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In efforts to combat eutrophication, the U.S. Environmental Protection Agency has established aggressive nitrogen, phosphorus, and sediment reduction goals for states and regulated dischargers within the Chesapeake Bay watershed. Chesapeake Bay jurisdictions are struggling to meet the nutrient (N, P) reduction goals. This paper evaluates the efficacy of removing legacy N from groundwater as a compliance strategy for three potential classes of "buyers" of N reductions in the Chesapeake Bay watershed: permitted point sources, permitted municipal stormwater systems (called MS4s), and state nonpoint source (NPS) managers. We compare denitrifying spring bioreactors with conventional agricultural and urban NPS removal technologies using evaluative criteria important to each of these buyers. Results indicate that spring bioreactors compare favorably to other N removal technologies based on cost effectiveness, administrative costs, and certainty of N removal performance. Most conventional NPS technologies provide greater ancillary benefits. On balance, denitrifying spring bioreactors add a valuable compliance option to those tasked with achieving Bay N reduction goals.

Effective watershed management and protection of water resources from non-point source pollution require identification, prioritization, and targeting of pollutant source areas. Process-based hydrology and water quality models are powerful heuristic tools for land and water resources managers. However, because of their complexity, such models are often under-utilized as management prioritization and planning tools. In this paper, we present a prioritization, interactive visualization, and analysis tool (Pi-VAT) that is programmed to synthesize multi-scenario, multi-watershed outputs from process-based geospatial models. We demonstrate the utility of Pi VAT to examine simulated hydrologic, sediment, and water quality response at the hillslope/hydrologic response unit (HRU) scale. We apply Pi-VAT to output from multiple watersheds and for multiple management scenarios and treatments from two geospatial models for watershed management: Water Erosion Prediction Project (WEPP) and Soil & Water Assessment Tool (SWAT). Pi-VAT was developed using the Shiny web application framework for the R programming language. In a matter of minutes, Pi-VAT can synthesize overwhelming amounts of output from process-based models into information useful for land and water resources managers. We illustrate the use of Pi-VAT to interactively identify, quantify, and visualize areas that are most susceptible to disturbance under different scenarios and provide a synthesis approach based on land use, soil type, and slope steepness. This approach guides land and water resources managers in prioritizing the areas of the watershed that provide the maximum reduction in pollutant loads while treating the least amount of area. Pi-VAT provides a flexible reactive platform for the development of decision support tools based on process-based models intended for watershed management and research applications. AFRI program [1009827]; USDA National Institute of Food and Agriculture Published version This work was supported by the AFRI program [grant no201667020-25320/project accession no. 1009827] from the USDA National Institute of Food and Agriculture. Public domain – authored by a U.S. government employee

Considerable efforts are underway to restore watersheds and estuaries downstream impacted by urban development; however, climate change (CC) may be undermining them. Current methods are limited in their ability to predict hydrology and water quality with CC and assess its effect on the efficiency of stormwater control measures (SCMs). We developed a method using downscaled global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5) to project precipitation and temperatures; these were used to force a Storm Water Management Model (SWMM). Three scenarios, a historical and two Representative Concentration Pathways (RCP 4.5 and 8.5) with five GCMs, were used to produce ensemble results. All GCMs in both RCP scenarios projected increases in precipitation and temperature compared to historical conditions. Both RCPs exhibited their largest increases in precipitation, streamflow, total suspended solids (TSS), total nitrogen (TN), and total phosphorous (TP) loads in the winter, summer exhibited the largest increase in temperature. Median loads of TSS, TN, and TP increased 3.1%, 2.5%, and 9.9%, respectively, for RCP 4.5, and increased 3.8%, 3.1%, and 10.4%, respectively, for RCP 8.5. Median reductions in TSS, TN, and TP SCM efficiency for RCP 4.5 were projected to be 6%, 7%, and 11%, respectively; and 11%, 12%, and 17% for RCP 8.5, respectively. Thus, it is likely that additional efforts will be needed to meet water quality goals in the future. Methods such as these can help create climate resilient watershed improvement strategies and guide urban stormwater planning against likely future changes as a result of CC.

Communities are coping with changes in runoff quantity and quality, stemming mainly from changes in climate and land use/land cover (LULC); there is a need to identify the most adaptable strategies that improve community resilience. However, the joint impacts of climate and LULC change have rarely been assessed at local scales. To address these needs, we assessed the response of runoff and pollutant loads from Broad Run, a rapidly developing watershed in northern Virginia, to projected climate and LULC change. Climate data from two downscaled Global Climate Models (GCMs) were used to force an urban watershed model, the Storm Water Management Model (SWMM), while forecasts of LULC change were derived from the Chesapeake Bay Land Change Model (CBLCM). Two Representative Concentration Pathways (RCPs), 4.5 and 8.5, a historic baseline (1995–2020), and projected periods (2040–2065); and four LULC change scenarios designated agricultural conservation (AC), forest conservation (FC), growth management (GM), and historical trend (HT) were used to create a series of ensemble simulations of coupled LULC and climate change. Results indicated that, under RCP 8.5, annual precipitation is projected to increase substantially more than RCP 4.5. Projected LULC change resulted in a projected increase in imperviousness from 6.3% to 13.1%. Results indicated that climate change will likely increase the seasonal variability of runoff, Total Suspended Solids (TSS), Total Nitrogen (TN), and Total Phosphorus (TP) for both RCPs. The largest increase for a single LULC change (without climate change) scenario for runoff, TSS, TN, and TP was 32.6%, 33.4%, 31.6%, and 35.8%, respectively. which occurred with the HT scenario. Results of LULC change also indicated that more pollutant loads were associated with increased imperviousness from increased urban development and loss of deciduous forests and grasslands. The largest increase for climate and LULC change scenarios in runoff, TSS, TN, and TP was 67.6%, 66.7%, 63.4%, and 69.4%, respectively, which occurred with the RCP 8.5 and HT scenarios. Similar, but smaller increases were obtained for other scenarios, suggesting that climate and LULC change may be synergistic, likely undermining watershed restoration efforts. The results of our study also indicate that runoff, TSS, TN, and TP are expected to be more affected by changes in future LULC than by projected changes in climate. Our study can be used to inform watershed restoration efforts, urban planning, and environmental policy. The combined impact of climate and LULC change will likely generate increased runoff, and nutrients and sediment loading, indicating that robust mitigation strategies are needed for watershed restoration to succeed. Published version

An increase in heavy precipitation associated with climate change has exacerbated flooding in the Eastern U.S. To assess regional flood risk with changing climatic conditions, we demonstrate the application of a novel hydrologic modeling framework that integrates climate projections with a coupled Noah-MP land surface model and a two-dimensional HEC-RAS hydrodynamic model. We employ this framework along a 41 km reach of the Susquehanna River near Harrisburg, Pennsylvania, where recent flood damages exceeded $2 billion (2011 Irene and Lee floods). Historical and future 30-year and 100-year peak-discharge estimates were compared to assess how flood risk might be altered due to climate change. Results indicate that precipitation increases from climate change do not always lead to increases in flood risk, because interplay of hydrological components in the watershed, which are considered by Noah-MP, largely controls flooding severity. However, climate change is expected to increase the severity of extreme events; if a 50-year flood (the recurrence interval of Tropical Storm Lee) occurred toward the end of the 21st century in the worst-case emission scenario, then flood volume would increase by 40% and flood extent by 15%, due to an increase in soil moisture from a wetter overall climate. Virginia Tech's Open Access Subvention Fund Published version The authors would like to thank Mark Roland from USGS and Craig Thomas from USACE for sharing the data from the USGS Scientific Investigation Report (2014-5046) and CISL at NCAR for supercomputing facilities. They also thank Editor Paul Samuels, Associate Editor (anonymous), and three anonymous reviewers for comments that helped the authors better generalize and improve the overall quality of this work. We thank Virginia Tech's Open Access Subvention Fund for providing financial support to publish this article.

