Accessing Recession Constant Sensitivity and Its Interaction with Data Adjustment Parameters in Continuous Hydrological Modeling in Data-Scarce Basins: A Case Study Using the Xinanjiang Model

Abstract

Data scarcity is a crucial problem in hydrological modeling, causing the uncertainties in hydrological model calibration and parameterization. Therefore, while considering the sensitivity of the parameter optimization, it is essential to determine which parameters have the most significant implications on model performance, especially when there is limited hydro-climatological information. Previous studies have underscored the significance of data adjustment parameter sensitivity and its consequential influence on both Xinanjiang (XAJ) model performance and the determination of the acceptable minimum data length, particularly in data-scarce regions. Nevertheless, it is essential to consider the recession constant sensitivity as it has been identified as one of the most sensitive parameter on an annual scale while keeping the data adjustment parameters constant during a period of data scarcity. Hence, the objective of this study is to extend the previous research by examining the relationship between recession constant sensitivity and data adjustment parameters in shorter datasets leading to more reliable parameter estimation for data-scarce basins.