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Spatial interpolation schemes of daily precipitation for hydrologic modeling

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Abstract

Distributed hydrologic models typically require spatial estimates of precipitation interpolated from sparsely located observational points to the specific grid points. We compare and contrast the performance of regression-based statistical methods for the spatial estimation of precipitation in two hydrologically different basins and confirmed that widely used regression-based estimation schemes fail to describe the realistic spatial variability of daily precipitation field. The methods assessed are: (1) inverse distance weighted average; (2) multiple linear regression (MLR); (3) climatological MLR; and (4) locally weighted polynomial regression (LWP). In order to improve the performance of the interpolations, the authors propose a two-step regression technique for effective daily precipitation estimation. In this simple two-step estimation process, precipitation occurrence is first generated via a logistic regression model before estimate the amount of precipitation separately on wet days. This process generated the precipitation occurrence, amount, and spatial correlation effectively. A distributed hydrologic model (PRMS) was used for the impact analysis in daily time step simulation. Multiple simulations suggested noticeable differences between the input alternatives generated by three different interpolation schemes. Differences are shown in overall simulation error against the observations, degree of explained variability, and seasonal volumes. Simulated streamflows also showed different characteristics in mean, maximum, minimum, and peak flows. Given the same parameter optimization technique, LWP input showed least streamflow error in Alapaha basin and CMLR input showed least error (still very close to LWP) in Animas basin. All of the two-step interpolation inputs resulted in lower streamflow error compared to the directly interpolated inputs.

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Acknowledgments

Partial support of this work by NOAA GAPP program (Award NA16GP2806) and the NOAA RISA Program (Award NA17RJ1229) is thankfully acknowledged. The authors also wish to thank Lauren Hay and Steve Markstrom at USGS Denver office, and Subhrendu Gangopadhyay at Hydrosphere for providing valuable comments and data that greatly enhanced the quality of the entire analysis.

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Authors and Affiliations

  1. College of Engineering, Arkansas State University, P.O. Box 1740, State University, AR, 72467, USA

    Yeonsang Hwang

  2. Research Applications Laboratory, National Center for Atmospheric Research, PO Box 3000, Boulder, CO, 80307-3000, USA

    Martyn Clark

  3. Department of Civil, Environmental and Architectural Engineering, University of Colorado, Boulder, CO, USA

    Balaji Rajagopalan

  4. U.S. Geological Survey, Denver, CO, USA

    George Leavesley

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  1. Yeonsang Hwang
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Correspondence to Yeonsang Hwang.

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Hwang, Y., Clark, M., Rajagopalan, B. et al. Spatial interpolation schemes of daily precipitation for hydrologic modeling. Stoch Environ Res Risk Assess 26, 295–320 (2012). https://doi.org/10.1007/s00477-011-0509-1

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