This study assessed the impacts of climate change on terrestrial hydrological components and Crop Water Use (CWU) over the Chesapeake Bay watershed using a combination of Global Climate Models (GCMs) and a land surface model. To better understand the impacts of climate change on the hydrological cycle, long-term simulations of multiple earth system models from the Coupled Model Intercomparison Project (CMIP Phase 5) are statistically downscaled and bias-corrected using Multivariate Adaptive Constructed Analogs (MACA) scheme for use as model forcing. Precipitation indices from the twenty MACA-based GCMs are used to identify six best performing models. A mesoscale approach is developed, where CWU is estimated by accounting for the impacts of changing climate conditions and rising CO2 levels. Daily grid-based crop coefficients are derived from evapotranspiration data. The findings indicate a significant annual increase in precipitation (10 %) and temperature (+4.5 K) for the RCP 8.5 scenario towards the end of the 21st century. A significant reduction (13 % and 17 % respectively) in CWU is estimated for corn and soybeans, resulting from increased total precipitation and rising CO2 levels suppressing evapotranspiration. Our results indicate that even in a warmer regime, crop water use decreased due to rising CO2 concentrations due to climate change. National Science Foundation (NSF)National Science Foundation (NSF) [CBET-1360280] Published version This work was supported in part by the National Science Foundation (NSF) , Water, Sustainability, and Climate Program (grant number CBET-1360280)

The United States Environmental Protection Agency has established aggressive nutrient reduction goals to achieve water quality objectives for the Chesapeake Bay estuary. Nitrogen (N) reduction goals are proving particularly difficult to meet with an additional 20.4 million kg of annual nitrogen reductions needed by 2025, and many of the easily achievable and low-cost N reductions have been realized. We assess the feasibility of employing woodchip denitrifying bioreactors to treat legacy N derived from spring discharge in the Mid-Atlantic region. We estimate that in excess of 6100 kg of soluble N is discharged daily from United States Geological Survey identified springs in four Mid-Atlantic states within the Chesapeake Bay watershed. Based on typical bioreactor removal efficiency (30–55%) and potentially treatable flows (

Watershed models are essential tools to understand, quantify, and predict hydrologic processes and water quality responses from scales ranging from field to large river basins. However, the reliability of watershed models in a management context depends largely on inherent uncertainties in model predictions. The objective of this study is to quantify prediction uncertainty for flow, sediment, total nitrogen (TN), and total phosphorus (TP) resulting from model structure. We do this using using three process-based models: the Soil and Water Assessment Tool-Variable Source Area model (SWAT-VSA), the standard Soil and Water Assessment Tool (SWAT-ST), and the Chesapeake Bay Program’s Phase 6 Watershed Model (CBP-Model). We initialize each of the models using meteorological, soil, and land use data, and analyze outputs of flow, sediment, TN, and TP fluxes at the U.S. Geological Survey stream gauge at the downstream end of the Susquehanna River Basin in Conowingo, Maryland. Using these three models, we develop and compare two types of Bayesian models, a Bayesian Generalized (Non-) Linear Multilevel Model (BGMM), and a Bayesian Model Averaging (BMA) for flow, sediment, TN, and TP and 95% credible intervals. We compare the Bayesian models results against the individual model results, and straight model averaging (SMA) using a split time period analysis to assess their predictive strengths. Both Bayesian models provided substantially better predictions than the individual process-based models, and estimates of prediction uncertainty, which can enhance decision-making and improve watershed management by providing a risk based assessment of outcomes.

Precision technologies for confinement animal agricultural systems have increased rapidly over the past decade, though precision technology solutions for pastured livestock remain limited. There are a number of reasons for this limited expansion of technologies for pastured animals, including networking availability and reliability, power requirements, and expense, among others. The objective of this work was to demonstrate a rapidly deployable long-range radio (LoRa) based, low-cost sensor suite that can be used to track location and activity of pastured livestock. The sensor is comprised of an inexpensive Arduino-compatible microprocessor, a generic MPU-9250 motion sensor which contains a 3-axis accelerometer, 3-axis magnetometer, and a 3-axis gyroscope, a generic GPS receiver, and a RFM95W generic LoRa radio. The microprocessor can be programmed flexibly using the open source Arduino IDE software to adjust the frequency of sampling, the data packet to send, and what conditions are needed to operate. The LoRa radio transmits to a Dragino LoRa gateway which can also be flexibly programmed through the Arduino IDE software to send data to local storage or, in cases where a web or cellular connection is available, to cloud storage. The sensor was powered using a USB cord connected to a 3,350 mAh lithium-ion battery pack. The Dragino gateway was programmed to upload data to the ThingSpeak IoT application programming interface for data storage, handling, and visualization. Evaluations showed minimal benefit associated with reducing sampling frequency as a strategy to preserve battery life. Packet loss ranged from 40% to 60%. In a 3 d evaluation on pastured sheep, the sensor suite was able to report GPS locations, inertial sensor readings, and temperature. Preliminary demonstrations of our system are satisfactory to detect animal location based on GPS data in real-time. This system has clear utility as a lower-cost strategy to deploy flexible, useful precision technologies for pasture-based livestock species.

The use of automated systems for monitoring animal behavior provides information on individual animal behavior and can be used to enhance animal productivity. However, the advancement of this industry is hampered by technology costs, challenges with power supplies, limited data accessibility, and inconsistent testing approaches for confirming the detection of livestock behaviors. Development of open-source research tools similar to commercially available wearable technologies may contribute to the development of more-efficient and affordable technologies. The objective of this study was to demonstrate an open-source, microprocessor-based sensor designed to monitor and enable differentiation among selected behaviors of adult wethers. The sensor was comprised of an inexpensive espressif ESP-32-WROOM-32 microprocessor with Bluetooth communication, a generic MPU92/50 motion sensor that contains a three-axis accelerometer, three-axis magnetometer, a three-axis gyroscope, and a 5-V rechargeable lithium-ion battery. The open-source Arduino IDE software was used to program the microprocessor and to adjust the frequency of sampling, the data packet to send, and the operating conditions. For demonstration purposes, sensors were placed on six housed sheep for three 1-h increments with concurrent visual behavioral observation. Sensor readings (x-, y-, and z-axis) were summarized (mean and SD) within a minute and compared to animal behavior observations (also on a by-minute basis) using a linear mixed-effect model with animal as a random effect and behavioral classifier as a fixed effect. This analysis demonstrated the basic utility of the sensor to differentiate among animal behaviors based on sensed data (P < 0.001). Although substantial additional work is needed for algorithm development, power source testing, and network optimization, this open-source platform appears to be a promising strategy to research wearable sensors in a generalizable manner.

