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Nos dernières publications - Avril 2020

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  • Pinel S., Bonnet Marie-Paule, Da Silva J. S., Sampaio T. C., Garnier J., Catry Thibault, Calmant Stéphane, Fragoso C. R., Moreira D., Marques D. M., Seyler Frédérique. Flooding dynamics within an Amazonian floodplain : water circulation patterns and inundation duration. Water Resources Research, 2020, 56 (1), p. e2019WR026081 [22 p.].

https://doi.org/10.1029/2019wr026081

Flooding dynamics across a medium-size (Janauaca Lake, 786 km(2)) floodplain system along the Amazon/Solimoes River over a 9-year period (2006-2015) is studied through integration of remote sensing and limited in situ data in hydrologic-hydrodynamic modelling based on Telemac-2D model. Model accuracy varies through the hydrological year. We focus on seasonal and interannual spatial variability of water circulation and inundation duration. We highlight strong heterogeneities in water velocity magnitude between the different morphological domains of the floodplain, the highest velocities being encountered in the river-floodplain channel. In addition to topography, we emphasize the importance of the main channel and the local runoff in controlling the water circulation, at least during part of the hydrological year. From low water to early rising period, local runoff constrains the river incursion across the floodplain, while the rates of main channel rising/receding controls the flood duration. The comparison of several hydrological years highlights the interannual changes of these hydraulic controls and also the influence exerted by prior inundation conditions. While we observed few changes in water velocity distribution among hydrological years, the inundation duration is highly variable. Usually defined by maximum water level, extreme flood events may paradoxically induce positive (up to 40 days) but also negative (up to -20 days) anomalies of inundation duration.

Affiliation IRD : UMR 228 (ESPACE-DEV) ; UMR 065 (LEGOS)
Descr. géo. : BRESIL; AMAZONE, SOLIMOES COURS D'EAU; JANAUACA LAC
Lien permanent : https://www.documentation.ird.fr/hor/fdi:010078886

 

  • Tavares M. H., Cunha A. H. F., Motta-Marques D., Ruhoff A. L., Fragoso C. R., Munar A. M., Bonnet Marie-Paule. Derivation of consistent, continuous daily river temperature data series by combining remote sensing and water temperature models. Remote Sensing of Environment, 2020, 241, p. art. 111721 [18 p.].

https://doi.org/10.1016/j.rse.2020.111721

Scarcity of water temperature data in rivers may limit a diversity of studies considering this property, which regulates many physical, chemical, and biological processes. We present a robust method to generate a consistent, continuous daily river water temperature (RWT) data series for medium and large rivers using the combined techniques of remote sensing and water temperature modelling. In order to validate our approach, we divided this study into two parts: (i) we evaluated methods to derive RWT from Landsat 7 ETM+ and Landsat 8 TIRS imagery; and (ii) we evaluated the calibration and validation of river temperature models, using these data, to generate the continuous RWT data series. A 1.2 km section of the White River located near Hazleton, IN, USA, was selected to assess this method mainly due to river width and data availability. We tested three methods to retrieve RWT from Landsat 7 and four from Landsat 8, and we also applied a simple thermal sharpening technique. For Landsat 7, the methods showed bias and RMSE of 0.01-0.46 degrees C and 1.32-1.84 degrees C, while for Landsat 8, the methods showed bias and RMSE of 0.08-1.27 degrees C and 1.74-2.17 degrees C, and in both cases, the best results were found applying the radiative transfer equation with NASA's Atmospheric Correction Parameter Calculator. For the second part of the validation process, we compared a stochastic model and a hybrid model, air2stream, using as input two datasets: the RWT data derived from Landsat 7 only, and a combined dataset of both Landsat 7 and 8 derived RWT. The air2stream model outperformed the stochastic model when calibrated with Landsat 7 data only, with RMSE of 1.83 degrees C, but both models showed similar results when calibrated with the combined Landsat data, when air2stream showed RMSE of 1.58 degrees C. Due to its physical basis, better calibration procedure, and higher consistency, air2stream was considered the best model for deriving the continuous RWT data series. When compared to the measured daily mean RWT data, there was no observed tendency in under or overestimating the RWT in low or high temperature conditions by the modelled series. While further tests are needed in order to evaluate if our approach can be applied to analyse past behaviour and present trends, and the impacts of climate change on the temperature of rivers, the consistent results indicate that this approach has the potential to be applied in rivers with no measured temperature data, for example, in the spatial modelling of longitudinal profiles of rivers and the modelling of tributary river temperatures.

Water surface temperature ; Temperature modelling ; Landsat ; Thermal infrared ; Atmospheric correction ; Temperature-based validation ; Thermal response

Affiliation IRD : UMR 228 (ESPACE-DEV)
Descr. géo. : ETATS UNIS
Lien permanent : https://www.documentation.ird.fr/hor/fdi:010078944

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