Precipitation and temperature are two important factors associated to snow hazards which block the transport infrastructure and cause loss of life and properties in the cold season.The in-situ observations are limited...Precipitation and temperature are two important factors associated to snow hazards which block the transport infrastructure and cause loss of life and properties in the cold season.The in-situ observations are limited in the alpine with complex topographic characteristics,while coarse satellite rainfall estimates,reanalysis rain datasets,and gridded in-situ rain gauge datasets obscure the understanding of the precipitation patterns in hazardprone areas.Considering the Karakoram Highway(KKH)region as a study area,a double nestedWeather Research and Forecasting(WRF)model with the high resolution of a 10-km horizontal grid was performed to investigate the spatial and temporal patterns of temperature and precipitation covering the Karakoram Highway region during the cold season.The results of WRF were compared with the in-situ observations and Multi-Source WeightedEnsemble Precipitation(MSWEP)datasets.The results demonstrated that the WRF model well reproduced the observed monthly temperature(R=0.96,mean bias=-3.92°C)and precipitation(R=0.57,mean bias=8.69 mm).The WRF model delineated the essential features of precipitation variability and extremes,although it overestimatedthe wet day frequency and underestimated the precipitation intensity.Two rain bands were exhibited in a northwest-to-southeast direction over the study area.High wet day frequency was found in January,February,and March in the section between Hunza and Khunjerab.In addition,the areas with extreme values are mainly located in the Dasu-Islamabad section in February,March,and April.The WRF model has the potential to compensate for the spatial and temporal gaps of the observational networks and to provide more accurate predictions on the meteorological variables for avoiding common coldweather hazards in the ungauged and high altitude areas at a regional scale.展开更多
The shrinkage of the Aral Sea,which is closely related to the Amu Darya River,strongly affects the sustainability of the local natural ecosystem,agricultural production,and human well-being.In this study,we used the B...The shrinkage of the Aral Sea,which is closely related to the Amu Darya River,strongly affects the sustainability of the local natural ecosystem,agricultural production,and human well-being.In this study,we used the Bayesian Estimator of Abrupt change,Seasonal change,and Trend(BEAST)model to detect the historical change points in the variation of the Aral Sea and the Amu Darya River and analyse the causes of the Aral Sea shrinkage during the 1950–2016 period.Further,we applied multifractal detrend cross-correlation analysis(MF-DCCA)and quantitative analysis to investigate the responses of the Aral Sea to the runoff in the Amu Darya River,which is the main source of recharge to the Aral Sea.Our results showed that two significant trend change points in the water volume change of the Aral Sea occurred,in 1961 and 1974.Before 1961,the water volume in the Aral Sea was stable,after which it began to shrink,with a shrinkage rate fluctuating around 15.21 km3/a.After 1974,the water volume of the Aral Sea decreased substantially at a rate of up to 48.97 km3/a,which was the highest value recorded in this study.In addition,although the response of the Aral Sea's water volume to its recharge runoff demonstrated a complex non-linear relationship,the replenishment of the Aral Sea by the runoff in the lower reaches of the Amu Darya River was identified as the dominant factor affecting the Aral Sea shrinkage.Based on the scenario analyses,we concluded that it is possible to slow down the retreat of the Aral Sea and restore its ecosystem by increasing the efficiency of agricultural water use,decreasing agricultural water use in the middle and lower reaches,reducing ineffective evaporation from reservoirs and wetlands,and increasing the water coming from the lower reaches of the Amu Darya River to the 1961–1973 level.These measures would maintain and stabilise the water area and water volume of the Aral Sea in a state of ecological restoration.Therefore,this study focuses on how human consumption of recharge runoff affects the Aral Sea and provides scientific perspective on its ecological conservation and sustainable development.展开更多
