A global rain-driven soil erosion investigation based on simulated breakpoint precipitation

Authors: Fullhart, Andrew; MCGEHEE, R. - Purdue University; NEARING, M.A. - Retired ARS Employee; HERNANDEZ, M. - University Of Arizona; WELTZ, M - Retired ARS Employee; Goodrich, David - Dave

Submitted to: American Society of Agricultural and Biological Engineers
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/15/2022
Publication Date: 10/11/2022
Citation: Fullhart, A.T., McGehee, R., Nearing, M., Hernandez, M., Weltz, M., Goodrich, D.C. 2022. A global rain-driven soil erosion investigation based on simulated breakpoint precipitation. American Society of Agricultural and Biological Engineers. 65(5):1081-1096. https://doi.org/10.13031/ja.14817.
DOI: https://doi.org/10.13031/ja.14817

Interpretive Summary: Climate inputs are a necessary part of soil erosion prediction and assessment, and the necessary data cannot always be found, particularly in international applications. In this study, we test a new international climate dataset, with particularly good coverage of the U.S., Mexico, Europe and Australia, for use with the ARS-Rangeland Hydrology and Erosion model (RHEM), and for determination of the climate factor in the ARS-Revised Universal Soil Loss Equation model (RUSLE). The international climate dataset represents numerous global climate types, and therefore, this study provides a generalization of global erosion rates that are useful for erosion risk assessment. This study also brings attention to the openly available international climate dataset, which may encourage soil erosion modeling in new locations.

Technical Abstract: Recent research has highlighted problems with erosion modeling applications that use coarser fixed-interval precipitation data (as opposed to breakpoint precipitation data which better preserve precipitation characteristics such as intensity and duration). Most, if not all, large scale erosion modeling applications and risk assessments are based on fixed-interval data due to their wider availability; and, if left uncorrected, these applications could be subject to substantial erosion underestimation bias related to time-averaging. Furthermore, recent research is increasingly finding that conventional correction procedures are not sufficiently addressing biases driven by fixed-interval precipitation data. Since there is currently no global, breakpoint precipitation dataset upon which new erosion analyses could be based, this manuscript presents a novel approach to global-scale erosion assessment based on simulated breakpoint precipitation data. A point-scale stochastic weather generator, CLIGEN, was used to generate precipitation events with characteristics more similar to breakpoint precipitation data than fixed-interval alternatives. An international CLIGEN dataset based on precipitation parameters from more than 10,000 long-term climate stations in numerous countries, was evaluated for potential use in this role. CLIGEN-simulated event characteristics were compared to high-quality, high-resolution NOAA-ASOS precipitation data where it was available. Average annual rainfall erosivity values of CLIGEN-simulated climates were compared with existing RUSLE2 and Panagos et al. (2017) databases. The Rangeland Hydrology and Erosion Model (RHEM) was used to predict runoff and soil loss based on the same CLIGEN inputs. RHEM was parameterized with undisturbed soil management, an arbitrary standard slope configuration, and site-specific soil properties from the global 250m SoilGrids product. Erosion results were analyzed according to climate type, revealing that predicted distributions of sediment yield and runoff were statistically unique for most global climate types. A multivariate regression model was developed to explore and understand the importance of various precipitation input factors. Peak precipitation intensity was the most important climate factor for determining sediment yield, and CLIGEN precipitation factors, when combined, had approximately as much predictive power as soil texture. Average annual rainfall erosivity values (i.e., the R-factor in USLE-based models) were calculated for each location and were, on average, 25% and 20% greater than values from RUSLE2 and Panagos et al. (2017) estimates, respectively, which is in agreement with the latest research on the topic.