Location: Southwest Watershed Research CenterTitle: Temporarily downscaling precipitation intensity factors for KÖPPEN Climate Regions in the U.S.
Submitted to: Journal of Soil and Water Conservation
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/17/2020
Publication Date: 1/31/2021
Citation: Fullhart, A.T., Nearing, M.A., Weltz, M.A. 2021. Temporarily downscaling precipitation intensity factors for KÖPPEN Climate Regions in the U.S.. Journal of Soil and Water Conservation. 76(1):39-51. https://doi.org/10.2489/jswc.2021.00156.
Interpretive Summary: Knowledge of the intensity of precipitation is an important part of using soil erosion and runoff models. The hypothesis of this work is that relationships that determine intensity of rainfall is specific to different climate regions, called Köppen-Geiger climate classifications. Much rainfall data from around the world does not have the needed information about precipitation intensity to allow the use of erosion models, therefore we are working to develop methods to use coarser resoultion data, such as hourly or daily, to estimate the information we need at the finer resolution. The findings allow users of relevant models to create precipitation intensity inputs using a variety of temporal resolutions and for a variety of climates. The intensity factors are applied to input parameters for the weather model CLIGEN, a stochastic weather generator, which is commonly used to drive a number of soil erosion models, such as the ARS-Rangeland Erosion and Hydrology model (RHEM), the ARS-Water Erosion Prediction Project (WEPP), and the ARS-Revised Universal Soil Loss Equation model (RUSLE/RUSLE2). The intensity factor is known as “monthly mean maximum 30-minute intensity” and normally requires high-resolution precipitation data to estimate. In the new analysis, the factor was estimated using hourly data which resulted in acceptable levels of error. Our plan now that we have these relationships, is to begin to analyze data from around the world so that ARS models may be more generally applied.
Technical Abstract: Model inputs for prediction of runoff and soil erosion commonly require precipitation intensity information. Intensity is often estimated if precipitation data with high temporal resolution is unavailable. However, when intensity is time-averaged for fixed measurement intervals, estimates become increasingly underestimated with longer intervals due to the assumption that event durations begin and end at measurement intervals. Addressing this, adjustment factors were determined for downscaling the temporal resolution of intensity values derived from selected resolutions within the range of 10-1440 min for Köppen-Geiger climate regions in the United States. In this case, monthly mean maximum 30-minute intensity (MX.5P) was downscaled, which is a parameter used to generate stochastic meteorological inputs for models that include the Rangeland Erosion and Hydrology model (RHEM) and the Water Erosion Prediction Project model (WEPP). The adjustment factors were given by regressions of reference MX.5P values derived from data with 5 min resolution against MX.5P values derived from data with lower temporal resolutions (>= 10 min). In addition to using a slope coefficient for intensity in the regression equation, permutations of the equation included use of an elevation coefficient and constants, resulting in four total permutations. For the 143 stations and 17 climate regions analyzed, the four regression equations had roughly equal performance, and all gave statistically significant results. Regressions for adjusting hourly data using only an intensity coefficient in the equation had standard error of the estimate ranging from 1.01-2.96 mm hr-1 with an average of 2.04 mm hr-1. When downscaling daily values, the error range was 2.50-10.20 mm hr-1 with an average of 5.63 mm hr-1. Also determined were average time-to-peak intensity probability distributions for each climate region. Finally, a stochastic weather generator, CLIGEN, was used to test the effectiveness of applying the climate-based factors as an alternative to using sub-hourly data.