Informing a hydrological model of the Ogooué with multi-mission remote sensing data

Abstract

Abstract. Remote sensing provides a unique opportunity to inform and constrain a hydrological model and to increase its value as a decision-support tool. In this study, we applied a multi-mission approach to force, calibrate and validate a hydrological model of the ungauged Ogooué river basin in Africa with publicly available and free remote sensing observations. We used a rainfall-runoff model based on the Budyko framework coupled with a Muskingum routing approach. We parametrized the model using the SRTM DEM, and forced it using precipitation from two satellite-based rainfall estimates, FEWS-RFE and 5 TRMM 3B42 v.7, and temperature from ECMWF ERA-Interim. We combined three different datasets to calibrate the model using an aggregated objective function with contributions from: (1) historical in-situ discharge observations from the period 1953-1984 at six locations in the basin, (2) radar altimetry measurements of river stages by Envisat and Jason-2 at 12 locations in the basin and (3) GRACE total water storage change. Additionally, we extracted CryoSat-2 observations throughout the basin using a Sentinel-1 SAR imagery water mask and used the observations for validation of the model. The use of new satel-10 lite missions, including Sentinel-1 and CryoSat-2, increased the spatial characterization of river stage. Throughout the basin, we achieved good agreement between observed and simulated discharge and river stage, with a RMSD between simulated and observed water amplitudes at virtual stations of 0.74 m for the TRMM forced model and 0.87 m for the FEWS-RFE forced model. The hydrological model also captures overall total water storage change patterns, although the amplitude of storage change is generally underestimated. By combining hydrological modelling with multi-mission remote sensing from ten dif-15 ferent satellite missions, we obtain new information on an otherwise unstudied basin. The proposed model is the best current baseline characterization of hydrological conditions in the Ogooué in light of the available observations.

Quality analysis of water level series obtained by altimetric radar satellite along the São Francisco River

“…The success of the Sentinel mission is attributed to the OLTM, synthetic aperture radar (SAR) and other altimeter configurations resulting from recent technological improvements in remote sensing instruments. Our results agree with those of recent studies that point out the improvements in Sentinel-3 configurations (Jiang et al, 2020;Kittel et al, 2021).…”

Using Remote Sensing Data‐Based Hydrological Model Calibrations for Predicting Runoff in Ungauged or Poorly Gauged Catchments

“…Remote sensing observation provides continuous data in both spatial and temporal scales, which make it possible to estimate regional surface data in a quick and widely applicable way (Stewart & Finch, 1993; Sun et al, 2018). Therefore, remote sensing data has been widely applied and combined with hydrological models (Beck et al, 2017; Kittel et al, 2018; Kumar & Lakshmi, 2018; Wanders et al, 2014). However, the quality of remote sensing data is not always guaranteed (Andersen et al, 2005; Beck et al, 2017; Liu et al, 2016; Sun et al, 2018), and the accuracy varies across regions, which can have important regional implications (Hijmans et al, 2005; Wang et al, 2015).…”

Informing a hydrological model of the Ogooué with multi-mission remote sensing data

“…All data sets used in this study are derived from publicly available resources. The model climate input files and river delineation as well as the GRACE and CryoSat-2 observations used in the study are available online https://doi.org/10.5281/zenodo.1157344 (Kittel et al, 2018).…”