Glaciers are crucial water resources for arid inland rivers in Northwest China.In recent decades,glaciers are largely experiencing shrinkage under the climate-warming scenario,thereby exerting tremendous influences on...Glaciers are crucial water resources for arid inland rivers in Northwest China.In recent decades,glaciers are largely experiencing shrinkage under the climate-warming scenario,thereby exerting tremendous influences on regional water resources.The primary role of understudying watershed scale glacier changes under changing climatic conditions is to ensure sustainable utilization of regional water resources,to prevent and mitigate glacier-related disasters.This study maps the current(2020)distribution of glacier boundaries across the Kaidu-Kongque river basin,south slope of Tianshan Mountains,and monitors the spatial evolution of glaciers over five time periods from 2000-2020 through thresholded band ratios approach,using 25 Landsat images at 30 m resolution.In addition,this study attempts to understand the role of climate characteristics for variable response of glacier area.The results show that the total area of glaciers was 398.21 km^(2)in 2020.The glaciers retreated by about 1.17 km^(2)/a(0.26%/a)from 2000 to 2020.The glaciers were reducing at a significantly rapid rate between 2000 and 2005,a slow rate from 2005 to 2015,and an accelerated rate during 2015-2020.The meteorological data shows slight increasing trends of mean annual temperature(0.02℃/a)and annual precipitation(2.07 mm/a).The correlation analysis demonstrates that the role of temperature presents more significant correlation with glacier recession than precipitation.There is a temporal hysteresis in the response of glacier change to climate change.Increasing trend of temperature in summer proves to be the driving force behind the Kaidu-Kongque basin glacier recession during the recent 20 years.展开更多
Hydrological processes in river basins of similar size and morphology may differ significantly due to different climatic conditions. This paper presents a comparative analysis of hydrological characteristics of two ri...Hydrological processes in river basins of similar size and morphology may differ significantly due to different climatic conditions. This paper presents a comparative analysis of hydrological characteristics of two river basins located in different climatic zones: the Wisok River Basin in the south-eastern Poland and the Chaohe River Basin in the northern China. The criteria of their choice were similarities in the basin area, main river length and topography. The results show that climate plays a key role in shaping fluvial conditions within the two basins. It is concluded that: 1) precipitation in the Wisok River Basin is more evenly distributed in the yearly cycle, while in the Chaohe River Basin it is highly concentrated in the few summer months; 2) spring snowmelt significantly contributes to runoff in the Wisok River Basin, while its role in the Chaohe River Basin is negligible; 3) in the Wisok River Basin, besides the peak flow in spring, there is also a period of high water in summer resulting from precipitation, while in the Chaohe River Basin there is only one high water period in summer; 4) the Wisok River Basin shows relatively higher stability in terms of the magnitude of intra- and inter-seasonal discharges; 5) during the multi-year observation period, a decrease in both precipitation and runoff was recorded in the two river basins.展开更多
Landslide is a serious natural disaster next only to earthquake and flood,which will cause a great threat to people’s lives and property safety.The traditional research of landslide disaster based on experience-drive...Landslide is a serious natural disaster next only to earthquake and flood,which will cause a great threat to people’s lives and property safety.The traditional research of landslide disaster based on experience-driven or statistical model and its assessment results are subjective,difficult to quantify,and no pertinence.As a new research method for landslide susceptibility assessment,machine learning can greatly improve the landslide susceptibility model’s accuracy by constructing statistical models.Taking Western Henan for example,the study selected 16 landslide influencing factors such as topography,geological environment,hydrological conditions,and human activities,and 11 landslide factors with the most significant influence on the landslide were selected by the recursive feature elimination(RFE)method.Five machine learning methods[Support Vector Machines(SVM),Logistic Regression(LR),Random Forest(RF),Extreme Gradient Boosting(XGBoost),and Linear Discriminant Analysis(LDA)]were used to construct the spatial distribution model of landslide susceptibility.The models were evaluated by the receiver operating characteristic curve and statistical index.After analysis and comparison,the XGBoost model(AUC 0.8759)performed the best and was suitable for dealing with regression problems.The model had a high adaptability to landslide data.According to the landslide susceptibility map of the five models,the overall distribution can be observed.The extremely high and high susceptibility areas are distributed in the Funiu Mountain range in the southwest,the Xiaoshan Mountain range in the west,and the Yellow River Basin in the north.These areas have large terrain fluctuations,complicated geological structural environments and frequent human engineering activities.The extremely high and highly prone areas were 12043.3 km^(2)and 3087.45 km^(2),accounting for 47.61%and 12.20%of the total area of the study area,respectively.Our study reflects the distribution of landslide susceptibility in western Henan Province,which provides a scientific basis for regional disaster warning,prediction,and resource protection.The study has important practical significance for subsequent landslide disaster management.展开更多
基金This work was supported by the project of China Geology Survey(DD20190315)Innovation Capability Support Program of Shaanxi(2019TD-040)+1 种基金“Integration of Groundwater Resources Assessment Results in Key Areas of Northwest China”programKey Laboratory of Groundwater and Ecology in Arid and Semi-arid Areas of China Geological Survey.
