Effects of Raingauge Distribution on Estimation Accuracy of Areal Rainfall

Abstract

Rainfall analysis is important to managing water resources. Mean rainfall is usually used to calculate the spatial rainfall status of a region and is the input into various rainfall-runoff models. However, this method relies on an adequate raingauge network. This study identifies the effects of raingauge distribution based on estimation results of areal rainfall using the Thiessen polygon and block Kriging methods. Twelve rainfall events with complete data from 14 raingauges were selected to complete the goal of this study. The block Kriging method in this study uses a dimensionless semivariogram to obtain hourly semivariograms based on a standardized rainfall depth. The power semivariogram model was used to describe the temporal-spatial variation of rainfall. The analytical process in this study uses raingauge weight and rainfall volume as evaluation criteria. All raingauges were in turn removed from the original raingauge network. The effects of removing each raingauge were compared with computations using all raingauges. Comparison results indicate that (1) the block Kriging method can accurately describe rainfall processes in terms of the spatiotemporal structure of a semivariogram. (2) the block Kriging method is better than the Thiessen polygon method at obtaining exact mean rainfall, and (3) the effects of different raingauge distributions on a mean hyetograph warrant further investigation.

Effects of urbanization variables on model parameters for watershed divisions

“…These methods have numerous applications in various research fields. Typical cases have included raingauge network design (Bastin et al 1984, Cheng et al 2008a, raingauge evaluation (Cheng 2011a, Cheng et al 2012, spatial interpolation of rainfall (Goovaerts 2000, Syed et al 2003, Basistha et al 2008, and space-time rainfall interpolation (Cheng et al 2007). The primary difference between the kriging method and traditional methods is the weighting computation.…”

A Step towards Integrating CMORPH Precipitation Estimation with Rain Gauge Measurements

“…However, CMORPH estimates tend to agree with rain gauge measurements beyond the monthly time scale [20]. Uncertainties in all satellite rainfall estimates [40] as well as in rain gauge measurements [71,72] can be reduced by SOAS rst at the daily and second at 30 min time scales, as suggested by Pereira Fo et al [24] who originally used SOAS to integrate weather radar rainfall estimates to rain gauge measurements from min to hr time scales. Furthermore, uncertainties in validating instantaneous rainfall estimates with the operational network have been reported in [73] and at relevant scales [74].…”

Spatial pattern of bias in areal rainfall estimations and its impact on hydrological modeling: a comparative analysis of estimating areal rainfall based on radar and weather station networks in South Korea

“…More importantly, the reliable calibration of hydrologic models is highly affected by the density of the weather station network, with denser networks providing an accurate representation of the rainfall-runoff process (Cheng et al, 2012;Lebel et al, 1987;Wood et al, 2000). Previous studies considered the spatio-temporal variability of continuous rainfall sequences across watersheds (Ahmed et al, 2022;Akgül and Aksu, 2021;Bližňák et al, 2022;Haberlandt, 2007;Malede et al, 2022;Schiemann et al, 2011;Sherman and Johnson, 1993;Valles et al, 2020;Verworn and Haberlandt, 2011).…”