Abstract
Catchment runoff is significantly affected by climate condition changes. Predicting the runoff and analyzing its variations under future climates play a vital role in water security, water resource management, and the sustainable development of the catchment. In traditional hydrological modeling, fixed model parameters are usually used to transfer the global climate models (GCMs) to runoff, while the hydrologic model parameters may be time-varying. It is more appropriate to use the time-variant parameter for runoff modeling. This is achieved by incorporating the time-variant parameter approach into a two-parameter water balance model (TWBM) through the construction of time-variant parameter functions based on the identified catchment climate indicators. Using the Ganjiang Basin with an outlet of the Dongbei Hydrological Station as the study area, we developed time-variant parameter scenarios of the TWBM model and selected the best-performed parameter functions to predict future runoff and analyze its variations under the climate model projection of the BCC-CSM1.1(m). To synthetically assess the model performance improvements using the time-variant parameter approach, an index Δ was developed by combining the Nash–Sutcliffe efficiency, the volume error, the Box–Cox transformed root-mean-square error, and the Kling–Gupta efficiency with equivalent weight. The results show that the TWBM model with time-variant C (evapotranspiration parameter) and SC (water storage capacity of catchment), where growing and non-growing seasons are considered for C, outperformed the model with constant parameters with a Δ value of approximately 5% and 10% for the calibration and validation periods, respectively. The mean annual values of runoff predictions under the four representative concentration pathways (RCPs) exhibited a decreasing trend over the future three decades (2021–2050) when compared to the runoff simulations in the baseline period (1982–2011), where the values were about −9.9%, −19.5%, −16.6%, and −11.4% for the RCP2.6, RCP4.5, RCP6.0, and RCP8.5, respectively. The decreasing trend of future precipitation exerts impacts on runoff decline. Generally, the mean monthly changes of runoff predictions showed a decreasing trend from January to August for almost all of the RCPs, while an increasing trend existed from September to November, along with fluctuations among different RCPs. This study can provide beneficial references to comprehensively understand the impacts of climate change on runoff prediction and thus improve the regional strategy for future water resource management.
Highlights
Climate changes like global warming are presently occurring due to continued emissions of greenhouse gases
The results demonstrate that the runoff volumes in the GJDB will decrease under the four representative concentration pathways (RCPs) in the future climate projection
This study focused on the runoff prediction and its change analysis in the GJDB for the future periods of 2021–2050
Summary
Climate changes like global warming are presently occurring due to continued emissions of greenhouse gases. The changes in the climate system will significantly affect climate variables such as precipitation and temperature. According to the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC) [1], the projected precipitation and temperature show great changes and exhibit heterogeneity in different regions. Increases of precipitation and temperature occur in high-latitude regions of the Northern Hemisphere; global surface temperature change for the end of 21st century is expected to exceed 1.5 ◦C for most of the representative concentration pathways (RCPs) [2]. The changes and variability of climate conditions can significantly affect catchment hydrological cycles, leading to changes in regional water resources [3]. It is of great importance to evaluate catchment runoff variations under the future climate projections
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