Designing regionally resilient cropping systems requires understanding the interactions of multiple factors important to crop production and soil erosion-susceptibility both spatially and temporally. The objectives of this study were to elucidate the relationships between regional hillslope soil loss and Daily Erosion Project (DEP) tillage management scenarios and assess the sensitivity of hillslope soil loss to different field residue covers associated with these tillage scenarios. DEP simulations used the same tillage management scenario for the entire domain. Scenarios consisted of the six tillage categories within DEP (T1 to T6), with increasing values implying more intense tillage (less residue). Results indicated a direct relationship between tillage intensity (less residue cover) and higher erosion rates at different spatial scales (statewide, major land resource areas [MLRA], and DEP watersheds). The study also revealed a significant spatial variability across the DEP domain, identifying erosion-prone regions along the Missouri River and eastern Iowa and Minnesota, surpassing in many scenarios 17.9 Mg/ha/yr. Other areas showed no response associated with increasing tillage intensity, demonstrating how local factors such as topography, soil, land use, and climate and their interaction could also affect erosion. The sensitivity analysis uncovered a very high erosion sensitivity to small changes in residue cover, especially in hilly watersheds, while transitioning from no-till to tilled systems. Overall, this regional study underscores the importance of residue cover management and minimal soil disturbance in reducing hillslope erosion and illustrates the critical role landscape and management interactions play in spatially variable regional hill slope soil erosion rates.

To appropriately place soil conservation measures, locating the most vulnerable areas prone to soil erosion is required. Available tools to locate vulnerable areas are tedious to use and time-consuming, and most water erosion estimations are based on empirical models with limited applicability. The present study takes advantage of two large-scale soil and water conservation tools available for the Midwest U.S.: the Daily Erosion Project (DEP) and the Agricultural Conservation Planning Framework (ACPF).In this study, we will showcase a recently developed large scale modeling approach implemented in the Midwest U.S. that currently downscales DEP from Hydrologic Unit Code (HUC) 12 (~90 km2) average estimation of hillslope runoff and soil loss into a much finer resolution, a field and pixel scale. The DEP uses the Water Erosion Prediction Project (WEPP) and simulates hundreds of thousands of hillslopes across the Midwest, covering the wide range of factors including topography, climate, soils and land use and management.This presentation will introduce the newly developed quantitative soil erosion assessment tool (named OFEtool - Overland Flow Element tool) that uses geographic information systems (GIS) and a physical-based model with real climate data (DEP). The OFEtool analyzes a watershed and groups areas with similar attributes, such as slope, soil type, land use, and management practices (information provided by the ACPF). Following watershed analysis, the tool uses DEP simulations to obtain average hillslope soil erosion or deposition rates for these grouped characteristics. Finally, it associates and assigns these rates to the respective areas within the watershed.The current version of the tool is used by the ACPF to locate the most vulnerable fields across the watershed for conservation planning scenarios to prioritize interventions in fields and specific areas with the highest erosion rates. The applicability of the tool will be shown for the state of Iowa (approximately 145,746 square kilometers). Preliminary results corroborate spatial variability of soil erosion within watersheds and Major Land Resource Areas (MLRA). The presentation will also provide new insights into the main factors governing soil erosion in Iowa (climate, soils, topography, land use and management). ReferencesGelder, B., Sklenar, T., James, D., Herzmann, D., Cruse, R., Gesch, K., & Laflen, J. (2018). The Daily Erosion Project – daily estimates of water runoff, soil detachment, and erosion. Earth Surface Processes and Landforms, 43(5), 1105–1117. https://doi.org/10.1002/esp.4286Daily Erosion Project. (n.d.). Retrieved January 9, 2024, from https://www.dailyerosion.org/Tomer, M. D., Porter, S. A., James, D. E., Boomer, K. M. B., Kostel, J. A., & McLellan, E. (2013). Combining precision conservation technologies into a flexible framework to facilitate agricultural watershed planning. Journal of Soil and Water Conservation, 68(5). https://doi.org/10.2489/jswc.68.5.113Agricultural Conservation Planning Framework. (n.d.). Retrieved January 9, 2024, from https://acpf4watersheds.org/

