special issue of Water Resources Bulletin on coping with severe sustained drought on the Colorado River. This retrospective paper reviews historical aspects of the dendrohydrology of the Colorado River, and the updates since 1995. A constantly expanding tree‐ring network has been subjected to an array of new statistical approaches to reconstruction. Climate change and increasing demand for water have meanwhile driven increased interest in the processing and presentation of reconstructions for optimal use in water resources planning and management. While highlighting the robustness of main findings of earlier studies, recent research yields improved estimates of magnitudes of flow anomalies, extends annual flows to more than 1200 years, and underscores unmatched drought duration in the medieval period. [ABSTRACT FROM AUTHOR]

The National Weather Service (NWS) Office of Water Prediction (OWP), in conjunction with the National Center for Atmospheric Research and the NWS National Centers for Environmental Prediction (NCEP) implemented version 2.1 of the National Water Model (NWM) into operations in April of 2021. As with the initial version implemented in 2016, NWM v2.1 is an hourly cycling analysis and forecast system that provides streamflow guidance for millions of river reaches and other hydrologic information on high‐resolution grids. The NWM provides complementary hydrologic guidance at current NWS river forecast locations and significantly expands guidance coverage and water budget information in underserved locations. It produces a full range of hydrologic fields, which can be leveraged by a broad cross section of stakeholders ranging from the emergency responder and water resource communities, to transportation, energy, recreation and agriculture interests, to other water‐oriented applications in the government, academic and private sectors. Version 2.1 of the NWM represents the fifth major version upgrade and more than doubles simulation skill with respect to hourly streamflow correlation, Nash Sutcliffe Efficiency, and bias reduction, over its original inception in 2016. This paper will discuss the driving factors underpinning the creation of the NWM, provide a brief overview of the model configuration and performance, and discuss future efforts to improve NWM components and services. [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]

Several models have been developed with streamflow as a covariate for predicting daily pesticide concentrations in surface water systems. Among these models, the SEAWAVE‐QEX model has been proposed by the United States Environmental Protection Agency for regulatory assessments. In this paper, the model was modified to include alternative transformations of streamflow data, and to include no streamflow covariates. The predictive performance of the modified models was evaluated and compared with the original SEAWAVE‐QEX model using a high frequency sampling dataset that includes 9 sites with 10 years of data from the Atrazine Ecological Monitoring Program. Streamflow transformations evaluated included those in the SEAWAVE‐QEX model (short‐term and mid‐term flow anomalies), reduced models with only short‐term flow anomaly or without any flow covariates, normalized Box‐Cox transformation of flow, and combinations of normalized Box‐Cox and flow anomalies. Loglinear interpolation was also evaluated. The normalized Box‐Cox transformation provided best predictive performance and significantly better predictive performance than that of the SEAWAVE‐QEX model for a target quantity of regulatory interest, such as the maximum 1‐day rolling average (similarly for the maximum 60‐day rolling average, but not significantly so). The no‐flow covariate model was only slightly worse than Box‐Cox. Significant differences in predictive performance of the SEAWAVE‐QEX model were detected across sites. [ABSTRACT FROM AUTHOR]

In recent decades, public and private environmental entities have been purchasing or leasing water rights across the Western United States (U.S.) in efforts to restore river flows and aquatic ecosystems. The need to pay for flow restoration arises from the fact that state governments did not begin to reserve water for instream purposes until the 1970s, long after water rights had become over‐appropriated and flows were substantially depleted in most rivers. As a consequence, flow depletion has become the leading cause of fish endangerment in the U.S., including the imperilment of two‐thirds of all native fish species in the Colorado River system. This paper takes stock of the progress made in buying water for the environment, specifically by reviewing and analyzing more than 50 transactions executed by public and private entities and the sources of funding underpinning these transactions. We conclude that nongovernmental actors — such as environmental organizations and state water trusts — are integral to regional efforts to restore river flows; these nongovernmental actors executed more than two‐thirds of the transactions we documented. However, we also conclude that the long‐term success of these nongovernmental actors depends upon the availability of sustained public funding that enables them to build capacity and engage in the large number of transactions needed to restore flows across each state. Research Impact Statement: River flow restoration is needed to stem widespread aquatic species declines but much greater public/private funding and transactional expertise is needed. [ABSTRACT FROM AUTHOR]

The exemption for groundwater wells for residential uses from the prior appropriations system, common in the western United States, has eroded in Washington State since about 2000 due to a series of legal cases. Water markets can allow the transfer of an existing water right, typically from an agricultural use, to compensate for the effect of a new residential well. But water must be legally and physically available in a way suitable to satisfy mitigation requirements. A recent court case in the Skagit basin in Northwestern Washington State has effectively halted residential development in rural areas of the basin because no suitable water rights are available to purchase for mitigation. This paper presents and examines the cost‐effectiveness of various water supply mitigation strategies. We find a small‐scale, distributed stream‐side storage system for augmenting instream flow purchased from downstream sources is relatively cost‐effective to mitigate against the effects of domestic groundwater use compared to more common alternatives. We consider transporting water to storage sites by both small‐gauge pipe and by truck. Overall, trucking water to stream‐side storage and release points tends to be more cost‐effective to mitigate against indoor‐use only given current subbasin housing densities, whereas piping for direct streamflow augmentation is more cost‐effective for higher mitigation needs associated with indoor and outdoor use and higher housing densities. Research Impact Statement: Small‐scale water storage is a cost‐effective approach for offsetting the impact of domestic wells on surface water rights. [ABSTRACT FROM AUTHOR]

