River Network Routing on the NHDPlus Dataset

Texas is a large state whose water resources vary from relatively abundant in the eastern half of the state to relatively scarce in the western half. In addition, Texas is one of five states nationwide that allocates surface water through a system that merges riparian rights and prior appropriation rights. In some locations and climatic conditions, water rights have been over-allocated, creating a predicament where the legal availability of water exceeds the physical availability. Complicating matters, in 2001, the Texas Legislature established an Instream Flow Program, which conducts studies to identify appropriate flow regimes to maintain an ecologically sound environment. The findings of these instream flow studies could create challenging streamflow requirements that might cause problems for water allocation planning and management. This case study analyzes the full execution of water rights in eleven of twenty-three total river basins in Texas and the corresponding relationship to water availability. Under the full execution scenario, each water rights holder diverts the full volume allocated by a water permit with zero return flow. While this full execution scenario is not necessarily practical since most water rights holders return a portion of the diverted water after use, the Texas Commission on Environmental Quality uses the full execution water availability model to evaluate new water rights applications. Using the full execution as a baseline, we created a model to estimate the potential decrease in total water diversions in Texas river basins through the implementation of three alternative cooling scenarios at thermoelectric power plants: 1) converting current open-loop cooling technologies to closed-loop cooling towers, 2) converting all current cooling technologies to hybrid wet-dry cooling, and 3) converting all current cooling technologies to dry cooling using air-cooled condensers. Total annual diversion savings for the three alternative cooling scenarios were determined and translated into human equivalence to show the significance of implementing these cooling technology changes. By implementing these alternative cooling technologies at the plants in all eleven of the river basins considered in this analysis, water diversion could be reduced by as much as 247 to 703 million m3 annually. These diversions can supply enough water for 1.3 to 3.7 million people for one year (each using 0.53 m3 per day). Improvement in volume reliability, the percentage of total demand that is actually supplied over a time period of interest, was also examined to determine the effectiveness of converting existing thermoelectric cooling technologies to alternative cooling technologies that reduce total water diversions. Our results suggest that implementation of alternative cooling technologies at Texas thermoelectric power plants do not translate into significant improvements in volume reliability but can dramatically reduce total water diversion volumes.

This study presents the results of comparing land use estimates between three different data sets for the Upper Mississippi River Basin (UMRB). The comparisons were performed between the U.S. Department of Agriculture (USDA) Natural Resource Conservation Service (NRCS) National Resource Inventory (NRI), the U.S. Geological Survey (USGS) National Land Cover Data (NLCD) database, and a combined USDA National Agricultural Statistics Service (NASS) Agricultural Census – NLCD dataset created to support applications of the Hydrologic Unit Model for the U.S. (HUMUS). The comparison was performed for 1992 versions of the datasets because that was the only consistent year available among all three data sources. The results show that differences in land use area estimates increased as comparisons shifted from the entire UMRB to smaller 4- and 8-digit watershed regions (as expected). However, the area estimates for the major land use categories remained generally consistent among all three data sets across each level of spatial comparison. Differences in specific crop and grass/forage land use categories were magnified with increasing refinement of the spatial unit of comparison, especially for close-grown crops, pasture, and alfalfa/hayland. The NLCD close-grown crop area estimates appear very weak relative to the NRI and HUMUS, and the lack of specific crop land use estimates limits its viability for UMRB agricultural-based modeling scenarios. However, the NLCD is a key source of non-agricultural land use data for HUMUS and supplemental wetland land use area estimates for the NRI. We conclude that comparisons between more recent versions of the data sets (i.e., 1997 NRI, 1997 or 2002 Agricultural Census, and 2001 NLCD) would not result in significant additional insights and that the 1997 NRI is a viable land use data source for current CARD UMRB water quality modeling studies. However, adoption of other land use data such as USDA-NASS remote sensing data should be investigated.

