took measurements only from January 2003 to August 2010. The short lifetime and low temporal resolution have limited the applications of using ICESat/GLAS to monitor reservoir elevations operationally at near real time. Similar to the radar altimeters, ICESat/GLAS has a large spacing between tracks, resulting in sparse spatial coverage.
3.2.2. Reservoir Storage
Storage is the reservoir variable that is the most direct indicator of drought severity. For instance, the Texas Water Development Board (TWDB) uses the Reservoir Storage Index (RSI) as a reference to support water management decisions. The RSI is defined as the percent of storage capacity that the reservoir conservation pool is filled to at a given time (TWDB, 2017). In Melo et al. (2016), the percentage of total reservoir storage (relative to the system’s maximum capacity) was used to quantify the drought severity in the Parana River Basin. The results were further compared with the SPI to identify the linkage between meteorological and hydrological drought. In Figure 3.2, the time series of observed total reservoir storage over the Brazos River Basin (Texas) are compared with the precipitation data and the SPI. While the onset of hydrological drought is usually slightly lagged behind that of meteorological drought (Van Loon, 2015), the recovery of the former can be much slower than that of the latter when conditions are extreme (e.g., the 2011 record drought). On one hand, a reservoir‐storage‐based drought index can offer valuable information to help mitigate socioeconomic drought at both local and regional scales. On the other hand, such efforts have not been pursued fully, largely because the observed reservoir storage data are typically not shared.
With remotely sensed elevation and/or surface area data, reservoir storage can be estimated using equation 3.1:
where V, h, and A represent storage, surface elevation, and area, while the subscript c stands for capacity. In some studies, elevations collected by radar altimeters and water area from image classifications were combined in order to calculate the storage. For instance, Birkett (2000) was the first to combine altimetry data obtained from the TOPEX/Poseidon satellite with area images from the Advanced Very High Resolution Radiometer (AVHRR) for estimating Lake Chad storage variations. More recently, Duan & Bastiaanssen (2013) calculated the water volume variations of Lake Mead, Lake Tana, and Lake Ijssel from Landsat imagery data and elevations collected from four operational satellite altimetry databases: G‐REALM, River Lake Hydrology (RLH), Hydroweb, and ICESat‐GLAS level 2 data. The drawback of this approach is that satellite elevation data and area imagery need to be available simultaneously during the study period. Despite their high accuracy, results based on ICESat are so sparse that they are suitable only for trend analysis and not for monitoring purposes. To overcome this constraint, alternative approaches were developed by first estimating the elevation–area relationships such that the storage can be calculated from elevation or area directly using equation (3.1) (Gao, 2015). Crétaux et al. (2011) showcased the capability of monitoring reservoir storage variations at a global scale by applying radar altimetry data to prior developed elevation–area relationships. Gao et al. (2012) leveraged the availability of both radar altimetry data and Moderate Resolution Imaging Spectroradiometer (MODIS) area data to maximize the temporal coverage. Zhang et al. (2014) established elevation–area relationships based on ICESat elevations and enhanced MODIS water area estimates, which allowed for monitoring relatively small reservoirs that do not have overpassing radar altimeter tracks. By combining the Joint Research Centre (JRC) Global Surface Water (GSW) data set and the Database for Hydrological Time Series of Inland Waters (DAHITI), time series of storage variations for 135 global lakes and reservoirs between 1984 and 2015 were generated by Busker et al. (2018). These studies serve as good examples of the benefits of utilizing observations from multiple sensors.
Figure 3.2 (a) Monthly average precipitation and SPI with a 6‐month timescale for the Brazos River Basin, Texas. (b) Total conservation storage and capacity for 24 monitored reservoirs in the basin.
With continuous reservoir storage records from satellite remote sensing, monitoring hydrological drought through the process of evaluating relative reservoir volume data holds promise. The advantage of bringing Landsat into the picture can be demonstrated by comparing Figures 3.1 and 3.3, in which the elevation and storage time series for Lake Powell are shown. In Figure 3.3, not only has the elevation data gap (in 2002‐2008; due to a prior lack of altimetry coverage) been closed, but also the storage variations before the satellite altimetry period (1984–1992) have been inferred from Landsat surface area. The severity and duration of three drought events that occurred over Lake Powell during the past three decades are distinctive. It may take many years for the second largest reservoir in the United States to recover from this latest situation.
Although Busker et al. (2018) offered a remotely sensed storage data set for 135 lakes, only a small proportion of them are manmade reservoirs. This lack of spatial coverage makes reservoir storage a less powerful drought indicator, when compared to other meteorological/agricultural drought indices (e.g., SPI, PDSI, and SMDI), for supporting holistic water management at a regional scale.
3.2.3. Reservoir Area
Although reservoir area is directly related to elevation and storage, to our best knowledge reservoir area has not been adopted in any previous studies as an indicator of drought severity. As reservoir operations are based on elevation/storage pools, existing reservoir observations in situ tend to focus on the measurement of elevations, which are subsequently converted into storage values. For a water manager who has access to collect elevation data concerning reservoirs of interest (from gauges or otherwise), measurements of water area are difficult to obtain and not so meaningful. Remotely sensed reservoir area time series, however, have great potential to be utilized for monitoring droughts.
Figure 3.3 Storage variations of Lake Powell estimated using radar altimetry and Landsat data from 1984 to 2017.
(Source: Busker, T., A. de Roo, E. Gelati, C. Schwatke, M. Adamovic, B. Bisselink, J.‐F. Pekel, and A. Cottam (2018), A global lake and reservoir volume analysis using a surface water dataset and satellite altimetry. Hydrol. Earth Syst. Sci., 23, 669–690, 2019. Licensed Under CCBY 4.0.)
Satellite imageries at the visible (VIS), near infrared (NIR), and shortwave infrared (SWIR) bands have been collected continuously for several decades. For instance, since the launch of Earth Resources Technology Satellite 1 (which was later renamed Landsat 1) in 1972, the well known Landsat satellite series have set the record (in terms of both quantity and quality) for acquiring satellite data about Earth. These high‐resolution imageries are both consistent and continuous,
