Determining Bathymetry of Shallow and Ephemeral Desert Lakes Using Satellite Imagery and Altimetry

Abstract

Water volume estimates of shallow desert lakes are the basis for water balance calculations, important both for water resource management and paleohydrology/climatology. Water volumes are typically inferred from bathymetry mapping; however, being shallow, ephemeral, and remote, bathymetric surveys are scarce in such lakes. We propose a new, remote-sensing-based, method to derive the bathymetry of such lakes using the relation between water occurrence, during >30 year of optical satellite data, and accurate elevation measurements from the new Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2). We demonstrate our method at three locations where we map bathymetries with~0.3 m error. This method complements other remotely sensed, bathymetry-mapping methods as it can be applied to: (a) complex lake systems with subbasins, (b) remote lakes with no in-situ records, and (c) flooded lakes. The proposed method can be easily implemented in other shallow lakes as it builds on publically accessible global data sets.Plain Language Summary Lakes in desert environments are often remote and shallow and only get filled once in a long while. They are an important water resource and could be used to decipher past environmental conditions. However, detailed maps of lake-floor terrain, which are required to effectively study these lakes are typically not available. The deepest parts of the lakes are filled with water more frequently than their shallow margins. Thus, we suggest here to relate water occurrence in those lakes with accurate satellite-based elevation measurements, to obtain a valuable lake-floor terrain map. We demonstrate the usefulness of our method by comparing results with other globally available data. Previous methods struggle with complex-terrain lakes or lakes that are partially flooded during their survey, while our method yields high-resolution accurate maps even in such lakes. Geophysical Research LettersARMON ET AL. 5 of 9surface readings (Text S3). The difference between the "dry" bathymetry and the SRTM data and the Figure 3. Hypsometric curves, extent maps and cross-sections for (a) Lake Eyre, (b) Sabkhat El-Mellah, and (c) Lago Coipasa. The maps show filling extent at heights (denoted by a gray line on the hypsometric curves) that exert major differences between bathymetries. Details of the preparation of the hypsometries are in Text S2.

Monitoring Annual Changes of Lake Water Levels and Volumes over 1984–2018 Using Landsat Imagery and ICESat-2 Data

“…In addition, benefitting from the bathymetric capacity and much denser ground/bottom points, the simulated ICESat-2 data (based on the airborne MABEL data) [51,52] or measured ICESat-2 data [40,[53][54][55] were used to obtain the along-track underwater bottom points and further generate the bathymetric maps with satellite images, e.g., the Landsat, Sentinel-2, and GSWD (Global Surface Water Dataset) products [4]. Although these used datasets and some steps of the method (e.g., detecting the signal photons) are similar to this study, the specific applications and other steps (e.g., the data fusion) are quite different.…”

Systematic Water Fraction Estimation for a Global and Daily Surface Water Time-Series

“…On a global level, such information can contribute significantly to the understanding of climate change [15] and the effects of human activities [16]. Furthermore, knowledge of water level variations (e.g., by altimetry time-series), in combination with surface water extent dynamics, enables the approximation of waterbody bathymetry, subsequently allowing the quantification of storage change [17][18][19][20][21][22][23]. Thus, essential water variables can be monitored, providing key indicators for the implementation of sustainable development goals [24].…”

Lake Topography and Active Storage From Satellite Observations of Flood Frequency

“…Furthermore, the direct retrieval of bathymetry from satellite imagery is limited for situations in clear and shallow water bodies where the remotely detected signal is dominated by bottom reflected radiation (Carbonneau et al, 2006;Dilbone et al, 2018;Gao, 2009;Legleiter et al, 2009;Legleiter & Overstreet, 2012;Sandidge & Holyer, 1998). Alternatively, the topography can be estimated between the smallest and largest water surface areas using water level and flood extent data, such as the approaches presented by Feng et al (2011), Arsen et al (2013), Getirana et al (2018), Tseng et al (2016), Li et al (2019Li et al ( , 2020, and Armon et al (2020). These methods, however, have limitations.…”

“…The method proposed by Tseng et al (2017) only partly resolves these limitations by estimating the bottom levels on a pixel by pixel basis from a flood frequency map computed from multitemporal satellite images, but this approach does not account for the temporal variation of water surface elevation. Recently, other approaches were developed relating terrain elevation from the ICESat-2, sampled at specific points, and flood frequency (Armon et al, 2020) or water surface areas (Li et al, 2019(Li et al, , 2020 derived from a flood frequency map. However, these approaches also require adjustment of functions.…”