The increase of irretrievable river water withdrawals and regulation of river flow has a negative effect on the natural regime of the Aral Sea. The Ainu Darya River and the Syr Darya River Basins are the largest irrig...The increase of irretrievable river water withdrawals and regulation of river flow has a negative effect on the natural regime of the Aral Sea. The Ainu Darya River and the Syr Darya River Basins are the largest irrigated farming areas. Their favorable soil and climatic conditions ensure guaranteed yields of various crops on irrigated lands. Since 1961, for the drastic increase of irretrievable river water withdrawal, mainly for irrigation, the inflow of fiver water into the Aral Sea has started to decrease significantly, accordingly the sea＇s hydrological and hydrochemical regimes disrupted dramatically. The sea level has continued to drop as evaporation exceeds inflow. This negatively transforms the natural environment and worsens socio-economic conditions in Priaralie as a whole, especially in the lower reaches of Amu Darya and Syr Darya, where natural conditions are largely determined by the sea＇s impact. At present, this causes desertification of the nonirrigated zone in the deltas, spreading to new areas as the Aral Sea dries out.展开更多
With the continuous development of economy and society, people's ability to transform society has been improved. To break the constraints of hydrological and climatic conditions, some hydrology and water conservan...With the continuous development of economy and society, people's ability to transform society has been improved. To break the constraints of hydrological and climatic conditions, some hydrology and water conservancy projects were constructed to meet the needs of human activities. In the construction of hydrological engineering, geological conditions are first surveyed to determine whether there are significant geological structure problems, in order to enhance the stability of hydrological engineering and reduce the probability of hydrological engineering leakage.展开更多
Copula functions have been widely used in stochastic simulation and prediction of streamflow.However,existing models are usually limited to single two-dimensional or three-dimensional copulas with the same bivariate b...Copula functions have been widely used in stochastic simulation and prediction of streamflow.However,existing models are usually limited to single two-dimensional or three-dimensional copulas with the same bivariate block for all months.To address this limitation,this study developed a mixed D-vine copula-based conditional quantile model that can capture temporal correlations.This model can generate streamflow by selecting different historical streamflow variables as the conditions for different months and by exploiting the conditional quantile functions of streamflows in different months with mixed D-vine copulas.The up-to-down sequential method,which couples the maximum weight approach with the Akaike information criteria and the maximum likelihood approach,was used to determine the structures of multivariate Dvine copulas.The developed model was used in a case study to synthesize the monthly streamflow at the Tangnaihai hydrological station,the inflow control station of the Longyangxia Reservoir in the Yellow River Basin.The results showed that the developed model outperformed the commonly used bivariate copula model in terms of the performance in simulating the seasonality and interannual variability of streamflow.This model provides useful information for water-related natural hazard risk assessment and integrated water resources management and utilization.展开更多
Geophysical techniques can help to bridge the inherent gap that exists with regard to spatial resolution and coverage for classical hydrological methods. This has led to the emergence of a new and rapidly growing rese...Geophysical techniques can help to bridge the inherent gap that exists with regard to spatial resolution and coverage for classical hydrological methods. This has led to the emergence of a new and rapidly growing research domain generally referred to as hydrogeophysics. Given the differing sensitivities of various geophysical techniques to hydrologically relevant parameters, their inherent trade-off between resolution and range, as well as the notoriously site-specific nature of petrophysical parameter relations, the fundamental usefulness of multi-method surveys for reducing uncertainties in data analysis and interpretation is widely accepted. A major challenge arising from such endeavors is the quantitative integration of the resulting vast and diverse database into a unified model of the probed subsurface region that is consistent with all available measurements. To this end, we present a novel approach toward hydrogeophysical data integration based on a Monte-Carlo-type conditional stochastic simulation method that we consider to be particularly suitable for high-resolution local-scale studies. Monte Carlo techniques are flexible and versatile, allowing for accounting for a wide variety of data and constraints of differing resolution and hardness, and thus have the potential of providing, in a geostatistical sense, realistic models of the pertinent target parameter distributions. Compared to more conventional approaches, such as co-kriging or cluster analysis, our approach provides significant ad- vancements in the way that larger-scale structural information eontained in the hydrogeophysieal data can be accounted for. After outlining the methodological background of our algorithm, we present the results of its application to the integration of porosity log and tomographic crosshole georadar data to generate stochastic realizations of the detailed local-scale porosity structure. Our procedure is first tested on pertinent synthetic data and then applied to a field dataset collected at the Boise Hydrogeophysical Research Site. Finally, we compare the performance of our data integration approach to that of more conventional methods with regard to the prediction of flow and transport phenomena in highly heterogeneous media and discuss the implications arising.展开更多
文摘The increase of irretrievable river water withdrawals and regulation of river flow has a negative effect on the natural regime of the Aral Sea. The Ainu Darya River and the Syr Darya River Basins are the largest irrigated farming areas. Their favorable soil and climatic conditions ensure guaranteed yields of various crops on irrigated lands. Since 1961, for the drastic increase of irretrievable river water withdrawal, mainly for irrigation, the inflow of fiver water into the Aral Sea has started to decrease significantly, accordingly the sea＇s hydrological and hydrochemical regimes disrupted dramatically. The sea level has continued to drop as evaporation exceeds inflow. This negatively transforms the natural environment and worsens socio-economic conditions in Priaralie as a whole, especially in the lower reaches of Amu Darya and Syr Darya, where natural conditions are largely determined by the sea＇s impact. At present, this causes desertification of the nonirrigated zone in the deltas, spreading to new areas as the Aral Sea dries out.
