Background
Type:

Assessment on the effect of climate change on streamflow in the source region of the Yangtze River, China

Journal: Water (Switzerland) (20734441)Year: 1 January 2017Volume: 9Issue:
Lü, HaishenSadeghi A.aZhu, YonghuaSu, Jianbin
Green • GoldDOI:10.3390/w9010070Language: English

Abstract

Tuotuo River basin, known as the source region of the Yangtze River, is the key area where the impact of climate change has been observed on many of the hydrological processes of this central region of the Tibetan Plateau. In this study, we examined six Global Climate Models (GCMs) under three Representative Concentration Pathways (RCPs) scenarios. First, the already impacted climate change was analyzed, based on the historical data available and then, the simulation results of the GCMs and RCPs were used for future scenario assessments. Results indicated that the annual mean temperature will likely be increased, ranging from -0.66 °C to 6.68 °C during the three future prediction periods (2020s, 2050s and 2080s), while the change in the annual precipitation ranged from -1.18% to 66.14%. Then, a well-known distributed hydrological soil vegetation model (DHSVM) was utilized to evaluate the effects of future climate change on the streamflow dynamics. The seasonal mean streamflows, predicted by the six GCMs and the three RCPs scenarios, were also shown to likely increase, ranging from -0.52% to 22.58%. Watershed managers and regulators can use the findings from this study to better implement their conservation practices in the face of climate change. © 2017 by the authors.


Author Keywords

CMIP5GCMsLARS-WG methodStreamflow dynamicsUncertainty

Other Keywords

ChinaQinghaiQinghai-Xizang PlateauTogton RiverYangtze RiverClimate modelsRiversStream flowTransport propertiesAnnual mean temperaturesAnnual precipitationCMIP5Conservation practicesGCMsLARS-WG methodSource region of the yangtze riversUncertaintyassessment methodclimate changeclimate modelingconservation managementhydrological cyclehydrological modelingstreamflowuncertainty analysiswatershed