The EarthCube BALTO broker (Brokered Alignment of Long-Tail Observations) provides streamlined access to both long-tail and big data using Web Services through several distinct mechanisms. First, w...

Nutrient export from agricultural landscapes is a water quality concern and the cause of mitigation activities worldwide. Climate change impacts hydrology and nutrient cycling by changing soil moisture, stoichiometric nutrient ratios, and soil temperature, potentially complicating mitigation measures. This research quantifies the impact of climate change and climate anomalies on hydrology, nutrient cycling, and greenhouse gas emissions in an agricultural catchment of the Chesapeake Bay watershed. We force a calibrated model with seven downscaled and bias-corrected regional climate models and derived climate anomalies to assess their impact on hydrology and the export of nitrate (NO3-), phosphorus (P), and sediment, and emissions of nitrous oxide (N2O) and di-nitrogen (N-2). Modelaverage (+/- standard deviation) results indicate that climate change, through an increase in precipitation and temperature, will result in substantial increases in winter/spring flow (10.6 +/- 12.3%), NO3-(17.3 +/- 6.4%), dissolved P (32.3 +/- 18.4%), total P (24.8 +/- 16.9%), and sediment (25.2 +/- 16.6%) export, and a slight increases in N2O (0.3 +/- 4.8%) and N-2 (0.2 +/- 11.8%) emissions. Conversely, decreases in summer flow (-29.1 +/- 24.6%) and the export of dissolved P (-15.5 +/- 26.4%), total P (-16.3 +/- 20.7%), sediment (-20.7 +/- 18.3%), and NO3-(-29.1 +/- 27.8%) are driven by greater evapotranspiration from increasing summer temperatures. Decreases in N2O (-26.9 +/- 15.7%) and N-2 (-36.6 +/- 22.9%) are predicted in the summer and driven by drier soils. While the changes in flow are related directly to changes in precipitation and temperature, the changes in nutrient and sediment export are, to some extent, driven by changes in agricultural management that climate change induces, such as earlier spring tillage and altered nutrient application timing and by alterations to nutrient cycling in the soil. (C) 2018 Elsevier B.V. All rights reserved. National Science FoundationNational Science Foundation (NSF) [1360415, 1343802]; USDAUnited States Department of Agriculture (USDA) [2012-67019-19434] We would like to acknowledge high-performance computing support from Yellowstone (http://n2t.net/ark:/85065/d7wd3xhc) provided by NCAR's Computational and Information Systems Laboratory, support from the National Science Foundation under award numbers 1360415 and 1343802, and funding support from the USDA under project number 2012-67019-19434. All data, methods, and code used in this manuscript are available upon request. Public domain – authored by a U.S. government employee

Increased adoption of denitrifying bioreactors to mitigate nutrient export in agricultural drainage can improve water quality but also raises concerns about potential unintended consequences of widespread implementation. Greenhouse gas (GHG) emissions, especially as nitrous oxide (N2O) produced during denitrification, are of particular concern. To gain understanding of the variables controlling bioreactor GHG production, the effects of nutrient loading, hydraulic residence time, and biochar addition on emissions of N2O, methane (CH4), and carbon dioxide (CO2) were tested in 6550 cm3 laboratory columns. Biochar, an organic carbon pyrolysis product, was hypothesized to reduce GHG emissions, as has been reported with its use as an agricultural soil amendment. GHG fluxes were measured during 5-day trials using the closed dynamic chamber technique. The 36 combinations of three media types (woodchips, 10% biochar, and 30% biochar), three residence times (3, 6, and 12 h), and four influent formulations, all combinations of high and low nitrogen as nitrate (16.1 and 4.5 mg NO3-N L−1) and phosphorus as orthophosphate (1.9 and 0.6 mg PO4-P L−1) concentrations, were tested in triplicate. Treatment effects were assessed with linear mixed effects models. Biochar addition, particularly at the 30% rate, was found to increase N2O and CO2 emissions and the proportion of removed NO3-N released as N2O-N. However, quantified GHG flux rates were not environmentally concerning. Methane fluxes were negligible, and CO2 fluxes were not considered to increase net GHG emissions. Nitrous oxide fluxes normalized to surface area averaged 2.92 mg N2O-N m−2 d−1, and 98% of measured fluxes were within the range previously reported for woodchip bioreactors and considered acceptable.

Agricultural conservation practices (CPs) are commonly implemented to reduce diffuse nutrient pollution. Climate change can complicate the development, implementation, and efficiency of agricultural CPs by altering hydrology, nutrient cycling, and erosion. This research quantifies the impact of climate change on hydrology, nutrient cycling, erosion, and the effectiveness of agricultural CP in the Susquehanna River Basin in the Chesapeake Bay Watershed, USA. We develop, calibrate, and test the Soil and Water Assessment Tool-Variable Source Area (SWAT-VSA) model and select four CPs; buffer strips, strip-cropping, no-till, and tile drainage, to test their effectiveness in reducing climate change impacts on water quality. We force the model with six downscaled global climate models (GCMs) for a historic period (1990-2014) and two future scenario periods (2041-2065 and 2075-2099) and quantify the impact of climate change on hydrology, nitrate-N (NO3-N), total N (TN), dissolved phosphorus (DP), total phosphorus (TP), and sediment export with and without CPs. We also test prioritizing CP installation on the 30% of agricultural lands that generate the most runoff (e.g., critical source areas-CSAs). Compared against the historical baseline and with no CPs, the ensemble model predictions indicate that climate change results in annual increases in flow (4.5±7.3%), surface runoff (3.5±6.1%), sediment export (28.5±18.2%) and TN export (9.5±5.1%), but decreases in NO3-N (12±12.8%), DP (14±11.5), and TP (2.5±7.4%) export. When agricultural CPs are simulated most do not appreciably change the water balance, however, tile drainage and strip-cropping decrease surface runoff, sediment export, and DP/TP, while buffer strips reduce N export. Installing CPs on CSAs results in nearly the same level of performance for most practices and most pollutants. These results suggest that climate change will influence the performance of agricultural CPs and that targeting agricultural CPs to CSAs can provide nearly the same level of water quality effects as more widespread adoption.