This paper presents a numerical investigation of approaches to enhance the mixing and dispersion processes in tidal areas by effecting changes in the natural estuary system. It compares the impact of various estuary modifications stemming from human intervention to pollutant dispersion and chaotic flow within the estuary including the implications of alteration of the original channel shape, change of the channel bathymetry, and modification of the tidal signal. Our findings indicate that chaotic flow analysis is similar in many regards, but not all, to conventional dispersion analysis. Specifically, we conclude that (1) simplification of the flow regime reduces chaotic flow patterns and tracer particle dispersion, (2) creation of extensively protruding barriers and/or installation of barriers on opposite sides of the main stem of the estuary enhances particle dispersion and chaotic mixing, (3) installation of underwater berms has relatively minor beneficial, but highly localized, effects on chaotic regime and particle dispersion, and (4) increasing the tidal signal amplitude was shown to increase chaotic and dispersion properties of the estuarine system. A parametric study investigating the effect of several geometrical configurations and tidal signals on characteristics of chaotic flows concludes the paper. [ABSTRACT FROM AUTHOR]

García, Jorge H., Matthew T. Heberling, and Hale W. Thurston, 2011. Optimal Pollution Trading Without Pollution Reductions: A Note. Journal of the American Water Resources Association (JAWRA) 47(1):52-58. DOI: 10.1111/j.1752-1688.2010.00476.x Various kinds of water pollution occur in pulses (e.g., agricultural and urban runoff). Ecosystems, such as wetlands, can serve to regulate these pulses and smooth pollution distributions over time. This smoothing reduces total environmental damages when 'instantaneous' damages are marginally increasing. This paper introduces a water quality trading model between a farm (a pulse-pollution source) and a firm (a more steady pollution source) where the object of exchange is the 'temporary' retention of runoff as opposed to total runoff reductions. The optimal trading ratio requires firm emissions to be offset by more than a proportional retention of the initial agricultural runoff pulse. The reason is twofold: (1) emissions are steady or constant over time and, in this sense, have relatively larger environmental impact; and (2) certain kinds of runoff management cause delayed environmental damages . [ABSTRACT FROM AUTHOR]

Snow course surveys in late winter provide streamflow forecasters with their best information for making water supply and flood forecasts for the subsequent spring and summer runoff period in mountainous regions of western North America. Snow survey data analyses are generally based on a 30-year "normal" period. It is well documented that forest cover changes over time will affect snow accumulation on the ground within forests. This paper seeks to determine if forest cover changes over decades at long term snow courses decrease measured peak snow water equivalent (SWE) enough to affect runoff prediction. Annual peak SWE records were analyzed at four snow courses in two different forest types having at least 25 years of snowpack data to detect any decreases in SWE due to forest growth. No statistically significant decreases in annual peak SWE over time were found at any of these four snow courses. The wide range of annual winter precipitation and correspondingly highly variable peak snowpack accumulation, as well as many other weather and site variables, masked any minor trends in the data. [ABSTRACT FROM AUTHOR]

Hydrologic modeling outputs are influenced by how a watershed system is represented. Channel routing is a typical example of the mathematical conceptualization of watershed landscape and processes in hydrologic modeling. We investigated the sensitivity of accuracy, equifinality, and uncertainty of Soil and Water Assessment Tool ( SWAT) modeling to channel dimensions to demonstrate how a conceptual representation of a watershed system affects streamflow and sediment modeling. Results showed the amount of uncertainty and equifinality strongly responded to channel dimensions. On the other hand, the model performance did not significantly vary with the changes in the channel representation due to the degree of freedom allowed by the conceptual nature of hydrologic modeling in the parameter calibration. Such findings demonstrated good modeling performance statistics do not necessarily mean small output uncertainty, and partial improvements in the watershed representation may neither increase modeling accuracy nor reduce uncertainty. We also showed the equifinality and uncertainty of hydrologic modeling are case-dependent rather than specific to models or regions, suggesting great caution should be used when attempting to transfer uncertainty analysis results to other modeling studies, especially for ungauged watersheds. Editor's note: This paper is part of the featured series on SWAT Applications for Emerging Hydrologic and Water Quality Challenges. See the February 2017 issue for the introduction and background to the series. [ABSTRACT FROM AUTHOR]

SWAT+ is a completely restructured version of the Soil and Water Assessment Tool ( SWAT) that was developed to face present and future challenges in water resources modeling and management and to meet the needs of the worldwide user community. It is expected to improve code development and maintenance; support data availability, analysis, and visualization; and enhance the model's capabilities in terms of the spatial representation of elements and processes within watersheds. The most important change is the implementation of landscape units and flow and pollutant routing across the landscape. Also, SWAT+ offers more flexibility than SWAT in defining management schedules, routing constituents, and connecting managed flow systems to the natural stream network. To test the basic hydrologic function of SWAT+, it was applied to the Little River Experimental Watershed (Georgia) without enhanced overland routing and compared with previous models. SWAT+ gave similar results and inaccuracies as these models did for streamflow and water balance. Taking full advantage of the new capabilities of SWAT+ regarding watershed discretization and landscape and river interactions is expected to improve simulations in future studies. While many capabilities of SWAT have already been enhanced in SWAT+ and new capabilities have been added, the model will continue to evolve in response to advancements in scientific knowledge and the demands of the growing worldwide user community. Editor's note: This paper is part of the featured series on SWAT Applications for Emerging Hydrologic and Water Quality Challenges. See the February 2017 issue for the introduction and background to the series. [ABSTRACT FROM AUTHOR]

