Geostatistical Interpolation Approach for Improving Flood Simulation Within a Data-Scarce Region in the Tibetan Plateau

The complex orography of the Tibetan plateau (TP) and the scarcity and uneven spatial distribution of meteorological stations present significant challenges in accurately estimating meteorological variables for hydrological simulations. This study aims to enhance the accuracy of daily precipitation and temperature interpolation for hydrological simulations in the Lhasa River Basin (LRB), particularly during flood events. We evaluate and compare the performance of deterministic Inverse Distance Weighting—IDW and geostatistical (Ordinary Kriging—OK and Kriging with External Drift—KED) interpolation methods for estimating precipitation and temperature patterns. Subsequently, we investigate the influence of different interpolation methods on hydrological simulations by using the interpolated meteorological data as input for the Water Balance Simulation Model (WaSiM) to simulate daily discharge in the LRB. Our results revealed that geostatistical methods, specifically OK and KED, are more effective in capturing the spatial variability and anisotropy inherent in precipitation patterns influenced by the Indian summer monsoons. In addition, the KED method effectively captured the daily variation of the temperature lapse rate, indicating the inadequacy of using a constant lapse rate for hydrological modelling in high-elevation regions like the TP. The geostatistical technique outperformed the Deterministic method, with KED realising the best temperature and precipitation interpolation performance based on cross-validation results. However, although KED provides superior results based on cross-validation performance, applying its precipitation interpolation as input into WaSiM led to the poorest discharge simulation. The combination of OK for precipitation and KED for temperature produced the most accurate discharge simulations in the LRB, highlighting the importance of not solely relying on cross-validation results but also considering the practical implications of interpolation methods on hydrological model outputs. Our study offers a robust framework for improving flood simulations and water resource management in a data-scarce, high-elevation region like the TP.