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Moctar Dembélé

Researcher at University of Lausanne

Publications -  17
Citations -  436

Moctar Dembélé is an academic researcher from University of Lausanne. The author has contributed to research in topics: Climate change & Environmental science. The author has an hindex of 6, co-authored 8 publications receiving 222 citations. Previous affiliations of Moctar Dembélé include Delft University of Technology & Rice University.

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Evaluation and comparison of satellite-based rainfall products in Burkina Faso, West Africa

TL;DR: In this paper, the performance of seven operational high-resolution satellite-based rainfall products (ARC, RFE, TARCAT, CHIRPS, PERSIANN, African Rainfall Estimation RFE 2.0, Tropical Applications of Meteorology using SATellite TAMSAT, AfricanRainfall Climatology and Time-series TARCat, and Tropical Rainfall Measuring Mission TRMM daily and monthly estimates) was investigated for Burkina Faso.
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Improving the Predictive Skill of a Distributed Hydrological Model by Calibration on Spatial Patterns With Multiple Satellite Data Sets

TL;DR: In this article, a multivariate calibration framework exploiting spatial patterns and simultaneously incorporating streamflow and three satellite products (i.e., Global Land Evaporation Amsterdam Model [GLEAM] evaporation, European Space Agency Climate Change Initiative [ESA CCI] soil moisture, and Gravity Recovery and Climate Experiment [GRACE] terrestrial water storage) is proposed.
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Suitability of 17 gridded rainfall and temperature datasets for large-scale hydrological modelling in West Africa

TL;DR: In this article, the authors evaluated the ability of different gridded rainfall datasets to plausibly represent the spatio-temporal patterns of multiple hydrological processes (i.e. streamflow, actual evaporation, soil moisture and terrestrial water storage) for large-scale hydrologogical modeling in the predominantly semi-arid Volta River basin (VRB) in West Africa.
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Gap-filling of daily streamflow time series using Direct Sampling in various hydroclimatic settings

TL;DR: In this paper, a thorough gap-filling framework including the selection of predictor stations and the optimization of the Direct Sampling (DS) parameters is developed and applied to data collected in the Volta River basin, West Africa.