Spatial variability of river network drainage density (Dd) is a key feature of river systems, yet few existing global hydrography datasets have properly accounted for it. Here, we present a new vector-based global hydrography that reasonably estimates the spatial variability of Dd worldwide. It is built by delineating channels from the latest 90-m Multi-Error-Removed Improved Terrain (MERIT) digital elevation model and flow direction/accumulation. A machine learning approach is developed to estimate Dd based on the global watershed-level climatic, topographic, hydrologic, and geologic conditions, where relationships between hydroclimate factors and Dd are trained using the high-quality National Hydrography Dataset Plus (NHDPlusV2) data. By benchmarking our dataset against HydroSHEDS and several regional hydrography datasets, we show the new river flowlines are in much better agreement with Landsat-derived centerlines, and improved Dd patterns of river networks (totaling ~75 million kilometers in length) are obtained. Basins and estimates of intermittent stream fraction are also delineated to support water resources management. This new dataset (MERIT Hydro–Vector) should enable full global modeling of river system processes at fine spatial resolutions.

Excessive nitrogen export from agricultural watersheds remains a critical water quality challenge, with the Upper Mississippi River Basin (UMRB) significantly contributing to downstream eutrophication and hypoxia in the Gulf. This study investigates the spatiotemporal dynamics of riverine nitrate plus nitrite (NO3- + NO2--N) export across the UMRB at high spatial resolution (12-digit Hydrologic Unit Codes or HUC12 subwatershed scale) during 2001-2020 and quantifies the effects of anthropogenic activities and hydrological variability on riverine NO3- + NO2--N export changes in the region between 2001-2005 and 2016-2020. Our results revealed hotspots of substantial increases in NO3- + NO2--N yields across the UMRB, with distinct regional patterns in driving factors. Over the entire UMRB, NO3- + NO2--N yields increased by 9.7 kg/ha/yr on average from 2001-2005 to 2016-2020, with anthropogenic activities contributing 4.8 kg/ha/yr and hydrological variability contributing 4.9 kg/ha/yr. The northern and western UMRB had combined influences from both anthropogenic activities and hydrological variability, while the east-central regions had predominantly hydrologically driven changes. Agricultural sources, including fertilizer, manure, and biological nitrogen fixation, collectively contributed over 80% of NO3- + NO2--N loading throughout the basin. This framework for disentangling human and hydrological impacts provides critical insights for developing effective and targeted watershed management strategies to reduce nutrient losses and improve water quality.

The Upper Mississippi River Basin (UMRB), located in the Midwestern U.S., is a nationally recognized region of concern for water quality and aquatic biological health. At 500,000 km2, geospatial characteristics and climate vary widely within the basin and thus, hydrological behavior and associated effects on freshwater ecosystems may also dramatically vary. So far, previous studies of long-term hydrological behavior in the UMRB have mostly considered changes for the basin rather than changes at finer watershed scales. This study systematically evaluated long term trends in indices of hydrological alteration (IHA) for 118 of 131 watersheds of the UMRB and compared them with 1) variabilities in geospatial features (land use, soil type, and topography), 2) land use changes, 3) climate, and 4) evapotranspiration trends using hierarchical clustering and correlation analysis. Compared to climate trends, land use and evapotranspiration were found to have stronger impacts on trends in low flow IHA indices in the watershed. The results demonstrate that while overall, low flow has been increasing in the basin, there is considerable variability in alterations in low flow across watersheds. Specifically, the results indicate that in forested or mixed-use watersheds, low flow has been decreasing while in agricultural and urban watersheds low flow is increasing. These results together with the literature on water quality in the UMRB, suggest both stressors, water quantity and water quality exist in the UMRB and vary by land use and evapotranspiration, implying that effective watershed management plans are local not regional and should be directed toward reducing the specific local stressors.

Precipitation, temperature, groundwater-level elevation, streamflow, and potential flood storage trends within the Brazos, Colorado, Big Cypress, Guadalupe, Neches, Sulphur, and Trinity River basins in Texas through 2017

