Vicente‐Serrano et al., 2011), to the Surface Water Supply Index (SWSI; Shafer, 1982), which integrates reservoir storage, streamflow, and precipitation. Because the quantification of socioeconomic drought is intriguingly complex, it is difficult to define a generalized drought index to represent the many aspects of demand and supply and their interactions.
A number of drought monitors have been developed at regional and global scales. For meteorological drought, some representative ones include: the U.S. Drought Monitor (Svoboda et al., 2002), the Standardized Precipitation–Evapotranspiration Index (SPEI) Global Drought Monitor (Vicente‐Serrano et al., 2010), and the Global Drought Information System (Heim Jr & Brewer, 2012). Most of the agricultural drought monitors are based on soil moisture outputs from hydrological models, such as the North America Land Data Assimilation System (NLDAS) Drought Monitor (Xia et al., 2014), the African Flood and Drought Monitor (Sheffield et al., 2014), and the Experimental Surface Water Monitor for the Continental United States (Mo, 2008). Taking advantage of satellite observations, agricultural droughts also can be monitored via remotely sensed soil moisture (Mishra et al., 2017) and evapotranspiration (Mu et al., 2013). For quantifying hydrological drought, a SRI based indicator has been calculated for the continental United States from model‐simulated runoff (Shukla & Wood, 2008). Meanwhile, satellite observations collected by the Gravity Recovery and Climate Experiment (GRACE) have offered an overall perspective about terrestrial water storage anomalies, in which signals from surface water, groundwater, soil moisture, and snow water are lumped across the globe from 2002 to 2017 (B. F. Thomas et al., 2017).
While these drought monitors have provided valuable information from various perspectives, there is a shortage of regional/global drought monitors that can support local scale decision‐making in a coherent manner. Meteorological drought is the “driver” (of each of the other drought categories), and we do not have a means to control these events. Actions can be taken, however, to mitigate the severity and impacts of agricultural drought and hydrological drought, and to help reduce the losses of socioeconomic drought. In this chain of relationships, reservoirs play a key role. Over the past century, more than 7320 reservoirs (with a surface area of 0.1 km2 or larger) have been constructed globally (Lehner et al., 2011). These reservoirs have redefined water resources management by damming and releasing river water to optimally support water supply (municipal, industrial, and agricultural), flood control, hydropower generation, and recreation purposes. On one hand, water is saved in reservoirs for anthropogenic usage during droughts. On the other hand, reservoir inflow and storage are directly affected by the severity of hydrological drought. This paradox is an even larger challenge for arid and semiarid regions where reservoir evaporative losses are significant (Friedrich et al., 2018). Despite the fact that in situ reservoir data (e.g., elevation, storage) are collected regularly, such information is rarely published or shared among regions with conflicts of interest (Zhang et al., 2014). Furthermore, consistent long‐term reservoir evaporation rate records are even harder to measure at a regional scale (Zhao & Gao, 2019a).
The advent of remote sensing has allowed for reservoir elevation, area, and storage using data collected from multiple sensors. Such near‐real‐time information, where available, can be directly used for supporting decision‐making under drought conditions. Thus, the overarching goal of this chapter is to review and explore how these remotely sensed reservoir data could be used for drought monitoring and decision‐making.
3.2. DROUGHT MONITORING USING REMOTELY SENSED RESERVOIR DATA
3.2.1. Reservoir Elevation
Reservoir elevation is the key factor for determining flow regulation rules. To optimize reservoir operation efficiency, a reservoir is typically divided into several “pools”: flood control, conservation, and inactive (Zhao et al., 2016). Water stored in the conservation pool is primarily used for agricultural irrigation, municipal uses, industrial supply, and hydropower generation. Because all of the operation rules are based on the reservoir stage condition, remotely sensed elevation measurements can be used directly as a drought indicator. As shown in Figure 3.1, when Lake Powell is under drought conditions (which encompassed most of the time from 2002 to 2018), the negative anomalies of the surface elevation are evident.
Figure 3.1 Time series of Lake Powell elevation variations based on observations collected by multiple radar altimeters.
(Source: USDA, G‐REALM, Time series of Lake Powell elevation, U.S. Department of Agriculture)
Satellite radar altimeters have been used for monitoring the elevation variations of large lakes and reservoirs since the 1990s (Birkett, 1994). The underlying principle used for radar altimetry is to infer the distance between the nadir pointing satellite and the water surface by measuring the travel time of a radar signal emitted and then reflected back to the sensor. This technology has been applied primarily to the remote sensing of ocean topography (Fu & Smith, 1996). Its usage in monitoring inland surface water levels, however, has been increasingly recognized as a practical option (Crétaux et al., 2011). To date, a suite of satellite altimeters has collected elevation data of several hundred lakes and reservoirs. Primarily in the frequencies from 5 GHz to 37 GHz, the repeat cycles of these sensors range from 10 days to 35 days. Despite the low spatial resolution, the narrow swath, and the large footprint size associated with radar altimeters, they have made great contributions in quantifying large inland water bodies globally (Gao et al., 2012). The U. S. Department of Agriculture’s Global Reservoirs/Lakes (G‐REALM; https://ipad.fas.usda.gov/cropexplorer/global_reservoir) and French Space Agency Centre National d’Etudes Spatiales’ (CNES) Hydroweb (http://hydroweb.theia‐land.fr/) databases have served as the representative data portals for historical and near‐real‐time observations. Without counting their overlaps, G‐REALM and Hydroweb have reported elevations of 379 and 268 large lakes and reservoirs, respectively.
Nonetheless, there are several limitations with regard to developing a generalized drought index directly from surface water elevations observed by radar altimeters. First, there is a dearth of data continuity due to the limited lifespan and limited spatial coverage of the sensors. Using Lake Powell as an example, even though it is the second largest reservoir in the United States (in terms of storage capacity), there was no altimeter overpassing it from 2002 to 2008 (Figure 3.1). Second, both the data quality and repeat period vary significantly among sensors. Third, although the reservoir elevation is uniquely related to the storage, the elevation–storage relationship varies drastically among reservoirs due to bathymetric differences. For instance, Gao et al. (2012) showed that the slope coefficients of the elevation–area relationships for the 34 global reservoirs range from 0.15 (Toktogul) to 210.28 (Aydarkul). This suggests that for an elevation increment of 1 m, the area of Lake Toktogul will increase 0.15 km2, while the area of Lake Aydarkul will increase 210.28 km2. Furthermore, the differences between the storage change values will be even much larger. As a result, it is difficult to compare drought severity among different reservoirs by comparing their elevation anomalies.
Reservoir/lake elevations also can be measured by the Geoscience Laser Altimeter System (GLAS) onboard the Ice Cloud and Land Elevation Satellite (ICESat). With a footprint of 70 m, the data collected by ICESat/GLAS has a much higher spatial resolution than those measured by radar altimeters. As such, ICESat/GLAS can observe more lakes/reservoirs of smaller sizes (Phan et al., 2012; Zhang et al., 2011), which offers a unique advantage over radar altimeters. However, ICESat has a long repeat
