Revisiting Spatial Estimation of Precipitation via Merging Adjusted IDW and Altitude Values in Complex Topographic Watershed

Accurate precipitation estimation in mountainous regions remains challenging due to sparse gauges and complex topography. This study presents enhanced variants of the Inverse Distance Weighting (IDW) method, including Orographic-based IDW (OIDW), Fuzzified Orographic-based IDW (FOIDW), and Adjusted Orographic-based IDW (AOIDW), which incorporate elevation effects, directional shielding, and fuzzy clustering to represent spatial heterogeneity better. Principal Component Analysis (PCA) was applied as a post-processing technique to merge and denoise model outputs. Monthly precipitation data from stations in the Karkheh River Basin (2000–2020) were used for evaluation. Compared with classical IDW and PRISM, AOIDW showed the highest individual performance (KGE = 0.92; MSE = 310 mm2), while the PCA-merged product achieved the best overall accuracy (NSE = 0.98; RMSE = 5.65 mm). Results demonstrate that integrating topographic factors and statistical merging significantly improves precipitation interpolation in complex mountainous regions, supporting more reliable hydrological modelling and water resource management.