Reassessment of Reservoir Sedimentation Rates under a Monsoon Climate with Combined Optical and Microwave Remote Sensing: A Case Study of Three Reservoirs in the Upper Godavari Basin, India

Remote sensing is widely used for monitoring reservoir capacities. The relationship between two water levels (WLs) and the corresponding satellite-derived water spread areas provides the reservoir volume between the two WLs. However, the accuracy of this method depends on the ability to capture the water spread areas at fine increments of WLs so that the cross section of a reservoir is represented in adequate detail. In a monsoon climate, persistent cloud cover during the rainy season limits the number of usable optical satellite images. Hence, the time interval between two successive WLs for which cloud-free images are available is generally large. Using only a few WLs increases the likelihood of missing a significant break of slope in the reservoirs that may lead to gross error in the storage measurement. We showed that the combination of freely available optical (e.g., Landsat 8) and cloud-penetrating microwave [e.g., European Space Agency (ESA) Sentinel-1] images may improve storage estimation and mitigate the inherent uncertainty of using remote sensing data for monitoring reservoir sedimentation. Three reservoirs in the Upper Godavari Basin in India constituted the study area. Findings show that the average annual sedimentation rate was overestimated by $\sim 10\%$ when using only Landsat-8 images compared to combined radar and optical remote sensing databases (2016 and 2017). The suggested method made significant improvements in the estimation of reservoir capacities for narrow-bottomed and convex-edged reservoirs.

The capacity of a reservoir is typically monitored on a periodic basis as a means of determining the reservoir’s overall health. This is a common application of satellite imagery. Satellite images are used to determine the volume of water stored in a reservoir by analyzing the relationship between two water levels and the area covered by the water. The greater the difference between the two water levels, the less precise this measurement will be. Under monsoon climate, cloud cover prevents the use of optical images during the rainy season when reservoir levels fluctuate greatly. Because there are fewer water levels to choose from due to the lack of images, the accuracy of the estimate suffers. Using three reservoirs in the monsoon region of peninsular India, this study shows that combining optical data from satellites like Landsat-8 with publicly available cloud-penetrating Sentinel-1 radar images greatly increases the number of water spread area–water level data points and decreases the error and uncertainty in the storage estimation. Based on our findings, it appears that the reported rate of capacity loss, derived from limited optical images, is likely to be inaccurate. The authors also found that the storage estimates of the narrow-bottomed conical-shaped reservoirs will be greatly improved by increasing the number of water levels in volume calculation.