Development of Sediment and Nutrient Export Coefficients for U.S. Ecoregions

Water quality impairment due to excessive nutrients and sediment is a major problem in the United States (U.S.). An important step in the mitigation of impairment in any given water body is determination of pollutant sources and amount. The sheer number of impaired waters and limited resources makes simplistic load estimation methods such as export coefficient (EC) methods attractive. Unfortunately ECs are typically based on small watershed monitoring data, which are very limited and/or often based on data collected from distant watersheds with drastically different conditions. In this research, we seek to improve the accuracy of these nutrient export estimation methods by developing a national database of localized EC for each ecoregion in the U.S. A stochastic sampling methodology loosely based on the Monte‐Carlo technique was used to construct a database of 45 million Soil and Water Assessment Tool (SWAT) simulations. These simulations consider a variety of climate, topography, soils, weather, land use, management, and conservation implementation conditions. SWAT model simulations were successfully validated with edge‐of‐field monitoring data. Simulated nutrient ECs compared favorably with previously published studies. These ECs may be used to rapidly estimate nutrient loading for any small catchment in the U.S. provided the location, area, and land‐use distribution are known.     

INFLUENCES OF SOIL DATASET RESOLUTION ON HYDROLOGIC MODELING1

ABSTRACT: Soils represent a fundamental abiotic parameter in defining the characteristics of an ecosystem. The U.S. Department of Agriculture (USDA) Natural Resources Conservation Service (NRCS) produces the most detailed digital spatial soil datasets that are publicly available. The Soil Survey Geographic (SSURGO) database contains basic attributes for the continuous coverage of soils across the United States. In its standard format, the SSURGO database is incompatible for use within the ArcView Soil and Water Assessment Tool (SWAT). A modified version of the State Soil and Geographic (STATSGO) database is the template soils dataset used by ArcView SWAT. This paper presents the methodology and development of a SSURGO database preprocessor extension for the ArcView SWAT model. A case study for the Upper Sabinal River Watershed near Uvalde, Texas, is given. Results indicate that hydro‐logic output parameter differences occur when comparing the STATSGO and SSURGO database information in the ArcView SWAT model under identical modeling conditions. Specifically, the SSURGO model produced a greater daily mean water yield with evapotranspiration and surface runoff being found consistently lower across the watershed. The most likely causes assigned to this phenomenon were higher percolation and resulting ground water return flow values due to significantly larger saturated hydraulic conductivity values associated with the SSURGO 2.x database.     

Ammonia assessment from agriculture: U.S. status and needs

Recent studies suggest that human activities accelerate the production of reactive nitrogen on a global scale. Increased nitrogen emissions may lead to environmental impacts including photochemical air pollution, reduced visibility, changes in biodiversity, and stratospheric ozone depletion. In the last 50 yr, emissions of ammonia (NH3), which is the most abundant form of reduced reactive nitrogen in the atmosphere, have significantly increased as a result of intensive agricultural management and greater livestock production in many developed countries. These agricultural production practices are increasingly subject to governmental regulations intended to protect air resources. It is therefore important that an accurate and robust agricultural emission factors database exist to provide valid scientific support of these regulations. This paper highlights some of the recent work that was presented at the 2006 Workshop on Agricultural Air Quality in Washington, D.C. regarding NH3 emissions estimates and emission factors from agricultural sources in the U.S. and Europe. In addition, several best management practices are explored as the scientific community attempts to maximize the beneficial use of reactive nitrogen while simultaneously minimizing negative environmental impacts.     

