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Research Project: Soil Erosion, Sediment Yield, and Decision Support Systems for Improved Land Management on Semiarid Rangeland Watersheds

Location: Southwest Watershed Research Center

Title: Rainfall erosivity estimation based on rainfall data collected over a range of temporal resolutions

Author
item YIN, S. - Beijing Normal University
item XIE, Y. - Beijing Normal University
item LIU, B. - Beijing Normal University
item Nearing, Mark

Submitted to: Hydrology and Earth System Sciences
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/27/2015
Publication Date: 9/15/2015
Citation: Yin, S., Xie, Y., Liu, B., Nearing, M.A. 2015. Rainfall erosivity estimation based on rainfall data collected over a range of temporal resolutions. Hydrology and Earth System Sciences. 19(10):4113-4126. doi:10.5194/hess-19-4113-2015.

Interpretive Summary: Soil erosion leads to land degradation and water pollution and also delivers sediment to streams and rivers, which increases the risks for flooding. Great efforts have been made in many parts of the world to reduce soil erosion by implementing biological, engineering, and tillage conservation practices. Soil erosion prediction models are effective tools for helping to guide and inform soil conservation planning and practice. The most widely used soil erosion models used for conservation planning are derived from the USDA-developed Universal Soil Loss Equation (USLE), including its revised version (RUSLE) and the Chinese Soil Loss Equation (CSLE). RUSLE is the official tool used by government conservation planners in the United States and the CSLE was successfully utilized in the first national water erosion sample survey in China. One very important piece of information needed to use these tools is rainfall erosivity, which is the power of rainfall to cause soil erosion by water. Unfortunately, the data needed to calculate erosivity are often unavailable in many areas of the world. The purpose of this study was to develop equations that relate more commonly available rainfall data resolutions, such as daily or monthly totals, to rainfall erosivity. Data from 18 meteorological stations in the eastern water-erosion areas of China were used to develop and calibrate 21 such equations. We made recommendations about the best equations to use depending on the objectives and data availability. These equations will greatly help conservation planners use these erosion models in places not previously possible in order to better conserve soil resources around the world.

Technical Abstract: Rainfall erosivity is the power of rainfall to cause soil erosion by water. The rainfall erosivity index for a rainfall event, EI30, is calculated from the total kinetic energy and maximum 30 minute intensity of individual events. However, these data are often unavailable in many areas of the world. The purpose of this study was to develop models that relate more commonly available rainfall data resolutions, such as daily or monthly totals, to rainfall erosivity. Eleven stations with one-minute temporal resolution rainfall data collected from 1961 through 2000 in the eastern water-erosion areas of China were used to develop and calibrate 21 models. Seven independent stations, also with one-minute data, were utilized to validate those models, together with 20 previously published equations. Results showed that models in this study performed better or similar to models from previous research to estimate rainfall erosivity for these data. Prediction capabilities, as determined using symmetric mean absolute percentage errors and Nash-Sutcliffe model efficiency coefficients, were demonstrated for the 41 models including those for estimating erosivity at event, daily, monthly, yearly, average monthly and average annual time scales. Prediction capabilities were generally better using higher resolution rainfall data as inputs. For example, models with rainfall amount and maximum 60-min rainfall amount as inputs performed better than models with rainfall amount and maximum daily rainfall amount, which performed better than those with only rainfall amount. Recommendations are made for choosing the appropriate estimation equation, which depend on objectives and data availability.