Agriculture continues to be one of the most important sources of nonpoint source pollution to surface water bodies. Consequently, it is critical to identify and prioritize high-contributing agricultural fields and sub-field areas for reducing soil erosion and sediment delivery by implementing best management practices (BMPs). Current erosion risk assessment tools are either complex modelling approaches or rely on a simplified reality and generalized assumption. The Daily Erosion Project (DEP) is a daily estimator of precipitation, hillslope runoff, detachment and soil loss covering similar to 630 000 km(2) across the Midwest United States. These estimations are reported daily and publicly at the hydrologic unit code 12 watershed resolution (approximately 100 km(2)). The main objective of this study was to develop a new tool (named Overland Flow Element tool [OFEtool]) that downscales the watershed scale of DEP to estimate average runoff and soil displacement within a field, helping to locate erosive hotspots at multiple scales. We also demonstrated the applicability of OFEtool in Bennet Creek-Sugar Creek in East Central Iowa (the United States) and compared its results with other erosion vulnerability tools such as the Soil Vulnerability Index for Cultivated Cropland (SVI-cc) and a GIS-based Revised Universal Soil Loss Equation (RUSLE). The same erosion risk classes and ranges (low, moderate, moderately high and high) were implemented for all indexes. The advantages of the OFEtool compared to the SVI-cc and RUSLE models are related to the use of an event-based modelling approach, such as DEP, with updated soil loss estimates based on temporal changes in climate inputs and land use and management. The OFEtool uses a 6-year time frame and a more up-to-date field inputs, while RUSLE provides a long-term average and SVI-cc only considers soil and topographical factors for risk assessment. Results indicated that the spatial distribution of vulnerable fields (and parts of the fields) followed a similar trend as other tested indices. However, the risk level associated with each tool differed (SVI-cc > RUSLE > OFEtool). These differences could arise from intrinsic disparities within the tools (inputs, timing, processes considered, assumptions). While currently limited to the DEP domain and relying on the DEP random sampling scheme, further research is warranted to validate the tool at other Midwest locations and ensure it captures the watershed's landscape variability (combination of terrain, soil, land use and management) required to identifying critical erosion hotspots.

The Daily Erosion Project (DEP) enables daily estimation of sheet and rill erosion across a large area in the US Midwest. However, DEP currently omits a potentially important source of erosion in the region by not considering wind erosion. In the work outlined here, we incorporated wind erosion into DEP by linking it with the Single-event Wind Erosion Evaluation Program (SWEEP). In this new integrated model known as DEP-SWEEP (Figure 1), daily predictions are made for rill, sheet, and wind erosion from farm fields in the US Midwest, given differing land-cover, weather, and land management conditions. Daily wind erosion estimates can be generated for one year‘s worth of climate and management conditions in DEP-SWEEP. Some parameters from DEP are used directly as input parameters into SWEEP (e.g. soil textural parameters), however others (including soil surface and aggregate parameters) needed to be calculated. Equations used in these calculations were based primarily on the theoretical processes of the NRCS‘s Wind Erosion Prediction (WEPS) model. Other important calculations added to the DEP-SWEEP model allow for estimation of residue and growing plant and plant biomass cover. Initially, DEP-SWEEP was run for three Hydrologic Unit Code 12 (HUC12) watershed test sites located in Nebraska and Minnesota on a daily basis for the year 2021. Soil types for these HUC12s included silty-loam and sandy-loam soils, and all HUC12s were planted in a corn-soybean crop rotation, with high-mulch tillage, and no wind barriers present. Results of these simulations showed realistic calculation of some soil surface parameters; however, wind erosion was only generated in one HUC 12 on one day. This result may not be representative of actual conditions given poor calculation of some key soil aggregate parameters. Additional planned work on the DEP-SWEEP model includes updating algorithms to account for climate and management practices for soil aggregate parameters, parameterization to allow for simulation of more field management practices, and simulation of more HUC12s in the US Midwest region.

