Kriging interpolated rainfall data in ArcGIS for a sustainable flood modelling prediction / Fahda Nurhani Ahmad Razan ... [et al.]

Rainfall data is the most important input for a hydrological modelling especially for a flood prediction. Conventionally, the most common rainfall measurement using ground based data namely rain gauges networking from a certain catchment. Although rain gauge data measurement are relatively accurate, the estimated rainfall value were prone to errors. Alternatively, kriging has become a widely used interpolation method to estimate the spatial distribution of climate variables including rainfall value. The objective of this study is to evaluate the application of geostatistical (ordinary kriging) method for rainfall value improvement for Upper Klang River Basin (UKRB), Malaysia. Th historical rainfall record from existing rain‐gauge stations of UKRB in a Monthly basis (January 2019) was selected and be as an input to the kriging method. Ordinary Kriging with the gaussian variogram model produces the lowest prediction error for rainfall estimation. Thus, it is found to be the most accurate
interpolator for estimating the monthly rainfalls over Upper Klang River Basin. This improved data is essential to be used as an input for sustainable flood prediction in the future to reduce the flood risk experience especially in the UKRB Catchment and other catchment that have a similar characteristic with this catchment. In addition, it could reduce the losses of property due to flood impacts and as one of the option for the sustainable flood planning, protection plan or rehabilitation work in the future.