Runoff simulation and projection in the source area of the Yellow River using the SWAT model and SSPs scenarios

Abstract

The source area of the Yellow River (SAYR) is one of the world´s largest wetlands containing the greatest diversity of high altitude marshlands. For this reason, its response to climate change is extremely significant. As revealed by different studies, the response of hydrological processes to global warming results in high uncertainties and complexities in the water cycle of the SAYR. Thus, understanding and projecting future runoff changes in this region has become increasingly important. In the present investigation, we used runoff and meteorological data of the SAYR from 1976 to 2014 (historical period). In addition, Digital Elevation Model (DEM), land-use, and soil data for the period 1976 to 2100 were used considering three future SSPs (Shared Socioeconomic Paths) scenarios of 8 models selected from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The Soil and Water Assessment Tool (SWAT) was used to simulate, project, and analyze potential variations and future runoff of the main hydrological stations (Jimai, Maqu, and Tangnaihai) located in the SAYR. The results showed that: 1) The SWAT model displayed good applicability in historical runoff simulation in the SAYR. A small runoff simulation uncertainty was observed as the simulated value was close to the measured value. 2) Under three different 2021–2100 SSPs scenarios, the yearly discharge of the three hydrological stations located in the SAYR showed an increasing trend with respect to the historical period. Future runoff is mainly affected by precipitation. 3) We compared the 1976–2014 average annual runoff with projected values for the periods 2021–2060 and 2061–2100. With respect to 2021–2060, the lowest and highest increases occurred at Tangnaihai and Maqu Stations in the emission scenarios without (SSP585) and with mitigation (SSP126), respectively. However, the highest and lowest increments at Jimai Station were observed in the intermediate emission (SSP245) and SSP126 scenarios, respectively. Moreover, in 2061–2100, the Maqu and Tangnaihai Stations showed the lowest and highest increments in the SSP585 and SSP245 scenarios, correspondingly. In Jimai Station, the lowest increment occurred in SSP126. The yearly average discharge in the near future will be smaller than that in the far future. Overall, this study provides scientific understanding of future hydrological responses to climate changes in the alpine area. This information can also be of help in the selection of actions for macro-control, planning, and management of water resources, and the protection of wetlands in the SAYR.

Modelling the future climate impacts on hydraulic infrastructure development in tropical (peri-)urban region: Case of Kigali, Rwanda

“…6,(a) ). Though the precipitation increment identified in the distant future might result in the flow rate increment, the temperature increment might also result in intensified evapotranspiration, which should lead to a slight peak flow rate decline in the distant future [ 77 ]. Refer to Icyimpaye, [ 79 ] and Icyimpaye et al [ 78 ] studies conducted on the same catchment, they obtained excess discharge because the model wasn't calibrated.…”

“…According to Ye et al [ 82 ], the study identified up to 400% and 18% flow rate increments resulting from rainfall variation and land use/cover change, respectively, by the end of the century. The study conducted by Li et al [ 77 ] identified up to 162.47% variation by end-century under ssp245, while the study conducted by Haider et al [ 83 ] identified up to 345.3% change factor under RCP8.5.…”

Xinanjiang-based interval forecasting model for daily streamflow considering climate change impacts

“…The results showed that most hydrological elements would increase under future climate patterns, such as precipitation, temperature, and streamflow. Li et al successfully simulated monthly streamflow in the HRYRB by using the SWAT model and conducted a sensitivity analysis on the model parameters (Li et al, 2022). They found that the monthly streamflow is notably sensitive to changes in precipitation, evaporation, and land cover.…”