Abstract
The University of Oklahoma’s Advanced Regional Prediction System (ARPS) was used to examine the impacts of varying mean soil moisture and model resolution on the magnitude and frequency of precipitation events in the U.S. Central Plains and to determine whether modeled soil moisture and precipitation fields exhibit scale invariance using the statistical moments. It was found that high soil moisture resulted in greater precipitation amounts and a higher frequency of events, suggesting the occurrence of a positive soil moisture–precipitation feedback. The scaling analysis performed on cumulative precipitation determined that these fields did not exhibit signs of self-similarity and, therefore, statistical properties cannot be predicted at other resolutions. The scaling properties of soil moisture were highly variable in time which has important implications for the use of remotely sensed data, as scaling properties from 1 day cannot necessarily be applied to subsequent days.
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Acknowledgements
This work was supported by the National Science Foundation EPSCOR grant (NSF EPS #0553722). We wish also would like to The Land Institute for their support.
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Jones, A.R., Brunsell, N.A. A scaling analysis of soil moisture–precipitation interactions in a regional climate model. Theor Appl Climatol 98, 221–235 (2009). https://doi.org/10.1007/s00704-009-0109-x
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DOI: https://doi.org/10.1007/s00704-009-0109-x