Denitrifying bioreactors are recently-established agricultural best management practices with growing acceptance in the US Midwest but less studied in other agriculturally significant regions, such as the US Mid-Atlantic. A bioreactor was installed in the Virginia Coastal Plain to evaluate performance in this geographically novel region facing challenges managing nutrient pollution. The 25.3 m3 woodchip bed amended with 10% biochar (v/v) intercepted subsurface drainage from 6.5 ha cultivated in soy. Influent and effluent nitrate-nitrogen (NO3–N) and total phosphorus (TP) concentrations and flowrate were monitored intensively during the second year of operation. Bed surface fluxes of greenhouse gases (GHGs) nitrous oxide (N2O), methane (CH4), and carbon dioxide (CO2) were measured periodically with the closed dynamic chamber technique. The bioreactor did not have a statistically or environmentally significant effect on TP export. Cumulative NO3–N removal efficiency (9.5%) and average removal rate (0.56 ± 0.25 g m−3 d−1) were low relative to Midwest tile bioreactors, but comparable to installations in the Maryland Coastal Plain. Underperformance was attributed mainly to low NO3–N loading (mean 9.4 ± 4.4 kg ha−1 yr−1), although intermittent flow, periods of low HRT, and low pH (mean 5.3) also likely contributed. N removal rates were correlated with influent NO3–N concentration and temperature, but decreased with hydraulic residence time, indicating that removal was often N-limited. GHG emissions were similar to other bioreactors and constructed wetlands and not considered environmentally concerning. This study suggests that expectations of NO3–N removal efficiency developed from bioreactors receiving moderate to high NO3–N loading with influent concentrations exceeding 10–20 mg L−1 are unlikely to be met by systems where N-limitation becomes significant.

The advent of real-time, short-term farm management tools is motivated by the need to protect water quality above and beyond the general guidance offered by existing nutrient management plans. Advances in high-performance computing and hydrologic or climate modeling have enabled rapid dissemination of real-time information that can assist landowners and conservation personnel with short-term management planning. This paper reviews short-term decision support tools for agriculture that are under various stages of development and implementation in the United States: (i) Wisconsin's Runoff Risk Advisory Forecast (RRAF) System, (ii) New York's Hydrologically Sensitive Area Prediction Tool, (iii) Virginia's Saturated Area Forecast Model, (iv) Pennsylvania's Fertilizer Forecaster, (v) Washington's Application Risk Management (ARM) System, and (vi) Missouri's Design Storm Notification System. Although these decision support tools differ in their underlying model structure, the resolution at which they are applied, and the hydroclimates to which they are relevant, all provide forecasts (range 24-120 h) of runoff risk or soil moisture saturation derived from National Weather Service Forecast models. Although this review highlights the need for further development of robust and well-supported short-term nutrient management tools, their potential for adoption and ultimate utility requires an understanding of the appropriate context of application, the strategic and operational needs of managers, access to weather forecasts, scales of application (e.g., regional vs. field level), data requirements, and outreach communication structure. USDA-NRCS Conservation Innovation Grants This review was supported, in part, by USDA-NRCS Conservation Innovation Grants to improve P indexing and modeling and to improve short-term forecasting tools for nutrient management planning.

Greenhouse gas (GHG) emissions from agroecosystems, particularly nitrous oxide (N2O), are an increasing concern. To quantify N2O emissions from agroecosystems, which occur as a result of nitrogen (N) cycling, a new physically-based routine was developed for the Soil and Water Assessment Tool (SWAT) model to predict N2O flux during denitrification and an existing nitrification routine was modified to capture N2O flux during this process. The new routines predict N2O emissions by coupling the carbon (C) and N cycles with soil moisture/temperature and pH in SWAT. The model uses reduction functions to predict total denitrification (N2+ N2O) and partitions N2 from N2O using a ratio method. The modified SWAT nitrification routine likewise predicts N2O emissions using reduction functions. The new denitrification routine and modified nitrification routine were tested using GRACEnet data at University Park, Pennsylvania, and West Lafayette, Indiana. Results showed strong correlations between plot measurements of N2O flux and the model predictions for both test sites and suggest that N2O emissions are particularly sensitive to soil pH and soil N, and moderately sensitive to soil temperature/moisture and total soil C levels. Nitrous oxide emissions are modeled with functional relationships between soil N, C, pH, temperature and moisture levels.The model is tested against high spatial and temporal resolution GRACEnet data at two sites.The model is particularly sensitive to soil pH and soil N levels.

Excess nonpoint source nutrient loss presents one of the most vexing water management challenges for water quality managers. Agriculture is the single largest contributor of nutrients to the Chesapeake Bay, and achieving nutrient reduction targets for agriculture will be costly. Biofilters offer new opportunities to reduce nutrient loads from artificially drained agricultural land. Nutrient biofilters consist of organic carbon (C) medium, typically woodchips, that when saturated with nitrogen (N)-enriched waters supports the activity of naturally occurring soil microorganisms that convert bioavailable N into N gases via denitrification. This study estimates the cost and N removal effectiveness for a biochar-amended woodchip biofilter draining a 10 ha (25 ac) field planted in a corn (Zea mays L.)–soy (Glycine max [L.] Merr.) rotation in eastern Virginia as well as for alternative design scenarios for biofilters ranging 25 to 150 m3 (883 to 5,297 ft3) with either woodchips alone or biochar-amended woodchip C substrates. Nitrogen removal effectiveness is estimated using modeled site-specific influent loads and N removal effectiveness estimates derived from experimental trials in a pilot scale system on the Delmarva Peninsula. This analysis estimates N removal costs as a function of biofilter size, which directly relates to residence time, per unit of N removed. Modeled N removal estimates over a five-year period (2009 to 2013) for five bed volumes range from 88 to 391 kg (194 to 862 lb) for the woodchip biofilters (21% to 95% removal) and 132 to 412 kg (291 to 908 lb) for the biochar-amended woodchip biofilters (32% to 100% removal) of the 412 kg total N load exported to the biofilters during the five-year period. The N removal costs range from US$15 to US$48 kg−1 y−1 (US$6.82 to US$21.82 lb−1 yr−1) for the woodchip biofilter and US$15 to US$33 kg−1 y−1 (US$6.82 to US$15 lb−1 yr−1) for biochar-amended woodchip filters. While biofilters are determined to be cost-effective for N removal compared to conventional agricultural best management practices, agricultural producers may be reluctant to voluntarily adopt biofilters because of the relatively high initial installation costs and the lack of direct incentive payments for pollutant removal effectiveness. Nutrient trading programs offer some potential to enhance adoption, but not in the immediate future.

Few current modeling tools are designed to predict short-term, high-risk runoff from hydrologically sensitive areas (HSAs) in watersheds. This study couples the Soil and Water Assessment Tool-Variable Source Area model with the Climate Forecast System Reanalysis model and the Global Forecast System-Model Output Statistics model short term weather forecast, to develop a HSA prediction tool designed to assist producers, landowners, and planners in identifying high-risk areas generating storm runoff and pollution. Short-term predictions for stream flow and soil moisture level were estimated in the South Fork of the Shenandoah river watershed. Daily volumetric flow forecasts were found to be satisfactory four days into the future, and distributed model predictions accurately captured sub-field scale HSAs. The model has the potential to provide valuable forecasts that can be used to improve the effectiveness of agricultural management practices and reduce the risk of non-point source pollution. A watershed model-Global Forecast System model are coupled to develop a critical source area prediction tool.All software is open source and the application applies globally.The tool predicts watershed discharge and spatially distributed soil moisture conditions 14 days into the future.The tool is designed to assist land managers in identifying high-risk areas generating storm runoff and pollution.

