Abstract In recent years, watershed modelers have put increasing emphasis on capturing the interaction of landscape hydrologic processes instead of focusing on streamflow at the watershed outlet alone. Understanding the hydrologic connectivity between landscape elements is important to explain the hydrologic response of a watershed to rainfall events. The Soil and Water Assessment Tool+ (SWAT+) is a new version of SWAT with improved runoff routing capabilities. Subbasins may be divided into landscape units (LSUs), e.g., upland areas and floodplains, and flow can be routed between these LSUs. We ran three scenarios representing different extents of connectivity between uplands, floodplains, and streams. In the first and second scenarios, the ratio of channelized flow from the upland to the stream and sheet flow from the upland to the floodplain was 70/30 and 30/70, respectively, for all upland/floodplain pairs. In the third scenario, the ratio was calculated for each upland/floodplain pair based on the upland/floodplain area ratio. Results indicate differences in streamflow were small, but the relative importance of flow components and upland areas and floodplains as sources of surface runoff changed. Also, the soil moisture in the floodplains was impacted. The third scenario was found to provide more realistic results than the other two. A realistic representation of connectivity in watershed models has important implications for the identification of pollution sources and sinks.
This study is a part of the Conservation Effects Assessment Project (CEAP) aimed to quantify the environmental and economic benefits of conservation practices implemented in the cultivated cropland throughout the United States. The Soil and Water Assessment Tool (SWAT) model under the Hydrologic United Modeling of the United States (HUMUS) framework was used in the study. An automated flow calibration procedure was developed and used to calibrate runoff for each 8-digit watershed (within 20% of calibration target) and the partitioning of runoff into surface and sub-surface flow components (within 10% of calibration target). Streamflow was validated at selected gauging stations along major rivers within the river basin with a target R2 of >0.6 and Nash and Sutcliffe Efficiency of >0.5. The study area covered the entire Mississippi and Atchafalaya River Basin (MARB). Based on the results obtained, our analysis pointed out multiple challenges to calibration such as: (1) availability of good quality data, (2) accounting for multiple reservoirs within a sub-watershed, (3) inadequate accounting of elevation and slopes in mountainous regions, (4) poor representation of carrying capacity of channels, (5) inadequate capturing of the irrigation return flows, (6) inadequate representation of vegetative cover, and (7) poor representation of water abstractions (both surface and groundwater). Additional outstanding challenges to large-scale hydrologic model calibration were the coarse spatial scale of soils, land cover, and topography.
The objective of this study was to evaluate soil nutrient loading and depth distributions of extractable nitrogen (N), phosphorus (P), and potassium (K) after long-term, continuous annual surface applications of anaerobically digested class B biosolids at a municipal recycling facility in central Texas. Commercial forage production fields of coastal bermudagrass (Cynodon dactylon L.) were surface applied at 0, 20, 40, or 60 Mg dry biosolids ha−1 y−1 for 8 years. Application duration was evaluated in fields treated with 20 Mg dry biosolids ha−1 y−1 for 0, 8, or 20 years. Total soil loads of extractable inorganic N and P increased linearly with application rate, but only extractable P increased with duration. Neither total load nor soil distribution of extractable K was affected by biosolid applications. Mineralization of biosolid-derived organic N and P likely contributed to elevated concentrations of nitrate throughout the soil profile (0–110 cm) and orthophosphate in surface soils (0–40 cm).
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.
Water quality simulation models such as the Soil and Water Assessment Tool (SWAT) and Agricultural Policy EXtender (APEX) are widely used in the US. These models require large amounts of spatial and tabular data to simulate the natural world. Accurate and seamless daily climatic data are critical for accurate depiction of the hydrologic cycle, yet these data are among the most difficult to obtain and process. In this paper we describe the development of a national (US) database of preprocessed climate data derived from monitoring stations applicable to USGS 12-digit watersheds. Various sources and processing methods are explored and discussed. A relatively simple method was employed to choose representative stations for each of the 83,000 12-digit watersheds in the continental US. Fully processed climate data resulting from this research were published online to facilitate other SWAT and APEX modeling efforts in the US.
Abstract Watershed simulation models such as the Soil & Water Assessment Tool ( SWAT ) can be calibrated using “hard data” such as temporal streamflow observations; however, users may find upon examination of model outputs, that the calibrated models may not reflect actual watershed behavior. Thus, it is often advantageous to use “soft data” (i.e., qualitative knowledge such as expected denitrification rates that observed time series do not typically exist) to ensure that the calibrated model is representative of the real world. The primary objective of this study is to evaluate the efficacy of coupling SWAT ‐Check (a post‐evaluation framework for SWAT outputs) and IPEAT ‐ SD (Integrated Parameter Estimation and Uncertainty Analysis Tool‐Soft & hard Data evaluation) to constrain the bounds of soft data during SWAT auto‐calibration. IPEAT ‐ SD integrates 59 soft data variables to ensure SWAT does not violate physical processes known to occur in watersheds. IPEAT ‐ SD was evaluated for two case studies where soft data such as denitrification rate, nitrate attributed from subsurface flow to total discharge ratio, and total sediment loading were used to conduct model calibration. Results indicated that SWAT model outputs may not satisfy reasonable soft data responses without providing pre‐defined bounds. IPEAT ‐ SD provides an efficient and rigorous framework for users to conduct future studies while considering both soft data and traditional hard information measures in watershed modeling.
