Sensitivity of bias correction step on generating hydrological scenarios

Abstract

Significant shifts in hydro-climatic regimes are expected in many parts of the world during the 21st century, affecting the water cycle. Vulnerability, impact, and adaptation studies often use tailored modeling chains to assess the expected effects of climate change, but the robustness of these chains is rarely investigated. This highlights the need for more rigorous evaluation of modeling chains to ensure that they are reliable for informed decision-making processes. To address this gap, we propose a framework for evaluating the sensitivity of hydrological scenario production to the bias correction step. We apply the framework to the Senegal River Basin, using three bias correction methods (linear scale, empirical quantile mapping, and nested bias correction) and three procedures (climate-correction, hydrological-correction, and climate-hydrological-correction). Our results show that the choice of modeling chain has a significant impact on future hydro-climatic trajectories. In particular, the combination of climate-and-hydrological-correction procedures may be optimal when both climate biases and hydrological model errors are significant. Moreover, using multiple bias correction methods can strengthen the ensemble of future hydro-climatic conditions. These findings have implications for vulnerability–impact–adaptation studies and underscore the importance of rigorous modeling chain design and sensitivity analysis. HIGHLIGHTS The choice of bias correction method can significantly impact the resulting hydrological scenarios.; Removing the biases with a climate-correction procedure, hydrological-correction procedure, or a combination of both also notably influences the hydrological-scenario outcomes.; Resorting to several bias correction methods can be seen as a factor to strengthen the ensemble of future hydro-climatic conditions.;

Keywords

• bias correction
• climate change
• hydrological scenario production
• senegal river basin