Approximately 34,000 aerial photographs covering large parts of Ethiopia and dating back to 1935–1941 have been recently recovered.These allow investigating environmental dynamics for a past period that until now is ...Approximately 34,000 aerial photographs covering large parts of Ethiopia and dating back to 1935–1941 have been recently recovered.These allow investigating environmental dynamics for a past period that until now is only accessible from terrestrial photographs or narratives.As the archive consists of both oblique and vertical aerial photographs that cover rather small areas,methods of image-based modelling were used to orthorectify the images.In this study,9 vertical and 18 low oblique aerial photographs were processed as an ortho-mosaic,covering an area of 25 km2,west of Wukro town in northern Ethiopia.Using 15 control points(derived from Google Earth),a Root Means Square Error of 28.5 m in X 35.4 m in Y were achieved.These values can be viewed as optimal,given the relatively low resolution and poor quality of the imagery,the lack of metadata,the geometric quality of the Google Earth imagery and the recording characteristics.Land use remained largely similar since 1936,with large parts of the land being used as cropland or extensive grazing areas.Most remarkable changes are the strong expansion of the settlements as well as land management improvements.In a larger effort,ortho-mosaics covering large parts of Ethiopia in 1935–1941 will be produced.展开更多
With the emergence of multisource data and the development of cloud computing platforms,accurate prediction of event-scale dust source regions based on machine learning(ML)methods should be considered,especially accou...With the emergence of multisource data and the development of cloud computing platforms,accurate prediction of event-scale dust source regions based on machine learning(ML)methods should be considered,especially accounting for the temporal variability in sample and predictor variables.Arid Central Asia(ACA)is recognized as one of the world’s primary potential sand and dust storm(SDS)sources.In this study,based on the Google Earth Engine(GEE)platform,four ML methods were used for SDS source prediction in ACA.Fourteen meteorological and terrestrial factors were selected as influencing factors controlling SDS source susceptibility and applied in the modeling process.Generally,the results revealed that the random forest(RF)algorithm performed best,followed by the gradient boosting tree(GBT),maximum entropy(MaxEnt)model and support vector machine(SVM).The Gini impurity index results of the RF model indicated that the wind speed played the most important role in SDS source prediction,followed by the normalized difference vegetation index(NDVI).This study could facilitate the development of programs to reduce SDS risks in arid and semiarid regions,particularly in ACA.展开更多
Extreme rainfall events are rare in inland arid regions, but have exhibited an increasing trend in recent years, causing many casualties and substantial socioeconomic losses. A series of heavy rains that began on July...Extreme rainfall events are rare in inland arid regions, but have exhibited an increasing trend in recent years, causing many casualties and substantial socioeconomic losses. A series of heavy rains that began on July 31st, 2018, battered the Hami prefecture of eastern Xinjiang, China for four days. These rains sparked devastating floods, caused 20 deaths, eight missing, and the evacuation of about 5500 people. This study examines the extreme rainfall event in a historical context and explores the anthropogenic causes based on analysis of multiple datasets (i.e., the observed daily data, the global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5), the NCEP/NCAR Reanalysis 1, and the satellite cloud data) and several statistical techniques. Results show that this extraordinarily heavy rainfall was due mainly to the abnormal weather system (e.g., the abnormal subtropical high) that transported abundant water vapor from the Indian Ocean and the East China Sea crossed the high mountains and formed extreme rainfall in Hami prefecture, causing the reservoir to break and form a flood event with treat loss, which is a typical example of a comprehensive analysis of the extreme rainfall event in summer in Northwest China. Also, the fraction of attributable risk (FAR) value was 1.00 when the 2018 July–August RX1day (11.52 mm) was marked as the threshold, supporting the claim of a significant anthropogenic influence on the risk of this extreme rainfall. The results offer insights into the variability of precipitation extremes in arid areas contributing to better manage water-related disasters.展开更多
