Search

Municipal stormwater systems are designed to collect, transport, and discharge precipitation from a defined catchment area into local surface waters. However, these discharges may contain unsafe levels of fecal waste. Paired measurements of Escherichia coli, precipitation, three land use metrics determined by geographic information system (GIS) mapping, and host-associated genetic markers indicative of human (HF183/BacR287 and HumM2), ruminant (Rum2Bac), dog (DG3), and avian (GFD) fecal sources were assessed in 231 urban stream samples impacted by two or more municipal stormwater outfalls. Receiving water samples were collected twice per month (n = 24) and after rain events (n = 9) from seven headwaters of the Anacostia River in the District of Columbia (United States) exhibiting a gradient of impervious surface, residential, and park surface areas. Almost 50% of stream samples (n = 103) were impaired, exceeding the local E. coli single sample maximum assessment level (410 MPN/100 ml). Fecal scores (average log10 copies per 100 ml) were determined to prioritize sites by pollution source and to evaluate potential links with land use, rainfall, and E. coli levels using a recently developed censored data analysis approach. Dog, ruminant, and avian fecal scores were almost always significantly increased after rain or when E. coli levels exceeded the local benchmark. Human fecal pollution trends showed the greatest variability with detections ranging from 9.1% to 96.7% across sites. Avian fecal scores exhibited the closest connection to land use, significantly increasing in catchments with larger residential areas after rain events (p = 0.038; R2 = 0.62). Overall, results demonstrate that combining genetic fecal source identification methods with GIS mapping complements routine E. coli monitoring to improve management of urban streams impacted by stormwater outfalls. [ABSTRACT FROM AUTHOR]

High frequency in-situ measurements of nitrate can greatly reduce uncertainty in nitrate flux estimates. Water quality databases maintained by various federal and state agencies often consist of pollutant concentration data obtained from periodic grab samples collected from gauged reaches of a stream. Regression models, such as the LOAD ESTimator (LOADEST), are frequently used to model variations in concentrations associated with changes in water discharge to provide integrated solute flux measurements. However, uncertainty in the relationships between nutrient concentration and flow may lead to errors in the corresponding flux estimates. In this study, a high frequency, in-situ measurement of nitrate concentration was implemented to ascertain uncertainty in the concentration/discharge relationship caused by nitrate hysteresis. It was found that observed nitrate hysteresis, as influenced by complex storm/watershed interactions, was not readily predictable. Therefore, it can lead to substantial nitrate flux uncertainty, based on periodic grab sample monitoring approaches. Scientists and engineers should take advantage of the proposed findings in future studies to enhance the quality of the associated decision making processes. [ABSTRACT FROM AUTHOR]

The health of the Chesapeake Bay ecosystem has been declining for several decades due to high levels of nutrients and sediments largely tied to agricultural production systems. Therefore, monitoring of agricultural water use and hydrologic connections between crop lands and Bay tributaries has received increasing attention. Remote sensing retrievals of actual evapotranspiration (ET) can provide valuable information in support of these hydrologic modeling efforts, spatially and temporally describing consumptive water use by crops and natural vegetation and quantifying response to expansion of irrigated area occurring with Bay watershed. In this study, a multisensor satellite data fusion methodology, combined with a multiscale ET retrieval algorithm, was applied over the Choptank River watershed located within the Lower Chesapeake Bay region on the Eastern Shore of Maryland, USA to produce daily 30 m resolution ET maps. ET estimates directly retrieved on Landsat satellite overpass dates have high accuracy with relative error (RE) of 9%, as evaluated using flux tower measurements. The fused daily ET time series have reasonable errors of 18% at the daily time step - an improvement from 27% errors using standard Landsat-only interpolation techniques. Annual water consumption by different land cover types was assessed, showing reasonable distributions of water use with cover class. Seasonal patterns in modeled crop transpiration and soil evaporation for dominant crop types were analyzed, and agree well with crop phenology at field scale. Additionally, effects of irrigation occurring during a period of rainfall shortage were captured by the fusion program. These results suggest that the ET fusion system will have utility for water management at field and regional scales over the Eastern Shore. Further efforts are underway to integrate these detailed water use data sets into watershed-scale hydrologic models to improve assessments of water quality and inform best management practices to reduce nutrient and sediment loads to the Chesapeake Bay. [ABSTRACT FROM AUTHOR]

Chesapeake Bay, the largest estuary in North America, is impaired by excess nutrient discharges, especially from urban and agricultural land. Watershed simulation models have provided key insights to understanding land-to-water connections, but rarely are these models applied to guide local land management to explore and communicate uncertainty in the model predictions. In this study, three watershed simulation models; the Soil and Water Assessment Tool (SWAT), the Generalized Watershed Loading Function (GWLF) model, and the Chesapeake Bay Program's Chesapeake Watershed Model (CBP-CWM) were implemented to predict water, total nitrogen, and total phosphorus discharges from small tributaries in the town of Queenstown, Maryland, USA. Based on our evaluation metrics, none of the models consistently provided better results. In general, there was a good agreement on annual average water flow between the SWAT and CBP-CWM models, and the GWLF and CBP-CWM models predicted similar TN and TP loads. Each model has strengths and weaknesses in flow and nutrient predictions, and predictions differed among models even when models were initialized with the same data. Using multiple models may enhance the quality of model predictions and the decision making process. However, it could also be the case that the complexity of implemented watershed models and resolution of our understanding currently are not yet suited to provide scientifically credible solutions. [ABSTRACT FROM AUTHOR]