The Total Maximum Daily Load (TMDL) program established by the United States Environmental Protection Agency (US EPA) to improve America's water quality is being applied to the Chesapeake Bay watershed to mitigate the "dead zone" problem. Agricultural activities are the major nonpoint source of nitrogen (N), contributing 44% of total N to the Bay. Best Management Practices (BMPs) are recognized as an effective way to mitigate N loss of agricultural activities. However, because of physical and economic heterogeneity in agricultural regions, targeting BMPs to areas that produce disproportionate nutrient losses has the potential to reduce the costs of achieving water quality goals. The purpose of this study is to examine the potential to reduce costs of meeting a regional water quality goal by targeting N load reductions within- and across-counties. Based on TMDL developed by the US EPA in 2010 for the Chesapeake Bay watershed, the N reduction goal is 35% for Pennsylvania by 2025. We examine the effects of targeting the required reductions within counties, across counties, and both within and across counties for the Susquehanna watershed. Using the uniform strategy to meet 35% N reduction as the baseline, results show that costs of achieving a regional 35% N reduction goal can be reduced by 13%, 31% and 36% with cross-county targeting, within-county targeting and within and across county targeting, respectively. Cost effectiveness of government subsidy programs for water quality improvement in agriculture can be increased by targeting them to areas with lower N abatement costs.

Hennig Brandt's discovery of phosphorus (P) occurred during the early European colonization of the Chesapeake Bay region. Today, P, an essential nutrient on land and water alike, is one of the principal threats to the health of the bay. Despite widespread implementation of best management practices across the Chesapeake Bay watershed following the implementation in 2010 of a total maximum daily load (TMDL) to improve the health of the bay, P load reductions across the bay's 166,000-km(2) watershed have been uneven, and dissolved P loads have increased in a number of the bay's tributaries. As the midpoint of the 15-yr TMDL process has now passed, some of the more stubborn sources of P must now be tackled. For nonpoint agricultural sources, strategies that not only address particulate P but also mitigate dissolved P losses are essential. Lingering concerns include legacy P stored in soils and reservoir sediments, mitigation of P in artificial drainage and stormwater from hotspots and converted farmland, manure management and animal heavy use areas, and critical source areas of P in agricultural landscapes. While opportunities exist to curtail transport of all forms of P, greater attention is required toward adapting P management to new hydrologic regimes and transport pathways imposed by climate change. Public domain – authored by a U.S. government employee

Mitigating the environmental impact of nonpoint source pollution from intensively managed urban and agricultural landscapes is of paramount concern to watershed managers. Golf course turfgrass systems, which receive significant fertilizer inputs, have been cited as significant sources of nutrient loading to groundwater and surface water, but a contemporary synthesis of golf course nutrient export rates is lacking. This review of nitrogen (N) and phosphorus (P) loss from golf courses and the factors affecting it aims to support watershed management efforts and decision making. We discuss previous literature reviews, examine seven golf course studies that quantify nutrient export from delineated drainage areas, and analyze the results of 40 turfgrass plot experiments. Studies were collected systematically and selected based on predetermined inclusion criteria. Combining evidence from both watershed- and plot-scale studies, typical inorganic N and P losses from golf courses via leaching and runoff are on the order of 2–12 kg ha−1 yr−1 and 0.1–1.0 kg ha−1 yr−1, respectively. Typical total N and P losses are around 2–20 kg ha−1 yr−1 and 1.5–5 kg ha−1 yr−1, respectively. However, the potential for large variation in export rates across 2–3 orders of magnitude must be emphasized. The body of turfgrass literature stresses the importance of best management practices (BMPs) related to applying fertilizer to match plant needs and reducing opportunities for its transport. Accounting for all sources of nutrients, especially soil P, in determining fertilizer application rates and avoiding excessive irrigation to prevent leaching of nutrients from the rootzone is particularly important. BMPs can also reduce nutrient leaching and runoff by controlling the movement of water across the landscape and promoting natural nutrient attenuation, such as with vegetative stream buffers.

Effective natural resource planning depends on understanding the prevalence of runoff generating processes. Within a specific area of interest, this demands reproducible, straightforward information that can complement available local data and can orient and guide stakeholders with diverse training and backgrounds. To address this demand within the contiguous United States (CONUS), we characterized and mapped the predominance of two primary runoff generating processes: infiltration-excess and saturation-excess runoff (IE vs. SE, respectively). Specifically, we constructed a gap-filled grid of surficial saturated hydraulic conductivity using the Soil Survey Geographic and State Soil Geographic soils databases. We then compared surficial saturated hydraulic conductivity values with 1-hr rainfall-frequency estimates across a range of return intervals derived from CONUS-scale random forest models. This assessment of the prevalence of IE versus SE runoff also incorporated a simple uncertainty analysis, as well as a case study of how the approach could be used to evaluate future alterations in runoff processes resulting from climate change. We found a low likelihood of IE runoff on undisturbed soils over much of CONUS for 1-hr storms with return intervals

As much as 66% of the Nile River flow that reaches Egypt originates in the Highlands of the Ethiopian Blue Nile Basin (BNB). This imbalance in water availability poses a threat to water security in the region and could be impacted by climate change. This study coupled a watershed model analysis with bias corrected and downscaled Intergovernmental Panel on Climate Change (IPCC) Coupled Model Intercomparison Project 5 (CMIP5) climate data to assess the potential impact of climate change on water resources and sediment dynamics in two critical headwater basins of the BNB. Climate scenarios analyzed include RCP2.6, RCP4.5, RCP6.0, and RCP8.5 from six climate models, which were used to force watershed models calibrated against historic streamflow for six gauged sub-watersheds in the Tana basin and four gauged sub-watersheds in the Beles basin. We developed distributed watershed model parameter estimates from the gauged sub-watersheds, which were applied to un-gauged portions of the basins using topographically informed parameter transfer functions. We analyzed the impact of climate change for two future time periods (2041–2065 and 2075–2099) by running each of the six downscaled and bias corrected CMIP5 model predicted climate forcings through the watershed models to assess the impact of ensemble model mean and variance in climate change prediction on water availability and sediment transport. Results indicate that the Tana and Beles basins will experience increases both in mean annual flow (22-27%) and sediment concentrations (16-19%). Interestingly, and of significance for water availability and hydropower development, the monsoon in the Tana and Beles basins will lengthen by approximately four (Tana) to six (Beles) weeks. These results highlight both the considerable variance in climate change impacts as well as the potential for beneficial outcomes in the region.

