Climate change will lead to a significant alteration in the temporal and spatial pattern variation in the regional hydrological cycle, and the subsequent lack of water, environmental deterioration, floods and droughts...Climate change will lead to a significant alteration in the temporal and spatial pattern variation in the regional hydrological cycle, and the subsequent lack of water, environmental deterioration, floods and droughts etc. And it is especially remarkable in semi-humid and semi-arid region. In this paper, the impacts of climate change on the hydrological cycle were analyzed for the Hai River Basin, a semi-humid and semi-arid basin and also the water receiving area of the middle route of South-to-North Water Diversion project. Meanwhile it is the most vulnerable to climate change. Firstly, the linear regression and Mann-Kendall non-parametric test methods were used to analyze the change characteristics of the hydrological and meteorological elements for the period from 1960 to 2009. The results show a significant increase in temperature, while precipitation decreases slightly, and runoff decreases drastically over the past 50 years. Secondly, the applicability of SWAT （Soil and Water Assessment Tool） model based on the DEM （Digital Elevation Model）, land use and soil type was verified in the basin. Results show the model performs well in this basin. Furthermore, the water balance model, Fu＇s theory and Koichiro＇s theory were used to calculate the actual evaporation, comparing to the simulated actual evaporation by SWAT model to validate the result for the lack of large-scale observed evaporation datasets. Possible reasons were also analyzed to explore the reasonable factor for the decline of the runoff. Finally the precipitation, temperature, runoff and evaporation response processes based on the IPCC AR4 multi-mode climate models and the verified SWAT model under different GHG emission scenarios （SRES-A2, AIB and B1） in the 21st century were discussed in three time periods： 2020s （2011-2040）, 20S0s （2041-2070）, 2080s （2071-2099）. Results show that there are systematic positive trends for precipitation and temperature while the trends for runoff and evaporation will differ among sub-areas. The results will offer some references for adaptive water management in a changing environment, also including adaptation of a cross-basin water transfer project.展开更多
Understanding the hydrological effects of the Three Gorges Dam operation in the entire reservoir area is significant to achieving optimal dam regulation. In this paper, a large-scale coupled hydrological-hydrodynamic-...Understanding the hydrological effects of the Three Gorges Dam operation in the entire reservoir area is significant to achieving optimal dam regulation. In this paper, a large-scale coupled hydrological-hydrodynamic-dam operation model is developed to comprehensively evaluate the hydrological effects of the river-type Three Gorges Reservoir. The results show that the coupled model is effective for hydrological, hydrodynamic regime and hydropower simulations in the reservoir area. Dam operation could have a notable positive effect on flood control and could reduce the maximum daily flood peak by up to 26.2%. It also contributes a large amount of hydropower, approximately 94.27 TWh/year, and a water supply increase for the downstream area of up to 22% during the dry season. In the flood season, the water level at Cuntan would increase under the condition that the water level of the dam is higher than approximately 158 m due to dam operation. In the dry season, attention should be paid to the low flow velocity near the dam in the reservoir area.展开更多
Lake water level is an essential indicator of environmental changes caused by natural and human factors.The water level of Poyang Lake,the largest freshwater lake in China,has exhibited a dramatic variation for the pa...Lake water level is an essential indicator of environmental changes caused by natural and human factors.The water level of Poyang Lake,the largest freshwater lake in China,has exhibited a dramatic variation for the past few years,especially after the completion of the Three Gorges Dam(TGD).However,there is a lack of more accurate assessment of the effect of the TGD on the Poyang Lake water level(PLWL)at finer temporal scales(e.g.,the daily scale).Here,we used three machine learning models,namely,an Artificial Neural Network(ANN),a Nonlinear Autoregressive model with exogenous input(NARX),and a Gated Recurrent Unit(GRU),to simulate the daily lake level during 2003-2016.We found that machine learning models with historical memory(i.e.,the GRU model)are more suitable for simulating the PLWL under the influence of the TGD.The GRU-based results show that the lake level is significantly affected by the TGD regulation in the different operation stages and in different periods.Although the TGD has had a slight but not very significant impact on the yearly decline of the PLWL,the blocking or releasing of water at the TGD at certain moments has caused large changes in the lake level.This machine-learning-based study sheds light on the interactions between Poyang Lake and the Yangtze River regulated by the TGD.展开更多
