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
Cities often have a substantial green infrastructure,which provides local ecosystem services that improve the quality of life of urban residents.These services should be explicitly addressed in urban development polic...Cities often have a substantial green infrastructure,which provides local ecosystem services that improve the quality of life of urban residents.These services should be explicitly addressed in urban development policies,and areas with insufficient vegetation and limited access to public green spaces should be identified.This paper presents two spatially explicit urban green indicators that are derived using remote sensing imagery,freely available map data and spatial analysis tools from open source geospatial libraries and commercial software.The first indicator represents proportional green cover(public as well as private)in the vicinity of each building within a city.The second indicator quantifies the proximity of public green spaces as walking distances from buildings to actual park entrances.A dasymetric mapping approach was used to take spatial variations in population density into account.This allows representing the indicators from the perspective of citizens instead of buildings,which may be more meaningful for deriving statistics at city level or at the level of neighbourhoods or administrative zones.The potential use of these indicators in a planning context is discussed on a case study carried out for the city of Brussels,Belgium.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)。
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.
文摘Cities often have a substantial green infrastructure,which provides local ecosystem services that improve the quality of life of urban residents.These services should be explicitly addressed in urban development policies,and areas with insufficient vegetation and limited access to public green spaces should be identified.This paper presents two spatially explicit urban green indicators that are derived using remote sensing imagery,freely available map data and spatial analysis tools from open source geospatial libraries and commercial software.The first indicator represents proportional green cover(public as well as private)in the vicinity of each building within a city.The second indicator quantifies the proximity of public green spaces as walking distances from buildings to actual park entrances.A dasymetric mapping approach was used to take spatial variations in population density into account.This allows representing the indicators from the perspective of citizens instead of buildings,which may be more meaningful for deriving statistics at city level or at the level of neighbourhoods or administrative zones.The potential use of these indicators in a planning context is discussed on a case study carried out for the city of Brussels,Belgium.
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.