The Daily Erosion Project (DEP) is a daily sheet and rill hillslope erosion estimator using radar precipitation, the Water Erosion Prediction Project (WEPP) model, field level crop rotation and management from the Agricultural Conservation Planning Framework (ACPF), the gridded Soil Survey Geographic Database (SSURGO), and LiDAR derived 3-m Digital Elevation Models (DEM) as inputs for erosion estimates on agricultural flowpaths across the Midwestern United States. There is interest in expanding DEP at many locations around the globe, but current DEP data requirements are greater than is readily available. For this paper, our goal was to analyze how distinct precipitation datasets will impact runoff and soil detachment estimates. Two precipitation scenarios were defined by editing WEPP climate files and replacing precipitation data with the sources: Integrated Multi-satellitE Retrievals for GPM (IMERG) and the Automated Surface Observing Systems (ASOS). Thirty Hydrologic Unit Codes 12 (HUC12) watersheds within Iowa were chosen with three HUC12s sampled per the ten Major Land Resource Areas found in the state. Precipitation accumulation differences showed an average precipitation decrease from 943 mm at the DEP baseline, to 883 mm (6% decrease), and 841 mm (11% decrease) for ASOS and IMERG, respectively, across all modeled hillslopes within each HUC12. Runoff decreased from an average of 144 mm at the DEP baseline to 107 mm for IMERG, totaling a 26% decrease. Furthermore, ASOS runoff estimates showed an average runoff of 120 mm, totaling a 17% decrease. Soil detachment revealed average values of 8.5 t ha-1 from the DEP baseline, to 5.5 t ha-1 from ASOS, and 3.3 t ha-1 from IMERG, decreases of 35% and 61%, respectively. Testing precipitation products with coarser spatial and temporal resolution substantially impacted runoff and soil detachment estimates due to decreases in rainfall intensity. IMERG results showed greater percentage difference estimates when compared to ASOS.

Evaluation of soil moisture information should consider when key crop development stages occur and, ultimately, when farmers must make decisions based on soil moisture status. Therefore, we assessed the performance of three microwave satellites (SMAP, SMOS, and METOP/ASCAT) and three reanalysis models (MERRA-2, NARR, and WEPP) in the U.S. Corn Belt in the context of agricultural management. Thermal time and crop progress reports from the USDA-NASS defined critical transition periods of crop growth and management decisions for the validation process. Contrary to calendar timelines like annual segments, these key events separate the year into five irregular segments. Measurements at the South Fork Core Validation Site from 2016 to 2020 show that the two passive microwave satellites are dry compared to in-situ observations, but the active satellite and reanalysis model products were almost always wetter. Overall, most products perform the best during the pre-planting and post-harvest segments when no crops are present and worst during the active management phase when crops are starting to grow.

Changing precipitation patterns associated with a changing climate result in altered rainfall erosivities that have direct impacts on soil erosion rates. Informed by observed climate change impacts on precipitation, three scenarios were run with the WEPP soil erosion model to assess changes in soil erosion estimates relative to the Daily Erosion Project (DEP) 2007-2020 baseline results over Iowa, USA. Unlike other studies which obtained results via statistical adjustment through a weather generator, these results were obtained via explicit adjustment of two-minute interval precipitation intensities. These scenarios found more than a 2% increase in average annual soil loss compared to the baseline for every 1% increase in precipitation intensity (Figure 1), a 0.7% increase in soil loss for every one day shift earlier in precipitation timing, and an 8% increase in soil loss for every additional 25 mm h-1 storm added during the spring season. Evidence suggests climate change impacts on precipitation characteristics affecting soil erosion must be considered a priority in planning conservation practices to maintain and/or improve soil health.

This paper describes a multi-site and multi-decadal dataset of artificially drained agricultural fields in seven Midwest states and North Carolina, USA. Thirty-nine research sites provided data on three conservation practices for cropland with subsurface tile drainage: saturated buffers, controlled drainage, and drainage water recycling. These practices utilize vegetation and/or infrastructure to minimize off-site nutrient losses and retain water in the landscape. A total of 219 variables are reported, including 90 field measurement variables and 129 management operations and metadata. Key measurements include subsurface drain flow (206 site-years), nitrate-N load (154 site-years) and other water quality metrics, as well as agronomic, soil, climate, farm management and metadata records. Data are published at the USDA National Agricultural Library Ag Data Commons repository and are also available through an interactive website at Iowa State University. These multi-disciplinary data have large reuse potential by the scientific community as well as for design of drainage systems and implementation in the US and globally.

