A global lake and reservoir volume analysis using a surface water dataset and satellite altimetry

Abstract

Abstract. Lakes and reservoirs are crucial elements of the hydrological and biochemical cycle and are a valuable resource for hydropower, domestic and industrial water use and irrigation. Although their monitoring is crucial in times of increased 10 pressure on water resources by both climate change and human interventions, publically available datasets of lakes and reservoir levels and volumes are scarce. Within this study, a time series of variation in lake and reservoir volume between 1984 and 2015 were analysed for 135 lakes over all continents by combining the JRC Global Surface Water (GSW) dataset and the satellite altimetry database DAHITI. The GSW dataset is a highly accurate surface water dataset at 30 m resolution compromising the whole L1T Landsat 5, 7 and 8 archive, which allowed for detailed lake area calculations globally over a 15 very long time period using Google Earth Engine. Therefore, the estimates in water volume fluctuations using the GSW dataset are expected to improve compared to current techniques as they are not constrained by complex and computationally intensive classification procedures. Lake areas and water levels were combined in a regression to derive the hypsometry relationship (dh/dA) for all lakes. Nearly all lakes showed a linear regression, and 42 % of the lakes showed a strong linear relationship with an R 2 > 0.8 and an average R 2 of 0.91. For these lakes and for lakes with a nearly constant lake area 20 (coefficient of variation < 0.008), volume variations were calculated. Lakes with a poor linear relationship were not considered. Reasons for low R 2 values were found to be (1) a nearly constant lake area, (2) winter ice coverage, (3) small lake sizes and (4) a predominance of no data within the GSW dataset for those lakes. Lake volume estimates were validated for 18 lakes in the U.S., Spain, Australia and Africa using in situ volume time series, and gave an excellent Pearson correlation coefficient of on average 0.97, and a normalized RMSE of 7.42 %. These results show a high potential for 25 measuring lake volume dynamics using a pre-classified GSW dataset, which easily allows the method to be scaled up to an extensive global volumetric dataset. This dataset will help to validate large scale hydrological models, to improve regional and global flood and drought forecasting systems and to update hydropower estimations.Hydrol. Earth Syst. Sci. Discuss., https://doi

High Resolution Modeling of River‐Floodplain‐Reservoir Inundation Dynamics in the Mekong River Basin

“…Future studies could improve reservoir bathymetry and operation of individual reservoirs if such data become available. Reservoir bathymetry could also potentially be improved using satellite altimetry and imagery (e.g., Busker et al, 2018;Li et al, 2019), but the application is limited to nonpermanent water areas. For permanent water areas, hydraulic geometry relationships (e.g., Schaperow et al, 2019) or known reservoir characteristics (e.g., Shin et al, 2019) could be used; however, such methods are generally not suited for deriving high-resolution (e.g., 90 m) reservoir bed elevations.…”

“…This is unavoidable in grid-based reservoir modeling, but the errors can be minimized by increasing the spatial resolution. Additionally, the spatial variations in inundation extent for reservoirs built before the SRTM launch can be better simulated by using improved reservoir bathymetry data where local information is available (Busker et al, 2018;Li et al, 2019;Shin et al, 2019;van Bemmelen et al, 2016).…”

Surface water monitoring in small water bodies: potential and limits of multi-sensor Landsat time series

“…These notably include 30 m topographic corrections, as did the Landsat imagery. Intensity-hue-saturation (IHS) pansharpening (Carper et al, 1990) was used on SWIR bands to allow water detection at 10 m (Du et al, 2016;Kaplan and Avdan, 2018) with the MNDWI and to compare performance with the 20 and 30 m MNDWI. The optimal MNDWI threshold on Sentinel-2 imagery was independently calibrated against k-means (unsupervised) classification (Jain, 2010) of flooded areas, and was substantially lower (−0.2) than with Landsat imagery (−0.09).…”

Authors' response to reviewer #2 C. Cudennec

“…Ogilvie et al: Surface water monitoring in small water bodies 30 m topographic corrections, as did the Landsat imagery. Intensity-hue-saturation (IHS) pansharpening (Carper et al, 1990) was used on SWIR bands to allow water detection at 10 m (Du et al, 2016;Kaplan and Avdan, 2018) with the MNDWI and to compare performance with the 20 and 30 m MNDWI. The optimal MNDWI threshold on Sentinel-2 imagery was independently calibrated against k-means (unsupervised) classification (Jain, 2010) of flooded areas, and was substantially lower (−0.2) than with Landsat imagery (−0.09).…”