Within the context of the Belt and Road Initiative(BRI)and the China-Myanmar Economic Corridor(CMEC),the Dulong-Ir-rawaddy(Ayeyarwady)River,an international river among China,India and Myanmar,plays a significant role...Within the context of the Belt and Road Initiative(BRI)and the China-Myanmar Economic Corridor(CMEC),the Dulong-Ir-rawaddy(Ayeyarwady)River,an international river among China,India and Myanmar,plays a significant role as both a valuable hydro-power resource and an essential ecological passageway.However,the water resources and security exhibit a high degree of vulnerabil-ity to climate change impacts.This research evaluates climate impacts on the hydrology of the Dulong-Irrawaddy River Basin(DIRB)by using a physical-based hydrologic model.We crafted future climate scenarios using the three latest global climate models(GCMs)from Coupled Model Intercomparison Project 6(CMIP6)under two shared socioeconomic pathways(SSP2-4.5 and SSP5-8.5)for the near(2025-2049),mid(2050-2074),and far future(2075-2099).The regional model using MIKE SHE based on historical hydrologic processes was developed to further project future streamflow,demonstrating reliable performance in streamflow simulations with a val-idation Nash-Sutcliffe Efficiency(NSE)of 0.72.Results showed that climate change projections showed increases in the annual precip-itation and potential evapotranspiration(PET),with precipitation increasing by 11.3%and 26.1%,and PET increasing by 3.2%and 4.9%,respectively,by the end of the century under SSP2-4.5 and SSP5-8.5.These changes are projected to result in increased annual streamflow at all stations,notably at the basin’s outlet(Pyay station)compared to the baseline period(with an increase of 16.1%and 37.0%at the end of the 21st century under SSP2-4.5 and SSP5-8.5,respectively).Seasonal analysis for Pyay station forecasts an in-crease in dry-season streamflow by 31.3%-48.9%and 22.5%-76.3%under SSP2-4.5 and SSP5-8.5,respectively,and an increase in wet-season streamflow by 5.8%-12.6%and 2.8%-33.3%,respectively.Moreover,the magnitude and frequency of flood events are pre-dicted to escalate,potentially impacting hydropower production and food security significantly.This research outlines the hydrological response to future climate change during the 21st century and offers a scientific basis for the water resource management strategies by decision-makers.展开更多
The imbalance in global streamflow gauge distribution and regional data scarcity,especially in large transboundary basins,challenge regional water resource management.Effectively utilizing these limited data to constr...The imbalance in global streamflow gauge distribution and regional data scarcity,especially in large transboundary basins,challenge regional water resource management.Effectively utilizing these limited data to construct reliable models is of crucial practical im-portance.This study employs a transfer learning(TL)framework to simulate daily streamflow in the Dulong-lrrawaddy River Basin(DIRB),a less-studied transboundary basin shared by Myanmar,China,and India.Our results show that TL significantly improves streamflow pre-dictions:the optimal TL model achieves an average Nash-Sutcliffe efficiency of 0.872,showing a marked improvement in the Hkamti sub-basin.Despite data scarcity,TL achieves a mean NSE of 0.817,surpassing the 0.655 of the process-based model MIKE SHE.Addition-ally,our study reveals the importance of source model selection in TL,as different parts of the flow are affected by the diversity and similarity of data in the source model.Deep learning models,particularly TL,exhibit complex sensitivities to meteorological inputs,more accu-rately capturing non-linear relationships among multiple variables than the process-based model.Integrated gradients(IG)analysis furtherillustrates TL's ability to capture spatial het-erogeneity in upstream and downstream sub-basins and its adeptness in characterizing dif-ferent flow regimes.This study underscores the potential of TL in enhancing the under-standing of hydrological processes in large-scale catchments and highlights its value for wa-ter resource management in transboundary basins under data scarcity.展开更多