Geographic information systems: a tool for strategic ground water quality management

Geographically‐related information is needed for several elements of an integrated ground water quality management programme, including ground water monitoring planning, prioritization of pollution sources, usage of permits and inspections for source control, and planning and completion of remedial actions. Geographic Information Systems (GISs) can be used to support these elements along with delineating wellhead protection areas (WHPAs), prioritizing existing contaminant sources and evaluating proposed changes in land usage in such areas. Eight case studies of the use of GISs in wellhead protection programmes are summarized, including examples from Rhode Island, Mississippi, New Jersey, New York, Pennsylvania, Kansas, Massachusetts and Texas. Six additional examples are mentioned relative to the use of GISs for evaluating ground water pollution potential, facilitating data analysis for environmental restoration of a large area with numerous waste sites, evaluating trends in ground water nitrate contamination, establishing a national database for ground water vulnerability to agricultural chemicals, simulating water table altitudes from stream and drainage basin locations, and selecting radioactive waste dump sites. The applicability of GISs and their associated advantages in wellhead protection and other ground water management studies are demonstrated via the case studies. The GIS technology provides a unique opportunity for analysing and visualizing spatial data. Contaminant and source prioritization within WHPAs is needed for both extant conditions and in the evaluation of proposed land use changes. The coupling of a GIS with contaminant/source prioritization would provide a strategic tool which could be used to plan targeted ground water monitoring programmes, to identify appropriate management or mitigation measures, minimize introduction of contaminants from existing sources into the subsurface environment, and to evaluate the potential of proposed land use activities for causing ground water contamination. GISs can be useful in providing current information for policy makers, planners and managers engaged in ground water quality decision making.     

SWATmodel: A Multi‐Operating System,Multi‐Platform SWAT Model Package in R

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.     

Regional Blue and Green Water Balances and Use by Selected Crops in the U.S.

The availability of freshwater is a prerequisite for municipal development and agricultural production, especially in the arid and semiarid portions of the western United States (U.S.). Agriculture is the leading user of water in the U.S. Agricultural water use can be partitioned into green (derived from rainfall) and blue water (irrigation). Blue water can be further subdivided by source. In this research, we develop a hydrologic balance by 8‐Digit Hydrologic Unit Code using a combination of Soil and Water Assessment Tool simulations and available human water use estimates. These data are used to partition agricultural groundwater usage by sustainability and surface water usage by local source or importation. These predictions coupled with reported agricultural yield data are used to predict the virtual water contained in each ton of corn, wheat, sorghum, and soybeans produced and its source. We estimate that these four crops consume 480 km³ of green water annually and 23 km³ of blue water, 12 km³ of which is from groundwater withdrawal. Regional trends in blue water use from groundwater depletion highlight heavy usage in the High Plains, and small pockets throughout the western U.S. This information is presented to inform water resources debate by estimating the cost of agricultural production in terms of water regionally. This research illustrates the variable water content of the crops we consume and export, and the source of that water.     

EFFECTS OF STATSGO AND SSURGO AS INPUTS ON SWAT MODEL'S SNOWMELT SIMULATION1

ABSTRACT: Soil data comprise a basic input of SWAT (Soil and Water Assessment Tool) for a watershed application. For watersheds where site specific soil data are unavailable, the two U.S. Department of Agriculture (USDA) soil databases, the State Soil Geographic (STATSGO) and Soil Survey Geographic (SSURGO) databases, may be the best alternatives. Although it has been noted that SWAT models using the STATSGO and SSURGO data may give different simulation results for water, sediment, and agricultural chemical yields, information is scarce on the effects of using these two databases in predicting streamflows that are predominantly generated from melting snow in spring. The objective of this study was to assess the effects of using STATSGO versus SSURGO as an input for the SWAT model's simulation of the streamflows in the upper 45 percent of the Elm River watershed in eastern North Dakota. Designating the model as SWAT‐STATSGO when the STATSGO data were used and SWAT‐SSURGO when the SSURGO data were used, SWAT‐STATSGO and SWAT‐SSURGO were separately calibrated and validated using the observed daily streamflows. The results indicated that SWAT‐SSURGO provided an overall better prediction of the discharges than SWAT‐STATSGO, although both did a good and comparable job of predicting the high streamflows. However, SWAT‐STATSGO predicted the low streamflows more accurately and had a slightly better performance during the validation period. In addition, the discrepancies between the discharges predicted by these two SWAT models tended to be larger at upstream locations than at those farther downstream within the study area.     

