To enable local water resource management and maintenance of ecosystem integrity and to protect and mitigate against flood and drought, it is necessary to determine changes in long-term series of streamflow and to dis...To enable local water resource management and maintenance of ecosystem integrity and to protect and mitigate against flood and drought, it is necessary to determine changes in long-term series of streamflow and to distinguish the roles that climate change and human disturbance play in these changes. A review of previous research on the detection and attribution of observed changes in annual runoff in China shows a decrease in annual runoff since the 1950s in northern China in areas such as the Songhuajiang River water resources zone, the Liaohe River water resources zone, the Haihe River water resources zone, the Yellow River water resources zone, and the Huaihe River water resources Zone. Furthermore, abrupt changes in annual runoff occurred mostly in the 1970s and 1980s in all the above zones, except for some of the sub-basins in the middle Yellow River where abrupt change occurred in the 1990s. Changes in annual runoff are found to be mainly caused by climate change in the western Songhuajiang River basin, the upper mainstream of the Yangtze River, and the western Pearl River basin, which shows that studies on the impact of climate change on future water resources under different climate change scenarios are required to enable planning and management by agencies in these river basins. However, changes in annual runoff were found to be mainly caused by human activities in most of the catchments in northern China (such as the southern Songhuajiang River, Liaohe River, Haihe River, the lower reach and some of the catchments within the middle Yellow River basin) and in middle-eastern China, such as the Huaihe River and lower mainstream of the Yangtze River. This suggests that current hydro-climatic data can continue to be used in water-use planning and that policymakers need to focus on water resource management and protection.展开更多
River water quality models based on remote sensing information models are superior to pure water quality models because they combine the inevitability and risk of geographical phenomena and can take complex geographic...River water quality models based on remote sensing information models are superior to pure water quality models because they combine the inevitability and risk of geographical phenomena and can take complex geographical characteristics into account. A water quality model for forecasting COD has been established with remote sensing in- formation modeling methods by monitoring and analyzing water quantity and water quality of the Lijing River reach which flows through a complicated Karst mountain area. This model provides a good tool to predict water quality of complex rivers. It is validated by simulating contaminant concentrations of the study area. The results show that remote sensing information models are suitable for complex geography. It is not only a combined model of inevitability and risk of the geographical phenomena, but also a semi-theoretical and semi-empirical formula, providing a good tool to study organic contaminants in complicated rivers. The coefficients and indices obtained have limited value and the model is not suitable for all situations. Some improvements are required.展开更多
A daily distributed hydrological model was developed using routine hydro-meteorological data on the basis of the raster DEM and land cover data. Then the model was used to model daily runoff of the Datong River Valley...A daily distributed hydrological model was developed using routine hydro-meteorological data on the basis of the raster DEM and land cover data. Then the model was used to model daily runoff of the Datong River Valley located in the upper catchment of the Yellow River Basin. The runoff comprises surface flow, subsurface flow and ground water flow. Evapotranspiration comprises canopy evaporation, snow sublimation and soil evapotranspiration. The infiltration to the soil was estimated with improved Green-Ampt model, and the potential evapotranspiration is estimated with Morton CRAE method, which only needs the routine meteorological data. Simulation results and the comparison with semi-distributed SLURP hydrological model show that the structure of the model presented herein is reasonable.展开更多
文摘To enable local water resource management and maintenance of ecosystem integrity and to protect and mitigate against flood and drought, it is necessary to determine changes in long-term series of streamflow and to distinguish the roles that climate change and human disturbance play in these changes. A review of previous research on the detection and attribution of observed changes in annual runoff in China shows a decrease in annual runoff since the 1950s in northern China in areas such as the Songhuajiang River water resources zone, the Liaohe River water resources zone, the Haihe River water resources zone, the Yellow River water resources zone, and the Huaihe River water resources Zone. Furthermore, abrupt changes in annual runoff occurred mostly in the 1970s and 1980s in all the above zones, except for some of the sub-basins in the middle Yellow River where abrupt change occurred in the 1990s. Changes in annual runoff are found to be mainly caused by climate change in the western Songhuajiang River basin, the upper mainstream of the Yangtze River, and the western Pearl River basin, which shows that studies on the impact of climate change on future water resources under different climate change scenarios are required to enable planning and management by agencies in these river basins. However, changes in annual runoff were found to be mainly caused by human activities in most of the catchments in northern China (such as the southern Songhuajiang River, Liaohe River, Haihe River, the lower reach and some of the catchments within the middle Yellow River basin) and in middle-eastern China, such as the Huaihe River and lower mainstream of the Yangtze River. This suggests that current hydro-climatic data can continue to be used in water-use planning and that policymakers need to focus on water resource management and protection.
文摘River water quality models based on remote sensing information models are superior to pure water quality models because they combine the inevitability and risk of geographical phenomena and can take complex geographical characteristics into account. A water quality model for forecasting COD has been established with remote sensing in- formation modeling methods by monitoring and analyzing water quantity and water quality of the Lijing River reach which flows through a complicated Karst mountain area. This model provides a good tool to predict water quality of complex rivers. It is validated by simulating contaminant concentrations of the study area. The results show that remote sensing information models are suitable for complex geography. It is not only a combined model of inevitability and risk of the geographical phenomena, but also a semi-theoretical and semi-empirical formula, providing a good tool to study organic contaminants in complicated rivers. The coefficients and indices obtained have limited value and the model is not suitable for all situations. Some improvements are required.
文摘论文应用第5次耦合模式比较计划(Coupled Model Intercomparison Project Phase 5,CMIP5)中5个全球气候模式(Global Climate Models,GCMs)和3种典型浓度路径(Representative Concentration Pathways,RCPs)在全球增温1.5℃和2.0℃下的预估结果,分析了淮河中上游地区未来的气候变化特征。进一步基于SWAT(Soil and Water Assessment Tool)水文模型定量预估了气候变化对该区域径流量的影响,并量化了预估结果的不确定性。结果表明:SWAT模型在淮河中上游对月径流量具有较好的模拟能力。在全球增温1.5℃和2.0℃下,淮河中上游年平均气温分别较基准期(1986—2005年)增加1.1℃和1.7℃;年降水量较基准期分别相应增加4%和7%;基于SWAT模型预估的年径流量较基准期分别增加5%和8%。未来气候变化不会改变月径流分布特征,年内径流仍集中在盛夏和初秋(6—9月)。预估的月丰水流量明显增加,尤其当全球增温达到2.0℃后,出现洪涝的风险明显增大。未来降水量和径流量预估都存在较大的不确定性,不确定性主要来源于GCMs,在全球增温2.0℃下预估的不确定性更大。
文摘A daily distributed hydrological model was developed using routine hydro-meteorological data on the basis of the raster DEM and land cover data. Then the model was used to model daily runoff of the Datong River Valley located in the upper catchment of the Yellow River Basin. The runoff comprises surface flow, subsurface flow and ground water flow. Evapotranspiration comprises canopy evaporation, snow sublimation and soil evapotranspiration. The infiltration to the soil was estimated with improved Green-Ampt model, and the potential evapotranspiration is estimated with Morton CRAE method, which only needs the routine meteorological data. Simulation results and the comparison with semi-distributed SLURP hydrological model show that the structure of the model presented herein is reasonable.