Precipitation is one of the most important drivers in watershed models. Our objective was to compare two sources of interpolated precipitation data in terms of their effect on calibration and validation of two Soil and Water Assessment Tool (SWAT) models. One model was a suburban watershed in metropolitan Atlanta, Georgia. The precipitation sources were Parameter-elevation Relationships on Independent Slopes Model ( PRISM) data on a 4-km grid and climate forecast system reanalysis ( CFSR) data on a 38-km grid. The PRISM data resulted in a better fit to the calibration data (Nash Sutcliffe efficiency [ NSE] = 0.64, Kling-Gupta efficiency [ KGE] = 0.74, p-factor = 0.84, and r-factor = 0.43) than the CFSR data ( NSE = 0.47, KGE = 0.53, p-factor = 0.67, and r-factor = 0.39). Validation results were similar. Sensitive parameters were similar in both the PRISM and CFSR models, but fitted values indicated more rapid groundwater flow to the streams with the PRISM data. The same comparison was made in the Big Creek watershed located approximately 1,000 km away, in central Louisiana. Results were similar with a more responsive groundwater system indicating PRISM data may produce better predictions of streamflow because of a more accurate estimate of rainfall within a watershed or because of a denser grid. Our study implies PRISM is providing a better estimate than CFSR of precipitation within a watershed when rain gauge data are not available, resulting in more accurate simulations of streamflows at the watershed outlet. Editor's note: This paper is part of the featured series on SWAT Applications for Emerging Hydrologic and Water Quality Challenges. See the February 2017 issue for the introduction and background to the series. [ABSTRACT FROM AUTHOR]

Khalili, Malika, François Brissette, and Robert Leconte, 2011. Effectiveness of Multi-site Weather Generator for Hydrological Modeling. Journal of the American Water Resources Association (JAWRA) 1-12. DOI: 10.1111/j.1752-1688.2010.00514.x A multi-site weather generator has been developed using the concept of spatial autocorrelation. The multi-site generation approach reproduces the spatial autocorrelations observed between a set of weather stations as well as the correlations between each pair of stations. Its performance has been assessed in two previous studies using both precipitation and temperature data. The main objective of this paper is to assess the efficiency of this multi-site weather generator compared to a uni-site generator with respect to hydrological modeling. A hydrological model, known as Hydrotel, was applied over the Chute du Diable watershed, located in the Canadian province of Quebec. The distributed nature of Hydrotel accounts for the spatial variations throughout the watershed, and thus allows a more in-depth assessment of the effect of spatially dependent meteorological input on runoff generation. Simulated streamflows using both the multi-site and uni-site generated weather data were statistically compared to flows modeled using observed data. Overall, the hydrological modeling using the multi-site weather generator significantly outperformed that using the uni-site generator. This latter combined to Hydrotel resulted in a significant underestimation of extreme streamflows in all seasons. [ABSTRACT FROM AUTHOR]

Masih Ilyas, Shreedhar Maskey, Stefan Uhlenbrook, and Vladimir Smakhtin, 2011. Assessing the Impact of Areal Precipitation Input on Streamflow Simulations Using the SWAT Model. Journal of the American Water Resources Association (JAWRA) 47(1):179-195. DOI: 10.1111/j.1752-1688.2010.00502.x Reduction of input uncertainty is a challenge in hydrological modeling. The widely used model Soil Water Assessment Tool (SWAT) uses the data of a precipitation gauge nearest to the centroid of each subcatchment as an input for that subcatchment. This may not represent overall catchment precipitation conditions well. This paper suggests an alternative - using areal precipitation obtained through interpolation. The effectiveness of this alternative is evaluated by comparing its simulations with those based on the standard SWAT precipitation input procedure. The model is applied to mountainous semiarid catchments in the Karkheh River basin, Iran. The model performance is evaluated at daily, monthly, and annual scales by using a number of performance indicators at 15 streamflow gauging stations each draining an area in the range of 590-42,620 km. The comparison suggests that the use of areal precipitation improves model performance particularly in small subcatchments in the range of 600-1,600 km. The modified areal precipitation input results in increased reliability of simulated streamflows in the areas of low rain gauge density. Both precipitation input methods result in reasonably good simulations for larger catchments (over 5,000 km). The use of areal precipitation input improves the accuracy of simulated streamflows with spatial resolution and density of rain gauges having significant impact on results. [ABSTRACT FROM AUTHOR]

Damodaram, Chandana, Marcio H. Giacomoni, C. Prakash Khedun, Hillary Holmes, Andrea Ryan, William Saour, and Emily M. Zechman, 2010. Simulation of Combined Best Management Practices and Low Impact Development for Sustainable Stormwater Management. Journal of the American Water Resources Association (JAWRA) 1-12. DOI: 10.1111/j.1752-1688.2010.00462.x Abstract: Urbanization causes increased stormwater runoff volumes, leading to erosion, flooding, and the degradation of instream ecosystem health. Although Best Management Practices (BMPs) are used widely as a means for controlling flood runoff events, Low Impact Development (LID) options have been proposed as an alternative approach to better mimic the natural flow regime by using decentralized designs to control stormwater runoff at the source, rather than at a centralized location in the watershed. For highly urbanized areas, LID practices such as rainwater harvesting, green roofs, and permeable pavements can be used to retrofit existing infrastructure and reduce runoff volumes and peak flows. This paper describes a modeling approach to incorporate these LID practices in an existing hydrologic model to estimate the effects of LID choices on streamflow. The modeling approach has been applied to a watershed located on the campus of Texas A&M University in College Station, Texas, to predict the stormwater reductions resulting from retrofitting existing infrastructure with LID technologies. Results demonstrate that use of these LID practices yield significant stormwater control for small events and less control for flood events. A combined BMP-LID approach is tested for runoff control for both flood and frequent rainfall events. [ABSTRACT FROM AUTHOR]

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 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Urbanization represents a strong and increasingly more prevalent impact on stream quality worldwide. One of the characteristic effects of increased urbanization is a consistent decline in biological stream condition. The characterization of this biological degradation with increasing urbanization presents a number of advantages for the study and management of urban streams and catchments. In this paper, the limitation of biological condition with urbanization, called observed biological potential, is characterized. Using an urban intensity index and a biological index developed specifically for urban systems in the Baltimore, Maryland; Cleveland, Ohio; and San Jose, California regions, two principal techniques were compared (quantile regression and bin regression) to define observed biological potential along urban gradients. Quantile regression was selected as the preferable tool for describing observed biological potential given the consistency with which it can be applied and its statistical efficiency, however, bin quantile regression performed similarly. Having identified a numeric approximation of observed biological potential, two methods for identifying factors related to distance from potential as a way of identifying critical environmental factors affecting biological condition in urban areas were explored. The results of this work can be used for identifying benchmarks for urban stream biological condition, identifying limiting catchment characteristics, and prioritizing urban stream management efforts. [ABSTRACT FROM AUTHOR]