Integration of SWAT and HSPF for Simulation of Sediment Sources in Legacy Sediment‐Impacted Agricultural Watersheds

A total maximum daily load for the Chesapeake Bay requires reduction in pollutant load from sources within the Bay watersheds. The Conestoga River watershed has been identified as a major source of sediment load to the Bay. Upland loads of sediment from agriculture are a concern; however, a large proportion of the sediment load in the Conestoga River has been linked to scour of legacy sediment associated with historic millpond sites. Clarifying this distinction and identifying specific segments associated with upland vs. channel sources has important implications for future management. In order to address this important question, we combined the strengths of two widely accepted watershed management models — Soil and Water Assessment Tool (SWAT) for upland agricultural processes, and Hydrologic Simulation Program FORTRAN (HSPF) for instream fate and transport — to create a novel linked modeling system to predict sediment loading from critical sources in the watershed including upland and channel sources, and to aid in targeted implementation of management practices. The model indicates approximately 66% of the total sediment load is derived from instream sources, in agreement with other studies in the region and can be used to support identification of these channel source segments vs. upland source segments, further improving targeted management. The innovated linked SWAT‐HSPF model implemented in this study is useful for other watersheds where both upland agriculture and instream processes are important sources of sediment load.     

增江流域非点源污染模拟研究

构建增江流域非点源污染数据库,包括DEM、土地利用,土壤类型,气象数据等,应用分布式流域水文模型SWAT(Soil andWater Assessment Tool,swat 2009版)对增江流域的非点源污染进行模拟。模型运行阶段为2000-2003年,分别应用2000-2001年和2002-2003年的实测月均流量及硝酸盐氮监测数据对模型的参数率定和验证,采用决定系数R2和Nash-Suttcliffe系数对模拟结果进行评定。其中水文模拟的R2均>0.9,Nash-Suttcliffe模型效率系数均>0.8;硝酸盐氮模拟的R2均>0.7,Nash-Suttcliffe模型效率系数均>0.6,表明SWAT模型在增江流域具有较好的适用性。     

Development of a Quantitative Pasture Phosphorus Management Tool Using the SWAT Model1

Abstract: Assessment tools to evaluate phosphorus loss from agricultural lands allow conservation planners to evaluate the impact of management decisions on water quality. Available tools to predict phosphorus loss from agricultural fields are either: (1) qualitative indices with limited applicability to address offsite water quality standards, or (2) models which are prohibitively complex for application by most conservation planners. The purpose of this research was to develop a simple interface for a comprehensive hydrologic/water quality model to allow its usage by farmers and conservation planners. The Pasture Phosphorus Management (PPM) Calculator was developed to predict average annual phosphorus (P) losses from pastures under a variety of field conditions and management options. PPM Calculator is a vastly simplified interface for the Soil and Water Assessment Tool (SWAT) model that requires no knowledge of SWAT by the user. PPM Calculator was validated using 33 months of data on four pasture fields in northwestern Arkansas. This tool has been extensively applied in the Lake Eucha/Spavinaw Basin in northeastern Oklahoma and northwestern Arkansas. PPM Calculator allows conservation planners to take advantage of the predictive capacity of a comprehensive hydrologic water quality model typically reserved for use by hydrologists and engineers. This research demonstrates the applicability of existing water quality models in the development of user friendly P management tools.     

SWATMOD‐Prep: Graphical User Interface for Preparing Coupled SWAT‐MODFLOW Simulations

This article presents SWATMOD‐Prep, a graphical user interface that couples a SWAT watershed model with a MODFLOW groundwater flow model. The interface is based on a recently published SWAT‐MODFLOW code that couples the models via mapping schemes. The spatial layout of SWATMOD‐Prep guides the user through the process of importing shape files (sub‐basins, hydrologic response units [HRUs], river network) from an existing SWAT model, creating a grid, performing necessary geo‐processing operations to link the models, writing out SWAT‐MODFLOW files, and running the simulation. The option of creating a new single‐layer MODFLOW model for near‐surface alluvial aquifers is available, with the user prompted to provide groundwater surface elevation (through a digital elevation model), aquifer thickness, and necessary aquifer parameter values. The option of simulating nitrate transport in the aquifer also is available, using the reactive transport model RT3D. The interface is in the public domain. It is programmed in Python, with various software packages used for geo‐processing operations (e.g., selection, intersection of rasters) and inputting/outputting data, and is written for Windows. The use of SWATMOD‐Prep is demonstrated for the Little River Experimental Watershed, Georgia. SWATMOD‐Prep and SWAT‐MODFLOW executables are available with an accompanying user's manual at: http://swat.tamu.edu/software/swat-modflow/ . The user's manual also accompanies this article as Supporting Information.     