Predicting ephemeral gully (EG) location is essential for erosion modeling because it helps confine portions of the hillslope segment above locations that gully and channel soil loss processes dominate. In the Water Erosion Prediction Project (WEPP), the prediction of EG occurrence location influences the model results by shorting or expanding the flow path, which the hillslope erosion modeling relies on. This research aimed to analyze the sensitivity of EG locations prediction accuracy on WEPP model output within the framework of the Daily Erosion Project (DEP) at the regional scale. DEP is a near real-time estimator of precipitation, soil detachment, hillslope soil loss, and water runoff using WEPP as the erosion model. The above estimations are conducted on randomly selected and spatially distributed flowpaths, and the means are reported at the HUC12 watershed level. The flowpaths are identified based on Digital Elevation Model (DEM) grid cell and D8 connectivity to adjacent cells. A flow path starts at a cell such that all adjacent cells are at a lower elevation, that is, no other adjacent cell directs flow into it and ends when sufficient flow concentration and soil conditions occur that channel erosion processes dominate soil loss where usually EGs occurrence. In this research, the DEP flowpaths, down to and including ephemeral gully heads, were surveyed in 8 HUC12 watersheds distributed in 8 different Iowa MLRAs using high-resolution imagery in-field measurement. A grid order model was used as a method for EG location prediction. The sensitivity of accuracy of EG location prediction on WEPP/DEP soil detachment, hillslope soil loss, and water runoff model output was explored at hillslope, watershed, and regional spatial scale with both extreme rainfall events and yearly average erosion modeling. This research will allow a more clear understanding of EG prediction influence on erosion modeling and help improve the accuracy of erosion modeling by using WEPP / DEP.

We used the Agricultural Production Systems sIMulator (APSIM) to predict and explain maize and soybean yields, phenology, and soil water and nitrogen (N) dynamics during the growing season in Iowa, USA. Historical, current and forecasted weather data were used to drive simulations, which were released in public four weeks after planting. In this paper, we 1) describe the methodology used to perform forecasts; 2) evaluate model prediction accuracy against data collected from 10 locations over 4-years; and 3) identify inputs that are key in forecasting yields and soil N dynamics. We found that the predicted median yield at planting was a very good indicator of end-of-season yields (relative root mean square error, RRMSE ~ 20%). For reference, the prediction at maturity, when all the weather was known, had a RRMSE of 14%. The good prediction at planting time was explained by the existence of shallow water tables, which decreased model sensitivity to unknown summer precipitation by 50 to 64%. Model initial conditions and management information accounted for 1⁄4 of the variation in maize yield. End of season model evaluations indicated that the model simulated well crop phenology (R2=0.88), root depth (R2=0.83), biomass production (R2=0.93), grain yield (R2=0.90), plant N uptake (R2=0.87), soil moisture (R2=0.42), soil temperature (R2=0.93), soil nitrate (R2=0.77), and water table depth (R2=0.41). We concluded that model set-up by the user (e.g. inclusion of water table), initial conditions, and early season measurements are very important for accurate predictions of soil water, N and crop yields in this environment. Disciplines Agriculture | Agronomy and Crop Sciences | Bioresource and Agricultural Engineering | Soil Science | Statistical Models Comments This is a manuscript of an article published as Archontoulis, Sotirios V., Michael J. Castellano, Mark A. Licht, Virginia Nichols, Mitch Baum, Isaiah Huber, Rafael Martinez‐Feria et al. "Predicting crop yields and soil‐plant nitrogen dynamics in the US Corn Belt." Crop Science (2020). doi: 10.1002/csc2.20039. Authors Sotirios V. Archontoulis, Michael J. Castellano, Mark A. Licht, Virginia Nichols, Mitch Baum, Isaiah Huber, Rafael Martinez-Feria, Laila Puntel, Raziel A. Ordonez, Javed Iqbal, Emily E. Wright, Ranae N. Dietzel, Matthew Helmers, Andy VanLoocke, Matt Liebman, Jerry L. Hatfield, Daryl Herzmann, S. Carolina Córdova, Patrick Edmonds, Kaitlin Togliatti, Ashlyn Kessler, Gerasimos Danalatos, Heather Pasley, Carl Pederson, and Kendall R. Lamkey This article is available at Iowa State University Digital Repository: https://lib.dr.iastate.edu/agron_pubs/623 This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi:

Abstract A strong need exists for tools to assess the efficacy of conservation practices across large regions supporting informed policy decisions that may lead to better soil and water conservation while optimizing agricultural production options. Perennial warm‐season grasses (WSGs) such as switchgrass (Panicum virgatum), can be grown on marginally productive and/or environmentally sensitive lands to meet growing bioenergy demands while reducing water runoff and soil erosion compared to current row crop systems. Quantifying the soil and water conservation effects of WSG when strategically placed on the landscape would help support decisions favoring both economic and environmental benefits. We used the Daily Erosion Project (DEP) to simulate the effects of WSGs on hillslope water runoff and soil loss for 2008–2016 across eight major land resource areas (MLRA) in the Midwest United States. Four different scenarios (baseline or existing conditions and switchgrass grown on slopes ≥3%, ≥6%, and ≥10%) were modeled. Across all hillslope groups replacing row crops with switchgrass reduced yearly water runoff and soil loss by 3.2%–12.1% and 43.7%–95.5% compared with the baseline levels, respectively. Water and soil conservation efficiency (water runoff reductions or soil loss reductions associated with 1% increase in switchgrass coverage) increased with slope as 10% > 6% > 3% for all MLRAs. Switchgrass replacement on slopes ≥10% reduced average soil loss estimates as much as 22.6 Mg ha−1 year−1 for the most erosive MLRA (baseline soil erosion rate of 28.6 Mg ha−1 year−1) and resulted in all MLRA erosion estimates ≤6.0 Mg ha−1 year−1. For soil loss, an apparent interaction existed between slope group and total annual precipitation; as annual precipitation increased, the difference in soil loss between slope groups increased. Soil loss was more sensitive to these factors than was water runoff. Policy supporting a renewable energy industry while strategically improving soil and water resources seems globally advantageous.

