资源环境科技发展态势分析平台

Drought monitoring is critical for early warning of drought hazard. This study attempted to develop an integrated remote sensing drought monitoring index (IRSDI), based on meteorological data for 2003-2013 from 40 meteorological stations and soil moisture data from 16 observatory stations, as well as Moderate Resolution Imaging Spectroradiometer data using a linear trend detection method, and standardized precipitation evapotranspiration index. The objective was to investigate drought conditions across the Huai River basin in both space and time. Results indicate that (1) the proposed IRSDI monitors and describes drought conditions across the Huai River basin reasonably well in both space and time; (2) frequency of drought and severe drought are observed during April-May and July-September. The northeastern and eastern parts of Huai River basin are dominated by frequent droughts and intensified drought events. These regions are dominated by dry croplands, grasslands, and highly dense population and are hence more sensitive to drought hazards; (3) intensified droughts are detected during almost all months except January, August, October, and December. Besides, significant intensification of droughts is discerned mainly in eastern and western Huai River basin. The duration and regions dominated by intensified drought events would be a challenge for water resources management in view of agricultural and other activities in these regions in a changing climate.

Plain Language Summary Drought hazard is disastrous but is little understood due to complicated influencing factors for drought development and monitor. This study presents a newly proposed integrated drought monitoring model which put multisource data into consideration such as meteorological data, soil moisture, and remote sensing data as well. This newly developed drought monitoring model can monitor drought in an ideal way, involving agricultural drought and meteorological drought. Therefore, the newly developed integrated drought monitoring model can greatly improve the drought monitoring practice in a changing climate and be helpful for human mitigation to drought hazards.