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) and Sentinel‐2B have a revisit time of 5 days at a high resolution (10–60 m). These greatly complement the Landsat sensors, and improve the chances of acquiring cloud‐free images. Another example involves CubeSat sensors, which collect daily imagery globally at 3–5 m resolution (McCabe et al., 2017). The key benefit of using CubeSat data is the capability of tracking the area variations of small reservoirs. There are numerous small reservoirs globally that are not documented in the Global Reservoir and Dam (GRanD) database (Lehner et al., 2011). Although these small reservoirs are essential for management at the local scale, there are almost no storage/elevation records available. Using the area information collected by CubeSat will help to fill in this gap.
Third, novel fusion algorithms can help to improve the spatial/temporal coverage and the accuracy of global reservoir products. As mentioned in sections 3.2.2 and 3.2.3, data fusion techniques have been used in a number of studies. These algorithms can be classified into two groups, the first of which focuses on fusing measurements of a single variable from different data sources. For elevation, the most common practice involves adopting data from all radar altimeters (Crétaux et al., 2015). For area, Zhang & Gao (2016) combined Advanced Microwave Scanning Radiometer and Earth Observing System (AMSR‐E) with MODIS observations to estimate reservoir area under all‐weather conditions. High‐resolution Landsat observations are also commonly used for downscaling the medium‐resolution MODIS results (Che et al., 2015). The second group is focused on taking advantage of elevation‐area‐storage relationships, such that a single variable with good accuracy (and/or good spatial/temporal coverage) can be used for inferring the values of the others (Gao et al., 2012). Powered by the ever increasing cloud‐computing platforms (such as the Google Earth Engine), the new generation of fusion algorithms will be able to incorporate elements from both groups.
Last, recent developments of the water management component used in earth system modeling tools has brought forth great promise for the investigation of human intervention in a holistic manner (Li et al., 2015; Voisin et al., 2017; Yigzaw et al., 2018). As such, the assimilation of remote sensing reservoir data is expected to be feasible, similar to the improved drought monitoring which has occurred using GRACE data incorporated into NLDAS (Kumar et al., 2016).
The pressing research questions related to reservoirs in the coming decades are:
1 What are the impacts of reservoir impoundments on the spatial and temporal distributions of the hydrological cycle?
2 How does reservoir storage respond to climate variability, climate change, extreme events, and human activities across scales?
3 How do we improve reservoir water management under the stress of future climate change and population growth?
To solve these questions, the science communities from different areas and disciplines need to collaborate for convergence research. For instance, remote sensing and modeling methods should be fully integrated, while the hydrology and water resources management communities should interact with decision makers more proactively.
3.5. DISCUSSION AND CONCLUSIONS
Remotely sensed reservoir data have the potential of being used with other drought indicators jointly to promote improved drought mitigation.
First, by combining the remotely sensed reservoir storage information with GRACE terrestrial water storage anomalies, optimal use of surface water and groundwater in combination would be feasible. In light of the ever‐increasing water needs and the uncertain supplies, leveraging water resources and