Many hydraulic structures are designed based on intensity-duration-frequency (IDF) curves. A design based on an inaccurate design storm can cause problems, such as malfunction of the infrastructure, excessive cost, or loss of life. In previous studies the authors developed IDF curves under future climate scenarios for Alabama using six different North American Regional Climate Change Assessment Program (NARCCAP)-based projections. Results demonstrated that these models do not project identical results, and there is uncertainty regarding future rainfall intensities projected by these six climate models. Understanding the uncertainties associated with climate model outputs can help decision makers to explain the impacts of climate change with more confidence. Therefore, the objective of this study was to develop probability-based IDF curves incorporating climate projections from six different climate models using a kernel density estimator. IDF curves were previously created using two different temporal disaggregation methods: a stochastic method and an artificial neural network (ANN) model. A kernel density estimator was applied to the resulting estimated rainfall intensities from both methods and probability-based IDFs were developed. In addition to the probability-based IDFs, typical IDF curves as a resultant of incorporating all models were also developed. A comparison of the results with the current (historical) IDF curves for the city of Auburn, Alabama, indicated that, when the stochastic method was used for rainfall disaggregation, future rainfall intensities are expected to decrease by 29 to 39% for durations less than 6 to 8 h, and to increase by 14 to 19% for longer durations. Analysis of the results of the ANN model for durations of less than 2 h indicates that the precipitation pattern for Alabama veers toward less intense rainfalls for the investigated durations and for all the return periods. This decrease is expected to be between 48 and 52% for the city of Auburn. [ABSTRACT FROM AUTHOR]

Highlights: [•] A new physically based model for carbon cycling in flooded wetlands is proposed. [•] Model fully reflects the dynamics of sediment–water interactions in flooded wetlands. [•] Model was validated using field collected data from a from a small wetland, collecting agricultural runoff. [•] Model behavior was scrutinized through in-depth sensitivity and uncertainty analysis. [•] Study wetland removed/retained equivalent to 47±12% of the OC intake over two years. [ABSTRACT FROM AUTHOR]

In many watersheds around the world where the importance of assessing a watershed as a whole is overlooked, new water resources projects are designed solely based on historic flow data. Such projects might fail to operate satisfactorily when designed without considering the uncertainties associated with future hydrologic changes. In this study, a system dynamics model (SDM) is developed to quantify the potential hydrologic impacts of future developments in parts of the Karkheh River basin, Iran, and assess their effects on hydroelectricity generation of existing and projected hydropower plants. Results indicate that upstream development could reduce future annual energy production by 254 GWh. Interbasin water transfer from the nearby Sirvan River basin to the Karkheh River basin was also investigated as a viable option to increase future energy production. Simulation results revealed that an average increase in electricity production can be achieved per of annual environmental flow release out of transferred water from Sirvan to Karkheh River basin. [ABSTRACT FROM AUTHOR]

In many watersheds around the world where the importance of assessing a watershed as a whole is overlooked, new water resources projects are designed solely based on historic flow data. Such projects might fail to operate satisfactorily when designed without considering the uncertainties associated with future hydrologic changes. In this study, a system dynamics model (SDM) is developed to quantify the potential hydrologic impacts of future developments in parts of the Karkheh River basin, Iran, and assess their effects on hydroelectricity generation of existing and projected hydropower plants. Results indicate that upstream development could reduce future annual energy production by 254 GWh. Interbasin water transfer from the nearby Sirvan River basin to the Karkheh River basin was also investigated as a viable option to increase future energy production. Simulation results revealed that an average increase in electricity production can be achieved per of annual environmental flow release out of transferred water from Sirvan to Karkheh River basin. [ABSTRACT FROM AUTHOR]

The Agricultural Policy/Environmental eXtender (APEX) model has been widely used to assess changes in agrochemical loadings in response to conservation and management led by US Department of Agriculture (USDA). However, the existing APEX model is limited in quantification of wetland water quality functions. This study improved the current model capacity to represent wetland water quality functions by addition of a new biogeochemical module into the APEX model. The performance of an enhanced APEX model was tested against five observed outgoing water quality variables (e.g., sediment, organic N, NO3, NH4 and PO4) from a wetland within the Eastern Shore of Maryland. Generalized Likelihood Uncertainty Estimation (GLUE) was implemented to assess model uncertainty. The enhanced APEX model demonstrated that it could effectively represent N and P cycling within the study wetland. Although improvement of model performance was limited, the additions of wetland biogeochemical routines to the APEX model improved our understanding of inner mass exchanges within N and P cycling for the study wetland. Overall, the updated APEX model can provide policymakers and managers with improved means for assessment of benefits delivered by wetland conservation. [ABSTRACT FROM AUTHOR]