Abstract 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.
ABSTRACT: Measured field scale data are increasingly used to guide policy and management decisions based on comparative pollutant load information from various land management alternatives. The primary objective of this study was to compile measured annual nitrogen (N) and phosphorus (P) load data representing field scale transport from agricultural land uses. This effort expanded previous work that established an initial nutrient export coefficient dataset. Only measured annual N and P load data published in scientific peer‐reviewed studies were included in the present compilation. Additional criteria for inclusion were: spatial scale (field scale or farm scale, minimum 0.009 ha); land use (homogeneous, either cultivated agriculture or pasture/rangeland/hay); natural rainfall (not rainfall simulation); and temporal scale (minimum one year). Annual N and P load data were obtained from 40 publications, resulting in a 163‐record database with more than 1,100 watershed years of data. Basic descriptive statistics in relation to N and P loads were tabulated for tillage management, conservation practices, fertilizer application, soil texture, watershed size, and land use (crop type). The resulting Measured Annual Nutrient loads from A Circumlittoral Environments (MANAGE) database provides readily accessible, easily queried watershed characteristic and nutrient load data and establishes a platform suitable for input of additional project specific data.
A probabilistic approach is presented to assess the performance validity of the empirical Curve Number (CN) and physically-based Green and Ampt (G&A) rainfall-runoff methods in the SWAT model. Specifically, the effects of modeling uncertainties on characterization of the hydrologic budgets and streamflow regimes at various spatial scales and upstream land use conditions are investigated. A Bayesian total uncertainty assessment framework, which explicitly accounts for uncertainties from model parameters, inputs, structure, and measurement data, was employed to explore uncertainties in streamflow simulation using SWAT with different rainfall-runoff methods in a mixed-land use watershed. While the models were trained for streamflow estimation only at the watershed outlet, the performances of the models were compared at different stream locations within the watershed. At the watershed outlet, the CN method had a slightly better, but not significant, performance in terms of streamflow error statistics. Similar results were obtained for the predominantly forested and agricultural tributaries. However, in tributaries with higher percentage of developed land, G&A outperformed the CN method in simulating streamflow based on various performance metrics. In general, the 95% prediction intervals from the models with G&A method covered a higher percentage of observed streamflow especially during the high flow events. However, they were approximately 20–45% wider than the corresponding 95% prediction intervals from the CN methods. Using 95% prediction interval for estimated flow duration curves, results indicated that the models with CN methods underestimated high flow events especially in tributaries with highly developed land use. However, the CN methods generated higher water yields to streams than the G&A method. The results of this study have important implications for the selection and application of appropriate rainfall-runoff methods within complex distributed hydrologic models particularly when simulating hydrologic responses in mixed-land use watersheds. In the present study, while CN and G&A methods in the SWAT model performed similarly at the outlet of a mixed-land use watershed, G&A captured the internal processes more realistically. The subsequent effects on the representation of internal hydrological processes and budgets are discussed.
Soils are the keystone of healthy and vibrant ecosystems, providing physical, chemical, and biological substrates and functions necessary to support life. In particular, it's the extensive and elaborate matrix of soil microorganisms and other life forms that contributes to soil health and utility.
But soils are under constant threat from heavy use, changing climate, and in some cases poor management (1, 2). In view of soil’s key role and threatened status, we believe that there is a need for the scientific community to undertake coordinated research and development efforts that will lead to a unique asset: a National Living Soil Repository (Fig. 1).
Fig. 1.
A National Living Soil Repository would store agricultural cryogenic and air-dried soil samples, analyze samples for microbial community composition, assess samples for microbial viability, and serve as a potential source of living organisms for various agricultural ecosystem services. Image courtesy of Jennifer Moore-Kucera (USDA Natural Resources Conservation Service) and Daniel Manter (USDA Agricultural Research Service).
Already local and national soil archives have been shown to be of great utility for studying, analyzing, and documenting long-term environmental and ecological trends. For example, the historical soil archive at Hubbard Brook helped researchers discover the link between fossil fuels and acidification of rain and snow (3); the Rothamsted Sample Archive in the United Kingdom has shown a steady increase in dioxins during the last century (4). And yet, a soil repository/archive designed to preserve native biological diversity does not currently exist.
Such an archive would provide the ability to acquire data on the current biological (e.g., soil health) state of soils around the country across soil types, cropping systems, and ecosystems and over time. Further, by maintaining soil archives and a catalog of their microbial communities, we will gain a better understanding of how soil organisms are distributed …
[↵][1]1To whom correspondence should be addressed. Email: Jorge.Delgado{at}ars.usda.gov.
[1]: #xref-corresp-1-1
Abstract 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 km 3 of green water annually and 23 km 3 of blue water, 12 km 3 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.
Abstract The “Measured Annual Nutrient loads from AG ricultural 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 .
