Towards Water Availability Predictions with Earth-Observed Open Data in Man-Made Reservoirs Worldwide

Towards Water Availability Predictions with Earth-Observed Open Data in Man-Made Reservoirs Worldwide

Klein Holkenborg, Emie (TU Delft Civil Engineering & Geosciences; TU Delft Water Management)

Winsemius, H.C. (mentor)
Uijlenhoet, R. (graduation committee)
van den Homberg, Marc (graduation committee)
Comes, T.C. (graduation committee)

Delft University of Technology

Water Management | Hydrology

Global Water Watch

2023-05-15

Small to medium-sized man-made freshwater reservoir are a reliable source for drinking water supply, hydropower generation and irrigation purposes worldwide. However, water volumes in these reservoirs can be significantly affected by prolonged droughts, resulting in severe impacts on society (Kozacek, 2014; Mahr, 2018). To mitigate the impact of such events it is crucial for decision makers to know when the available water resources are lacking. Although many reservoirs are closely monitored, this data is not always readily available. Inadequate information sharing, inaccessibility, and a lack of tools to predict future reservoir storages contribute to this problem.

Remote sensing has the potential to address this problem. The Global Water Watch is a platform that and provides earth-observed surface area dynamics that can be used to monitor small to medium-sized reservoirs worldwide and detect trends in water availability. While this method serves as a valuable indicator of water availability, it falls short in providing decision-makers with the necessary absolute volume time series and volume predictions. Currently, no platform exists beyond in-situ measurements to meet this essential need.

This thesis presents a novel method for retrieving near real-time volume time series in small to medium-sized man-made reservoirs worldwide using remotely sensed open data. The method utilises the MERIT-Hydro digital elevation model, HydroMT and stream flow methods by Eilander et al. (2023), and literature by Messager et al. (2016) to reconstruct reservoir bathymetry. This novel approach in reconstructing reservoir bathymetry enables the conversion of available reservoir area time series into volume time series. These were employed in autoregressive and multi-linear regression models to predict water availability up to six months in advance. The models incorporate ERA5 precipitation data by Hersbach's (2020) and the Standardised Precipitation and Evaporation Index (SPEI) by Beguería et al. (2021) to improve the accuracy of the volume predictions.

When comparing the novel method to the method proposed by Messager et al. (2016), the novel method yielded more accurate reservoir volume estimations. The method successfully obtained bathymetries and accurate volume estimations when validating using 2 reservoirs in Zambia and 48 in India, demonstrating the potential of this novel approach. However, some reservoirs with complex shapes faced initial delineation challenges, resulting in inaccurate volume predictions. These issues could be resolved by manually delineating the area for bathymetry reconstruction. Moreover, regression models were applied to case study reservoirs in Eswatini and Lesotho, demonstrating reasonable predictive capabilities with the Heidke Skill Scores ranging from 0.77 to 1 for up to 2 months ahead. However, precise prediction of extreme decreases in reservoir levels requires a physically based approach that incorporates the volumetric time series provided by this novel method. The study emphasises the necessity of considering the volume time series’ memory to predict water availability and provides a valuable foundation for volume time series analysis using remotely sensed data.

Water Availability
Predictions
Reservoirs
Bathymetry
Earth Observation
Decision Making
Anticipatory action

http://resolver.tudelft.nl/uuid:ed4ba706-9efb-4c0a-8fbd-331accd99f5b

Reservoir bathymetry plots, cross-sections, hypsometry for the Indian reservoirs provided in the supplementary materials.

Student theses

master thesis

© 2023 Emie Klein Holkenborg