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

Abstract

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.

Spatial distribution and trends of different precipitation variability indices based on daily data in Northern Chile between 1966 and 2015

“…In recognizing that multivariate regression models generate spatially distributed residuals (the differences between the meteorological stations and the values determined by the models), they were corrected following an inverse distance weighting interpolation with a power magnitude of 2 (Chen et al, ). This enabled us to obtain interpolations that more effectively attended to spatial heterogeneity by reducing errors in the model and deleting the outliers (Crespi et al, ).…”

“…This information was based on continuous variables obtained from the Shuttle Radar Topography Mission, such as elevation (E lev ), curvature (C urv ) and orientation (O ri ) (Ninyerola et al, 2000(Ninyerola et al, , 2007, latitude (L at ) and distance to the Amazon Basin (D AB ). The spatial resolution of these continuous variables had a spatial resolution of 90 m x 90 m. In recognizing that multivariate regression models generate spatially distributed residuals (the differences between the meteorological stations and the values determined by the models), they were corrected following an inverse distance weighting interpolation with a power magnitude of 2 (Chen et al, 2017). This enabled us to obtain interpolations that more effectively attended to spatial heterogeneity by reducing errors in the model and deleting the outliers (Crespi et al, 2018).…”

Performance Assessment of Spatial Interpolation of Precipitation for Hydrological Process Simulation in the Three Gorges Basin

“…Large differences in flow were evident during the peaks. The same conclusion was obtained by Chen et al that the hydrological response in the catchment associated with different interpolation methods could reflect a large difference [53]. Based on differences in the specific characteristics of streamflow, the year 2012 could be approximately divided into three periods.…”

Exploring the Spatial-Seasonal Dynamics of Water Quality, Submerged Aquatic Plants and Their Influencing Factors in Different Areas of a Lake

“…Using this method, we obtained the spatial distribution of TN, TP, COD Mn , submerged aquatic plant biomass and composite water quality (CWQ) across all of Honghu Lake. The formula for the IDW method is as follows [35]:…”