基金supported by National Basic Research Program of China(2010CB428406)the National Natural Science Foundation of China (No. 41071025/40730632)MWR Commonweal Project (200801001)
文摘Climate change will lead to a significant alteration in the temporal and spatial pattern variation in the regional hydrological cycle, and the subsequent lack of water, environmental deterioration, floods and droughts etc. And it is especially remarkable in semi-humid and semi-arid region. In this paper, the impacts of climate change on the hydrological cycle were analyzed for the Hai River Basin, a semi-humid and semi-arid basin and also the water receiving area of the middle route of South-to-North Water Diversion project. Meanwhile it is the most vulnerable to climate change. Firstly, the linear regression and Mann-Kendall non-parametric test methods were used to analyze the change characteristics of the hydrological and meteorological elements for the period from 1960 to 2009. The results show a significant increase in temperature, while precipitation decreases slightly, and runoff decreases drastically over the past 50 years. Secondly, the applicability of SWAT （Soil and Water Assessment Tool） model based on the DEM （Digital Elevation Model）, land use and soil type was verified in the basin. Results show the model performs well in this basin. Furthermore, the water balance model, Fu＇s theory and Koichiro＇s theory were used to calculate the actual evaporation, comparing to the simulated actual evaporation by SWAT model to validate the result for the lack of large-scale observed evaporation datasets. Possible reasons were also analyzed to explore the reasonable factor for the decline of the runoff. Finally the precipitation, temperature, runoff and evaporation response processes based on the IPCC AR4 multi-mode climate models and the verified SWAT model under different GHG emission scenarios （SRES-A2, AIB and B1） in the 21st century were discussed in three time periods： 2020s （2011-2040）, 20S0s （2041-2070）, 2080s （2071-2099）. Results show that there are systematic positive trends for precipitation and temperature while the trends for runoff and evaporation will differ among sub-areas. The results will offer some references for adaptive water management in a changing environment, also including adaptation of a cross-basin water transfer project.
基金Strategic Priority Research Program of the Chinese Academy of Sciences,No.XDA23040500Youth Innovation Promotion Association,CAS,No.2021385Central Guidance on Local Science and Technology Development Fund of Chongqing City,No.2021000069。
文摘Understanding the hydrological effects of the Three Gorges Dam operation in the entire reservoir area is significant to achieving optimal dam regulation. In this paper, a large-scale coupled hydrological-hydrodynamic-dam operation model is developed to comprehensively evaluate the hydrological effects of the river-type Three Gorges Reservoir. The results show that the coupled model is effective for hydrological, hydrodynamic regime and hydropower simulations in the reservoir area. Dam operation could have a notable positive effect on flood control and could reduce the maximum daily flood peak by up to 26.2%. It also contributes a large amount of hydropower, approximately 94.27 TWh/year, and a water supply increase for the downstream area of up to 22% during the dry season. In the flood season, the water level at Cuntan would increase under the condition that the water level of the dam is higher than approximately 158 m due to dam operation. In the dry season, attention should be paid to the low flow velocity near the dam in the reservoir area.
基金Strategic Priority Research Program of the Chinese Academy of Sciences,No.XDA23040500National Natural Science Foundation of China,No.41890823。
文摘Lake water level is an essential indicator of environmental changes caused by natural and human factors.The water level of Poyang Lake,the largest freshwater lake in China,has exhibited a dramatic variation for the past few years,especially after the completion of the Three Gorges Dam(TGD).However,there is a lack of more accurate assessment of the effect of the TGD on the Poyang Lake water level(PLWL)at finer temporal scales(e.g.,the daily scale).Here,we used three machine learning models,namely,an Artificial Neural Network(ANN),a Nonlinear Autoregressive model with exogenous input(NARX),and a Gated Recurrent Unit(GRU),to simulate the daily lake level during 2003-2016.We found that machine learning models with historical memory(i.e.,the GRU model)are more suitable for simulating the PLWL under the influence of the TGD.The GRU-based results show that the lake level is significantly affected by the TGD regulation in the different operation stages and in different periods.Although the TGD has had a slight but not very significant impact on the yearly decline of the PLWL,the blocking or releasing of water at the TGD at certain moments has caused large changes in the lake level.This machine-learning-based study sheds light on the interactions between Poyang Lake and the Yangtze River regulated by the TGD.
