Monitoring small reservoirs' storage with satellite remote sensing in inaccessible areas

Abstract

Abstract. In river basins with water storage facilities, the availability of regularly updated information on reservoir level and capacity is of paramount importance for the effective management of those systems. However, for the vast majority of reservoirs around the world, storage levels are either not measured or not readily available due to financial, political, or legal considerations. This paper proposes a novel approach using Landsat imagery and digital elevation models (DEMs) to retrieve information on storage variations in any inaccessible region. Unlike existing approaches, the method does not require any in situ measurement and is appropriate for monitoring small, and often undocumented, irrigation reservoirs. It consists of three recovery steps: (i) a 2-D dynamic classification of Landsat spectral band information to quantify the surface area of water, (ii) a statistical correction of DEM data to characterize the topography of each reservoir, and (iii) a 3-D reconstruction algorithm to correct for clouds and Landsat 7 Scan Line Corrector failure. The method is applied to quantify reservoir storage in the Yarmouk basin in southern Syria, where ground monitoring is impeded by the ongoing civil war. It is validated against available in situ measurements in neighbouring Jordanian reservoirs. Coefficients of determination range from 0.69 to 0.84, and the normalized root-mean-square error from 10 to 16 % for storage estimations on six Jordanian reservoirs with maximal water surface areas ranging from 0.59 to 3.79 km 2 .

Modeling the role of reservoirs versus floodplains on large-scale river hydrodynamics

“…Simplified operation rules have been proposed by recent studies with regional/global hydrologic models (Hanasaki et al 2006;Döll et al 2009;Zajac et al 2017), considering, for example, the reservoir storage and water demands. For estimating reservoir storage (or bathymetry), remote sensing data processing is very promising (Gao et al 2012;Rodrigues et al 2012;Duan and Bastiaanssen 2013;Zhang et al 2014;Bonnema et al 2016;Avisse et al 2017;Bonnema and Hossain 2019), for instance, by combining surface water from optical imagery and water level from satellite altimetry. New global datasets and methods describing reservoirs and elevation-area-volume relationships present also interesting opportunities for future developments (Lehner et al 2011;Gao et al 2012;Yigzaw et al 2018).…”

Small reservoirs for a sustainable water resources management

“…Both Sentinel-2 platforms are equipped with a multispectral sensor that acquires 13 spectral bands with 12 bit radiometric resolution and different geometric resolutions at 10 m (blue, green, red and NIR), 20 m (six bands including SWIR) and 60 m, as reported in Table 1. Remote sensed data are used for lake identification through water index (WI) evaluation by means of the NDWI (Normalized Difference Water Index) and the MNDWI (Modified Normalized Difference Water Index; Xiucheng et al, 2017;Gordana and Ugur, 2017;Yun et al, 2016;Dominici et al, 2019;Avisse et al, 2017;Gideon and Maurice, 2015;Mishra and Prasad, 2015).…”

Revision of Vajiralongkorn Dam’s Reservoir Characteristic Curves Using NDWI Derived from Landsat 8 Data

“…The previous studies demonstrated the potential of remote sensing technique in assessing the reservoir sedimentation and analyzing its spatio-temporal variation. One of the common data source was the Landsat image (Avisse et al, 2017;Du et al, 2014;Rodrigues et al, 2012;Gupta and Banerji, 1985), especially for estimating reservoir storage loss (Zhang et al, 2018;El-Shazli and Hoermann, 2016;Ran and Lu, 2012).…”