The relationship between forests and streamflows has long been an important research interest in China. The purpose of this paper is to summarize progress and lessons learned from the forest-streamflow studies over the past four decades in China. To better measure the research gaps between China and other parts of the world, a brief global review on the findings from paired watershed studies over the past 100 years was also provided. In China, forest management shifted in the later 1990s from timber harvesting to forest restoration. Forest-streamflow research was accordingly changed from assessing harvesting impacts to evaluating both harvesting and forestation effects. Over the past four decades, Chinese forest hydrology research has grown substantially. Significant progress has been made on measuring individual processes, but little solid, long-term data were available to assess the relationship between forest changes and streamflows because of an absence of standard paired watersheds. In addition, misuse of statistical analyses was often found in the literature. A unique opportunity exists in China to study the forestation effects on streamflow as several large-scale forestation programs are being implemented. Such an opportunity should include a robust paired watershed design under an integrated watershed ecosystem framework to avoid repeating the lessons already learned. Recommendations on future forest-streamflow research directions in China are provided. [ABSTRACT FROM AUTHOR]

Natural forests in southern China have been severely logged due to high human demand for timber, food, and fuels during the past century, but are recovering in the past decade. The objective of this study was to investigate how vegetation cover changes in composition and structure affected the water budgets of a 9.6-km2 Dakeng watershed located in a humid subtropical mountainous region in southern China. We analyzed 27 years (i.e., 1967-1993) of streamflow and climate data and associated vegetation cover change in the watershed. Land use/land cover census and Normalized Difference of Vegetation Index (NDVI) data derived from remote sensing were used to construct historic land cover change patterns. We found that over the period of record, annual streamflow ( Q) and runoff/precipitation ratio did not change significantly, nor did the climatic variables, including air temperature, Hamon’s potential evapotranspiration (ET), pan evaporation, sunshine hours, and radiation. However, annual ET estimated as the differences between P and Q showed a statistically significant increasing trend. Overall, the NDVI of the watershed had a significant increasing trend in the peak spring growing season. This study concluded that watershed ecosystem ET increased as the vegetation cover shifted from low stock forests to shrub and grasslands that had higher ET rates. A conceptual model was developed for the study watershed to describe the vegetation cover-streamflow relationships during a 50-year time frame. This paper highlighted the importance of eco-physiologically based studies in understanding transitory, nonstationary effects of deforestation or forestation on watershed water balances. [ABSTRACT FROM AUTHOR]

The article discusses various reports published within the issue including one by Mary C. Freeman and Catherine M. Pringle on hydrologic connectivity and the contribution of stream headwaters to ecological integrity and another one by Thomas C. Winter on the role of groundwater in generating streamflow in headwater areas.

Distributed parameter watershed models are often used for evaluating the effectiveness of various best management practices (BMPs). Streamflow, sediment, and nutrient yield predictions of a watershed model can be affected by spatial resolution as dictated by watershed subdivision. The objectives of this paper are to show that evaluation of BMPs using a model is strongly linked to the level of watershed subdivision; to suggest a methodology for identifying an appropriate subdivision level; and to examine the efficacy of different BMPs at field and watershed scales. In this study, the Soil and Water Assessment Tool (SWAT) model was calibrated and validated for streamf low, sediment, and nutrient yields at the outlet of the Dreisbach (623 ha) and Smith Fry (730 ha) watersheds in Maumee River Basin, Indiana. Grassed waterways, grade stabilization structures, field borders, and parallel terraces are the BMPs that were installed in the study area in the 1970s. Sediment and nutrient outputs from the calibrated model were compared at various watershed subdivision levels, both with and without implementation of these BMPs. Results for the study watersheds indicated that evaluation of the impacts of these BMPs on sediment and nutrient yields was very sensitive to the level of subdivision that was implemented in SWAT. An optimal watershed subdivision level for representation of the BMPs was identified through numerical simulations. For the study watersheds, it would appear that the average subwatershed area corresponding to approximately 4 percent of total watershed area is needed to represent the influence of these BMPs when using the SWAT model. [ABSTRACT FROM AUTHOR]

The term flashiness reflects the frequency and rapidity of short term changes in streamflow, especially during runoff events. Flashiness is an important component of a stream's hydrologic regime. A variety of land use and land management changes may lead to increased or decreased flashiness, often to the detriment of aquatic life. This paper presents a newly developed flashiness index, which is based on mean daily flows. The index is calculated by dividing the pathlength of flow oscillations for a time interval (i.e., the sum of the absolute values of day-to-day changes in mean daily flow) by total discharge during that time interval. This index has low interannual variability, relative to most flow regime indicators, and thus greater power to detect trends. Index values were calculated for 515 Midwestern streams for the 27-year period from 1975 through 2001. Statistically significant increases were present in 22 percent of the streams, primarily in the eastern portion of the study area, while decreases were present in 9 percent, primarily in the western portion. Index values tend to decrease with increasing watershed area and with increasing unit area ground water inputs. Area compensated index values often shift at ecoregion boundaries. Potential index applications include evaluation of programs to restore more natural flow regimes. [ABSTRACT FROM AUTHOR]