The frequency and intensity of extreme weather years, characterized by abnormal precipitation and temperature, are increasing. In isolation, these years have disproportionately large effects on environmental N losses. However, the sequence of extreme weather years (e.g., wet-dry vs. dry-wet) may affect cumulative N losses. We calibrated and validated the DAYCENT ecosystem process model with a comprehensive set of biogeophysical measurements from a corn-soybean rotation managed at three N fertilizer inputs with and without a winter cover crop in Iowa, USA. Our objectives were to determine: (i) how 2-year sequences of extreme weather affect 2-year cumulative N losses across the crop rotation, and (ii) if N fertilizer management and the inclusion of a winter cover crop between corn and soybean mitigate the effect of extreme weather on N losses. Using historical weather (1951-2013), we created nine 2-year scenarios with all possible combinations of the driest ("dry"), wettest ("wet"), and average ("normal") weather years. We analyzed the effects of these scenarios following several consecutive years of relatively normal weather. Compared with the normal-normal 2-year weather scenario, 2-year extreme weather scenarios affected 2-year cumulative NO3- leaching (range: -93 to +290%) more than N2O emissions (range: -49 to +18%). The 2-year weather scenarios had nonadditive effects on N losses: compared with the normal-normal scenario, the dry-wet sequence decreased 2-year cumulative N2O emissions while the wet-dry sequence increased 2-year cumulative N2O emissions. Although dry weather decreased NO3- leaching and N2O emissions in isolation, 2-year cumulative N losses from the wet-dry scenario were greater than the dry-wet scenario. Cover crops reduced the effects of extreme weather on NO3- leaching but had a lesser effect on N2O emissions. As the frequency of extreme weather is expected to increase, these data suggest that the sequence of interannual weather patterns can be used to develop short-term mitigation strategies that manipulate N fertilizer and crop rotation to maximize crop N uptake while reducing environmental N losses.

Water runoff and sediment transport from agricultural uplands are substantial threats to water quality and sustained crop production. To improve soil and water resources, farmers, conservationists, and policy‐makers must understand how landforms, soil types, farming practices, and rainfall interact with water runoff and soil erosion processes. To that end, the Iowa Daily Erosion Project (IDEP) was designed and implemented in 2003 to inventory these factors across Iowa in the United States. IDEP utilized the Water Erosion Prediction Project (WEPP) soil erosion model along with radar‐derived precipitation data and government‐provided slope, soil, and management information to produce daily estimates of soil erosion and runoff at the township scale (93 km2 [36 mi2]). Improved national databases and evolving remote sensing technology now permit the derivation of slope, soil, and field‐level management inputs for WEPP. These remotely sensed parameters, along with more detailed meteorological data, now drive daily WEPP hillslope soil erosion and water runoff estimates at the small watershed scale, approximately 90 km2 (35 mi2), across sections of multiple Midwest states. The revisions constitute a substantial improvement as more realistic field conditions are reflected, more detailed weather data are utilized, hill slope sampling density is an order of magnitude greater, and results are aggregated based on surface hydrology enabling further watershed research and analysis. Considering these improvements and the expansion of the project beyond Iowa it was renamed the Daily Erosion Project (DEP). Statistical and comparative evaluations of soil erosion simulations indicate that the sampling density is adequate and the results are defendable. The modeling framework developed is readily adaptable to other regions given suitable inputs. © 2017 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd.

