**4. Results**

The MODIS-SRTM-based reservoir storage dataset was examined comprehensively from three perspectives: First, the reliability of the dataset was tested by validating the MODIS-SRTM based reservoir storage results with both in situ gauge data and the MODIS-ICESat based results. Second, the enhanced spatial coverage from this new dataset was compared with the existing reservoir storage dataset in South Asia. Third, the uncertainties associated with the algorithm and dataset were analyzed.

#### *4.1. Validation Results*

The MODIS-SRTM-based reservoir storage was validated over 11 reservoirs (Table 2) where gauge observation data were available. The performance of the results was evaluated using Equations (3)–(5), which represent three statistical criteria: the coefficient of determination (*R*2), the relative bias (*B*), and the normalized root mean square error (NRMSE):

$$R^2 = \frac{\sum\_{i=1}^{n} \left( RS\_i - \overline{RS\_i} \right) \left( OBS\_i - \overline{Obs} \right)}{\sqrt{\sum\_{i=1}^{n} \left( RS\_i - \overline{RS\_i} \right)^2} \sqrt{\sum\_{i=1}^{n} \left( Obs\_i - \overline{Obs} \right)^2}} \tag{3}$$

$$B = \frac{\overline{RS} - \overline{Obs}}{\overline{Obs}} \times 100\% \tag{4}$$

$$NRMSE = \frac{\sqrt{\sum\_{i=1}^{n} \frac{\left(RS\_i - Obs\_i\right)^2}{n}}}{\overline{Obs}} \times 100\% \tag{5}$$

where *RS* represents the remotely sensed results, *Obs* is the gauge data, *i* denotes the *ith* record, *n* is the total number of data points, and *RS* and *Obs* are the average values of the remote sensing results and the gauge data, respectively.



As shown in Table 2, most of these results were highly correlated with CEA gauge observations. The *R*<sup>2</sup> values ranged from 0.47 to 0.91, with a mean of 0.8. The lowest *R*<sup>2</sup> was found over the Ranjit Sagar reservoir. This reservoir has a relatively small area (56 km<sup>2</sup> at capacity) and is very meandering with a high shoreline to area ratio, complicating the accurate estimation of the surface area from the medium spatial resolution MODIS data [9]. This multicriteria evaluation provided a comprehensive understanding of the results. Using the Srisailam reservoir as an example, its *R*<sup>2</sup> value was the second highest among all of the validated reservoirs, but its NRMSE was relatively large. Because the slope of the *A-H* relationship (*k*, in Equation (2)) is constant, a high *R*<sup>2</sup> value suggests that the area estimations are accurate. Thus, the large NRMSE was mainly caused by errors associated with the slope of the *A*-*H* relationship. Because the area error was proven to be small as indicated by the large *R*2, the SRTM DEM was thus the primary error source for the storage results for this reservoir. Another example is the Ranjit Sagar reservoir. Although it had an extremely low *R*<sup>2</sup> value due to the large amount of error in the surface area estimations, the storage bias was close to those of the Pong and Rihand reservoirs, which indicates a relatively more accurate *A*-*H* relationship over this reservoir.

The performance of this algorithm was also compared with the MODIS-ICESat algorithm by Zhang et al. [25] (Table 3). The remotely sensed reservoir storage data from these two algorithms

were validated over five reservoirs (Hirakud, N. J. Sagar, Pong, Rengali, and R. P. Sagar) where gauge observations and *A*-*H* values were available (from both MODIS-ICESat and SRTM).