基金financially supported by the project of the National Natural Science Foundation of China(U1703241)the Strategic Priority Research Program of the Chinese Academy of Sciences+2 种基金the Pan-Third Pole Environment Study for a Green Silk Road(Pan-TPE)(XDA2004030202)the Chinese Academy of Sciences President’s International Fellowship Initiative(PIFI,Grant No.2017VCA0002)the China Scholarship Council(CSC,Grant No.201904910896)。
文摘Precipitation and temperature are two important factors associated to snow hazards which block the transport infrastructure and cause loss of life and properties in the cold season.The in-situ observations are limited in the alpine with complex topographic characteristics,while coarse satellite rainfall estimates,reanalysis rain datasets,and gridded in-situ rain gauge datasets obscure the understanding of the precipitation patterns in hazardprone areas.Considering the Karakoram Highway(KKH)region as a study area,a double nestedWeather Research and Forecasting(WRF)model with the high resolution of a 10-km horizontal grid was performed to investigate the spatial and temporal patterns of temperature and precipitation covering the Karakoram Highway region during the cold season.The results of WRF were compared with the in-situ observations and Multi-Source WeightedEnsemble Precipitation(MSWEP)datasets.The results demonstrated that the WRF model well reproduced the observed monthly temperature(R=0.96,mean bias=-3.92°C)and precipitation(R=0.57,mean bias=8.69 mm).The WRF model delineated the essential features of precipitation variability and extremes,although it overestimatedthe wet day frequency and underestimated the precipitation intensity.Two rain bands were exhibited in a northwest-to-southeast direction over the study area.High wet day frequency was found in January,February,and March in the section between Hunza and Khunjerab.In addition,the areas with extreme values are mainly located in the Dasu-Islamabad section in February,March,and April.The WRF model has the potential to compensate for the spatial and temporal gaps of the observational networks and to provide more accurate predictions on the meteorological variables for avoiding common coldweather hazards in the ungauged and high altitude areas at a regional scale.
基金supported by the National Natural Science Foundation of China (42230708)the Joint CAS (Chinese Academy of Sciences) & MPG (Max-Planck-Gesellschaft) Research Project (HZXM20225001MI)the Tianshan Talent Project of Xinjiang Uygur Autonomous Region, China (2022TSYCLJ0056)。
文摘The shrinkage of the Aral Sea,which is closely related to the Amu Darya River,strongly affects the sustainability of the local natural ecosystem,agricultural production,and human well-being.In this study,we used the Bayesian Estimator of Abrupt change,Seasonal change,and Trend(BEAST)model to detect the historical change points in the variation of the Aral Sea and the Amu Darya River and analyse the causes of the Aral Sea shrinkage during the 1950–2016 period.Further,we applied multifractal detrend cross-correlation analysis(MF-DCCA)and quantitative analysis to investigate the responses of the Aral Sea to the runoff in the Amu Darya River,which is the main source of recharge to the Aral Sea.Our results showed that two significant trend change points in the water volume change of the Aral Sea occurred,in 1961 and 1974.Before 1961,the water volume in the Aral Sea was stable,after which it began to shrink,with a shrinkage rate fluctuating around 15.21 km3/a.After 1974,the water volume of the Aral Sea decreased substantially at a rate of up to 48.97 km3/a,which was the highest value recorded in this study.In addition,although the response of the Aral Sea's water volume to its recharge runoff demonstrated a complex non-linear relationship,the replenishment of the Aral Sea by the runoff in the lower reaches of the Amu Darya River was identified as the dominant factor affecting the Aral Sea shrinkage.Based on the scenario analyses,we concluded that it is possible to slow down the retreat of the Aral Sea and restore its ecosystem by increasing the efficiency of agricultural water use,decreasing agricultural water use in the middle and lower reaches,reducing ineffective evaporation from reservoirs and wetlands,and increasing the water coming from the lower reaches of the Amu Darya River to the 1961–1973 level.These measures would maintain and stabilise the water area and water volume of the Aral Sea in a state of ecological restoration.Therefore,this study focuses on how human consumption of recharge runoff affects the Aral Sea and provides scientific perspective on its ecological conservation and sustainable development.