文摘Glaciers are crucial water resources for arid inland rivers in Northwest China.In recent decades,glaciers are largely experiencing shrinkage under the climate-warming scenario,thereby exerting tremendous influences on regional water resources.The primary role of understudying watershed scale glacier changes under changing climatic conditions is to ensure sustainable utilization of regional water resources,to prevent and mitigate glacier-related disasters.This study maps the current(2020)distribution of glacier boundaries across the Kaidu-Kongque river basin,south slope of Tianshan Mountains,and monitors the spatial evolution of glaciers over five time periods from 2000-2020 through thresholded band ratios approach,using 25 Landsat images at 30 m resolution.In addition,this study attempts to understand the role of climate characteristics for variable response of glacier area.The results show that the total area of glaciers was 398.21 km^(2)in 2020.The glaciers retreated by about 1.17 km^(2)/a(0.26%/a)from 2000 to 2020.The glaciers were reducing at a significantly rapid rate between 2000 and 2005,a slow rate from 2005 to 2015,and an accelerated rate during 2015-2020.The meteorological data shows slight increasing trends of mean annual temperature(0.02℃/a)and annual precipitation(2.07 mm/a).The correlation analysis demonstrates that the role of temperature presents more significant correlation with glacier recession than precipitation.There is a temporal hysteresis in the response of glacier change to climate change.Increasing trend of temperature in summer proves to be the driving force behind the Kaidu-Kongque basin glacier recession during the recent 20 years.
基金Under the auspices of Fellowship for Young International Scientists of Chinese Academy of Sciences(No.2010Y12A10)
文摘Hydrological processes in river basins of similar size and morphology may differ significantly due to different climatic conditions. This paper presents a comparative analysis of hydrological characteristics of two river basins located in different climatic zones: the Wisok River Basin in the south-eastern Poland and the Chaohe River Basin in the northern China. The criteria of their choice were similarities in the basin area, main river length and topography. The results show that climate plays a key role in shaping fluvial conditions within the two basins. It is concluded that: 1) precipitation in the Wisok River Basin is more evenly distributed in the yearly cycle, while in the Chaohe River Basin it is highly concentrated in the few summer months; 2) spring snowmelt significantly contributes to runoff in the Wisok River Basin, while its role in the Chaohe River Basin is negligible; 3) in the Wisok River Basin, besides the peak flow in spring, there is also a period of high water in summer resulting from precipitation, while in the Chaohe River Basin there is only one high water period in summer; 4) the Wisok River Basin shows relatively higher stability in terms of the magnitude of intra- and inter-seasonal discharges; 5) during the multi-year observation period, a decrease in both precipitation and runoff was recorded in the two river basins.
基金This work was financially supported by National Natural Science Foundation of China(41972262)Hebei Natural Science Foundation for Excellent Young Scholars(D2020504032)+1 种基金Central Plains Science and technology innovation leader Project(214200510030)Key research and development Project of Henan province(221111321500).
文摘Landslide is a serious natural disaster next only to earthquake and flood,which will cause a great threat to people’s lives and property safety.The traditional research of landslide disaster based on experience-driven or statistical model and its assessment results are subjective,difficult to quantify,and no pertinence.As a new research method for landslide susceptibility assessment,machine learning can greatly improve the landslide susceptibility model’s accuracy by constructing statistical models.Taking Western Henan for example,the study selected 16 landslide influencing factors such as topography,geological environment,hydrological conditions,and human activities,and 11 landslide factors with the most significant influence on the landslide were selected by the recursive feature elimination(RFE)method.Five machine learning methods[Support Vector Machines(SVM),Logistic Regression(LR),Random Forest(RF),Extreme Gradient Boosting(XGBoost),and Linear Discriminant Analysis(LDA)]were used to construct the spatial distribution model of landslide susceptibility.The models were evaluated by the receiver operating characteristic curve and statistical index.After analysis and comparison,the XGBoost model(AUC 0.8759)performed the best and was suitable for dealing with regression problems.The model had a high adaptability to landslide data.According to the landslide susceptibility map of the five models,the overall distribution can be observed.The extremely high and high susceptibility areas are distributed in the Funiu Mountain range in the southwest,the Xiaoshan Mountain range in the west,and the Yellow River Basin in the north.These areas have large terrain fluctuations,complicated geological structural environments and frequent human engineering activities.The extremely high and highly prone areas were 12043.3 km^(2)and 3087.45 km^(2),accounting for 47.61%and 12.20%of the total area of the study area,respectively.Our study reflects the distribution of landslide susceptibility in western Henan Province,which provides a scientific basis for regional disaster warning,prediction,and resource protection.The study has important practical significance for subsequent landslide disaster management.
