there is a strong agreement between the Landsat based RAFI and U.S. Geological Survey observed elevation values at Lake Whitney (Texas). By developing drought indices from the remotely sensed reservoir area, true global monitoring capability can be achieved. Furthermore, the decades of available Landsat data can offer insightful information for long‐term planning.
3.3. ADOPTING REMOTELY SENSED RESERVOIR DATA TO SUPPORT DROUGHT MODELING APPLICATIONS
Since the early 1990s, a number of large‐scale hydrological models (LHMs) have been developed to address the global water scarcity issue (Bierkens, 2015). During the early stage, most LHMs focused on solving the issues related to water balance and the routing of runoff water without considering reservoir flow regulations (Vörösmarty et al., 1989). In the early 2000s, more LHMs started to consider the human impacts on the hydrological cycle. Haddeland et al. (2014) found that the impacts from reservoir management are often as large, or larger than, those from global warming. This was based on results from seven global LHMs, H08 (Hanasaki et al., 2008), LPJmL (Bondeau et al., 2007), MPI‐HM (Stacke & Hagemann, 2012), PCR‐GLOBWB (Wada et al., 2011), VIC (Liang et al., 1994), WaterGAP (Döll et al., 2001), and WBMplus (Wisser et al., 2008). However, because reservoir operation rules are usually not shared, they have virtually always been represented in a rather simplistic manner within these global models. This has prevented these models from supporting purposes related to the monitoring of hydrological droughts.
Figure 3.4 Comparison of remotely sensed surface area with observed storage/elevation for nine reservoirs. The two numbers in parentheses indicate the values of R2 between observed storages (or elevations) and water areas before and after enhancement.
(Source: From Zhao, G., & Gao, H. (2018). Automatic Correction of Contaminated Images for Assessment of Reservoir Surface Area Dynamics. Geophysical Research Letters. 45(12), 6092–6099. © 2018, John Wiley & Sons.)
In contrast, the physically based, distributed LHMs, which do not have a reservoir component, have been well adopted for monitoring agricultural drought at the continental and global scales. Examples include (but are not limited to) the North American Land Data Assimilation System (NLDAS) Drought Monitor (http://www.emc.ncep.noaa.gov/mmb/nldas/drought/), the Princeton United States and Global Drought Monitor (http://hydrology.princeton.edu/forecast/current.php), and the Global Integrated Drought Monitoring and Prediction System (GIDMaPS) (http://drought.eng.uci.edu/). Although some of these drought monitors also use modeled streamflows as indicators, they are often biased because they do not consider the effects of reservoir flow regulations in their routing scheme. The most reliable streamflow based monitor is the U.S. Geological Survey Water Watch (https://waterwatch.usgs.gov), which collects national scale observed gauge data. However, it is impossible to set up monitors of this kind over most of the world due to the lack of gauge data (Kugler & De Groeve, 2007). Indeed, as pointed out by Wada et al. (2017) and other studies (Fekete et al., 2015; Lawford et al., 2013), there is a critical need for comprehensive data for purposes of calibrating and evaluating hydrological models over continental to global scales.
Remotely sensed reservoir data can be used to support drought‐modeling applications in two ways. First, the models can be calibrated and validated using remotely sensed reservoir elevation and/or storage data. For instance, by validating the modeled results with both directly observed flows and satellite‐based reservoir storage seasonal cycles, Zhou et al. (2016) generated the first ever long‐term hydroclimate record for 166 reservoirs over 32 global river basins. In Gao et al. (2011), radar altimeter data were used to calibrate the hydrological model over Lake Chad. Second, satellite reservoir data can be used in conjunction with LHM‐based information to provide valuable information for irrigation allocation under drought conditions. On one hand, irrigation water demand escalates as drought severity increases. On the other hand, the water availability (both in terms of reservoir storage and groundwater storage) is likely to be impacted negatively during a drought. Thus, a drought monitor based on reservoir storage/area is valuable for informing irrigation scheduling in a realistic manner (when under drought conditions).
Figure 3.5 (a) Monthly average precipitation and SPI with a 6‐month timescale for the upstream area of Lake Whitney, Texas. (b) Comparison of RAFI with U.S. Geological Survey monitored elevation for Lake Whitney.
3.4. FUTURE DIRECTIONS
The future of observing reservoirs from space is very promising for several reasons. First, the process of measuring surface water elevation is moving into a new era. In addition to the traditional radar altimeter instruments, such as Sentinel‐3A (2016), Sentinel‐3B (2017), and Jason‐CS/Sentinel‐6 (2020), the recently launched ICESat‐2 mission (Markus et al., 2017) and the Surface Water and Ocean Topography (SWOT) mission (Biancamaria et al., 2016) to be launched in 2022, will allow a record number of global lakes and reservoirs to have their spatial coverage measured. The Advanced Topographic Laser Altimeter System (ATLAS), which is the sole instrument on ICESat‐2, uses the travel time from multiple pulses to determine elevation. The ATLAS generates six beams arranged in three pairs. Each of the three beam pairs is 90 m wide, with a spacing of 3.3 km between adjacent pairs. With such a design, ICESat‐2 ground tracks can cover most water bodies that have an area larger than a few square kilometers (Figure 3.6). Furthermore, its water penetration capability makes ICESat‐2 the best sensor for generating high quality reservoir/lake elevation–area relationships (Li et al., 2019). The SWOT satellite mission will be the first with the primary goal of monitoring surface water. With its Ka‐band radar interferometer, the SWOT mission will be able to map water level elevations for all bodies of water greater than 250 m × 250 m at a 21 day repeat orbit. This will result in a considerable leap forward with regard to the monitoring of reservoir storage variations, and thus drought conditions.
Figure 3.6 ICESat‐2 ground tracks for: (a) some natural lakes on the Tibetan Plateau, China (0.4 km2 < area < 498.06 km2); (b) Lake Mead, Nevada, USA (area = 580.95 km2); (c) Timnath Reservoir, Colorado, United States (area = 2.33 km2). The line colors represent the different tracks for the different passes.
(Source: From Li, Y., Gao, H., Jasinski, M. F., Zhang, S., & Stoll, J. D. (2019). Deriving High‐Resolution Reservoir Bathymetry from ICESat‐2 Prototype Photon‐Counting Lidar and Landsat Imagery. IEEE Transactions on Geoscience and Remote Sensing, 57(10), 7883–7893. © IEEE.)
Second, the number of VIS/NIR/SWIR sensors in orbit has been increasing drastically, and high‐resolution imagery is now being collected more frequently than ever. For instance, the twin satellites Sentinel‐2A (since 2015)