Combining satellite altimetry, in-situ observations, and models to improve hydrological forecasting in West Africa

Water level information is highly sought-after by operational hydrologists and emergency managers to improve flood management in, for example, West Africa (Lienert et al 2020). A main constraint of large-scale flood forecasting systems is an inability to convert streamflow volumes to water level at specific locations. Accurately representing water levels – and hence potential impacts of peak flows on local scale – is possible through detailed field work and hydraulic simulations (e.g. Massazza et al 2020). However, large-scale implementation of such approaches is typically constrained by lack of detailed topographic data. In this study we therefore develop a pragmatic method to estimate water levels using rating curves created through a combination of ground-based (in-situ) hydrometric gauge observations, hydrological simulations, and satellite altimetry data. Specifically, rating curves are created based on simulated discharge from HYPE models and 305 in-situ discharge observations from 1980 to 2020, in addition to 42 in-situ and 558 virtual water level stations (i.e. locations where Sentinel-3 missions intersect large rivers) from 2018 to 2020. The rating curves were estimated by fitting a conventional power-law equation. For the in-situ data this could be done directly from the two variables. These were, however, very scarce. We therefore exploited the EO-based virtual stations to be able to predict water levels at many more locations. To this end, rating curves were estimated using simulated discharge together with EO-based water level data at the virtual station locations. The inverted rating curve equation was subsequently used to transform simulated discharge to water level. The water levels estimated from simulated discharge were finally compared with the in-situ and virtual altimetry stations using accuracy performance metrics such as Nash-Sutcliffe efficiency (NSE) and Kling-Gupta efficiency (KGE). Furthermore, we examine and compare the rating curve uncertainty obtained from different data sources (in-situ, modelled and satellite data). This pragmatic methodology can be used in operational hydrology, specifically flood forecasting, to render forecasts more relevant at local scale and hence enable better flood risk management.