This paper presents the results of a study on the use of continuous stage data to describe the relation between urban development and three aspects of hydrologic condition that are thought to influence stream ecosystems -- overall stage variability, stream flashiness, and the duration of extreme-stage conditions. This relation is examined using data from more than 70 watersheds in three contrasting environmental settings - the humid Northeast (the metropolitan Boston, Massachusetts, area); the very humid Southeast (the metropolitan Birmingham, Alabama, area); and the semiarid West (the metropolitan Salt Lake City, Utah, area). Results from the Birmingham and Boston studies provide evidence linking increased urbanization with stream flashiness. Fragmentation of developed land cover patches appears to ameliorate the effects of urbanization on overall variability and flashiness. There was less success in relating urbanization and streamflow conditions in the Salt Lake City study. A related investigation of six North Carolina sites with long term discharge and stage data indicated that hydrologic condition metrics developed using continuous stage data are comparable to flow based metrics, particularly for stream flashiness measures. [ABSTRACT FROM AUTHOR]

Abstract: We present a Digital Elevation Model‐based hydrologic analysis methodology for continental flood inundation mapping (CFIM), implemented as a cyberGIS scientific workflow in which a 1/3rd arc‐second (10 m) height above nearest drainage (HAND) raster data for the conterminous United States (CONUS) was computed and employed for subsequent inundation mapping. A cyberGIS framework was developed to enable spatiotemporal integration and scalable computing of the entire inundation mapping process on a hybrid supercomputing architecture. The first 1/3rd arc‐second CONUS HAND raster dataset was computed in 1.5 days on the cyberGIS Resourcing Open Geospatial Education and Research supercomputer. The inundation mapping process developed in our exploratory study couples HAND with National Water Model forecast data to enable near real‐time inundation forecasts for CONUS. The computational performance of HAND and the inundation mapping process were profiled to gain insights into the computational characteristics in high‐performance parallel computing scenarios. The establishment of the CFIM computational framework has broad and significant research implications that may lead to further development and improvement of flood inundation mapping methodologies. [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]

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 Soil and Water Assessment Tool (SWAT) model is commonly used to predict the impacts of agricultural practices on water quality and quantity. Although widely applied, the data framework used to drive SWAT in the United States (U.S.) is fragmented and inconsistent, varying by user and model interface. This research describes the development of the National Agroecosystems Model (NAM), which provides a unified field to national scale modeling computational framework for research and decision support by using the latest SWAT platform, dubbed SWAT+. NAM has sufficient detail to capture field‐level processes and management actions and spans the full extent of the contiguous U.S. NAM contains 7 million computational units, 4 million of which represent specific cultivated fields. It contains 3 million individually identifiable stream segments and more than 5,000 reservoirs. NAM is intended to serve as a reasonable base framework to be refined for specific applications. This work describes the individual data sources, assumptions, and the processing steps for their inclusion. NAM is constructed with 2,121 individual SWAT+ models which can be executed in a parallel hierarchical structure to dramatically improve runtime. This framework was tested in a case study of the Little River Watershed, Tifton, GA. NAM is developed using only publicly available data sources such that subsets of it can be shared to support research with other government agencies, universities, and others in the public domain. [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]

Multiple instruments and methods exist for collecting discrete streamflow measurements in small streams with low flows, defined here as less than 5.7 m3/s (200 ft3/s). Included in the available methods are low‐cost approaches that are infrequently used, in part, because their uncertainty is not well known. In this work, we evaluated the accuracy and suitability of three low‐cost velocity measurement methods (surface float [SF], velocity head rod [VR], and rising body [RB]) and three conventional current meters (acoustic Doppler velocimeter, and mechanical Price type AA and Price Pygmy meters) relative to discharge calculated from stable artificial hydraulic controls. A total of 231 measurements were made by 20 individuals during 88 site visits to 24 sites in eight states. Accuracies were assessed for all methods and precision was evaluated for the low‐cost methods. The median percent error was below 5% for conventional methods, and below 20% for the low‐cost methods. The SF was the most accurate (median absolute percent error 14%) and precise (mean percent precision of 11%) low‐cost method. The RB and VR, respectively, had 15% and 20% median absolute percent error and 29% and 12% mean percent precision. Results suggest that low‐cost methods, when used appropriately, can be used to estimate discharge data under low flow conditions when measurements with conventional methods are not feasible and the associated accuracies meet end‐user measurement objectives. [ABSTRACT FROM AUTHOR]

Beaver‐mimicry stream restoration (BMR) involves the alteration of a stream channel to approximate the effects of beaver activity. Project objectives often include increasing groundwater storage and dry‐season streamflow, but limited data are available to understand the nature of its effects on groundwater dynamics. We developed generic groundwater models of mountain headwater streams to investigate the effects of installing a single beaver‐mimicry structure (BMS) using different restoration designs in varied hydrogeologic settings. The magnitude of changes in dry‐season net stream gains from a single BMS was always a minor component of the channel water balance, and would be too small to measure in the field; however, the modeled patterns of change caused by a single BMS help to understand the underlying mechanisms. All tested scenarios caused increases in groundwater recharge from the stream, which resulted in increased groundwater levels, and groundwater outflow from the model domain. For scenarios that did not include evapotranspiration, most treatments in gaining and losing settings caused slight increases in dry‐season net stream gains, but in strongly losing settings net stream gains were reduced. The addition of simulated evapotranspiration often resulted in decreased dry‐season net stream gains, since evapotranspiration increased with groundwater elevations. BMR design and siting influence the types of hydrologic effects that should be anticipated. [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]

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]

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]

