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3 Drought Monitoring Using Reservoir Data Collected via Satellite Remote Sensing
Huilin Gao1, Gang Zhao1, Yao Li1, and Shuai Zhang2
1 Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, Texas, USA
2 Department of Geological Sciences, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
ABSTRACT
Drought can significantly impair water availability, agricultural productivity, ecosystem health, and the economy. The advent of satellite remote sensing has meant that reservoirs can be observed from space, which offers a unique promise for monitoring hydrological drought. Thus, the overarching goal of this chapter is to review and explore how these remotely sensed reservoir data (elevation, area, and storage) can be used for drought monitoring and decision making. Although reservoir storage is deemed to be the best indicator of drought severity, such data are only available for a small portion of reservoirs globally, mainly limited by the availability of altimetry measurements. Reservoir area data, which have better spatial and temporal coverage than elevation/storage data, can be used to derive a drought index suitable for monitoring purposes at local and regional scales. A new surface‐area‐based hydrological drought index has been introduced and compared with the meteorological drought index. The skills of hydrological‐modeling‐based drought monitors can be enhanced by incorporating remotely sensed reservoir information. Furthermore, new and future satellite missions, such as Ice Cloud and Land Elevation Satellite 2 and Surface Water and Ocean Topography, will make the global monitoring of reservoirs storage feasible.
3.1. INTRODUCTION
Drought, which is caused by a lack of precipitation over an extended period in a large region, can significantly impair water availability, agricultural productivity, ecosystem health, and the economy (Mishra & Singh, 2010). It has become a pressing global issue given the fast growing population (Gleick, 2003) and the fact that more than 38% of world’s population live in dryland regions (Reynolds et al., 2007). Under global warming, both observations and model simulations have shown an increasing trend of aridity (Dai, 2013; Sheffield & Wood, 2008; Trenberth et al., 2014).
Droughts are typically classified into four types: meteorological, agricultural, hydrological, and socioeconomic (Wilhite & Glantz, 1985). Meteorological drought is defined based on the amount and duration of a precipitation shortfall. Agricultural drought usually focuses on quantifying the decline of soil moisture (by examining its magnitude and duration) or vegetation water stress (by comparing with normal conditions) during a dry period. Hydrological drought is used to quantify the extreme events through the “lens” of water resources, by estimating the deficits of surface water and ground water. With elements of meteorological, agricultural, and hydrological droughts, socioeconomic drought is associated with the imbalance between the supply and demand of water resources by using water as the principle economic “good” (AMS, 2004).
Many drought indices have been designed for quantifying the severity of different types of drought events (Heim Jr, 2002; Mishra & Singh, 2010). Representative meteorological drought indices include (just to name a few) the Standardized Precipitation Index (SPI; McKee, 1995; McKee et al., 1993), the Rainfall Anomaly Index (RAI; Van Rooy, 1965), the Palmer Drought Severity Index (PDSI; Palmer, 1965), the deciles (Gibbs & Maher, 1967), and the National Rainfall Index (NRI; Gommes & Petrassi, 1994). Some agriculture drought indices primarily examine soil moisture values, such as the Soil Moisture Deficit Index (SMDI; Narasimhan & Srinivasan, 2005) and the Soil Moisture Index (SMI; Hunt et al., 2009), while others focus on quantifying crop or vegetation stress, such as the crop moisture index (CMI; Palmer, 1968) and the Evaporative Drought Index (EDI; Yao et al., 2010). There are a variety of hydrological drought indices, ranging from the Standardized Runoff Index (SRI; Shukla &
