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ARS Home » Midwest Area » West Lafayette, Indiana » National Soil Erosion Research Laboratory » Research » Publications at this Location » Publication #337469

Title: Parsimonious stochastic storm generator and application in soil erosion modeling

item REVUELTA, JOSEPT - Purdue University
item Flanagan, Dennis
item ENGEL, BERNARD - Purdue University

Submitted to: ASABE Annual International Meeting
Publication Type: Abstract Only
Publication Acceptance Date: 2/20/2017
Publication Date: 7/16/2017
Citation: Revuelta, J., Flanagan, D.C., Engel, B.A. 2017. Parsimonious stochastic storm generator and application in soil erosion modeling. ASABE Annual International Meeting. CDROM.

Interpretive Summary:

Technical Abstract: Sophisticated field and watershed scale models for runoff, erosion control, environmental, and global-change investigations require detailed continuous temporal and spatial inputs of precipitation to drive the hydrologic processes. For accurate estimates of these processes, the resolution of the input data must allow the representation of the variability of precipitation as it represents a major source of variability in the model outputs. Currently, the use of stochastic weather generators is wide spread to generate continuous series of meteorological data at gauged and ungauged locations. These weather simulators are designed to replicate the statistical properties of real weather data at monthly or daily time resolutions. However, daily values of precipitation do not represent the variability of storm parameters within a day, which is assumed to significantly influence the predictions of environmental or agricultural models where processes are sensitive to sub-daily values. This research proposes a parsimonious stochastic storm generator based on 15-min time resolution and Monte Carlo simulation. The proposed model considers correlated non-normal random rainstorm characteristics such as time between storms, duration, and amount of precipitation, as well as the dependence of storm structure on rainfall depth and duration. The accuracy of the model was verified by comparing the generated rainfall with rainfall data from five randomly selected 15-min weather stations in Indiana, US. Additionally, the generated storms were compared with those from the same location generated by the Climate Generator (CLIGEN), extensively used as the input in a variety of hydrological models. Ultimately, the applicability of the stochastic storm generator in runoff and soil loss estimations was tested by setting 100 years of synthetic climate as input of the well-known hydrologic model Water Erosion Prediction Project (WEPP) integrated with CLIGEN. Current results have shown that through numerical experiments of correlated rainstorm variables, the proposed storm generator is capable of capturing the essential statistical features of rainstorms, preserving the first two moments of monthly storms events and good annual extreme event correspondence. It is expected that future results from runoff and soil loss simulations agree with observed quantities. Finally, as the proposed model depends on statistical properties at a site, this may allow the use of the synthetic storms in ungauged locations provided relevant information from a previously done regional analysis is available.