The Natural Resources Conservation Service's Dimensionless Unit Hydrograph is used extensively in industry by design engineers for the purpose of determining the runoff hydrograph from a rainfall event. As part of the methodology, the user is required to input a Peak Rate Factor (PRF) which describes the runoff response (volume distribution) of the corresponding unit hydrograph. Unfortunately, little guidance is given in the literature for selecting an appropriate PRF for a given watershed. Additionally, consideration has not been given to determine if the PRF should remain constant for a given watershed or be allowed to vary as a function of return period. The goal of this study was to back‐calculate site‐specific PRFs for various watersheds which have experienced two or more significant rain events since the year 2000. A total of 14 watersheds were investigated with results showing PRFs remained consistent for five of the watersheds regardless of the storm event. Nine of the watersheds showed a significant variation in PRF with an average variation of approximately 440 across all storm events within a given watershed. Additional analysis showed there is a general trend of increasing PRF with decreasing storm‐specific curve number. Finally, the complacent‐violent runoff response was shown to occur for storms with resulting PRF's in excess of 1,000. Research Impact Statement: Peak Rate Factors used as part of the NRCS unit hydrograph method are shown to vary as a function of both storm size and curve number. [ABSTRACT FROM AUTHOR]

Channel roughness, often described by Manning's n, is used to represent the amount of resistance that flow encounters, and has direct implications on velocity and discharge. Ideally, n is calculated from a long-term record of channel discharge and hydraulic geometry. In the absence of these data, a combination of photo references and a validated qualitative method is preferable to simply choosing n arbitrarily or from a table. The purpose of this study was to use United States Geological Survey ( USGS) streamflow data to calculate roughness coefficients for streams in the mountains of North Carolina. Five USGS gage stations were selected for this study, representing drainage areas between 71.5 and 337 km2. Photo references of the study sites are presented. Measured discharges were combined with hydraulic geometry at a cross-section to calculate roughness coefficients for flows of interest. At bankfull flow, n ranged between 0.039 and 0.064 for the five study sites. Roughness coefficients were not constant for all flows in a channel, and fluctuated over a large range. At all sites, roughness was highest during low-flow conditions, then quickly decreased as flow increased, up to the bankfull elevation. [ABSTRACT FROM AUTHOR]

Groundwater pumping can cause reductions in streamflow ("streamflow depletion") that must be quantified for conjunctive management of groundwater and surface water resources. However, streamflow depletion cannot be measured directly and is challenging to estimate because pumping impacts are masked by streamflow variability due to other factors. Here, we conduct a management‐focused review of analytical, numerical, and statistical models for estimating streamflow depletion and highlight promising emerging approaches. Analytical models are easy to implement, but include many assumptions about the stream and aquifer. Numerical models are widely used for streamflow depletion assessment and can represent many processes affecting streamflow, but have high data, expertise, and computational needs. Statistical approaches are a historically underutilized tool due to difficulty in attributing causality, but emerging causal inference techniques merit future research and development. We propose that streamflow depletion‐related management questions can be divided into three broad categories (attribution, impacts, and mitigation) that influence which methodology is most appropriate. We then develop decision criteria for method selection based on suitability for local conditions and the management goal, actionability with current or obtainable data and resources, transparency with respect to process and uncertainties, and reproducibility. [ABSTRACT FROM AUTHOR]

Small impoundments are numerous throughout river corridors. Limited data on their size and outflow have made it difficult to assess how these smaller impoundments cumulatively impact streamflow. This study combined rainfall‐runoff data from the Chesapeake Bay Watershed Model with a routing framework to develop a hydrologic model capable of analyzing flow alteration induced by cumulative impoundment. Using standard design processes and satellite imagery, simulations of impoundment hydraulics were assembled for both small and large impoundments. Eleven of the 170 impoundments in the Difficult Run watershed were modeled to assess their cumulative impact on downstream flow from 1984 to 2005. Multiple simulations were analyzed to determine incremental effects. We found that flow alteration increased with impoundment density. Impoundments prolonged storm hydrographs, increasing median and baseflows, and decreasing peak streamflow downstream. Impoundment size and location impacted downstream flow alteration as headwater channels ran dry and evaporation from impoundments with high surface area to storage ratios decreased extreme low flows. Large reservoirs had a greater impact on median flows, as small ponds dominated low flow alteration. Including a greater fraction of the watershed's 170 small impoundments may further increase flow alteration. These results suggest that small impoundments play a large role in determining water availability, particularly during droughts. [ABSTRACT FROM AUTHOR]

Hydrodynamic models play a key role in simulating total water level (TWL), that is, a combination of river flow, tide, surge, wind and wave‐induced water level, and representing flood inundation dynamics in coastal areas. An appropriate selection of two‐dimensional (2D) models that integrate riverine and estuarine interactions with ocean dynamics is crucial to generate accurate TWL predictions and assist stakeholders and federal agencies in decision making and flood emergency responses. In this study, we compare the performance of two widely used hydrodynamic models (e.g., 2D HEC‐RAS and Delft3D‐Flexible Mesh [FM]) with respect to their ability of predicting TWL in Delaware Bay, United States. Based on a previously established model configuration, we simulate Hurricane Sandy and Isabel that affected the Bay and led to considerable damages and economic losses. We then evaluate model capabilities with tidal analysis, compare observed vs. simulated TWL and analyze spatiotemporal variations of TWL through scenario‐based simulations. Our results suggest that atmospheric forcing input in Delft3D‐FM significantly improves TWL predictions as compared to those of 2D HEC‐RAS. Furthermore, model simulations with Delft3D‐FM can be faster than 2D HEC‐RAS by a factor of 6–10. Despite these advantages, 2D HEC‐RAS (version 5.07) is a noncommercial software easier to implement and can be a simpler alternative for modeling extreme events when atmospheric forcing is not relevant in the model domain. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Water scarcity in the Sevier River Basin in south-central Utah has led water managers to seek advanced techniques for identifying optimal forecasting and management measures. To more efficiently use the limited quantity of water in the basin, better methods for control and forecasting are imperative. Basin scale management requires advanced forecasts of the availability of water. Information about long term water availability is important for decision making in terms of how much land to plant and what crops to grow; advanced daily predictions of streamflows and hydraulic characteristics of irrigation canals are of importance for managing water delivery and reservoir releases; and hourly forecasts of flows in tributary streams to account for diurnal fluctuations are vital to more precisely meet the day-to-day expectations of downstream farmers. A priori streamflow information and exogenous climate data have been used to predict future streamflows and required reservoir releases at different timescales. Data on snow water equivalent, sea surface temperatures, temperature, total solar radiation, and precipitation are fused by applying artificial neural networks to enhance long term and real time basin scale water management information. This approach has not previously been used in water resources management at the basin-scale and could be valuable to water users in semi-arid areas to more efficiently utilize and manage scarce water resources. [ABSTRACT FROM AUTHOR]