The Intergovernmental Panel on Climate Change (IPCC) has predicted that changes in climate patterns (higher temperatures, changes in extreme precipitation events, and higher level of humidity) will adversely impact water quality. Given the implications of climate change on water and soil quality, it is important for watershed managers, stakeholders, and policymakers to understand not only the effectiveness of different conservation practices in improving water quality, but also the cost-effectiveness of a watershed-level policy program designed for implementing conservation practices. A system of points measuring the efficiency of five conservation practices in reducing nutrient (nitrogen [N] and phosphorus [P]) runoff are estimated using the Soil and Water Assessment Tool (SWAT) model in this study for the Boone River watershed (BRW) in north central Iowa. The points can be interpreted as indices of the environmental benefits associated with each conservation practice. Among the various market instruments proposed as resource and cost-revelation mechanisms, competitive biddings, also referred to as "reverse auctions" or procurement auctions, have come to the attention of researchers and policymakers. Competitive bidding mechanisms induce landowners to submit bids close to their opportunity costs, thus increasing the budgetary cost-effectiveness and revealing the true costs of adopting different conservation practices. This study considers the cost efficiency of reverse auction programs designed for improving water quality in the BRW, where the bids are constructed using the system of points.

土壤侵蚀发布系统作为定量研究土壤侵蚀过程的重要工具,一直是土壤侵蚀学科的前沿领域,其对于估算土壤流失速度、 评估水土保持措施效益具有重要的科学意义.然而,现有土壤侵蚀发布系统多仅依靠一年内降雨事件或利用多年平均降雨数据进行潜在土壤侵蚀程度估算,无法对一次平均水平的土壤侵蚀事件进行定量描述,更无法回答土壤侵蚀在何时何地发生,侵蚀动态如何变化,以及降雨、 地形、 土壤、 作物管理方式和极端事件的时空变化对土壤侵蚀的影响.作为土壤侵蚀发布系统领域最新研究进展的土壤侵蚀日发布系统DEP(Daily Erosion Project),解决了以往土壤侵蚀发布系统中出现的上述问题.DEP基于侵蚀模型、 地理信息、 土地利用、 植被变化等信息,逐日模拟降雨、 径流、 剥离、 土壤流失等定量指标,结合第二代气象雷达云图生成土壤侵蚀分布图,实现以日为步长的土壤侵蚀发布,成为首个土壤侵蚀日发布系统,已在美国成功应用.本文详细介绍了土壤侵蚀日发布系统,并对实例进行了分析,探讨了该系统在我国土壤侵蚀发布研究与应用中的前景.图4,参42.

The Daily Erosion Project (DEP) estimates precipitation, runoff, sheet and rill erosion, and hillslope delivery in near real time, on over 2000 Hydrologic Unit Code (HUC) 12 watersheds in the Midwest (Figure 1). It does this by running the Water Erosion Prediction Project (WEPP) model with a combination of remotely-sensed precipitation weather stations, remotely-sensed crop and residue cover, remotely-sensed topography, and soils databases. It is an update and expansion to the Iowa Daily Erosion Project (Cruse et al., 2006) that is designed to further investigate large scale erosion dynamics while maintaining hillslope level input resolution. The DEP has a climate database extending from 2007 (thus 2007 is considered a ‘warm-up’ year) to the present day, enabling investigation of single event and single year runoff and soil erosion dynamics over a large time range and spatial extent. The current DEP weather input files are derived from 5-minute, 1x1 kilometer NEXRAD Level 3 precipitation product and county level wind speed and temperature. These files are available for download and use in regular WEPP runs. Slope length and elevation is derived from 3x3-meter resolution, LiDAR based Digital Elevation Models that have been hydro-enforced using the procedure of Gelder (2015). HUC12 watersheds are divided into subcatchments for stratified sampling and one random flowpath is chosen for modeling in each subcatchment. Slopes are truncated when a flowpath leaves agricultural management or grid order is greater than 4 as flow characteristics are assumed to transition to concentrated flow at this point. Crop rotations and field boundaries come from the Ag Conservation Planning Framework (Tomer et al, 2015a; Tomer et al, 2015b), and soils information from the USDA NRCS 10-meter SSURGO database. Crop residue/tillage information is determined with the procedure of Gelder et al. (2009) using estimated residue cover to divide corn into 4 tillage classes and soybeans into 3 tillage classes. WEPP overland flow elements (OFEs) and slopes are calculated wherever soil properties or management practices change. Analysis of yearly summaries indicate that some years on a domain-wide basis deliver up to 6 times more sediment to the end of the hillslope and produce about 4 times more runoff than others. Spatial trends generally followed precipitation and topographic drivers of erosion.