**Table 3.** Comparison of the validation results between the MODIS-SRTM and MODIS-ICESat approaches.

As shown in Figure 5a, both the MODIS-SRTM and MODIS-ICESat-based approaches performed well overall. The time series from these two algorithms closely matched the gauge values for reservoir storage. To highlight the differences between the DEM and ICESat based algorithms, Figure 5b compares the storage errors against the gauge observations from these two datasets. The error statistics are provided in Table 3. Among each of the five reservoirs, the NRMSE of the MODIS-SRTM algorithm ranged from 18.14% to 27.95%, with a mean value of 21.94%. The relative bias values ranged from −11.07% to 19.25%. The NRMSE of the MODIS-ICESat algorithm ranged from 14.58% to 26.50%, with a mean value of 18.83%. The bias values ranged from −8.97% to 4.41%. In terms of accuracy, the two approaches performed relatively similarly, with the MODIS-ICESat algorithm slightly better than the DEM based algorithm. For the N. J. Sagar reservoir, the NRMSE was 27.95% for the MODIS-SRTM algorithm and 26.50% for the ICESat-based algorithm. For this reservoir, the DEM results were more accurate than the ICESat results. The NRMSE was 15.00%, which was 3.18% better than the ICESat based algorithm. For the Hirakud, Pong, and R. P. Sagar reservoirs, the MODIS-ICESat algorithm showed a superior accuracy when validated against the gauge data. The higher accuracy of the MODIS-ICESat algorithm at these three reservoirs may be attributed to the higher vertical accuracy of the ICESat elevation values, and/or the longer observation period of ICESat (than the DEM, which results in a more representative *A*-*H* relationship). Because the ICESat and SRTM approaches both use the same MODIS water area values, the larger bias of storage from the SRTM DEM implies that the lower accuracy of SRTM could reduce the quality of the reservoir storage product. As stated by the authors of [44], the components of the SRTM error include baseline roll error, phase error, beam differential errors, and timing and position errors. However, the SRTM DEM errors related to terrain, timing, and position—along with the low vertical resolution (1-m intervals)—still influenced the accuracy of the *A*-*H* relationship, which led to a higher bias of the storage calculation. Overall, the MODIS-SRTM algorithm performed reasonably well.

#### *4.2. Spatial Coverage of the Reservoir Storage Dataset*

With full-coverage of two-dimensional elevation data at a fine spatial resolution (30 m), the MODIS-DEM algorithm generated storage time series for the 28 reservoirs in South Asia from 2000 to 2015 (Figure 6). These reservoirs had an integrated capacity of 124.17 km<sup>3</sup> (46.6% of the region's total capacity). Compared with the MODIS-ICESat algorithm, the MODIS-SRTM algorithm enabled the monitoring of eight additional reservoirs (Figure 1), which represented a 18.6% increase of the overall storage capacity. Sriram Sagar, which was almost at its maximum level during the SRTM flight time, was the only reservoir for which the *A*-*H* relationship could be generated by MODIS-ICESat but not by the DEM.

**Figure 5.** Validation results by comparing the remotely sensed storage values with gauge observations: (**a**) Comparison among absolute storage values; (**b**) comparison of storage difference (remotely sensed storage minus gauge data).

**Figure 6.** Combined remotely sensed storage time series of the South Asian reservoirs analyzed in this study.

The new dataset contains the storage variation information over multiple reservoirs at the basin scale, which is essential for regional water managemen<sup>t</sup> purposes. For instance, with two additional reservoirs included in the dataset, the total storage of the monitored reservoirs in the Krishna river basin (KRB) increased from 33.4% to 67.0% (i.e., from 9.70 to 19.44 km3) of the basin's capacity (29 km3). The Krishna River is the fourth largest river in India, with its basin extending over an area of 259,948 km<sup>2</sup> (about 8% of India). Most of the KRB is relatively flat, with about 76% of the basin covered by agricultural land. Many hydroelectric power stations are distributed along the Krishna River, providing clean energy to a large area of India. Therefore, the improved spatial representativeness of reservoirs in this river basin is essential for hydrologic modeling and water management. The Ukai Dam across the Tapti River was constructed for the purposes of irrigation, hydropower generation, and flood control. The Tapti River basin accounts for nearly two percent of the total area of India. However, before this study, no reservoir in this basin had remotely sensed elevation or storage data from space. In 2000, a severe drought occurred in the Tapti basin, causing drinking water scarcity in some villages [45]. In 2009, many districts in this basin were declared to be under drought conditions due to the deficiency of rainfall from June to September [46]. The low storage values around 2000 and 2009 (Figure 6) reflect this water scarcity. Figure 6 also shows an increase of maximum storage in the Mangla Reservoir after 2012. This is attributed to the enhanced storage capacity, that was used to increase the reservoir's irrigation capability [47]. Another example is the Yeldari reservoir. According to media reports, two severe drought events occurred in the region in 2004 and from 2012 to 2015—and, in both cases, the Yeldari reservoir almost dried up [48,49].