NSFNational Science Foundation (NSF) [1343802]; USDA NRCS Conservation Innovation Grants Program [69-3A75-13-232]; NatureNet postdoctoral fellowship from The Nature Conservancy

Correctly representing weather is critical to hydrological modelling, but scarce or poor quality observations can often compromise model accuracy. Reanalysis datasets may help to address this basic challenge. The Climate Forecast System Reanalysis (CFSR) dataset provides continuous, globally available records, and CFSR data have produced satisfactory hydrological model performance in some temperate and monsoonal locations. However, the use of CFSR for hydrological modelling in tropical and semi-tropical basins has not been adequately evaluated. Taking advantage of exceptionally high rainfall station density in the catchments of the Rio Grande de Loiza above San Juan, Puerto Rico, we compared model performance based on CFSR records with that based on publicly available weather stations in the Global Historical Climate Network (GHCN, n = 21) and on a dataset of rainfall records maintained by the United States Geological Survey Caribbean Water Science Center (USGS, n = 24). For an implementation of the Soil and Water Assessment Tool (SWAT) with subbasins defined at 11 streamflow gages, uncalibrated measures of Nash–Sutcliffe efficiency (NSE) were >0 at 8 of 11 gages using USGS precipitation data for daily simulations over the period 1998–2012, but were 0 using all precipitation inputs, including CFSR. However, the ground weather station closest to the geographic basin centre produced the highest NSE values in only 5 of 11 cases. The spatially interpolated CFSR data performed as well or better than single ground observations made further than 20–30 km, and sometimes better than individual weather stations

Streamflow forecasts are essential for water resources management. Although there are many methods for forecasting streamflow, real-time forecasts remain challenging. This study evaluates streamflow forecasts using a process-based model (Soil and Water Assessment Tool-Variable Source Area model-SWAT-VSA), a stochastic model (Artificial Neural Network -ANN), an Auto-Regressive Moving-Average (ARMA) model, and a Bayesian ensemble model that utilizes the SWAT-VSA, ANN, and ARMA results. Streamflow is forecast from 1 to 8 d, forced with Quantitative Precipitation Forecasts from the US National Weather Service. Of the individual models, SWAT-VSA and the ANN provide better predictions of total streamflow (NSE 0.60-0.70) and peak flow, but underpredicted low flows. During the forecast period the ANN had the highest predictive power (NSE 0.44-0.64), however all three models underpredicted peak flow. The Bayesian ensemble forecast streamflow with the most skill for all forecast lead times (NSE 0.49-0.67) and provided a quantification of prediction uncertainty. National Science FoundationNational Science Foundation (NSF) [1360415, 1343802]; USDAUnited States Department of Agriculture (USDA) [2012-67019-19434] We would like to acknowledge high-performance computing support from Yellowstone (http://n2t.net/ark:/85065/d7wd3xhc) provided by NCAR's Computational and Information Systems Laboratory, support from the National Science Foundation under award numbers 1360415 and 1343802, and funding support from the USDA under project number 2012-67019-19434. All data and methods used in this manuscript are available upon request. Public domain – authored by a U.S. government employee

Denitrifying bioreactors are edge-of-field structures that remove excess nitrogen (N) from intercepted agricultural drainage by supporting the activity of denitrifying microorganisms with a saturated organic carbon substrate. Although these bioreactors successfully mitigate N export, the typical woodchip systems have little effect on phosphorus (P), which is also often present in environmentally harmful quantities in drainage waters. Currently, the evidence that amending woodchip bioreactors with biochar enhances both N and P removal rates is mixed, but more work is required to test this hypothesis under controlled conditions. To determine the effect of biochar amendment on nitrate (NO3-N) and phosphate (PO4-P) removal in woodchip bioreactors, three media types-aged woodchips (W), 10% (B10) and 30% (B30) biochar by volume-were tested under different operational conditions during five-day laboratory trials with horizontal, flow-through columns. Nutrient removal was observed under different flow rates yielding hydraulic residence times of 3, 6, and 12 hours with four formulations of simulated agricultural drainage, all combination of 16.1 or 4.5 mg L-1 NO3-N and 1.9 or 0.6 mg L-1 PO4-P. Each unique treatment with respect to media type, HRT, and influent formulation was tested in triplicate using independent columns. All treatments successfully removed NO3-N, but PO4-P removal was inconsistent. Cumulative NO3-N removal efficiencies ranged 15-98% with an average removal rate of 11.0 g m-3 d-1; biochar amendment enhanced removal only in response to sufficiently high loading rates. Cumulative PO4-P removal efficiencies ranged from 66% removal to 170% export of the influent load; biochar addition was associated with increased export. These results indicate that pine-feedstock biochar poses a substantial increase to PO4-P leaching risk and only modestly enhances NO3-N removal given sufficiently high loading.

Agricultural production is a major source of nonpoint source pollution contributing 44% of total nitrogen (N) discharged to the Chesapeake Bay. The United States Environmental Protection Agency (US EPA) established the Total Maximum Daily Load (TMDL) program to control this problem. For the Chesapeake Bay watershed, the TMDL program requires that nitrogen loadings be reduced by 25% by 2025. Climate change may affect the cost of achieving such reductions. Thus, it is necessary to develop cost-effective strategies to meet water quality goals under climate change. We investigate landscape targeting of best management practices (BMPs) based on topographic index (TI) to determine how targeting would affect costs of meeting N loading goals for Mahantango watershed, PA. We use the results from two climate models, CRCM and WRFG, and the mean of the ensemble of seven climate models (Ensemble Mean) to estimate expected climate changes and the Soil and Water Assessment Tool-Variable Source Area (SWAT-VSA) model to predict crop yields and N export. Costs of targeting and uniform placement of BMPs across the entire study area (423 ha) were compared under historical and future climate scenarios. Targeting BMP placement based on TI classes reduces costs for achieving water quality goals relative to uniform placement strategies under historical and future conditions. Compared with uniform placement, targeting methods reduce costs by 30, 34, and 27% under historical climate as estimated by the Ensemble Mean, CRCM and WRFG, respectively, and by 37, 43, and 33% under the corresponding estimates of future climate scenarios.

Critical source area identification through phosphorus (P) site assessment is a fundamental part of modern nutrient management planning in the United States, yet there has been only sparse testing of the many versions of the P Index that now exist. Each P site assessment tool was developed to be applicable across a range of field conditions found in a given geographic area, making evaluation extremely difficult. In general, evaluation with in-field monitoring data has been limited, focusing primarily on corroborating manure and fertilizer "source" factors. Thus, a multiregional effort (Chesapeake Bay, Heartland, and Southern States) was undertaken to evaluate P Indices using a combination of limited field data, as well as output from simulation models (i.e., Agricultural Policy Environmental eXtender, Annual P Loss Estimator, Soil and Water Assessment Tool [SWAT], and Texas Best Management Practice Evaluation Tool [TBET]) to compare against P Index ratings. These comparisons show promise for advancing the weighting and formulation of qualitative P Index components but require careful vetting of the simulation models. Differences among regional conclusions highlight model strengths and weaknesses. For example, the Southern States region found that, although models could simulate the effects of nutrient management on P runoff, they often more accurately predicted hydrology than total P loads. Furthermore, SWAT and TBET overpredicted particulate P and underpredicted dissolved P, resulting in correct total P predictions but for the wrong reasons. Experience in the United States supports expanded regional approaches to P site assessment, assuming closely coordinated efforts that engage science, policy, and implementation communities, but limited scientific validity exists for uniform national P site assessment tools at the present time. USDA-NRCS Conservation Innovation Grants This research was supported by four USDA-NRCS Conservation Innovation Grants to assess the accuracy of P Indices to determine the risk of P runoff, to identify shortcomings, and to provide options to refine and improve indices. Mention of trade names or commercial products in this publication is solely to provide specific information and does not imply recommendation or endorsement by the USDA. The USDA is an equal opportunity provider and employer. Public domain – authored by a U.S. government employee

