Comparison of Spatial Interpolation Schemes for Rainfall Data and Application in Hydrological Modeling

Tao Chen,Xiaoli Yang,Chongxu Zhao,Liming Zhang,Liliang Ren,Shanhu Jiang,Yi Liu,Fei Yuan,Tiantian Tang

doi:10.3390/w9050342

Abstract

The spatial distribution of precipitation is an important aspect of water-related research. The use of different interpolation schemes in the same catchment may cause large differences and deviations from the actual spatial distribution of rainfall. Our study analyzes different methods of spatial rainfall interpolation at annual, daily, and hourly time scales to provide a comprehensive evaluation. An improved regression-based scheme is proposed using principal component regression with residual correction (PCRR) and is compared with inverse distance weighting (IDW) and multiple linear regression (MLR) interpolation methods. In this study, the meso-scale catchment of the Fuhe River in southeastern China was selected as a typical region. Furthermore, a hydrological model HEC-HMS was used to calculate streamflow and to evaluate the impact of rainfall interpolation methods on the results of the hydrological model. Results show that the PCRR method performed better than the other methods tested in the study and can effectively eliminate the interpolation anomalies caused by terrain differences between observation points and surrounding areas. Simulated streamflow showed different characteristics based on the mean, maximum, minimum, and peak flows. The results simulated by PCRR exhibited the lowest streamflow error and highest correlation with measured values at the daily time scale. The application of the PCRR method is found to be promising because it considers multicollinearity among variables.

Highlights

The spatial distribution of precipitation plays an important role in hydrological modeling, disaster prediction, and watershed management
This phenomenon can be avoided by using the principal component regression with residual correction (PCRR) method
The results showed that the PCRR model yielded the minimum relative error in average annual runoff depth (0.97%)

Summary

Introduction

The spatial distribution of precipitation plays an important role in hydrological modeling, disaster prediction, and watershed management. Many uncertain factors, including topographic factors such as latitude, longitude, altitude, slope, aspect, and large-scale circulation, have variable effects on the spatial distribution of precipitation. Spatial interpolation schemes are required to provide accurate spatial distributions of rainfall. Various interpolation methods have been developed for this purpose, ranging from simple techniques such as Thiessen polygons [1] and inverse distance weighting schemes [2] to complex statistical methods such as multiple linear regression [3] and geostatistical kriging [4,5]. The more complex approaches use additional information such as elevation, slope, or radar-estimated rainfall as covariates [6].

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Conclusion