基金supported by the Strategic Priority Research Programme of the Chinese Academy of Sciences(XDA20060302)the Tianshan Talent Cultivation(2022TSYCLJ0001)+2 种基金the Key Projects of Natural Science Foundation of Xinjiang Uygur Autonomous Region(2022D01D01)the National Natural Science Foundation of China(U1803243)the High-End Foreign Experts Project(G2022045012L)。
文摘Approximately 34,000 aerial photographs covering large parts of Ethiopia and dating back to 1935–1941 have been recently recovered.These allow investigating environmental dynamics for a past period that until now is only accessible from terrestrial photographs or narratives.As the archive consists of both oblique and vertical aerial photographs that cover rather small areas,methods of image-based modelling were used to orthorectify the images.In this study,9 vertical and 18 low oblique aerial photographs were processed as an ortho-mosaic,covering an area of 25 km2,west of Wukro town in northern Ethiopia.Using 15 control points(derived from Google Earth),a Root Means Square Error of 28.5 m in X 35.4 m in Y were achieved.These values can be viewed as optimal,given the relatively low resolution and poor quality of the imagery,the lack of metadata,the geometric quality of the Google Earth imagery and the recording characteristics.Land use remained largely similar since 1936,with large parts of the land being used as cropland or extensive grazing areas.Most remarkable changes are the strong expansion of the settlements as well as land management improvements.In a larger effort,ortho-mosaics covering large parts of Ethiopia in 1935–1941 will be produced.
基金supported by the National Natural Science Foundation of China(42171014)the UNEPNSFC International Cooperation Project(42161144004)+2 种基金the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA20060301)National Natural Science Foundation of China(42071424)the China Scholarship Council(202104910412).
文摘With the emergence of multisource data and the development of cloud computing platforms,accurate prediction of event-scale dust source regions based on machine learning(ML)methods should be considered,especially accounting for the temporal variability in sample and predictor variables.Arid Central Asia(ACA)is recognized as one of the world’s primary potential sand and dust storm(SDS)sources.In this study,based on the Google Earth Engine(GEE)platform,four ML methods were used for SDS source prediction in ACA.Fourteen meteorological and terrestrial factors were selected as influencing factors controlling SDS source susceptibility and applied in the modeling process.Generally,the results revealed that the random forest(RF)algorithm performed best,followed by the gradient boosting tree(GBT),maximum entropy(MaxEnt)model and support vector machine(SVM).The Gini impurity index results of the RF model indicated that the wind speed played the most important role in SDS source prediction,followed by the normalized difference vegetation index(NDVI).This study could facilitate the development of programs to reduce SDS risks in arid and semiarid regions,particularly in ACA.
基金This study was sponsored by the Project of Tianshan Innovation Team in Xinjiang(202113050)the Chinese Academy of Sciences President's International Fellowship Initiative(2017VCA0002).
文摘Extreme rainfall events are rare in inland arid regions, but have exhibited an increasing trend in recent years, causing many casualties and substantial socioeconomic losses. A series of heavy rains that began on July 31st, 2018, battered the Hami prefecture of eastern Xinjiang, China for four days. These rains sparked devastating floods, caused 20 deaths, eight missing, and the evacuation of about 5500 people. This study examines the extreme rainfall event in a historical context and explores the anthropogenic causes based on analysis of multiple datasets (i.e., the observed daily data, the global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5), the NCEP/NCAR Reanalysis 1, and the satellite cloud data) and several statistical techniques. Results show that this extraordinarily heavy rainfall was due mainly to the abnormal weather system (e.g., the abnormal subtropical high) that transported abundant water vapor from the Indian Ocean and the East China Sea crossed the high mountains and formed extreme rainfall in Hami prefecture, causing the reservoir to break and form a flood event with treat loss, which is a typical example of a comprehensive analysis of the extreme rainfall event in summer in Northwest China. Also, the fraction of attributable risk (FAR) value was 1.00 when the 2018 July–August RX1day (11.52 mm) was marked as the threshold, supporting the claim of a significant anthropogenic influence on the risk of this extreme rainfall. The results offer insights into the variability of precipitation extremes in arid areas contributing to better manage water-related disasters.