Over the past 20 yr, there has been a proliferation of phosphorus (P) site assessment tools for nutrient management planning, particularly in the United States. The 19 papers that make up this special section on P site assessment include decision support tools ranging from the P Index to fate-and-transport models to weather-forecast-based risk calculators. All require objective evaluation to ensure that they are effective in achieving intended benefits to protecting water quality. In the United States, efforts have been underway to compare, evaluate, and advance an array of P site assessment tools. Efforts to corroborate their performance using water quality monitoring data confirms previously documented discrepancies between different P site assessment tools but also highlights a surprisingly strong performance of many versions of the P Index as a predictor of water quality. At the same time, fate-and-transport models, often considered to be superior in their prediction of hydrology and water quality due to their complexity, reveal limitations when applied to site assessment. Indeed, one consistent theme from recent experience is the need to calibrate highly parameterized models. As P site assessment evolves, so too do routines representing important aspects of P cycling and transport. New classes of P site assessment tools are an opportunity to move P site assessment from general, strategic goals to web-based tools supporting daily, operational decisions. USDA's Natural Resource Conservation Service The authors are grateful to USDA's Natural Resource Conservation Service for support of many of the studies reported in this special collection. Public domain – authored by a U.S. government employee

There is strong interest in adapting denitrifying bioreactors to mid-Atlantic drainage systems to help address Chesapeake Bay water quality goals. Three ditch drainage-oriented bioreactors were constructed in 2015 in Maryland to evaluate site-specific design and installation concerns and nitrate (NO3-N) removal. All three bioreactor types removed NO3-N, as measured by load and/or concentration reduction, showing promise for denitrifying bioreactors in the mid-Atlantic's low gradient Coastal Plain landscape. The ditch diversion bioreactor (25% NO3-N load reduction; 0.97 g NO3-N removed m(-3) d(-1)) and the sawdust denitrification wall adjacent to a ditch (> 90% NO3-N concentration reduction; 1.9-2.9 g NO3-N removed m(-3) d(-1)) had removal rates within range of the literature. The in-ditch bioreactor averaged 65% NO3-N concentration reduction, but sedimentation is expected to be one of the biggest challenges. A robust water balance is critical for future assessment of bioreactors' contribution to water quality improvement in low gradient mid-Atlantic landscapes. USDA-NRCS Conservation Innovation Grant [69-3A75-13-232] Funding was from USDA-NRCS Conservation Innovation Grant # 69-3A75-13-232. Thanks to Mr. Don Mahan (UMES) for sample collection and producers who allowed use of their land as test sites. Public domain – authored by a U.S. government employee

Engineered denitrifying environments, such as constructed wetland, biofilters or bioreactors have been increasingly recognized as an efficient means to improve water quality via facilitated denitrification. These environments function by providing a carbon substrate that serves as an energy source for microbial denitrifiers to utilize during denitrification. However, under sulfate (SO42−) reducing redox potentials, methane (CH4), hydrogen sulfide (H2S) and methyl mercury production can result. Amorphous ferric hydroxide (Fe(OH)3) has been shown to poise soil systems above the redox potential (Eh) of microbial SO42 reduction. This mineral was added in a denitrification column experiment in order to assess the ability of amorphous Fe(OH)3 to prevent or mitigate microbiological SO42− reduction. Given biochar’s ability to enhance microbial activity, this experiment was also designed to determine whether biochar enhances microbiological SO42− and NO3− − N reduction, as well as mitigate N2O emissions. Four denitrification substrate mixtures were evaluated in a laboratory column experiment: woodchips only (WC), woodchips + 15% (v/v) sand (Sand), woodchips + 15%(v/v) hardwood 26 mesh biochar (Biochar) and woodchips + 15%(v/v) hardwood 26 mesh biochar + 15 % (v/v) amorphous Fe(OH)3 (Fe). Columns were sampled for NO3− − N, N2O and SO42− at an initial interval of 3 hrs gradually decreasing to 12 h over a 120 h sampling period. Analysis of the data show that Biochar enhanced SO42− reduction (to sulfide) by 85% compared to the initial concentration while Fe largely prevented SO42− reduction, displaying a 18% reduction, which was not statistically significant. These results indicated that Fe(OH)3 addition significantly mitigated SO42− reduction. Similar to Fe, the Sand column exhibited lower microbially reduced SO42 than Biochar, at 20%, but this schema resulted in significantly higher N2O production. The WC treatment reduced SO42 by 47%, but also produced more N2O than other treatments. As expected, both treatments with biochar (Biochar and Fe) accelerated NO3− − N removal while reducing the production of N2O, and there was no discernable difference between the two, indicating that the addition of Fe(OH)3 to the system does not negatively impact performance. These results indicate that biochar, while enhancing denitrification, significantly increases microbiological reduction of SO42−, a precursor to methyl mercury production in. Thus, methyl mercury production may increase with increased biochar addition unless amorphous Fe(OH)3 is added to the substrate matrix.

We present a conceptual framework that relates agricultural best management practice (BMP) effectiveness with dominant hydrological flow paths to improve nonpoint source (NPS) pollution management. We use the framework to analyze plot, field and watershed scale published studies on BMP effectiveness to develop transferable recommendations for BMP selection and placement at the watershed scale. The framework is based on the location of the restrictive layer in the soil profile and distinguishes three hydrologic land types. Hydrologic land type A has the restrictive layer at the surface and BMPs that increase infiltration are effective. In land type B1, the surface soil has an infiltration rate greater than the prevailing precipitation intensity, but there is a shallow restrictive layer causing lateral flow and saturation excess overland flow. Few structural practices are effective for these land types, but pollutant source management plans can significantly reduce pollutant loading. Hydrologic land type B2 has deep, well-draining soils without restrictive layers that transport pollutants to groundwater via percolation. Practices that increased pollutant residence time in the mixing layer or increased plant water uptake were found as the most effective BMPs in B2 land types. Matching BMPs to the appropriate land type allows for better targeting of hydrologically sensitive areas within a watershed, and potentially more significant reductions of NPS pollutant loading.