文摘With the continuous development of economy and society, people's ability to transform society has been improved. To break the constraints of hydrological and climatic conditions, some hydrology and water conservancy projects were constructed to meet the needs of human activities. In the construction of hydrological engineering, geological conditions are first surveyed to determine whether there are significant geological structure problems, in order to enhance the stability of hydrological engineering and reduce the probability of hydrological engineering leakage.
基金supported by the National Natural Science Foundation of China(Grant No.52109010)the Postdoctoral Science Foundation of China(Grant No.2021M701047)the China National Postdoctoral Program for Innovative Talents(Grant No.BX20200113).
文摘Copula functions have been widely used in stochastic simulation and prediction of streamflow.However,existing models are usually limited to single two-dimensional or three-dimensional copulas with the same bivariate block for all months.To address this limitation,this study developed a mixed D-vine copula-based conditional quantile model that can capture temporal correlations.This model can generate streamflow by selecting different historical streamflow variables as the conditions for different months and by exploiting the conditional quantile functions of streamflows in different months with mixed D-vine copulas.The up-to-down sequential method,which couples the maximum weight approach with the Akaike information criteria and the maximum likelihood approach,was used to determine the structures of multivariate Dvine copulas.The developed model was used in a case study to synthesize the monthly streamflow at the Tangnaihai hydrological station,the inflow control station of the Longyangxia Reservoir in the Yellow River Basin.The results showed that the developed model outperformed the commonly used bivariate copula model in terms of the performance in simulating the seasonality and interannual variability of streamflow.This model provides useful information for water-related natural hazard risk assessment and integrated water resources management and utilization.
基金supported by the Swiss National Science Foundation
文摘Geophysical techniques can help to bridge the inherent gap that exists with regard to spatial resolution and coverage for classical hydrological methods. This has led to the emergence of a new and rapidly growing research domain generally referred to as hydrogeophysics. Given the differing sensitivities of various geophysical techniques to hydrologically relevant parameters, their inherent trade-off between resolution and range, as well as the notoriously site-specific nature of petrophysical parameter relations, the fundamental usefulness of multi-method surveys for reducing uncertainties in data analysis and interpretation is widely accepted. A major challenge arising from such endeavors is the quantitative integration of the resulting vast and diverse database into a unified model of the probed subsurface region that is consistent with all available measurements. To this end, we present a novel approach toward hydrogeophysical data integration based on a Monte-Carlo-type conditional stochastic simulation method that we consider to be particularly suitable for high-resolution local-scale studies. Monte Carlo techniques are flexible and versatile, allowing for accounting for a wide variety of data and constraints of differing resolution and hardness, and thus have the potential of providing, in a geostatistical sense, realistic models of the pertinent target parameter distributions. Compared to more conventional approaches, such as co-kriging or cluster analysis, our approach provides significant ad- vancements in the way that larger-scale structural information eontained in the hydrogeophysieal data can be accounted for. After outlining the methodological background of our algorithm, we present the results of its application to the integration of porosity log and tomographic crosshole georadar data to generate stochastic realizations of the detailed local-scale porosity structure. Our procedure is first tested on pertinent synthetic data and then applied to a field dataset collected at the Boise Hydrogeophysical Research Site. Finally, we compare the performance of our data integration approach to that of more conventional methods with regard to the prediction of flow and transport phenomena in highly heterogeneous media and discuss the implications arising.
