作 者：Fang, WeiHuang, ShengzhiHuang, QiangHuang, GuoheWang, HaoLeng, GuoyongWang, LuGuo, Yi
作者机构：Xian Univ TechnolSch Water Resources & Hydropower State Key Lab Ecohydraul Northwest Arid Reg China Xian 710048 Shaanxi Peoples R ChinaUniv ReginaInst Energy Environm & Sustainable Communities Regina SK S4S 0A2 CanadaChina Inst Water Resources & Hydropower ResState Key Lab Simulat & Regulat Water Cycle River Beijing 100038 Peoples R ChinaChinese Acad SciInst Geog Sci & Nat Resources Res Key Lab Water Cycle & Related Land Surface Proc Beijing 100101 Peoples R ChinaUniv OxfordEnvironm Change Inst Oxford OX1 3QY England
出 版 物：《Remote Sensing of Environment》
年 卷 期：2019年第232卷
核心收录：
中图分类：TP[工业技术-自动化技术、计算机技术]
学科分类：08[工学]
基 金：Belt and Road Special Foundation of the State Key Laboratory of Hydrology Water Resources and Hydraulic Engineering [2018490711]
主 题：Vegetation healthDrought stressCopula methodConditional probabilityVulnerability analysisSTANDARDIZED PRECIPITATION INDEXCLIMATE-CHANGESOIL-MOISTUREECOLOGICAL RESTORATIONMETEOROLOGICAL DROUGHTAGRICULTURAL DROUGHTRIPARIAN VEGETATIONECO-ENVIRONMENTCARBON BALANCEWATER DEMAND
摘 要：Quantitative understanding of vegetation vulnerability under drought stress is essential to initiating drought preparedness and mitigation. In this study, a bivariate probabilistic framework is developed for assessing vegetation vulnerability and mapping drought-prone ecosystems more informatively, which is different from previous studies conducted in a deterministic way. The Normalized Difference Vegetation Index (NDVI) is initially correlated to the Standardized Precipitation Index (SPI) at contrasting timescales to evaluate the degree of vegetation dependence on water availability and screen out the vegetation response time. Afterward, the monthly NDVI series is connected with the most correlated SPI to derive joint distributions using a copula method. On such basis, conditional probabilities of vegetation losses are estimated under multiple drought scenarios and used for revealing tempo-spatial patterns of vegetation vulnerability. Particular focus is directed to the Loess Plateau (LP), China, which is a world-famous environmentally fragile area. Results indicate that the proposed framework is valid for vegetation vulnerability assessment as the pair-wise SPI-NDVI observations fall within high-density areas of the estimated NDVI distributions. From a probabilistic perspective, roughly 95% of the LP exhibits greater probability of vegetation losses when suffering from water deficits rather than water surplus. Vegetation loss probabilities reaching their peak (39.7%) in summer indicate the highest vegetation vulnerability to drought stress in summer months sequentially followed by autumn (32.9%) and spring (31.0%), which is linked to marked variations in water requirement at different stages of vegetation growth. Spatially, drought-vulnerable regions are identified in the western edge with vegetation loss probability 20.6% higher than the LP mean value, suggesting higher vulnerability in more arid areas. Irrigation practices and large-scale vegetation restoration, as two important sources of anthropogenic distu