We build three long short‐term memory (LSTM) daily streamflow prediction models (deep learning networks) for 531 basins across the contiguous United States (CONUS), and compare their performance: (1) a LSTM post‐processor trained on the United States National Water Model (NWM) outputs (LSTM_PP), (2) a LSTM post‐processor trained on the NWM outputs and atmospheric forcings (LSTM_PPA), and (3) a LSTM model trained only on atmospheric forcing (LSTM_A). We trained the LSTMs for the period 2004–2014 and evaluated on 1994–2002, and compared several performance metrics to the NWM reanalysis. Overall performance of the three LSTMs is similar, with median NSE scores of 0.73 (LSTM_PP), 0.75 (LSTM_PPA), and 0.74 (LSTM_A), and all three LSTMs outperform the NWM validation scores of 0.62. Additionally, LSTM_A outperforms LSTM_PP and LSTM_PPA in ungauged basins. While LSTM as a post‐processor improves NWM predictions substantially, we achieved comparable performance with the LSTM trained without the NWM outputs (LSTM_A). Finally, we performed a sensitivity analysis to diagnose the land surface component of the NWM as the source of mass bias error and the channel router as a source of simulation timing error. This indicates that the NWM channel routing scheme should be considered a priority for NWM improvement. [ABSTRACT FROM AUTHOR]

Riffle crests are important hydraulic controls in riffle‐pool‐dominated streams, influencing pool hydraulics and water quality that collectively control lotic habitat for many organisms. We define a simple stream depth measurement, the riffle crest thalweg (RCT), describe measurement methods and utility, and suggest that RCT depth is a better independent variable than streamflow (Q) for many instream flow and habitat assessment applications. Using RCT depth as an independent variable, rather than streamflow, reduces the need for gauging or streamflow measurements in many management applications. Unlike streamflow, RCT depth varies directly with fundamental elements of riverine habitat such as channel morphology and bed roughness. We also suggest that relationships between RCT depth and streamflow (RCT‐Q curves) can be used to evaluate the risk of streamflow alteration at ungauged sites. We describe three case studies to demonstrate the utility of RCT depth and RCT‐Q rating curves in stream ecosystem management: (1) evaluating the effect of a top‐down flow prescription on modeled salmonid habitat, (2) estimating the risk from the incremental reduction of RCT depth on habitat and ecological responses, and (3) identifying relationships between RCT depth and dissolved oxygen in ungauged streams. An easy‐to‐measure, inexpensive, and ecologically sensitive metric, RCT depth holds promise as a useful tool in stream ecosystem management. [ABSTRACT FROM AUTHOR]

In the Colorado River Basin (CRB), ensemble streamflow prediction (ESP) forecasts drive operational planning models that project future reservoir system conditions. CRB operational seasonal streamflow forecasts are produced using ESP, which represents climate using an ensemble of meteorological sequences of historical temperature and precipitation, but do not typically leverage additional real‐time subseasonal‐to‐seasonal climate forecasts. Any improvements to streamflow forecasts would help stakeholders who depend on operational projections for decision making. We explore incorporating climate forecasts into ESP through variations on an ESP trace weighting approach, focusing on Colorado River unregulated inflows forecasts to Lake Powell. The k‐nearest neighbors (kNN) technique is employed using North American Multi‐Model Ensemble one‐ and three‐month temperature and precipitation forecasts, and preceding three‐month historical streamflow, as weighting factors. The benefit of disaggregated climate forecast information is assessed through the comparison of two kNN weighting strategies; a basin‐wide kNN uses the same ESP weights over the entire basin, and a disaggregated‐basin kNN applies ESP weights separately to four subbasins. We find in general that climate‐informed forecasts add greater marginal skill in late winter and early spring, and that more spatially granular disaggregated‐basin use of climate forecasts slightly improves skill over the basin‐wide method at most lead times. [ABSTRACT FROM AUTHOR]

Streamflow predictions derived from a hydrologic model are subjected to many sources of errors, including uncertainties in meteorological inputs, representation of physical processes, and model parameters. To reduce the effects of these uncertainties and thus improve the accuracy of model prediction, the United States (U.S.) National Water Model (NWM) incorporates streamflow observations in the modeling framework and updates model‐simulated values using the observed ones. This updating procedure is called streamflow data assimilation (DA). This study evaluates the prediction performance of streamflow DA realized in the NWM. We implemented the model using WRF‐Hydro® with the NWM modeling elements and assimilated 15‐min streamflow data into the model, observed during 2016–2018 at 140 U.S. Geological Survey stream gauge stations in Iowa. In its current DA scheme, known as "nudging," the assimilation effect is propagated downstream only, which allows us to assess the performance of streamflow predictions generated at 70 downstream stations in the study domain. These 70 locations cover basins of a range of scales, thus enabling a multi‐scale hydrologic evaluation by inspecting annual total volume, peak discharge magnitude and timing, and an overall performance indicator represented by the Kling–Gupta efficiency. The evaluation results show that DA improves the prediction skill significantly, compared to open‐loop simulation, and the improvements increase with areal coverage of upstream assimilation points. [ABSTRACT FROM AUTHOR]

