Abrupt near-surface temperature changes in mountainous areas are a special component of the mountain climate system.Fast and accurate measurements of the locations,intensity,and width of the near-surface changes are n...Abrupt near-surface temperature changes in mountainous areas are a special component of the mountain climate system.Fast and accurate measurements of the locations,intensity,and width of the near-surface changes are necessary but highly difficult due to the complicated environmental conditions and instrumental issues.This paper develops a spatial pattern recognition method to measure the near-surface high temperature increase(NSHTI),one of the lesser-attended changes.First,raster window measurement was proposed to calculate the temperature lapse rate using MODIS land surface temperature and SRTM DEM data.It fully considers the terrain heights of two neighboring cells on opposite or adjacent slopes with a moving window of 3×3 cell size.Second,a threshold selection was performed to identify the NSHTI cells using a threshold of-0.65℃/100 m.Then,the NSHTI strips were parameterized through raster vectorization and spatial analysis.Taking Yunnan,a mountainous province in southwestern China,as the study area,the results indicate that the NSHTI cells concentrate in a strip-like pattern along the mountains and valleys,and the strips are almost parallel to the altitude contours with a slight northward uplift.Also,they are located mostly at a 3/5 height of high mountains or within 400 m from the valley floors,where the controlling topographic index is the altitude of the terrain trend surface but not the absolute elevation and the topographic uplift height and cutting depth.Additionally,the NSHTI intensity varies with the geographic locations and the proportions increase with an exponential trend,and the horizontal width has a mean of about 1000 m and a maximum of over 5000 m.The result demonstrates that the proposed method can effectively recognize NSHTI boundaries over mountains,providing support for the modeling of weather and climate systems and the development of mountain resources.展开更多
Steep-slope cropland plays a vital role in food production,economic development,ecosystem diversity,and Eu-ropean cultural heritage.However,these systems are susceptible to extreme weather events.The 2022 summer droug...Steep-slope cropland plays a vital role in food production,economic development,ecosystem diversity,and Eu-ropean cultural heritage.However,these systems are susceptible to extreme weather events.The 2022 summer drought significantly impacted European agriculture,but the specific effects on steep-slope crops remain uncer-tain.Clarifying this is essential for comprehending similar future events and for implementing effective water management strategies to ensure the sustainability of steep-slope agriculture and associated ecosystem services.This study quantitatively analyzes the spatial distribution of twelve major European steep-slope(>12%)crops and assesses agricultural drought severity during the 2022 events using open-access spatial data.The satellite-based Vegetation Health Index(VHI)is utilized to identify critical hotspots.Results show that olive grove is the most widespread crop in steep slope agriculture(34%of total area),followed by wheat(24%),maize(16%),and vineyard(11%).Almost half of the steep-slope agriculture in Europe suffered drought during summer 2022.Vineyards were hardest affected at 79%,primarily in northern Portugal,northern Spain,southern France,and central Italy.Sunflowers followed at 62%,mainly in Spain,central Italy,southern France,and northern Roma-nia.Olive groves ranked third at 59%,with the most impact in northern Portugal,southern and central Spain,and southern Italy.Maize was also significantly affected at 54%.In this paper,we therefore highlight the need to increase steep-slope agriculture resilience by improving water management and promoting sustainable land practices.展开更多
基金supported by the National Natural Science Foundation of China (Grant No. 42061004)the Joint Special Project of Agricultural Basic Research of Yunnan Province (Grant No. 202101BD070001093)the Youth Special Project of Xingdian Talent Support Program of Yunnan Province
文摘Abrupt near-surface temperature changes in mountainous areas are a special component of the mountain climate system.Fast and accurate measurements of the locations,intensity,and width of the near-surface changes are necessary but highly difficult due to the complicated environmental conditions and instrumental issues.This paper develops a spatial pattern recognition method to measure the near-surface high temperature increase(NSHTI),one of the lesser-attended changes.First,raster window measurement was proposed to calculate the temperature lapse rate using MODIS land surface temperature and SRTM DEM data.It fully considers the terrain heights of two neighboring cells on opposite or adjacent slopes with a moving window of 3×3 cell size.Second,a threshold selection was performed to identify the NSHTI cells using a threshold of-0.65℃/100 m.Then,the NSHTI strips were parameterized through raster vectorization and spatial analysis.Taking Yunnan,a mountainous province in southwestern China,as the study area,the results indicate that the NSHTI cells concentrate in a strip-like pattern along the mountains and valleys,and the strips are almost parallel to the altitude contours with a slight northward uplift.Also,they are located mostly at a 3/5 height of high mountains or within 400 m from the valley floors,where the controlling topographic index is the altitude of the terrain trend surface but not the absolute elevation and the topographic uplift height and cutting depth.Additionally,the NSHTI intensity varies with the geographic locations and the proportions increase with an exponential trend,and the horizontal width has a mean of about 1000 m and a maximum of over 5000 m.The result demonstrates that the proposed method can effectively recognize NSHTI boundaries over mountains,providing support for the modeling of weather and climate systems and the development of mountain resources.
基金funding from the European Union Next-GenerationEU(PIANO NAZIONALE DI RIPRESA E RESILIENZA(PNRR)-MISSIONE 4 COMPONENTE 2,INVESTIMENTO 1.4-D.D.103217/06/2022,CN00000022).
文摘Steep-slope cropland plays a vital role in food production,economic development,ecosystem diversity,and Eu-ropean cultural heritage.However,these systems are susceptible to extreme weather events.The 2022 summer drought significantly impacted European agriculture,but the specific effects on steep-slope crops remain uncer-tain.Clarifying this is essential for comprehending similar future events and for implementing effective water management strategies to ensure the sustainability of steep-slope agriculture and associated ecosystem services.This study quantitatively analyzes the spatial distribution of twelve major European steep-slope(>12%)crops and assesses agricultural drought severity during the 2022 events using open-access spatial data.The satellite-based Vegetation Health Index(VHI)is utilized to identify critical hotspots.Results show that olive grove is the most widespread crop in steep slope agriculture(34%of total area),followed by wheat(24%),maize(16%),and vineyard(11%).Almost half of the steep-slope agriculture in Europe suffered drought during summer 2022.Vineyards were hardest affected at 79%,primarily in northern Portugal,northern Spain,southern France,and central Italy.Sunflowers followed at 62%,mainly in Spain,central Italy,southern France,and northern Roma-nia.Olive groves ranked third at 59%,with the most impact in northern Portugal,southern and central Spain,and southern Italy.Maize was also significantly affected at 54%.In this paper,we therefore highlight the need to increase steep-slope agriculture resilience by improving water management and promoting sustainable land practices.