There is an increasing need for improved process-based planning tools to assist watershed managers in the selection and placement of effective best management practices (BMPs). In this article, we present an approach, based on the Water Erosion Prediction Project model and a pesticide transport model, to identify dominant hydrologic flow paths and critical source areas for a variety of pollutant types. We use this approach to compare the relative impacts of BMPs on hydrology, erosion, sediment, and pollutant delivery within different landscapes. Specifically, we focus on using this approach to understand what factors promoted and/or hindered BMP effectiveness at three Conservation Effects Assessment Project watersheds: Paradise Creek Watershed in Idaho, Walnut Creek Watershed in Iowa, and Goodwater Creek Experimental Watershed in Missouri. These watersheds were first broken down into unique land types based on soil and topographic characteristics. We used the model to assess BMP effectiveness in each of these land types. This simple process-based modeling approach provided valuable insights that are not generally available to planners when selecting and locating BMPs and helped explain fundamental reasons why long-term improvement in water quality of these three watersheds has yet to be completely realized.

Agricultural non-point source (NPS) pollution is a source of water quality impairment, and demonstrates widely varying spatial and temporal pollution potential. Many efforts to protect water quality are based on seasonal and annual estimates of pollutant loss potential (NRCS 590 nutrient management standard, P-Index) that inadequately address the hydrologic processes driving NPS pollution. One barrier to adopting practices that address NPS pollution is a lack of tools capable of transferring information at sufficient spatial and temporal resolution so that end-users can make informed decisions. We introduce a web-based system displaying distributed hydrologic forecasts using free and open source software. The system consists of three primary components: (1) a hydrology model that provides short-term distributed forecasts; (2) a data structure capable of re-structuring large, high resolution rasters; (3) an interface employing adaptive map-viewing technology that allows end-users to interact with the data to avoid high-risk areas when planning agricultural practices.

Assessing climate change (CC) impacts on urban watersheds is difficult due to differences in model spatial and temporal scales, making prediction of hydrologic restoration a challenge. A methodology was developed using an autocalibration tool to calibrate a previously developed Storm Water Management Model (SWMM) of Difficult Run in Fairfax, Virginia. Calibration was assisted by use of multi-objective optimization. Results showed a good agreement between simulated and observed data. Simulations of CC for the 2041–2068 period were developed using dynamically downscaled North American Regional CC Assessment Program models. Washoff loads were used to simulate water quality, and a method was developed to estimate treatment performed in stormwater control measures (SCMs) to assess water quality impacts from CC. CC simulations indicated that annual runoff volume would increase by 6.5%, while total suspended solids, total nitrogen, and total phosphorus would increase by 7.6%, 7.1%, and 8.1%, respectively. The simulations also indicated that within season variability would increase by a larger percentage. Treatment practices (e.g., bioswale) that were intended to mitigate the negative effects of urban development will need to deal with additional runoff volumes and nutrient loads from CC to achieve the required water quality goals. Published version

An understanding of how dissolved mineral nutrient ions (solutes) move through pine bark substrates during the application of irrigation water is vital to better understand nutrient transport and leaching from containerized crops during an irrigation event. However, current theories on solute transport processes in soilless systems are largely based on research in mineral soils and thus do not necessarily explain solute transport in soilless substrates. A study was conducted to characterize solute transport through a 9 pine bark:1 sand (by volume) substrate by developing and analyzing breakthrough curves (BTCs). Columns filled with pine bark substrate were subjected to the application of a nutrient solution (tracer) and deionized water under saturated and unsaturated conditions. Effluent drained from the columns during these applications was collected and analyzed to determine the effluent concentration (C) of the bulk ions in solution through electrical conductivity (EC) and nitrate (NO3–), phosphate, and potassium (K+) concentrations. The BTCs were developed by plotting C relative to the concentration of the input solution (Co) (i.e., relative concentration = C/Co) as a function of the cumulative effluent volume. Solutes broke through the column earlier (i.e., with less cumulative effluent) and the transition from C/Co = 0 to 1 occurred more abruptly under unsaturated than saturated conditions. Movement of the anion, NO3–, through the substrate was observed to occur more quickly than the cation K+. Throughout the experiment, 37% of the applied K+ was retained by the pine bark. The adsorption of K+ to pine bark cation exchange sites displaced calcium (Ca2+) and magnesium (Mg2+), of which the combined equivalent charge accounted for 43.1% of the retained K+. These results demonstrate the relative ease that negatively charged fertilizer ions could move through a pine bark substrate while solution is actively flowing through substrate pores such as during irrigation events. This approach to evaluating solute transport may be used in horticultural research to better understand how mineral nutrients move through and subsequently leach from soilless substrates during irrigation. Expanding this knowledge base may lead to the refinement of production practices that improve nutrient and water use efficiency in container nurseries.

The Soil and Water Assessment Tool (SWAT) model (Arnold et al., 1998) is a popular watershed management tool. Currently, the SWAT model, actively supported by the U.S. Department of Agriculture and Texas A&M, operates only on Microsoft® Windows, which hinders modelers that use other operating systems (OS). This technical note introduces the Comprehensive R Archive Network (CRAN) distributed “SWATmodel” package which allows SWAT 2005 and 2012 to be widely distributed and run as a linear model-like function on multiple OS and processor platforms. This allows researchers anywhere in the world using virtually any OS to run SWAT. In addition to simplifying the use of SWAT across computational platforms, the SWATmodel package allows SWAT modelers to utilize the analytical capabilities, statistical libraries, modeling tools, and programming flexibility inherent to R. The software allows watershed modelers to develop a simple hydrological watershed model conceptualization of the SWAT model and to obtain a first approximation of the minimum expected results a more complicated model should deliver. As a proof of concept, we test the SWAT model by initializing and calibrating 314 U.S. Geological Survey stream gages in the Chesapeake Bay watershed and present the results.

Phosphorus (P) loss from agricultural watersheds has long been a critical water quality problem, the control of which has been the focus of considerable research and investment. Preventing P loss depends on accurately representing the hydrological and chemical processes governing P mobilization and transport. The Soil and Water Assessment Tool (SWAT) is a watershed model commonly used to predict run-off and non-point source pollution transport. SWAT simulates run-off employing either the curve number (CN) or the Green and Ampt methods, both assume infiltration-excess run-off, although shallow soils underlain by a restricting layer commonly generate saturation-excess run-off from variable source areas (VSA). In this study, we compared traditional SWAT with a re-conceptualized version, SWAT-VSA, that represents VSA hydrology, in a complex agricultural watershed in east central Pennsylvania. The objectives of this research were to provide further evidence of SWAT-VSA's integrated and distributed predictive capabilities against measured surface run-off and stream P loads and to highlight the model's ability to drive sub-field management of P. Thus, we relied on a detailed field management database to parameterize the models. SWAT and SWAT-VSA predicted discharge similarly well (daily Nash–Sutcliffe efficiencies of 0.61 and 0.66, respectively), but SWAT-VSA outperformed SWAT in predicting P export from the watershed. SWAT estimated lower P loss (0.0–0.25 kg ha−1) from agricultural fields than SWAT-VSA (0.0–1.0+ kg ha−1), which also identified critical source areas – those areas generating large run-off and P losses at the sub-field level. These results support the use of SWAT-VSA in predicting watershed-scale P losses and identifying critical source areas of P loss in landscapes with VSA hydrology. Copyright © 2014 John Wiley & Sons, Ltd.