The assessment of annual rainfall field by applying different interpolation methods in the state of Rio Grande do Sul, Brazil

An accurate analysis of spatial rainfall distribution is of great importance for managing watershed water resources, in addition to giving support to meteorological studies and agricultural planning. This work compares the performance of two interpolation methods: Inverse distance weighted (IDW) and Kriging, in the analysis of annual rainfall spatial distribution. We use annual rainfall data for the state of Rio Grande do Sul (Brazil) from 1961 to 2017. To determine which proportion of the sample results in more accurate rainfall distribution maps, we use a certain amount of points close to the estimated point. We use mean squared error (MSE), coefficient of determination (R2), root mean squared error (RMSE) and modified Willmott's concordance index (md). We conduct random fields simulations study, and the performance of the geostatistics and classic methods for the exposed case was evaluated in terms of precision and accuracy obtained by Monte Carlo simulation to support the results. The results indicate that the co-ordinary Kriging interpolator showed better goodness of fit, assuming altitude as a covariate. We concluded that the geostatistical method of Kriging using nine closer points (50% of nearest neighbors) was the one that better represented annual rainfall spatial distribution in the state of Rio Grande do Sul.

Effects of Rainfall Spatial Distribution on the Relationship between Rainfall Spatiotemporal Resolution and Runoff Prediction Accuracy

We studied how rainfall spatial distribution affects the relationship between rainfall spatiotemporal resolution and runoff prediction accuracy under real field conditions. We gathered radar rainfall and discharge data for three rainfall events. These rainfall-runoff events were then reproduced using a kinematic wave model. Modeling accuracy was estimated quantitatively using the Nash–Sutcliffe model efficiency coefficient and peak discharge ratio. Normalized root-mean-square error ( nRMSE ), skewness ( S k ), and second scaled spatial moment of catchment rainfall ( δ 2 ) were employed to quantify rainfall spatial distribution characteristics. By relating the accuracy of modeling results to the rainfall spatial characteristics using various rainfall spatiotemporal resolutions, we found that the modeling results converged to a value as the nRMSE , | S k | and | 1 − δ 2 | decreased. That is, rainfall spatial distributions affect the relationship between lower limit of rainfall spatiotemporal resolution for runoff models and runoff prediction accuracy.

Influence of Rainfall Spatial Distribution on Total Suspended Solid (TSS in Ci lutung Watershed

The study aims to analyze the influence of rainfall spatial distribution on total suspended solid and the connection between total suspended solid with river stream discharge on the condition of physical characteristics in Cilutung stream area, Majalengka regency. The extraction process of Himawari 8 Imagery used to find out the rainfall spatial distribution pattern while taking samples in April, 2018. Curve Number Data have the form of hydrologic soil groups and land use required to give score to each five sub-watershed that has been delineated for region physical characteristic. The Rainfall spatial distribution pattern has strong correlation with total suspended solid concentration generated through runoff discharge with coefficient of determination number r2 = 0,8416. The varied rainfall spatial distribution pattern take part to the occurrence of fluctuation of total suspended solid concentration with average 190 mg/l, and amount of total suspended sediment yield 820 kg/m3.

Analysis on Effect of Spatial Distribution of Rainfall on Runoff Modeling in Xixi Watershed of Jinjiang Basin

The effect of spatial distribution of rainfall is considered to be main uncertain factor for hydrologic simulation. Based on calibration and validation of the SWAT model, using the 15 rainfall stations data ，analyzed the effect of spatial distribution of rainfall on runoff modeling in Xixi Watershed of Jinjiang basin. The results show that the difference of the rainfall spatial distribution is very obvious by calculating standard deviation. The effect of the difference of rainfall spatial distribution to simulation results is significant by comparing runoff simulation of different years in which the average of all rainfall stations precipitation was similar. The uncertain spatial distribution of rainfall must be considered in the hydrological simulation.