Paper No. JAWRA-08-0173-P of the Journal of the American Water Resources Association (JAWRA). Discussions are open until six months from print publication.
GIS-Based Spatial Precipitation Estimation: A Comparison of Geostatistical Approaches1
Article first published online: 1 JUN 2009
© 2009 American Water Resources Association
JAWRA Journal of the American Water Resources Association
Volume 45, Issue 4, pages 894–906, August 2009
How to Cite
Zhang, X. and Srinivasan, R. (2009), GIS-Based Spatial Precipitation Estimation: A Comparison of Geostatistical Approaches. JAWRA Journal of the American Water Resources Association, 45: 894–906. doi: 10.1111/j.1752-1688.2009.00335.x
- Issue published online: 29 JUL 2009
- Article first published online: 1 JUN 2009
- Received September 16, 2008; accepted February 4, 2009.
- Geographic Information System;
- spatial precipitation
Abstract: As one of the primary inputs that drive watershed dynamics, the estimation of spatial variability of precipitation has been shown to be crucial for accurate distributed hydrologic modeling. In this study, a Geographic Information System program, which incorporates Nearest Neighborhood (NN), Inverse Distance Weighted (IDW), Simple Kriging (SK), Ordinary Kriging (OK), Simple Kriging with Local Means (SKlm), and Kriging with External Drift (KED), was developed to facilitate automatic spatial precipitation estimation. Elevation and spatial coordinate information were used as auxiliary variables in SKlm and KED methods. The above spatial interpolation methods were applied in the Luohe watershed with an area of 5,239 km2, which is located downstream of the Yellow River basin, for estimating 10 years’ (1991-2000) daily spatial precipitation using 41 rain gauges. The results obtained in this study show that the spatial precipitation maps estimated by different interpolation methods have similar areal mean precipitation depth, but significantly different values of maximum precipitation, minimum precipitation, and coefficient of variation. The accuracy of the spatial precipitation estimated by different interpolation methods was evaluated using a correlation coefficient, Nash-Sutcliffe efficiency, and relative mean absolute error. Compared with NN and IDW methods that are widely used in distributed hydrologic modeling systems, the geostatistical methods incorporated in this GIS program can provide more accurate spatial precipitation estimation. Overall, the SKlm_EL_X and KED_EL_X, which incorporate both elevation and spatial coordinate as auxiliary into SKlm and KED, respectively, obtained higher correlation coefficient and Nash-Sutcliffe efficiency, and lower relative mean absolute error than other methods tested. The GIS program developed in this study can serve as an effective and efficient tool to implement advanced geostatistics methods that incorporate auxiliary information to improve spatial precipitation estimation for hydrologic models.