Kenneth J. Forshay

Reservoirs are globally important sources of greenhouse gases, but the magnitude of their emissions is highly uncertain. Here, we present data for 146 reservoirs from two surveys of reservoir methane and carbon dioxide emissions, one at the regional scale in the midwestern United States and one at the national scale in the United States, plus data from two hand‐picked sites in Washington and Puerto Rico. At all reservoirs, ebullitive and diffusive emissions and basic physicochemistry were measured at 15–55 locations during one 22 to 64‐h period during the summers of 2016–2023, with four reservoirs revisited a second time. Contemporaneous water chemistry measurements were made at one or two locations in each reservoir. The dataset consists of two geospatial files and seven CSV files containing greenhouse gas emissions, water chemistry, morphology, and other relevant data. To date, these data comprise the largest multi‐reservoir emissions dataset assembled using consistent measurement methods.

Constructed water quality wetlands, designed to accept tile drainage and surface runoff, are a promising solution for reducing surface water nutrient loading from agricultural systems. In addition to their water quality benefits, these systems may also offset losses of migratory waterbird stopover sites resulting from historical and future agricultural drainage modernization. To assess this possibility, we developed spatially explicit habitat models informed with expert opinion to explore the: 1) potential of water quality wetlands to provide suitable stopover resources for waterbirds during spring migration; and 2) the extent these wetlands can offset likely losses of stopover resources due to drainage modernization. We focused our modeling on the Iowa portion of the Prairie Pothole Region of North America as it was a historically important area within this vital region for waterbirds, but it has experienced widespread subsurface drainage. Model results indicate that unmitigated drainage modernization is likely to have a large negative effect on spring migratory resources for dabbling ducks and shorebirds and minimal effect on diving ducks. Water quality wetland installations are likely to provide habitat for dabbling and diving ducks, but wetland installation is unlikely to completely offset habitat losses for dabbling ducks and shorebirds. Drainage modernization aside, our results indicate that water quality wetlands can address several environmental issues associated with agricultural expansion and intensification by improving water quality and providing wetland resources for waterbirds and other organisms. Field-scale research is needed to validate these results.

Nitrogen (N) retention is a common goal of stream-wetland restoration projects in systems with excess nitrate (NO₃⁻), however N retention depends on habitats with high denitrification and uptake rates that interact with NO₃⁻. Legacy sediments deposited along formerly impounded streams bury and disconnect historic floodplain-wetland systems. This disconnection limits sediment-water interactions, decreases N retention and increases N delivery. Restoration with legacy sediment removal should lead to greater N retention due to the reestablishment of wet habitats that interact with NO₃⁻-rich water, but the formation of biogeochemically retentive soils under modern conditions of high NO₃⁻, N retention rates, and recovery time are unclear. An experimental restoration approach undertaken at Big Spring Run in Lancaster, PA, USA was used to test the hypothesis that reconnection of a stream to its historic floodplain with legacy sediment removal enhances N processing and retention. We describe changes in sediment and water concentrations of N and organic carbon (C) along with the changes in sediment biogeochemical processing rates of denitrification, nitrification, and C mineralization, before and for five years following restoration. Our results show that biogeochemical processing increased and higher NO₃⁻ retention developed following stream-wetland restoration. NO₃⁻ retention improved after several years as organic matter accumulated to ultimately support higher rates of denitrification that transitioned from organic C limitation to NO₃⁻ limitation. We conclude that, in systems with high contemporary NO₃⁻, restoration via legacy sediment removal and floodplain reconnection can lead to the accumulation of organic matter and improved biogeochemical NO₃⁻ retention over time

Reliable and accurate streamflow prediction plays a critical role in watershed water resources planning and management. We developed a new hybrid SWAT-WSVR model based on 12 hydrological sites in the Illinois River watershed (IRW), U.S., that integrated the Soil and Water Assessment Tool (SWAT) model with a Support Vector Regression (SVR) calibration method coupled with discrete wavelet transforms (DWT) to better support modeling watersheds with limited data availability. Wavelet components of the simulated streamflow from the SWAT-Calibration Uncertainty Procedure (SWAT-CUP) and precipitation time series were used as inputs to SVR to build a hybrid SWAT-WSVR. We examined the performance and potential of the SWAT-WSVR model and compared it with observations, SWAT-CUP, and SWAT-SVR using statistical metrics, Taylor diagrams, and hydrography. The results showed that the average of RMSE-observation’s standard deviation ratio (RSR), Nash–Sutcliffe efficiency (NSE), percent bias (PBIAS), and root mean square error (RMSE) from SWAT-WSVR is 0.02, 1.00, −0.15, and 0.27 m³ s⁻¹ in calibration and 0.14, 0.98, −1.88, and 2.91 m³ s⁻¹ in validation on 12 sites, respectively. Compared with the other two models, the proposed SWAT-WSVR model possessed lower discrepancy and higher accuracy. The rank of the overall performance of the three SWAT-based models during the whole study period was SWAT-WSVR > SWAT-SVR > SWAT-CUP. The developed SWAT-WSVR model supplies an additional calibration approach that can improve the accuracy of the SWAT streamflow simulation of watersheds with limited data.

