Comparison of spatial interpolation methods for the estimation of precipitation patterns at different time scales to improve the accuracy of discharge simulations

Abstract

Interpolating precipitation data is of prime importance to hydrological design, modeling, and water resource management. Various models have been developed that estimate spatial precipitation patterns. The purpose of this study is to analyze different precipitation interpolation schemes at different time scales in order to improve the accuracy of discharge simulations. The study was carried out in the upstream area of the Changjiang River basin. The performance of all selected methods was assessed using cross-validation schemes, with the mixed methods ultimately displaying the best performance at all three time scales. However, the differences in performance between the spatial interpolation methods decreased with increasing time scales. The unifying catchment Soil and Water Assessment Tool (SWAT), ‘abcd’, and the Budyko equation were employed at the daily, monthly, and annual scales, respectively, to simulate discharge. The performance of the discharge simulation at the monthly and annual time scales was consistent with their ranks of spatial precipitation estimation. For coarse, or long period, precipitation, there were no significant differences. However, the mixed methods performed better than the single model for the daily, or short, time scale with respect to the accuracy of the discharge simulation.

A Multimethod Analysis for Average Annual Precipitation Mapping in the Khorasan Razavi Province (Northeastern Iran)

“…With respect to Iran, for some regions with a high correlation between precipitation and elevation, regression models turned out to be the best [4,14-17], whereas in most Generally, there is no model that can decisively be selected as the best one for all regions. With respect to Iran, for some regions with a high correlation between precipitation and elevation, regression models turned out to be the best [4,[14][15][16][17], whereas in most regions, Kriging and Co-Kriging gave more accurate results compared with the other methods [15,16,20]. Similarly, despite some studies describing IDW as the best method [45][46][47], many studies emphasize the high accuracy of geostatistical methods (Kriging and Co-Kriging) for precipitation estimation for other regions as well, with Co-Kriging using the auxiliary elevation variable often showing the highest accuracy [9,12,[48][49][50][51][52][53][54][55].…”

“…However, the accuracy of the production of these maps depends on the methods used for the interpolation of the observed precipitation for the total considered area. Many different interpolation methods have been used in climate analysis so far [5][6][7][8][9][10][11][12], and finding the most suitable precipitation interpolation method is still a research desideratum for many regions [4,[13][14][15][16][17].…”

Spatializing the Coffee Yield Model SAFERNAC with Soil Fertility Data Across Tanzania

“…The latter uses the sampled points to predict the values at locations where no samples were taken, according to Tobler's Law of Geography. It has been used successfully in many areas such as geology, hydrology [22], environment [23], mining, climatology and meteorology [24]- [25], biology [26], forestry, agriculture [27]- [28], etc. Spatial interpolation uses a variety of methods, and in this case [29] noted that to-date there is no rule of thumb on the most appropriate interpolation technique for certain situations though general suggestions have been published.…”

Leveraging machine learning for quantitative precipitation estimation from Fengyun-4 geostationary observations and ground meteorological measurements

“…According to previous studies (Yang et al, 2020;Zeng et al, 2020), a ten-fold cross-validation (10-fold cv) method was used to to test the model estimation performance. The 10-fold cv method makes maximum use of the existing sample data and ensures that each sample is used as a training sample and a test sample respectively, effectively avoiding the result of 170 over-fitting.…”