Overall uncertainty of climate change impacts on watershed hydrology in China

Abstract

The hydrological projections provided by the outputs of Global Climate Models (GCMs) combining hydrological models include multi-source uncertainties, which may challenge the formulation of relevant adaption and mitigation policies. In this paper, the overall uncertainty and the relative contribution of each uncertainty component were investigated for hydrological projections over 408 watersheds in China by using 3 shared socioeconomic pathway emission scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5), 21 GCMs, 8 bias correction methods, 4 hydrological models, and 2 sets of optimized hydrological model parameters. The results show that the total uncertainty (T) is mainly contributed by uncertainty related to global climate models (G), with the mean percentage ranging from 60.4 to 64.1%, followed by the interaction uncertainties among all components, with the mean percentage ranging from 22.0 to 26.4%.The uncertainty contribution of hydrological models (H) (6.1-9.4%) ranks third, followed by emission scenarios (S) (2.9-5.9%) and bias correction methods (B) (0.2-1.1%). The uncertainty contribution of the optimized hydrological model parameters (P) (0.2-0.3%) is almost negligible. In terms of spatial variability, the relative contribution of uncertainty related to global climate models (G) is the highest in the near future for northern and in the far future for southern China (66.1-66.7%). However, it was found to be lower for the Tibetan Plateau and northwestern China (45.3-57.9%) in the near and far future. The relative contribution of hydrological model uncertainty is higher for southwestern and northwestern China and the Tibetan Plateau (7.2-19.5%) and lower for northern, eastern, and southern China (2.5-6.6%). This study highlights the importance of including multiple GCMs and hydrological models in hydrological impact studies to consider their overall uncertainty. The development of global climate models and hydrological models is still the best way to reduce the uncertainty of climate change impact studies.

Predicting the Hydrological Impacts of Future Climate Change in a Humid-Subtropical Watershed

“…Uncertainties in the precipitation data of the GCMs are widely reported [5,11,[41][42][43]. Large uncertainties existed in precipitation data even after using the bias correction methods.…”

Assessing the Uncertainty of Hydropower-Environmental Conflict-Resolution Management under Climate Change

“…Many studies have investigated these uncertainties on watershed hydrology [18,35,36]. Zhang et al [37] investigated the overall uncertainty and the relative contribution of each uncertainty component for hydrological simulations over 408 watersheds in China by using 3 emission scenarios, 21 GCMs, 8 downscaling methods, 4 hydrological models, and 2 sets of optimized hydrological model parameters. They found that the uncertainty related to GCMs contributes most with the percentage of 60%, followed by the uncertainty related to hydrological models (approximately 7%) and downscaling methods (less than 1%).…”

Reliability of simulating internal precipitation variability over multi‐timescales using multiple global climate model large ensembles in China

“…To better understand both simulated and observed ICV in different regions, it is necessary to divide the whole Chinese territory into subregions. This study used seven subregions: northeast China (NE), north China (N), east China (E), south China (S), northwest China (NW), southwest China (SW) and Qinghai-Tibet Plateau (QT), as shown in Figure 1 (Wang & Li, 2007;Zhang et al, 2021). Regional average values in this study are described by spatially averaging the values of grids within the same region with latitudinal weight.…”