Animals can have pervasive effects on ecosystems as they modify their biogeochemical and physical environments. In particular, when animals occur in high densities these effects can result in dramatic changes in the physical environment and biogeochemical hotspots or hot moments. While most research to date has focused on the direct role of animals in biogeochemical cycles, few have examined how animals indirectly influence biogeochemical cycles across scales. Freshwater mussels occur as spatially heterogeneous, dense and species‐rich aggregations in many river ecosystems world‐wide. Here we examined how mussel communities (a) directly influence the flux of particulate and dissolved nutrients and (b) indirectly effect the flux of N₂production, via denitrification, across a gradient of mussel biomass and differences in community composition at the patch‐ (0.25 m²) and stream reach‐scales (60–80 m). We combined measurements of ammonia (N) and soluble reactive phosphorous (P) excretion and C, N and P biodeposition rates for 10 species with biomass and distribution estimates for seven mixed‐species aggregations to quantify direct mussel contributions to biogeochemical cycling and the spatial heterogeneity of their impact. Additionally, we sampled sediments at a fine spatial scale to determine how mussel biomass and richness influence potential denitrification (indirect flux) rates at the patch‐ and reach‐scales. We predicted that increasing mussel biomass would lead to greater direct and indirect fluxes of nutrients, manifesting in heterogeneous nutrient redistribution within and among stream reaches. We also predicted that variation in community composition would result in differential nutrient excretion and egestion stoichiometries. Our results indicate that mussel aggregations directly influence soluble and particulate nutrient fluxes with community composition, particularly phylogenetic tribe composition, controlling the stoichiometry. Mussel aggregations also indirectly influenced nutrient fluxes as greater mussel biomass and species richness resulted in higher denitrification rates as mediated by their interactions with the sediments and enhancement of nutrient availability. Our results underscore the importance of patchy communities in acting as biogeochemical control points. A freePlain Language Summarycan be found within the Supporting Information of this article.

Accurate streamflow prediction plays a pivotal role in hydraulic project design, nonpoint source pollution estimation, and water resources planning and management. However, the highly non-linear relationship between rainfall and runoff makes prediction difficult with desirable accuracy. To improve the accuracy of monthly streamflow prediction, a seasonal Support Vector Regression (SVR) model coupled to the Soil and Water Assessment Tool (SWAT) model was developed for 13 subwatersheds in the Illinois River watershed (IRW), U.S. Terrain, precipitation, soil, land use and land cover, and monthly streamflow data were used to build the SWAT model. SWAT Streamflow output and the upstream drainage area were used as two input variables into SVR to build the hybrid SWAT-SVR model. The Calibration Uncertainty Procedure (SWAT-CUP) and Sequential Uncertainty Fitting-2 (SUFI-2) algorithms were applied to compare the model performance against SWAT-SVR. The spatial calibration and leave-one-out sampling methods were used to calibrate and validate the hybrid SWAT-SVR model. The results showed that the SWAT-SVR model had less deviation and better performance than SWAT-CUP simulations. SWAT-SVR predicted streamflow more accurately during the wet season than the dry season. The model worked well when it was applied to simulate medium flows with discharge between 5 m³ s^-1 and 30 m³ s^-1, and its applicable spatial scale fell between 500 to 3000 km². The overall performance of the model on yearly time series is “Satisfactory”. This new SWAT-SVR model has not only the ability to capture intrinsic non-linear behaviors between rainfall and runoff while considering the mechanism of runoff generation but also can serve as a reliable regional tool for an ungauged or limited data watershed that has similar hydrologic characteristics with the IRW.

