Abstract
Drought, as an extreme climate event, affects the ecological environment for vegetation and agricultural production. Studies of the vegetative response to drought are paramount to providing scientific information for drought risk mitigation. In this paper, the spatial-temporal pattern of drought and the response lag of vegetation in Nebraska were analyzed from 2000 to 2015. Based on the long-term Daymet data set, the standard precipitation index (SPI) was computed to identify precipitation anomalies, and the Gaussian function was applied to obtain temperature anomalies. Vegetation anomaly was identified by dynamic time warping technique using a remote sensing Normalized Difference Vegetation Index (NDVI) time series. Finally, multilayer correlation analysis was applied to obtain the response lag of different vegetation types. The results show that Nebraska suffered severe drought events in 2002 and 2012. The response lag of vegetation to drought typically ranged from 30 to 45 days varying for different vegetation types and human activities (water use and management). Grasslands had the shortest response lag (~35 days), while forests had the longest lag period (~48 days). For specific crop types, the response lag of winter wheat varied among different regions of Nebraska (35–45 days), while soybeans, corn and alfalfa had similar response lag times of approximately 40 days.
Highlights
Drought is one of the world’s most costly and widespread natural hazards, impacting water resources, agricultural production, ecosystems, human health, and the global economy [1,2,3,4]
The methodological workflow consisted of the following steps (Figure 1): (1) Data preprocessing and Normalized Difference Vegetation Index (NDVI) time-series smoothing; (2) calculation of Standardized Precipitation Index (SPI); (3) calculation of the temperature anomaly; (4) calculation of the vegetation anomaly, which involves the extraction of average phenology NDVI profile, Dynamic time warping (DTW) match of NDVI observed time-series and phenology profile, and the anomaly calculation of NDVI at daily scale; and (5) extraction of vegetation response lag based on lag correlation coefficient
The methodological workflow consisted of the following steps (Figure 2): (1) Data preprocessing and NDVI time-series smoothing; (2) calculation of SPI; (3) calculation of the temperature anomaly; (4) calculation of the vegetation anomaly, which involves the extraction of average phenology NDVI profile, DTW match of NDVI observed time-series and phenology profile, and the anomaly calculation of NDVI on a daily scale; and (5) extraction of vegetation response lag based on lag’s correlation coefficient
Summary
Drought is one of the world’s most costly and widespread natural hazards, impacting water resources, agricultural production, ecosystems, human health, and the global economy [1,2,3,4]. Drought events are projected to be more intense and frequent for many regions of the world under global warming, placing further pressure on agricultural systems and natural resources, due to increasing demands from an ever-increasing global population [5]. Semiarid regions such as Australia’s Murray-Darling River Basin, South Africa, and the America’s Middle West with vegetation cover face a greater threat from drought, especially agricultural regions because of insufficient rainfall, water management, and vulnerable vegetation [6,7,8]. As a simple calculation using commonly available precipitation data, the traditional Standardized Precipitation Index (SPI) is widely used as a drought indicator, since it can be calculated at multiple time scales and adapted to different climates and drought types [21,22,23]
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