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Using remote sensing and modeling to monitor and understand harmful algal blooms. Application to Karaoun Reservoir (Lebanon).

Abstract : Reservoirs are strategic water resources in particular for drinking water and hydropower production. Nevertheless, their physical and biogeochemical processes have been long influenced by anthropogenic pressures. A complete and regular monitoring of reservoir water quality in the context of current climate change, eutrophication and higher water demand, has become crucial for optimal management strategies. Recent progress in the satellite remote sensing field made it possible to enhance data acquisition on a synoptic scale and to perform retrospective studies. Satellite data can complement measurements however over a limited depth of the water column. In addition, three-dimensional (3D) numerical models which integrate physical, chemical and biological processes can fill temporal gaps and extend the information into the vertical domain.In this context, this PhD thesis focuses on the combined use of techniques and data derived from field monitoring, satellite remote sensing and 3D modeling. The overreaching objective of this work is to propose a combined approach for surveying the water quality of medium-sized reservoirs (~ 14 km2).The study site is Karaoun Reservoir, Lebanon (semi-arid climate, surface 12 km2, capacity 110 hm3). It mainly serves for hydropower however with possibly a future drinking water production. It is eutrophic and has been experiencing regular events of toxic cyanobacterial blooms. The following methodological approach was adopted:i)In situ measurements were regularly collected from spring to fall for the calibration and the validation of remote sensing algorithms and of the model.ii)In order to calibrate and validate remote sensing algorithms, Landsat 8 and Sentinel-2 imagery were atmospherically corrected using a single-channel algorithm and the 6SV code respectively.a.Four algorithms from literature for deriving surface temperature were validated using Landsat 8 thermal data.b.A previously calibrated and validated Sentinel-2 algorithm was applied to retrieve chlorophyll-a concentrations.c.An empirical algorithm was calibrated and validated in order to retrieve transparency from Sentinel-2 data.iii)In order to conduct a retrospective analysis of surface temperature, the validated single channel algorithm was applied to a series of Landsat images from 1984 to 2018.iv)In order to reproduce the hydrodynamics and ecological processes, including cyanobacterial biomass in space and time, the Delft3D model was configured, calibrated and validated for summer and fall. The spatial distribution of surface temperature and chlorophyll-a concentrations from the satellite and the model were investigated.The results of this study revealed that, among the four tested algorithms, the single channel algorithm dependent on atmospheric water vapor content and lake water emissivity yielded the best estimations of surface temperature. Using this validated algorithm, the retrospective analysis of surface temperature did not reveal any warming trend over the 1984-2018 period at the study site. Compared to in situ profiles, the Delft3D model represented well the evolution of the water level fluctuations, and the time and vertical distribution of temperature and phytoplankton biomass. Satellite data and model simulations showed minor spatial heterogeneities of surface temperature (< 2 °C) and considerable ones for chlorophyll-a concentrations (~ 50 mg.m-3). Their comparison revealed an overall good correlation in space and time.This work showed the good performance of the Delft3D model for simulating the thermal structure and phytoplankton biomass in a reservoir. It highlighted the value of satellite images as complementary to in situ measurements for validating 3D models and for the survey of reservoirs. The proposed approach is transferrable to other freshwater ecosystems and is particularly beneficial for poorly monitored ecosystems
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Submitted on : Tuesday, October 26, 2021 - 4:58:25 PM
Last modification on : Friday, August 5, 2022 - 2:38:11 PM
Long-term archiving on: : Thursday, January 27, 2022 - 8:09:57 PM


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  • HAL Id : tel-03404563, version 1


Najwa Sharaf. Using remote sensing and modeling to monitor and understand harmful algal blooms. Application to Karaoun Reservoir (Lebanon).. Environmental Engineering. École des Ponts ParisTech, 2021. English. ⟨NNT : 2021ENPC0008⟩. ⟨tel-03404563⟩



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