Lake Topography and Active Storage From Satellite Observations of Flood Frequency

Abstract

Topography is critical information for water resources management in lakes, and remote sensing provides a unique opportunity to estimate topography in ungauged regions. We introduce here a new method that estimates nearshore topography of water bodies based on a flood frequency map and time series of water levels by assuming the equivalence between flood frequency and water level exceedance probability at a given area. Test cases are performed for two lakes and 12 hydropower reservoirs using the proposed Flood2Topo app. This new application generates the bottom level pixel by pixel and level‐area‐active storage relationship directly from the topography map, without the need to fit functions. Flood extent estimates from the Landsat based Global Surface Water (GSW) data set, the current state‐of‐the‐art, were used to run Flood2Topo, together with water levels from satellite altimetry and in situ gauges. Results show bottom level root mean square deviation (RMSD) values of 18.5 and 146 cm for Lake Poopó (Bolivia) and Lake Curuai (Amazon basin), respectively. For reservoir active storage, RMSD normalized values ranged from 2% to 11% for 11 reservoirs (average NRMSD of 6.4%). The method can be applied to any seasonally flooded area using any data set. Considering the surface water occurrence map from the GSW data set, the method is applicable to 35.8% (86%) of the global seasonally flooded areas delimited by flood frequencies between 0 and 95% (99%) over 35 years. The flood frequency‐based method is a promising tool for obtaining data for hydrodynamic simulations and monitoring of ungauged water bodies.

Comment on essd-2021-32

“…This boundary is a polygon defined considering a flood frequency comprised between 96% and 100%. The flood frequency map was calculated from the Global Surface Water (GSW) Monthly Water History v1.1 data (Pekel et al, 2016; available at https://global-surface-water.appspot.com), which represents the space-borne Landsat-based monthly record of water presence on a global scale with a spatial resolution of 30 m. A Google-Earth engine code (Gorelick et al, 2017), described in Fassoni- Andrade et al (2020b), was used to create it considering all GSW monthly images from the period from January 2015 to December 2018, hereby totalizing 48 months.…”

“…Then, the water level duration curves were extrapolated through a nearest-neighbor interpolation over the whole of the estuary inter-tidal areas and floodplains, i.e., everywhere upstream estuary mouths. Therefore, the terrain elevation at any pixel was estimated considering the water level, in which the probability of exceedance is equal to the flood frequency at the same pixel 165 (Fassoni- Andrade et al, 2020b). In permanently flooded areas, i.e., where the flood frequency is 100%, the method considers the topography equal to the lowest WSE observed, as in the river.…”

“…The challenge to estimate the topography in the coastal area using the methodology of Fassoni- Andrade et al (2020b), where vertically-leveled tide gauges are lacking, is to infer spatially-distributed water level exceedance functions. We considered the downstream-most Amazon estuary station of Ponta do Céu (Table A2; location in Fig.…”

A Low-Cost Approach for Lake Volume Estimation on the Tibetan Plateau: Coupling the Lake Hypsometric Curve and Bottom Elevation

“…Given the high cost of field measurement in full coverage and the low accuracy of the spatial prediction method for local scale, this study aims to propose an efficient method for water volume estimation for lakes on the TP. Inspired by the application of the lake hypsometric curve method in monitoring lake volume change trajectory (Yigzaw et al, 2018;Fassoni-Andrade et al, 2020;Li et al, 2021), we tried to estimate the lake volume by extrapolating the hypsometric curve fitting to the near-bottom constrained by the minimized water depth surveys. The most commonly used hypsometric curve method is to combine the water area estimations from satellite imagery (e.g., Landsat and MODIS) with elevations from altimetry datasets (e.g., Hydroweb, G-REALM, and DAHITI) (Crétaux et al, 2011;Busker et al, 2019).…”

Coupling Historical Maps and LiDAR Data to Identify Man-Made Landforms in Urban Areas

“…In this way, the same vector file gathered elevations extracted from contour lines: at the interface between landfill material and natural sediment/rock substrate and in the two riverbeds, bearing in mind that no significant natural process occurred, such as severe erosion or intense sediment accumulation due to flooding, that changed the floodplain terrace elevations between Porro's survey epoch and the late 1800s, when intense urban sprawls began, unlike the phenomena described in [58][59][60]. -Interpolation and DoDs with GRASS GIS Since hand-drawn graphical elements of the Porro map have an equivalent resolution of 2 m (1 mm to the naked eye on a scale of 1:2'000), previously obtained elevation data were interpolated on a raster map with a 1-m resolution, also to reduce potential vertical errors due to horizontal offset between the old and current map, according to a methodology explained by Stoker et al [61].…”