MANAGEMENT AND ANALYSIS OF WATER-USE DATA USING A GEOGRAPHIC INFORMATION SYSTEM1

ASSTRACT: As part of its mission, the U.S. Geological Survey conducts water-resources research. Site-specific and aggregate water-use data are used in the Survey's National Water-Use Information Program and in various hydrologic investigations. Both types of activities have specific requirements in terms of water-use data access, analysis, and display. In Kansas, the Survey obtains water-use information from several sources. Trpically, this information is in a format that is not readily usable by the Survey. Geographic information system (GIS) technology is being used to restructure the available water-use data into a format that allows users to readily access and summarize site-specific water-use data by source (i.e., surface or ground water), type of use, and user-defined area.     

Data integration across borders: a case study of the Abbotsford‐Sumas aquifer (British Columbia/Washington State)1

Abstract: Integrating spatial datasets from diverse sources is essential for cross‐border environmental investigations and decision‐making. This is a little investigated topic that has profound implications for the availability and reliability of spatial data. At present, ground‐water hydrostratigraphic models exist for both the Canadian or for the United States (U.S.) portion of the aquifer but few are integrated across the border. In this paper, we describe the challenges of integrating multiple source, large datasets for development of a ground‐water hydrostratigraphic model for the Abbotsford‐Sumas Aquifer. Growing concerns in Canada regarding excessive withdrawal south of the border and in the U.S. regarding nitrate contamination originating north of the border make this particular aquifer one of international interest. While much emphasis in GIScience is on theoretical solutions to data integration, such as current ontology research, this study addresses pragmatic ways of integrating data across borders. Numerous interoperability challenges including the availability of data, metadata, data formats and quality, database structure, semantics, policies, and cooperation are identified as inhibitors of data integration for cross‐border studies. The final section of the paper outlines two possible solutions for standardizing classification schemes for ground‐water models – once data heterogeneity has been addressed.     

Integrated Measures of Anthropogenic Stress in the U.S. Great Lakes Basin

Integrated, quantitative expressions of anthropogenic stress over large geographic regions can be valuable tools in environmental research and management. Despite the fundamental appeal of a regional approach, development of regional stress measures remains one of the most important current challenges in environmental science. Using publicly available, pre-existing spatial datasets, we developed a geographic information system database of 86 variables related to five classes of anthropogenic stress in the U.S. Great Lakes basin: agriculture, atmospheric deposition, human population, land cover, and point source pollution. The original variables were quantified by a variety of data types over a broad range of spatial and classification resolutions. We summarized the original data for 762 watershed-based units that comprise the U.S. portion of the basin and then used principal components analysis to develop overall stress measures within each stress category. We developed a cumulative stress index by combining the first principal component from each of the five stress categories. Maps of the stress measures illustrate strong spatial patterns across the basin, with the greatest amount of stress occurring on the western shore of Lake Michigan, southwest Lake Erie, and southeastern Lake Ontario. We found strong relationships between the stress measures and characteristics of bird communities, fish communities, and water chemistry measurements from the coastal region. The stress measures are taken to represent the major threats to coastal ecosystems in the U.S. Great Lakes. Such regional-scale efforts are critical for understanding relationships between human disturbance and ecosystem response, and can be used to guide environmental decision-making at both regional and local scales.     

