**1. Introduction**

Human-made reservoirs, which are managed by storing and releasing water under predetermined operation rules, play an important role in mitigating floods and improving the efficiency of the water supply for municipal, industrial, and agricultural demands [1–4]. Although most (if not all) human operated reservoirs are monitored in real-time, reservoir storage information is not commonly available to the public. Indeed, this directly limits the effectiveness of reservoir flow regulation with regard to flood control, water supply, and other purposes—especially for those reservoirs located within transboundary river basins. For instance, the lack of reservoir information for the Mekong River delta has created challenges with regard to flood forecasting in this region [5,6]. In addition, when assessing and predicting the impacts of droughts, the lack of reservoir storage information reduces the reliability of drought analysis systems [7,8].

Due to the limited availability of gauge observations—especially with regard to remote locations, restricted locations, and/or observations over large geographical areas—remote sensing technology provides a promising alternative by monitoring reservoirs from space [4,9–12]. With remotely sensed water surface area and elevation data, reservoir storage information can be inferred. Reservoir surface area is commonly estimated by classifying optical satellite imageries [13,14] and surface elevation values are typically obtained from satellite radar altimetry [15,16]. The Geoscience Laser Altimeter System (GLAS) onboard the Ice, Cloud, and Land Elevation Satellite (ICESat) and the Advanced Topographic Laser Altimeter System (ATLAS) onboard ICESat-2 were used to measure the elevation values of relatively small lakes and reservoirs [4,17–20].

Even though a variety of remote sensing approaches were developed to monitor reservoir storage from space [21–23], they are still insufficient in terms of spatial and temporal coverage—which hinders their applications when high-density reservoir network information is required. For radar altimetry, the restrictions are mainly due to the coarse spatial resolution. With about 3–20 km footprints, it is difficult to capture water surface level values using radar altimetry over reservoirs that are either not large enough or do not overlap with the satellite tracks [24]. Even for lakes that are detectable by radar altimeters, the data may not be accurate enough for applications if the surrounding topography is complex. Consequently, as of 2015 less than 200 large lakes and reservoirs have been observed using the past and current set of radar altimeters [24]. Compared with radar altimeters, the ICESat/GLAS instrument has a distinct advantage with its small footprint (70 m)—but this comes at the cost of a very long return period (91 days). By combining ICESat elevation values and Moderate Resolution Imaging Spectroradiometer (MODIS) area estimations, Zhang et al. [25] developed an algorithm which is partially capable of monitoring South Asian reservoirs at 16-day intervals, with 28% of the total capacity of in the region covered. Despite such progress, the reservoir observation network is still too sparse due to the large spaces between satellites tracks. Water surface area from Landsat and the area-elevation relationship provided by the Shuttle Radar Topography Mission (SRTM) were combined to infer the water level and reservoir storage variations [26–28]. Landsat can be used to estimate water surface area for smaller reservoirs and lakes due to its high spatial resolution (30 m). However, its repeat period of 16 days limits its ability to monitor reservoir storage at high temporal resolution—especially when cloud coverage is too thick. Therefore, the lack of dense spatial and temporal representation from satellite altimeters remains a major challenge for collecting reservoir storage information on a large scale.

South Asia, which contains one of the largest and densest populations, suffers the most from the dearth of reservoir storage data sharing. The deficient communications with regard to reservoir storage (and managemen<sup>t</sup> decisions) further exacerbate the casualties and economic losses from flood events. According to past statistical records, South Asia experiences one of the highest fatality rates in the world caused by floods [29]. The available remotely sensed reservoir storage datasets only sparsely cover the region. For instance, radar altimetry data are only available for six reservoirs in this region, which accounts for 10.70% of the total capacity in South Asia (according to Hydrology by altimetry data from Laboratoire d'Etudes en Géophysique et Océanographie Spatiales (LEGOS) [30] and the Global Reservoir Lake Monitor [31]). Although the use of ICESat elevation data improved the coverage to around 28% of South Asian reservoirs [25], it still does not meet the strong societal need. Therefore, acquiring reservoir storage information with large spatial coverage is critical for minimizing the vulnerabilities and maximizing the benefits to communities in this region through good reservoir managemen<sup>t</sup> practices.

To extend the spatial coverage where remote sensing reservoir storage data are available, a reservoir storage dataset was developed by leveraging the global coverage capability of the Digital Elevation Model (DEM) collected by SRTM. Although DEMs have been most commonly used for generating river routing networks [32,33], they have also been adopted in studies to estimate glacier variations [34,35] and surface water storage change [36]. Due to its high consistency, accuracy, and global coverage [35,37], the SRTM DEM was used to extract the area-elevation (*A*-*H*) relationship for calculating reservoir storage in this study.

Our overarching goal was to improve the spatial coverage of the remotely sensed reservoir storage dataset in the South Asia region. To this end, the *A-H* relationship of a given reservoir was first derived from MODIS water surface area values and SRTM DEM surface heights, and then combined with the area time series to estimate storage variations. The results were validated with gauge

observations. The performance of the generated reservoir dataset was compared with the ICESat based algorithm reported by Zhang et al. [25]. In addition to the data analysis and the results validation, storage estimation uncertainties due to reservoir surface area retrieval algorithm parameterization and elevation measurement errors were also quantified.