<p>Humans have fundamentally altered global river flow by constructing reservoirs and building water-diversion schemes for irrigation. Reservoir operation and the regulation of river flow is important for estimating global water fluxes and water availability. Reservoirs and dam management are however generally not represented in Earth System Models. Recently, efforts are made to incorporate human water management in Land Surface Models by improving the irrigation representation and including high-resolution river networks.</p><p>Here, we present the integration of a reservoir routine in the vector-based river routing model mizuRoute, to be coupled with the Community Terrestrial Systems Model (CTSM). We use the Hydrologic Derivatives for Modeling and Applications (HDMA) vector-based river network, which is intersected with lake and reservoir polygons from the HydroLAKES and GRanD databases to model both natural lakes and reservoirs. We implement reservoir management based on the parametrization of Hanasaki et al. (2006) and develop an irrigation topology to determine the irrigation water demand for every individual reservoir based on gridded water demands modeled by CTSM.</p><p>We then evaluate our reservoir implementation both in a local setup, driven by observed inflow for 26 reservoirs, and in a global-scale setup, driven by gridded runoff from CTSM and using the Hydrologic Derivatives for Modeling and Applications (HDMA) river network. The local simulations show that accounting for reservoirs improves the skill compared to resolving reservoirs with a natural lake parametrization and not accounting for lakes/reservoirs. In the global-scale simulation, the reservoir management and natural lake parametrizations show however similar results, which could be attributed to biases in modeled reservoir inflow. These biases originate from biases in runoff simulated by CTSM and/or unresolved reservoirs on the river network.</p><p>This study overall underlines the need to further develop and test water management parametrizations for improving the representation of anthropogenic interference with the terrestrial water cycle in Earth system models.</p><p><strong>

Soil Water Assessment Tool (SWAT) are being extensively used by hydrologists and environmentalists to simulate river discharge and water quality at watershed/basin scale across the world. The SWAT is a physically based semi-distributed rainfall runoff model and require watershed related characteristics (elevation, land cover, and soil information for the entire river basin) and meteorological variables (rainfall, temperature, relative humidity, solar radiation and windspeed) information to simulation runoff and water quality data at the basin outlet. One drawback of SWAT is that the default database for the model is available for United States and the modeller needs to develop a separate database to implement the model at river basins located outside the USA. This study generates soil and landcover database that can be used for the SWAT modelling for river basins located in Ireland. The soil database has been created based on soil testing experiments conducted during the STRIVE programme by Teagasc and Environmental Protection Agency Ireland. The landcover database has been created by relating the landcover data obtained from the CORINE database with the default SWAT landcover database. Furthermore, detailed information on the five meteorological data covering Ireland has been provided. A newly created SWAT geodatabase has been generated that can be used as a replacement from the default SWAT database for simulating runoff and water quality at river basins in Ireland. The database contains digital elevation model, soil and landcover maps along with river network and river subbasins for Ireland and is publicly available at: https://doi.org/10.5281/zenodo.4767926 (Basu, 2021).

Reliable river discharge forecasts are crucial for effective water resource management and flood risk mitigation. However, uncertainty in the forecasts is inevitable due to limitations in hydrological process understanding, errors in the meteorological forcings, observation uncertainty, and computational constraints. For flood forecasting, this forecast uncertainty can lead to ineffective or delayed preparatory actions. The primary aim of this thesis is to improve the actionable information from ensemble river discharge forecasts at gauged and ungauged locations using post-processing – a technique used to statistically correct the forecasts and reduce the uncertainty. Post-processing is already part of the forecasting system of the Copernicus Emergency Management Service’s European Flood Awareness System (EFAS). The skill of the EFAS operational at-gauge post-processing method is evaluated, finding that post-processing improves the skill of the forecasts particularly for large rivers for which hydrological errors dominate. Barriers to the use of the operational post-processed forecasts are identified via a co-production workshop, including lack of local relevance, and difficulty accessing the forecasts and associated documentation. A key limitation of the operational post-processing method is that it is only applicable at gauged locations. To overcome this limitation, a data-assimilation-inspired technique is developed to propagate observation information along the river network. Combined with the at-gauge post-processing method, this new information propagation technique allows the correction of river discharge forecasts at ungauged locations. This new post-processing method was evaluated and found to improve forecasts up to a 5-day lead-time. The new method is computationally efficient, adapts to the flow situation, and is applicable to any ensemble river discharge forecast. By improving the skill of the forecast at ungauged locations, this work aims to support more informed decision-making in flood risk management and water resource planning, ultimately helping to protect people, infrastructure, and economies from hydrological extremes.