基金Under the auspices of the Yunnan Scientist Workstation on International River Research of Daming He(No.KXJGZS-2019-005)National Natural Science Foundation of China(No.42201040)+1 种基金National Key Research and Development Project of China(No.2016YFA0601601)China Postdoctoral Science Foundation(No.2023M733006)。
文摘Within the context of the Belt and Road Initiative(BRI)and the China-Myanmar Economic Corridor(CMEC),the Dulong-Ir-rawaddy(Ayeyarwady)River,an international river among China,India and Myanmar,plays a significant role as both a valuable hydro-power resource and an essential ecological passageway.However,the water resources and security exhibit a high degree of vulnerabil-ity to climate change impacts.This research evaluates climate impacts on the hydrology of the Dulong-Irrawaddy River Basin(DIRB)by using a physical-based hydrologic model.We crafted future climate scenarios using the three latest global climate models(GCMs)from Coupled Model Intercomparison Project 6(CMIP6)under two shared socioeconomic pathways(SSP2-4.5 and SSP5-8.5)for the near(2025-2049),mid(2050-2074),and far future(2075-2099).The regional model using MIKE SHE based on historical hydrologic processes was developed to further project future streamflow,demonstrating reliable performance in streamflow simulations with a val-idation Nash-Sutcliffe Efficiency(NSE)of 0.72.Results showed that climate change projections showed increases in the annual precip-itation and potential evapotranspiration(PET),with precipitation increasing by 11.3%and 26.1%,and PET increasing by 3.2%and 4.9%,respectively,by the end of the century under SSP2-4.5 and SSP5-8.5.These changes are projected to result in increased annual streamflow at all stations,notably at the basin’s outlet(Pyay station)compared to the baseline period(with an increase of 16.1%and 37.0%at the end of the 21st century under SSP2-4.5 and SSP5-8.5,respectively).Seasonal analysis for Pyay station forecasts an in-crease in dry-season streamflow by 31.3%-48.9%and 22.5%-76.3%under SSP2-4.5 and SSP5-8.5,respectively,and an increase in wet-season streamflow by 5.8%-12.6%and 2.8%-33.3%,respectively.Moreover,the magnitude and frequency of flood events are pre-dicted to escalate,potentially impacting hydropower production and food security significantly.This research outlines the hydrological response to future climate change during the 21st century and offers a scientific basis for the water resource management strategies by decision-makers.
基金National Key Research and Development Program of China,No.2022YFF1302405National Natural Science Foundation of China,No.42201040+1 种基金The National Key Research and Development Program of China,No.2016YFA0601601The China Postdoctoral Science Foundation,No.2023M733006。
文摘The imbalance in global streamflow gauge distribution and regional data scarcity,especially in large transboundary basins,challenge regional water resource management.Effectively utilizing these limited data to construct reliable models is of crucial practical im-portance.This study employs a transfer learning(TL)framework to simulate daily streamflow in the Dulong-lrrawaddy River Basin(DIRB),a less-studied transboundary basin shared by Myanmar,China,and India.Our results show that TL significantly improves streamflow pre-dictions:the optimal TL model achieves an average Nash-Sutcliffe efficiency of 0.872,showing a marked improvement in the Hkamti sub-basin.Despite data scarcity,TL achieves a mean NSE of 0.817,surpassing the 0.655 of the process-based model MIKE SHE.Addition-ally,our study reveals the importance of source model selection in TL,as different parts of the flow are affected by the diversity and similarity of data in the source model.Deep learning models,particularly TL,exhibit complex sensitivities to meteorological inputs,more accu-rately capturing non-linear relationships among multiple variables than the process-based model.Integrated gradients(IG)analysis furtherillustrates TL's ability to capture spatial het-erogeneity in upstream and downstream sub-basins and its adeptness in characterizing dif-ferent flow regimes.This study underscores the potential of TL in enhancing the under-standing of hydrological processes in large-scale catchments and highlights its value for wa-ter resource management in transboundary basins under data scarcity.