Watershed-scale nonpoint source (NPS) pollution models have become important tools to understand, evaluate, and predict the negative impacts of NPS pollution on water quality. Today, there are many NPS models available for users. However, different types of models possess different form and structure as well as complexity of computation. It is difficult for users to select an appropriate model for a specific application without a clear understanding of the limitations or strengths for each model or tool. This review evaluates 14 more commonly used watershed-scale NPS pollution models to explain how and when the application of these different models are appropriate for a given effort. The models that are assessed have a wide range of capacities that include simple models used as rapid screening tools (e.g., Long-Term Hydrologic Impact Assessment (L-THIA) and Nonpoint Source Pollution and Erosion Comparison Tool (N-SPECT/OpenNSPECT)), medium-complexity models that require detail data input and limited calibration (e.g., Generalized Watershed Loading Function (GWLF), Loading Simulation Program C (LSPC), Source Loading and Management Model (SLAMM), and Watershed Analysis Risk Management Frame (WARMF)), complex models that provide sophisticated simulation for NPS pollution processes with intensive data and rigorous calibration (e.g., Agricultural Nonpoint Source pollution model (AGNPS/AnnAGNPS), Soil and Water Assessment Tool (SWAT), Stormwater Management Model (SWMM), and Hydrologic Simulation Program Fortran (HSPF)), and modeling systems that integrate various sub-models and tools, and contain the highest complexity to solve all phases of hydrologic, hydraulic, and chemical dynamic processes (e.g., Automated Geospatial Watershed Assessment Tool (AGWA), Better Assessment Science Integrating Point and Nonpoint Sources (BASINS) and Watershed Modeling System (WMS)). This assessment includes model intended use, components or capabilities, suitable land-use type, input parameter type, spatial and temporal scale, simulated pollutants, strengths and limitations, and software availability. Understanding the strengths and weaknesses of each watershed-scale NPS model will lead to better model selection for suitability and help to avoid misinterpretation or misapplication in practice. The article further explains the crucial criteria for model selection, including spatial and temporal considerations, calibration and validation, uncertainty analysis, and future research direction of NPS pollution models. The goal of this work is to provide accurate and concise insight for watershed managers and planners to select the best-suited model to reduce the harm of NPS pollution to watershed ecosystems.

Hyporheic zones contribute to lower temperatures in many rivers, creating a longitudinal heterogeneous array of thermal refuges. In this study, we had the unique opportunity to show temperature reduction along hyporheic zone pathlines in a large river system that contribute to the maintenance of refuges through discharge into off‐channel habitats. Temperature was monitored in a dense network of wells that were located along pathlines in small islands, determined from a calibrated groundwater flow model. Temperature along one 600‐m pathline was reduced about 7° C. Among three islands that were adjacent to the river, the northern two showed exponential decrease in temperature with distance, with fitted thermal Péclet numbers of 2.7 and 6.5, whereas the southern island showed no significant decrease. We suggest that this is due to the higher infiltration rate in the wet season in this larger, more mature island, which suppresses hyporheic flow in the wet season.Stable isotope sampling showed that values of δ²H were higher in areas where we observed lower temperatures. The overall relationship of δ²H versus temperature was significant with a slope of −0.329. This implies that lower temperatures are associated with water that has had contact with deeper groundwater or that lower temperatures have been affected by local rainfall infiltration or water that has entered the hyporheic zone in winter. These findings are important because they allow estimation of the temperature benefit that may be achieved in similar geomorphic settings, providing implications for riparian restoration.

Soil erosion and lake sediment loading are primary concerns of watershed managers around the world. In the Xinjiang River Basin of China, severe soil erosion occurs primarily during monsoon periods, resulting in sediment flow into Poyang Lake and subsequently causing lake water quality deterioration. Here, we identified high-risk soil erosion areas and conditions that drive sediment yield in a watershed system with limited available data to guide localized soil erosion control measures intended to support reduced sediment load into Poyang Lake. We used the Soil and Water Assessment Tool (SWAT) model to simulate monthly and annual sediment yield based on a calibrated SWAT streamflow model, identified where sediment originated, and determined what geographic factors drove the loading within the watershed. We applied monthly and daily streamflow discharge (1985–2009) and monthly suspended sediment load data (1985–2001) to Meigang station to conduct parameter sensitivity analysis, calibration, validation, and uncertainty analysis of the model. The coefficient of determination (R²), Nash–Sutcliffe efficiency (NSE), percent bias (PBIAS), and RMSE -observation’s standard deviation ratio (RSR) values of the monthly sediment load were 0.63, 0.62, 3.8%, and 0.61 during calibration, respectively. Spatially, the annual sediment yield rate ranged from 3 ton ha⁻¹year⁻¹ on riparian lowlands of the Xinjiang main channel to 33 ton ha⁻¹year⁻¹ on mountain highlands, with a basin-wide mean of 19 ton ha⁻¹year⁻¹. The study showed that 99.9% of the total land area suffered soil loss (greater than 5 ton ha⁻¹year⁻¹). More sediment originated from the southern mountain highlands than from the northern mountain highlands of the Xinjiang river channel. These results suggest that specific land use types and geographic conditions can be identified as hotspots of sediment source with relatively scarce data; in this case, orchards, barren lands, and mountain highlands with slopes greater than 25° were the primary sediment source areas. This study developed a reliable, physically-based streamflow model and illustrates critical source areas and conditions that influence sediment yield.