The Upper Mississippi River Basin and Ohio‐Tennessee River Basin comprise the majority of the United States Corn Belt region, resulting in degraded Mississippi River and Gulf of Mexico water quality. To address the water quality implications of increased biofuel production, biofuel scenarios were tested with a Soil and Water Assessment Tool (SWAT) model revision featuring improved biofuel crop representation. Scenarios included corn stover removal and the inclusion of two perennial bioenergy crops, switchgrass and Miscanthus, grown on marginal lands (slopes >2% and erosion rates >2 t/ha) and nonmarginal lands. The SWAT model estimates show water quality is not very sensitive to stover removal. The perennial bioenergy crops reduce simulated sediment, nitrogen (N), and phosphorus (P) yields by up to 60%. Simulated sediment and P reductions in marginal lands were generally twice that occurring in the nonmarginal lands. The highest unit area reductions of N occurred in the less sloping tile‐drained lands. Productivity showed corn grain yield was independent from stover removal, while yields of the two perennial bioenergy crops were similar in the marginal and nonmarginal lands. The results suggest planning for biofuel production in the Corn Belt could include the removal of stover in productive corn areas, and the planting of perennial bioenergy crops in marginal land and in low‐sloped tile‐drained areas characterized by high N pollution. 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.

Existing methods for connectivity analysis still encounter difficulties in explaining functional relationships between network structure and ecological patterns over larger territories or complex structures like dendritic river networks. We propose a method that addresses the problem of scale and resolution in the connectivity analysis of dendritic network structures, illustrated here for the re-colonization of the French Loire river basin by the European otter. The ecological niche factor approach is applied to infer favourable habitat in the river network based on large scale data of land use and hydro-morphology of river segments for the entire river basin. These analyses identified the stressors to the riparian zone of channel straightening, urbanisation and forest fragmentation as the principal factors explaining otter occurrence. Using this estimate of habitat favourability, we used the Integral Index of Connectivity to quantify habitat availability and connectivity in the dendritic river network. When we calculate the integral index of connectivity over different spatial extents by constraining network distances, the scale-sensitivity of the network’s connectivity emerges. Accounting for high mobility by entering larger network distances in the analysis identifies conservation networks and priorities mainly in downstream parts of the river basin, whereas with smaller network distances, more restricted high quality areas in central and upstream parts are highlighted. The presented approach performed better than distribution modelling approaches in explaining species occurrence over the river network and confirms the crucial aspect of connectivity in otter re-colonization.

A numerical simulation of dust storms in China

Spatiotemporal variation in nitrogen loads and their impacts on river water quality in the upper Yangtze River basin

Riverine network size determined major driving factors of the composition and diversity of aquatic invertebrate communities in a multi-tributary mountain river basin.

Purpose of ReviewThis review aims to better understand the utility of machine learning algorithms for predicting spatial patterns of contaminants in the United States (U.S.) drinking water.Recent FindingsWe found 27 U.S. drinking water studies in the past ten years that used machine learning algorithms to predict water quality. Most studies (42%) developed random forest classification models for groundwater. Continuous models show low predictive power, suggesting that larger datasets and additional predictors are needed. Categorical/classification models for arsenic and nitrate that predict exceedances of pollution thresholds are most common in the literature because of good national scale data coverage and priority as environmental health concerns. Most groundwater data used to develop models were obtained from the United States Geological Survey (USGS) National Water Information System (NWIS). Predictors were similar across contaminants but challenges are posed by the lack of a standard methodology for imputation, pre-processing, and differing availability of data across regions.SummaryWe reviewed 27 articles that focused on seven drinking water contaminants. Good performance metrics were reported for binary models that classified chemical concentrations above a threshold value by finding significant predictors. Classification models are especially useful for assisting in the design of sampling efforts by identifying high-risk areas. Only a few studies have developed continuous models and obtaining good predictive performance for such models is still challenging. Improving continuous models is important for potential future use in epidemiological studies to supplement data gaps in exposure assessments for drinking water contaminants. While significant progress has been made over the past decade, methodological advances are still needed for selecting appropriate model performance metrics and accounting for spatial autocorrelations in data. Finally, improved infrastructure for code and data sharing would spearhead more rapid advances in machine-learning models for drinking water quality.