Impact of Different Geospatial Meteorological Input Configurations on a Semi-Distributed hydrological model output

Hydrological models are influenced by multiple choices, which can significantly affect all phases of their application. These choices impact the calibration process by influencing the estimation of optimized parameters, the validation phase, and the model's overall performance in forecasting applications. Among these sources, input data, such as meteorological variables, play a pivotal role. While accurate collection and validation of such data are essential, they are often insufficient. For example, in the case of semi-distributed hydrological models applied to a basin divided into multiple hydrological response units (HRUs), most of them typically lack adequate instrumentation. Consequently, it becomes necessary to estimate or simulate meteorological inputs, such as precipitation and air temperature through appropriate geostatistical modeling. Beyond the choice of estimation method, a critical consideration is what constitutes a representative value for an HRU: whether it originates from a single point (e.g. the HRU centroid) or is an appropriate statistic from a grid of points. This decision has substantial implications for the model's computational time, performance, and reliability and can introduce uncertainties in the final modeled product.

This study investigates how different configurations of input data may affect model performance in the upper part of the Noce River, located in the Trento province of Italy. The analysis was conducted using the GEOframe framework, its kriging method and semi-distributed model. Four configurations were analyzed moving from the most simplified and computationally convenient (one representative point over the subbasin) towards the most complex (average of gridded values over the subbasin). The effects of the different scenarios are evaluated over several hydrological processes (river discharges, soil moisture, snow evolution), quantifying the trade-offs between computational efficiency and the accuracy of input data representation. The work offers insights into how different configurations can influence the reliability of hydrological forecasts and the uncertainties in the final results.