Assessing 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 plays the crucial role 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 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. Five U.S. river basins were analyzed using the 28-year dataset and shorter subsets to highlight the impacts of recession constant sensitivities with different data lengths. This study explores the impact of recession constant sensitivities over the hydrological parameter estimation using two approaches (cg): (i) assessing the relationship between the recession constant (cg) and the data adjustment parameter (Cep), for the 28-year dataset, and (ii) investigating the significant impacts of the sensitivity of cg over Cep in shorter datasets, which can affect the estimation of the acceptable minimum data length in the data-scarce basins. The polynomial regression analysis was applied to compare and evaluate the model results, varying over the recession constant with different data lengths. The findings indicated that the influence of the recession constant over the data adjustment parameters in the 28-year dataset is limited in the annual scale. However, there is a significant impact of recession constant sensitivity over the model performance while calibrating the model with subsets, particularly in the worst-case scenario. This study underscores the importance of the recession constant sensitivity for reliable continuous hydrological model predictions, especially in data-scarce areas.