**2. Data**

#### *2.1. Remote Sensing Data*

In this study, the two primary remote sensing datasets were the STRM DEM and the MODIS imageries. The DEM was used for inferring the *A-H* relationship. The DEM data were collected by SRTM during an 11-day mission in February 2000, covering a near-global domain from 56◦ S to 60◦ N [38]. The relative vertical accuracy was ~6m, and the absolute accuracy was ~16 m [37]. The NASA SRTM V3.0 dataset provides land surface elevation values at a 30-m spatial resolution globally. Here, the global SRTM DEM dataset was obtained from the U.S. Geological Survey's Long Term Archive [39].

For each given reservoir, MODIS imageries were used to derive surface area estimations, which were then applied to the *A-H* relationship to generate a long-term time series of reservoir storage. The reservoir surface area was calculated from the MODIS/Terra 16-day, 250-m resolution vegetation indices product (MOD13Q1). Specifically, an image classification algorithm (Section 3.2.1) was applied to the Normalized Di fference Vegetation Index (NDVI) imageries to extract the reservoir area. From 2000 to 2015, a total of 365 imageries were processed for each reservoir.

#### *2.2. Data for Validations*

Gauge observations released by the Indian Central Electricity Authority (CEA, [40]) were used to validate the remotely sensed reservoir storage dataset. This gauge data contained daily reservoir water level and storage information for 30 hydropower reservoirs. We downloaded the record from 2008 to 2011 and from 2013 to 2016 in May 2016.

Additionally, the reservoir storage results derived from MODIS and SRTM were compared against the previous results from MODIS and ICESat [25]. Because the Zhang dataset contains results from 21 South Asian reservoirs, this cross-validation helped us to better understand the overall performance of this new dataset on a regional scale.

#### **3. Reservoir Selection and Methodology**

## *3.1. Reservoir Selection*

Two criteria were used to identify the reservoirs included in this study: First, the reservoir maximum area at capacity needed to be larger than 55 km2. The threshold of 55 km<sup>2</sup> was based on a comprehensive consideration of both estimation accuracy and spatial coverage. This would guarantee that the surface area could be estimated with high accuracy using medium-resolution MODIS imageries. Reservoirs larger than 55 km<sup>2</sup> account for ~46.6% of the total South Asian reservoir capacity. Second, the surface area according to the SRTM DEM for a reservoir of interest should not reach its maximum surface area (estimated from MODIS). Otherwise, the respective ranges of area and elevation detected by SRTM DEM would have been too small to infer the *A*-*H* relationship accurately. Following the above criteria, a total of 28 reservoirs were chosen from the Global Reservoir and Dam (GRanD) database [41]. Figure 1 shows the locations of these reservoirs, and compares the reservoirs from this study with those in Zhang et al. [25], with details shown in Table 1.

**Figure 1.** Locations of 28 reservoirs that can be monitored using a remote sensing approach. Yellow dots represent reservoirs that can only be monitored by the Moderate Resolution Imaging Spectroradiometer-Ice, Cloud, and Land Elevation Satellite (MODIS-ICESat). Green dots are reservoirs that can only be monitored through the MODIS-Shuttle Radar Topography Mission (STRM). Red points are reservoirs that can be monitored by both approaches. For each reservoir, detailed information is provided in Table 1.


**Table 1.** Detailed information for the 28 reservoirs.

a I, irrigation; E, electricity generation; W, water supply; F, flood control; b y, water surface height; *x*, area.

#### *3.2. Methodology for Reservoir Storage Estimation*

The MODIS-SRTM-based reservoir storage estimation algorithm—referred to as the "MODIS-SRTM algorithm" hereafter—is illustrated using the flowchart in Figure 2. It mainly contains three step: First, the water surface area was estimated from MODIS NDVI imageries via an enhanced classification procedure; second, the *A*–*H* relationship was generated from the DEM information by regressing the cumulative area values against their corresponding elevation values (within the delineated reservoir maximum domain; and third, by applying the water surface area estimations to the *A*–*H* relationship, the reservoir storage variations were calculated. Further details of these steps are provided as below.

**Figure 2.** Flowchart of the MODIS-SRTM based reservoir storage estimation algorithm.

#### 3.2.1. Surface Area Estimation

For each given reservoir, the water surface area was estimated using the enhanced K-means classification approach developed by Zhang et al. [25]. First, a threshold of 0.1 was applied to each 16-day MODIS NDVI image from 2000 to 2015, where pixels with NDVI values less than 0.1 were considered water. Based on these simplified classifications, a mask image was created to represent the water coverage percentile and to delineate the domain of the reservoir. Then, the K-means clustering algorithm [42] was used to identify all water pixels within the masked area of the MODIS NDVI images. Finally, a classification enhancement procedure was applied to finetune the results from the K-means clustering. The main purpose of the enhancement was to use the water occurrence map as a reference to correct misclassified pixels and/or to assign an appropriate class to the unclassified pixels [25].

#### 3.2.2. Area-Elevation (*A-H*) Relationship Development

The SRTM DEM data were used to develop the *A-H* relationship for each reservoir. As a valid approximation, the relationships for all reservoirs were assumed to be linear (*H* = *kA* + *b*, where *k* is the slope of the *A*-*H* relationship, and *b* is the intercept) [43]. To capture the relationship, we first delineated the water surface area from the DEM for each reservoir of interest. For a given reservoir, the water surface area during the SRTM acquisition time was expanded to include its surrounding pixels by gradually increasing the surface elevation threshold, with the water surface elevation corresponding to the DEM area as the initial value. During this process, all pixels that were not directly connected to the increasing water area were discarded as noise. This expansion continued until the new area on this DEM reached the maximum reservoir area estimated from the MODIS images (from 2000 to 2015). This maximum reservoir area was then delineated from the SRTM DEM. A simplified example

of a delineated reservoir is shown in Figure 3a. After delineating the maximum coverage of the reservoir from the DEM, the cumulative area (e.g., *A*3) at any given elevation value (e g., *H*3) could be estimated by counting the number of pixels with elevations equal to or smaller than that value (i.e., *H*3). By regressing the cumulative area values against the elevation values, the *A-H* relationship for the reservoir of interest was established (Figure 3b). A real example of the *A-H* relationship development for the Pong reservoir is provided in Figure 3c,d.

**Figure 3.** (**a**) A simplified example of a delineated reservoir from the SRTM Digital Elevation Model (DEM), where *H*1 > *H*2 > *H*3 > *H*4; (**b**) the corresponding *A*-*H* relationship inferred from a simplified example; (**c**) real example of a delineated reservoir from the SRTM DEM over the Pong reservoir; (**d**) the corresponding *A*-*H* relationship inferred from the Pong reservoir.

An example of the *A*-*H* relationship over the Hirakud reservoir is shown in Figure 4a. This *A-H* relationship was also compared with that derived from MODIS area values and ICESat elevations for cross-validation purposes. The MODIS-ICESat-based *A-H* relationship was adopted from Zhang et al. [2014]. The *A*-*H* relationship from the MODIS-ICESat algorithm is capable of capturing a larger range of water surface elevation values due to its longer temporal coverage period (seven years). The range of elevation values associated with the SRTM based *A*-*H* relationship depends on how full the reservoir was during the SRTM flight time—the fuller the reservoir at the overpass time, the smaller the elevation range above the water. The slopes for the two relationships are fairly similar with only with a small bias.

**Figure 4.** (**a**) The *A*-*H* relationship developed from SRTM compared with the relationship derived from ICESat, (**b**) time series of the storage estimation values for the Hirakud reservoir from both the SRTMand the ICESat-based approaches.
