A combined use of in situ and satellite-derived observations to characterize surface hydrology and its variability in the Congo River Basin

Abstract. The Congo River Basin (CRB) is the second largest river system in the world, but its hydroclimatic characteristics remain relatively poorly known. Here, we jointly analyze a large record of in situ and satellite-derived observations, including long term time series of Surface Water Height (SWH) from radar altimetry (a total of 2,311 virtual stations) and surface water extent (SWE) from a multi-satellite technique to better characterize CRB surface hydrology and its variability. Firstly, we show that SWH from radar altimetry multi-missions agree well with in situ water stage at various locations, with root mean square deviation varying from 10 cm (with Sentinel-3A) to 75 cm (with European Remote Sensing-2). SWE from multi-satellite observations also shows a good behavior over a ~25-year period against in situ observations from sub-basin to basin scale. Both datasets help to better characterize the large spatial and temporal variability of hydrological patterns across the basin, with SWH exhibiting annual amplitude of more than 5 m in the northern sub-basins while Congo main-stream and Cuvette Centrale tributaries vary in smaller proportions (1.5 m to 4.5 m). Furthermore, SWH and SWE help better illustrate the spatial distribution and different timings of the CRB annual flood dynamic and how each sub-basin and tributary contribute to the hydrological regime at the outlet of the basin (the Brazzaville/Kinshasa station), including its peculiar bi-modal pattern. Across the basin, we jointly use SWH and SWE to estimate time lag and water travel time to reach the Brazzaville/Kinshasa station, ranging from 0–1 month in its vicinity downstream the basin up to 3 months in remote areas and small tributaries. Northern sub-basins and the central Congo region highly contribute to the large peak in December–January while the southern part of the basin supplies water to both hydrological peaks, in particular to the moderate one in April–May. The results are supported using in situ observations at various locations in the basin. Our results contribute to a better characterization of the hydrological variability in the CRB and represent an unprecedented source of information for hydrological modeling and to study hydrological processes over the region.

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A Methodological Framework to Combine Multiple Precipitation Datasets for Improving Streamflow Simulations: A test study in the Saskatchewan River basin, Canada

10.5194/egusphere-egu2020-12547 ◽

2020 ◽

Author(s):

Jefferson Wong ◽

Fuad Yassin ◽

James Famiglietti

Keyword(s):

River Basin ◽

Land Surface ◽

Large River ◽

Combination Method ◽

Methodological Framework ◽

Surface Hydrology ◽

Test Study ◽

Basin Scale ◽

Precipitation Estimation

<p>Obtaining reliable precipitation measurements and accurate spatiotemporal distribution of precipitation remains as a challenging task for driving Hydrologic-Land Surface Models (H-LSMs) and better hydrological simulations and predictions. To further improve the accuracy of precipitation estimation for hydrological applications, the idea of generating a hybrid dataset by combining existing precipitation products has become a more appealing approach in recent years. The reliability of the hybrid dataset is evaluated against in-situ climate stations and error characteristics are calculated to compare to the existing products. However, the robustness of the hybrid dataset in representing spatial details could be problematic when evaluated only using a sparse network of in-situ observations at regional or basin scales. This study aims to develop a methodological framework that combines multiple precipitation products based on evaluation against not only climate stations but also streamflow stations that are spatially representative across large river basin. The framework is illustrated using a Canadian H-LSM named MESH (Mod&#233;lisation Environmentale communautaire - Surface Hydrology) in the Saskatchewan River basin, Canada over the period of 2002 to 2012. Five existing precipitation datasets are considered as the candidates for generating the hybrid dataset. The framework consists of three components. The first component evaluates each precipitation candidate against the local gauge data for benchmarking, runs each candidate through MESH with 10 km spatial resolution and default parameterization, and calculates the overall streamflow performance in each sub-basins with equal weighting of three evaluation metrics. The second component generates the hybrid dataset by combining the best performing candidates (annual or seasonal) at sub-basin scale. The third component assesses the performance of the hybrid dataset at downstream gauge stations along the mainstream as a validation mechanism for comparison with the performance of the candidate datasets. Results shows that the hybrid dataset is able to perform equally well with the existing precipitation products in the headwater while improve the streamflow performance downstream. The successful application of the framework in this river basin could build the foundation and the confidence in applying the combination method to data-limited river basins in northern Canada.</p>

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