F
Faisal Hossain
Researcher at University of Washington
Publications - 246
Citations - 6396
Faisal Hossain is an academic researcher from University of Washington. The author has contributed to research in topics: Precipitation & Flood forecasting. The author has an hindex of 38, co-authored 230 publications receiving 5251 citations. Previous affiliations of Faisal Hossain include University of Chittagong & Tennessee Technological University.
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Impacts of Postdam Land Use/Land Cover Changes on Modification of Extreme Precipitation in Contrasting Hydroclimate and Terrain Features
TL;DR: In this paper, the impact of postdam climate feedbacks, resulting from land use/land cover variability, on modification of extreme precipitation (EP) remains a challenge for a twenty-first-century approach to dam design and operation.
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Understanding satellite-based monthly-to-seasonal reservoir outflow estimation as a function of hydrologic controls
Matthew Bonnema,Safat Sikder,Yabin Miao,Xiaodong Chen,Faisal Hossain,Ismat Ara Pervin,S. M. Mahbubur Rahman,Hyongki Lee +7 more
TL;DR: In this paper, the authors used a mass balance approach of three hydrologic controls to estimate reservoir outflow from satellite data at monthly and annual time scales: precipitation-induced inflow, evaporation, and reservoir storage change.
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Validation of a TRMM-based global Flood Detection System in Bangladesh
TL;DR: The quantitative assessment of the TRMM-based Flood Detection System over Bangladesh showed that the system had a high probability of detection overall, but produced increased false alarms during the monsoon period and in regulated basins (Ganges), undermining the credibility of the FDS flood warnings for these situations.
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Investigating the Optimal Configuration of Conceptual Hydrologic Models for Satellite-Rainfall-Based Flood Prediction
A. Harris,Faisal Hossain +1 more
TL;DR: The impact of integrating NASA's real-time global satellite rainfall product (IR-3B41RT), available at 0.25deg-hourly resolution, is explored in four conceptual model configurations, with the NRCS CN method found to be most effective in terms of minimizing flood prediction uncertainty, followed by the Green-Ampt infiltration and deficit/constant loss methods.