Urbanization increases stormwater runoff into streams, resulting in channel erosion, and increases in sediment and nutrient delivery to receiving water bodies. Stream restoration is widely used as a Best Management Practice to stabilize banks and reduce sediment and nutrient loads. While most instream nutrient retention measurements are often limited to low flow conditions, most of the nutrient load is mobilized at high stream flows in urban settings. We, therefore, use a process‐based stream ecosystem model in conjunction with measurements at low flows and focus on estimation of stream nitrogen retention over the full streamflow distribution. The model provides a theoretical framework to evaluate the geomorphic, hydrologic, and ecological factors that are manipulated by stream restoration, and drive nitrogen retention. We set a model for a pool‐riffle sequence restored stream (190 m) in Baltimore County, Maryland and calibrated the model to the in situ measured primary production (Nash–Sutcliffe model efficiency coefficient [NSE] NSE = 0.89), respiration (NSE = 0.74), and nitrate uptake lengths (R2 = 0.88). At the daily scale, simulations showed low nitrogen retention during high flows due to high transport rates, mobilization of stored hyporheic nitrogen, and scouring of periphyton biomass. This result underscores the need to reduce contributing watershed runoff flashiness to promote aquatic nutrient cycling and retention. At monthly and yearly time scale, model predicted a higher percent reduction in summer than in winter and estimated 5.7%–9.5% of annual nitrate reductions. While the model was tested in a pool‐riffle sequence restoration design, the approach can be adapted to evaluate a range of channel restoration design characteristics, and the effects of upland watershed restoration to mitigate stormwater loading through both restored and unrestored streams. Research Impact Statement: This study provides a coupled data and process modeling‐based framework to investigate stream ecosystem over a full flow regime, and to evaluate the benefits of stream restoration to increase nutrient retention. [ABSTRACT FROM AUTHOR]

The National Water Model (NWM) is a high‐resolution hydrological model capable of providing streamflow forecast at 2.7 million reaches across the conterminous United States (U.S.). It utilizes a conceptual (not physically explicit) module for estimating groundwater discharge (baseflow) to streams, and the baseflow module only allows for one‐way flux from the aquifer to the streams, with no interflow between surficial or groundwater catchments. This study evaluated the ability of the NWM to simulate baseflow in different geologic conditions with a case study in the Northern High Plains region. The study also evaluated two alternative formulations for the baseflow module in the NWM with the goal of improving simulated baseflow accuracy. The NWM simulated baseflow more accurately in a clayey catchment than in a sandy catchment. The model, however, over simulated baseflow during storm runoff events and under simulated during low flow events in both catchments. Analysis of groundwater recharge simulation showed that the NWM over simulated groundwater recharge for both catchment types, and reducing groundwater recharge using U.S. Geological Survey Baseflow Indexes improved the model performance for baseflow simulation, especially in the sandy catchment. Evaluation of the alteranative formulations showed that a modified Rorabaugh‐Rutledge formulation was able to improve the baseflow simulation with RMSE of 7.98 m3/s and R2 of 0.41 and also improved the timing of peak flows. Research Impact Statement: Assessment of baseflow simulation in the National Water Model and evaluating alternative functions to provide improved physical representation using a case study in the Northern High Plains region. [ABSTRACT FROM AUTHOR]

The flat topography, large natural storage features, backwater effects, and boundary conditions all play different roles in the flood response of rivers in low‐gradient environments. The combined effects of these factors result in frequent episodes of reverse flows, slow recession of flood waters, and complex flow interactions. This study investigates the value of varying degrees of model complexity and setup features on the model ability to reproduce some of the unique flooding characteristics in low‐gradient basins. The study focuses on (1) effect of streamflow routing techniques; (2) effect of incorporating large natural storage areas; (3) effect of model dimensionality; and (4) effect of downstream boundary conditions. The study assessed six different model setups for the Vermilion River in south Louisiana, during a series of flood‐inducing storm events in May–June 2014. A successful simulation of the repetitive reverse flows in the river was only possible after incorporating the large swamp areas within the basin. The slow recession of the flood peaks was accurately reproduced with the use of a two‐dimensional representation in characterizing the swamp areas. The results of this study have implications for understanding flood dynamics in low‐gradient basins, and for guiding the development of reliable flood models that take advantage of available technologies and information without adding unwarranted complexities that require extensive, yet typically unavailable calibration data. Research Impact Statement: Incorporating large natural storage areas into flood models of low‐gradient rivers is critical to capture complex flow patterns, including reverse flows and prolonged recession of flood water. [ABSTRACT FROM AUTHOR]

Surface water and groundwater are intimately connected by a two‐way flux between the stream and underlying aquifers. The National Water Model (NWM) currently only considers a one‐way flux, where groundwater can enter a stream but cannot return to the aquifer. The Northern High Plains Aquifer, USA is used as a case study to investigate the consequences of omitting two‐way stream–aquifer fluxes on streamflow prediction capabilities of the NWM during hydrologic extremes. Instead of traditional field techniques to identify stream–aquifer fluxes, this study presents an integrated approach to classify likely stream regimes using three identification methods: United States Geological Survey (USGS) gage data, simulated stream–aquifer fluxes from an existing USGS Groundwater Availability Model, and the normalized difference vegetation index from remote sensing. For flood events, the modeled flood response for losing streams is characterized by statistically significant earlier peak discharges and an overestimate of the observed flood volume when compared to gaining streams. For drought events, our study found no statistical difference between losing and gaining streams, however, modeled streamflow from the NWM overestimated the observed USGS hydrograph. The systematic overestimate of streamflow by the NWM could be, in part, due to the lack of a losing stream mechanism which was on average 0.1% streamflow loss per streamwise km along the river. Research Impact Statement: The National Water Model significantly overestimates streamflow in the Northern High Plains Aquifer, the consequence of ignoring a two‐way groundwater‐surface water exchange mechanism is quantified. [ABSTRACT FROM AUTHOR]