Country-scale phosphorus balancing as a base for resources conservation

In order to effectively conserve the non-renewable resource phosphorus (P), flows and stocks of P must be known at national, regional and global scales. P is a key non-renewable resource because its use as fertilizer cannot be substituted posing a constraint on the global food production in the long-term. This paper presents a methodology to establish country-wide P balances that emphasises resource use. We develop a material flow analysis (MFA) model that comprises all relevant flows and stocks of P in five subsystems, seven processes and 36 material flows. For quantification, statistical data from economic and agricultural sources as well as available information about P partitioning in natural and anthropogenic processes are used. Special attention is paid to data gaps and uncertainties. The model was tested in two case studies on P management in Turkey and Austria. MFA appears to be a tool well suited for establishing country-wide P balances, provided that national statistics are well-structured and accessible. If a common approach is used for modelling P-flows and stocks, regional and national balances can be compared and linked towards larger scale P balances for an improved management of the resource.     

Conceptual Framework of Connectivity for a National Agroecosystem Model Based on Transport Processes and Management Practices

There is a critical need for a national agroecosystem model for conservation policy and environmental planning, driven by issues including harmful algal blooms, water scarcity, flooding, and other weather‐related extremes. In this study, we illustrate the feasibility of a national agroecosystem model that will downscale processes to individual fields and first‐order channels. We propose to conceptually divide the conterminous United States (U.S.) into process domains as a framework for simulating processes and management at relevant scales. Specifically, we are proposing five domains: field (1–50 ha), transition (0.2–2.0 km²), headwater (1–15 km²), tributaries (15–150 km²), and main river (>150 km²). The proposed conceptual framework hydrologically connects fields across the U.S. using the National Hydrography Dataset (NHDPlus version 2). Parameterizing the Soil and Water Assessment Tool for the national agroecosystem model resulted in 4,880,000 agricultural fields, 2,250,000 non‐agricultural hydrologic response units, and 7,130,000 transition, 1,610,000 headwater, 591,000 tributary, and 432,400 main channels. Application of this framework was shown for Hydrologic Unit Code 07120002 in central Illinois and Indiana to demonstrate the feasibility of the approach using data that is readily available across the U.S. The new connectivity framework has the potential to dramatically improve national conservation and environmental assessments performed by U.S. Department of Agriculture and U.S. Environmental Protection Agency.     

Expansion of the MANAGE Database with Forest and Drainage Studies

The “Measured Annual Nutrient loads from AGricultural Environments” (MANAGE) database was published in 2006 to expand an early 1980s compilation of nutrient export (load) data from cultivated and pasture/range land at the field or farm scale. Then in 2008, MANAGE was updated with 15 additional studies, and nitrogen (N) and phosphorus (P) concentrations in runoff were added. Since then, MANAGE has undergone significant expansion adding N and P water quality along with relevant management and site characteristic data from: (1) 30 runoff studies from forested land uses, (2) 91 drainage water quality studies from drained land, and (3) 12 additional runoff studies from cultivated and pasture/range land uses. In this expansion, an application timing category was added to the existing fertilizer data categories (rate, placement, formulation) to facilitate analysis of 4R Nutrient Stewardship, which emphasizes right fertilizer source, rate, time, and place. In addition, crop yield and N and P uptake data were added, although this information was only available for 21 and 7% of studies, respectively. Inclusion of these additional data from cultivated, pasture/range, and forest land uses as well as artificially drained agricultural land should facilitate expanded spatial analyses and improved understanding of regional differences, management practice effectiveness, and impacts of land use conversions and management techniques. The current version is available at www.ars.usda.gov/spa/manage-nutrient .     

A Review of the United States' Past and Projected Water Use

Good information and data on water demands are needed to perform good analyses, yet collecting and compiling spatially and temporally consistent water demand data are challenges. The objective of our work was to understand the limitations associated with water‐use estimates and projections. We performed a comprehensive literature review of national and regional United States (U.S.) water‐use estimates and projections. We explored trends in past regional projections of freshwater withdrawals and compared these values to regional estimates of freshwater withdrawals made by the U.S. Geological Survey. Our results suggest a suite of limitations exist that have the potential for influencing analyses aiming to extract explanatory variables from the data or using the data to make projections and forecasts. As we explored regional projections, we paid special attention to the two largest water demand‐side sectors — thermoelectric energy and irrigation — and found thermoelectric projections are more spread out than irrigation projections. All data related to water use have limitations, and there is no alternative to making the best use that we can of the available data; our article provides a comprehensive review of these limitations so that water managers can be more informed.