Impacts of changes in the watershed partitioning level and optimization algorithm on runoff simulation: decomposition of uncertainties

Abstract

Hydrological modeling has provided key insights into the mechanisms of model state, such as the watershed partitioning level and optimization algorithm, and their impacts on the hydrological process, but the uncertainty of this impact is poorly understood. To this end, in this study, the effects of the watershed partitioning level and optimization algorithm for hydrological simulation uncertainty were assessed based on the semi-distributed TOPMODEL model, i.e., six watershed partitioning levels and three intelligent global optimization algorithms were used in the source region of the Yellow River. Meanwhile, the uncertainty contribution of the individual and interaction of the watershed partitioning levels and optimization algorithms on the hydrological process were dynamically evaluated using the variance decomposition method based on subsampling. Results showed that the impacts of the watershed partitioning level and optimization algorithm on the runoff simulation were particularly obvious for different characteristic periods. In the flood period, the optimization algorithm was the dominant factor affecting the runoff simulation uncertainty, with the proportion of up to 0.50, whereas the contribution of the watershed partitioning level was only 0.22. In the non-flood period, they contributed substantially to the uncertainty of the runoff simulation, accounting for about 0.30. Moreover, the interactions between the watershed partitioning level and optimization algorithm had a strong influence throughout the year, especially in the non-flood period, which may be because the hydrological model amplifies the output error and increases the interaction effect. Generally, the results shed important insight into reducing the uncertainty of the runoff simulation in future research.

Projections of Global Land Runoff Changes and Their Uncertainty Characteristics During the 21st Century

“…Most such studies have focused on temperature and precipitation (Evin et al., 2019; Lehner et al., 2020; Zhou, Lu, et al., 2020), and they have found that model uncertainty dominates the uncertainty of temperature and precipitation projections, while the contribution of internal variability decreases with time, and the contribution of scenario uncertainty increases with time. The decomposition of uncertainty in projections of runoff changes based on the CMIP models has mostly not been explored; the sources of decomposition vary and mostly do not involve internal variability and scenario uncertainty (Finger et al., 2012; Zhou, Wang, et al., 2020; Zhou, Wang, Li, Chang, & Guo, 2021). Furthermore, these studies have focused mainly on the watershed scale—looking at, for example, the Alpine Rhine (Bosshard et al., 2013), the Swiss Alps (Finger et al., 2012), the Jialing River (Zhou, Wang, Li, Chang, & Guo, 2021), and 12 large‐scale river basins (Vetter et al., 2017)—while studies on the global scale are not yet available.…”