**5. Conclusions**

In this study, an algorithm that leverages the SRTM DEM data was developed to improve the spatial coverage of the reservoir monitoring network in South Asia. By combining water surface area from MODIS for reservoir storage estimations, we were able to take the advantage of high temporal resolution of MODIS and large spatial coverage of SRTM. Furthermore, validation results against gauge observations over 11 reservoirs in South Asia suggested that the storage estimations had a good level of accuracy (with *R*<sup>2</sup> values ranging from 0.47 to 0.91). The integrated storage capacity of these reservoirs was 118.76 km3, which represents 46.6% of the overall storage in the region.

This algorithm still has some limitations that need to be noted. First, the accuracy of the proposed algorithm depends on the water level at the time the DEM data were collected. For certain reservoirs that were almost full during the SRTM acquisition time, this approach did not work. Due to the assumption that the *A*-*H* relationship derived from the DEM above the water surface represented the full bathymetry, uncertainties in storage estimations were introduced in addition to those from the area retrieval algorithm. Second, the low vertical resolution of SRTM DEM and the errors from different sources may reduce the accuracy of the storage estimation [44]. Therefore, examining the DEM errors with respect to the terrain of the reservoirs could help us to better understand the error characteristics of the storage estimation bias. Third, due to the medium resolution of MODIS, the accuracy of reservoir storage estimation decreased for the reservoir with the smallest surface area (56 km2). Nonetheless, the benefits of the extended number of reservoirs outweigh the constraints.

The algorithm proposed in this study can provide reservoir storage products that support water managemen<sup>t</sup> on a large scale. For instance, given the long-term availability of high spatial resolution sensors, this approach could be used to monitor much smaller sized reservoirs than possible using existing techniques. This algorithm may also contribute to future satellite missions such as the Surface Water Ocean Topography (SWOT) mission, which will provide a direct water surface measurement for about two-thirds of global lakes and reservoirs, including those with an individual water area > 0.06 km2.

**Author Contributions:** Conceptualization, S.Z. and H.G.; Methodology, S.Z. and H.G.; Writing—Original Draft Preparation, S.Z.; Writing—Review & Editing, S.Z. and H.G.; Funding Acquisition, H.G. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by the NASA Science of Terra, Aqua, and Suomi NPP (TASNPP) Program (80NSSC18K0939) provided to Texas A&M University. It has benefitted from the computing resources of the Texas A&M Supercomputing Facility (http://sc.tamu.edu). The authors would also like to thank the Central Electricity Authority of India for providing the reservoir gauge observations.

**Conflicts of Interest:** The authors declare no conflict of interest.
