EGU26-6166, updated on 13 Mar 2026
https://doi.org/10.5194/egusphere-egu26-6166
EGU General Assembly 2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Poster | Wednesday, 06 May, 08:30–10:15 (CEST), Display time Wednesday, 06 May, 08:30–12:30
 
Hall X3, X3.90
Advancing Revised Universal Soil Loss Equation, Version 2 (RUSLE2) Development: Integrating Cutting-Edge Science and Cloud-Based Innovations for Transformative Soil Erosion Modeling and Land Management
Christophe Darnault1, Mahsa Ghorbani1, Gizem Genc Kildirgici1, Bigyan Ghimire2, Carson Sisk2, Kevin Cunningham2, Jon Calhoun2, Henrique Momm3, Daniel Yoder4, Dalmo Vieira5, Ronald Bingner6, Martin Locke6, Robert Wells6, and Giulio Ferruzzi7
Christophe Darnault et al.
  • 1Clemson University, School of Civil and Environmental Engineering and Earth Sciences, Clemson, United States of America
  • 2Clemson University, Holcombe Department of Electrical and Computer Engineering, Clemson, United States of America
  • 3Middle Tennessee State University, Department of Geosciences, Murfreesboro, United States of America
  • 4University of Tennessee, Department of Biosystems Engineering and Soil Science, Knoxville, United States of America
  • 5USDA-ARS National Sedimentation Laboratory, Oxford, United States of America, (Former)
  • 6USDA-ARS-National Sedimentation Laboratory, Oxford, United States of America
  • 7USDA-NRCS, West National Technology Support Center, Portland, United States of America

The Revised Universal Soil Loss Equation, Version 2 (RUSLE2), is the primary water erosion prediction tool used by the USDA Natural Resources Conservation Service (NRCS) for land management planning across the United States. Despite its widespread adoption, RUSLE2’s reliance on a personal computer-based model limits its capacity for large-scale, dynamic applications. This research addresses these constraints by developing a novel cloud-based platform to host and enhance RUSLE2, enabling server-based computation, geospatial data integration, and scalable modeling capabilities. Built on Amazon Web Services (AWS), the platform integrates web-based user interfaces, spatial databases, and geoprocessing tools to streamline soil erosion modeling. It incorporates historical data on soil properties, weather patterns, and land use practices to support precise assessments of rill and interrill erosion. A redesigned database architecture ensures computational efficiency, data security, and collaborative development. Scientific advancements in RUSLE2 include quantifying the effects of precipitation variability and land use on the spatiotemporal dynamics of key soil properties. Leveraging advanced field and laboratory methods, remote sensing, and machine learning, the platform improves the measurement and mapping of soil erodibility and soil loss across diverse U.S. agricultural landscapes. These enhancements enable more accurate forecasts of erosion risk under evolving environmental scenarios and support flexible land management strategies. This transformative, cloud-based platform delivers innovative tools to guide land use practices and improve long-term agricultural productivity. By integrating cutting-edge technologies and data-driven modeling, this work addresses longstanding challenges in erosion science and enhances regional and national resilience in soil resource management.

How to cite: Darnault, C., Ghorbani, M., Genc Kildirgici, G., Ghimire, B., Sisk, C., Cunningham, K., Calhoun, J., Momm, H., Yoder, D., Vieira, D., Bingner, R., Locke, M., Wells, R., and Ferruzzi, G.: Advancing Revised Universal Soil Loss Equation, Version 2 (RUSLE2) Development: Integrating Cutting-Edge Science and Cloud-Based Innovations for Transformative Soil Erosion Modeling and Land Management, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6166, https://doi.org/10.5194/egusphere-egu26-6166, 2026.