22 research outputs found
Hydrological Reanalysis for the Congo River Basin (1983-2020)
No full text<p>The Congo River basin (CRB), the world's second-largest river system, is subject to extreme hydrological events that strongly impact its ecosystems and population. Here we present an unprecedented 38-year (1983-2020) hydrological reanalysis of CRB daily discharge that helped us to analyze the spatiotemporal dynamics of recent major CRB floods and droughts, and their teleconnection with El Niño Southern Oscillation (ENSO), the dominant driver of tropical precipitation.</p>
<p>To develp this dataset, we employ a large-scale hydrologic-hydrodynamic model (MGB) with lake storage dynamics representation and a data assimilation (DA) technique using in-situ discharge and water level and remote sensing observations from radar altimetry.</p>
<p>The performance of MGB is satisfactory, with KGE indices of 0.84 and 0.71 for calibration and validation, respectively. Incorporating lake representation significantly improves the simulations, from 0.3 to 0.63 for the Pearson correlation coefficient. Furthermore, DA shows a ~13% reduction in simulated discharge errors through cross-validation.</p>
<p>This final version of the product uses all in-situ and radar altimetry observations during the simulation period, after satisfactory performance was verified in the DA cross-validation process. The original simulation period was from 1981 but the first two years were removed to avoid inconsistencies due to model initial conditions.<br>
</p>
<p> </p>
<p>Please cite as:</p>
<p>Wongchuig, S.; Kitambo, B.; Papa, F.; Paris, A.; Fleischmann, A.; Gal, L.; Boucharel, J.; Paiva, R.; Oliveira, R.; Tshimanga, R. M.; Calmant, S. 2023. Improved modeling of Congo's hydrology for floods and droughts analysis and ENSO teleconnections.</p>We make available the daily discharge data set of the Hydrological Reanalysis for the Congo Basin in its v1.0 version, which correspond to the "HR_Congo_1983-2020.nc" and and its version in csv format "HR_Congo_1983-2020.nc" and "coordinates.csv" files.
The matrix of daily discharge contains 13880 columns (one per day, starting at 01Jan/1983) and 11580 rows (one per unit-catchment). These irregular unit-catchments have an average spatial resolution of ~15km.
Shapefiles containing the "Basin_boundary", "Centroids", "Drainage" and "Unitcatchments" are attached.
Authors:
Sly Wongchuig Correa
Benjamin Kitambo
Fabrice Papa
Adrien Paris
Ayan Santos Fleischmann
Laetitia Gal
Julien Boucharel
Rodrigo Cauduro Dias de Paiva
Romulo Jucá Oliveira
Raphael M. Tshimanga
Stéphane Calman
Hydrological Reanalysis for the Congo River Basin (1983-2020)
No full text<p>The Congo River basin (CRB), the world's second-largest river system, is subject to extreme hydrological events that strongly impact its ecosystems and population. Here we present an unprecedented 38-year (1983-2020) hydrological reanalysis of CRB daily discharge that helped us to analyze the spatiotemporal dynamics of recent major CRB floods and droughts, and their teleconnection with El Niño Southern Oscillation (ENSO), the dominant driver of tropical precipitation.</p>
<p>To develp this dataset, we employ a large-scale hydrologic-hydrodynamic model (MGB) with lake storage dynamics representation and a data assimilation (DA) technique using in-situ discharge and water level and remote sensing observations from radar altimetry.</p>
<p>The performance of MGB is satisfactory, with KGE indices of 0.84 and 0.71 for calibration and validation, respectively. Incorporating lake representation significantly improves the simulations, from 0.3 to 0.63 for the Pearson correlation coefficient. Furthermore, DA shows a ~13% reduction in simulated discharge errors through cross-validation.</p>
<p>This final version of the product uses all in-situ and radar altimetry observations during the simulation period, after satisfactory performance was verified in the DA cross-validation process. The original simulation period was from 1981 but the first two years were removed to avoid inconsistencies due to model initial conditions.<br>
</p>
<p> </p>
<p>Please cite as:</p>
<p>Wongchuig, S.; Kitambo, B.; Papa, F.; Paris, A.; Fleischmann, A.; Gal, L.; Boucharel, J.; Paiva, R.; Oliveira, R.; Tshimanga, R. M.; Calmant, S. 2023. Improved modeling of Congo's hydrology for floods and droughts analysis and ENSO teleconnections.</p>We make available the daily discharge data set of the Hydrological Reanalysis for the Congo Basin in its v1.0 version, which correspond to the "HR_Congo_1983-2020.nc" and and its version in csv format "HR_Congo_1983-2020.nc" and "coordinates.csv" files.
The matrix of daily discharge contains 13880 columns (one per day, starting at 01Jan/1983) and 11580 rows (one per unit-catchment). These irregular unit-catchments have an average spatial resolution of ~15km.
Shapefiles containing the "Basin_boundary", "Centroids", "Drainage" and "Unitcatchments" are attached.
Authors:
Sly Wongchuig Correa
Benjamin Kitambo
Fabrice Papa
Adrien Paris
Ayan Santos Fleischmann
Laetitia Gal
Julien Boucharel
Rodrigo Cauduro Dias de Paiva
Romulo Jucá Oliveira
Raphael M. Tshimanga
Stéphane Calman
Hydrological Reanalysis for the Congo River Basin (1983-2020)
No full text<p>The Congo River basin (CRB), the world's second-largest river system, is subject to extreme hydrological events that strongly impact its ecosystems and population. Here we present an unprecedented 38-year (1983-2020) hydrological reanalysis of CRB daily discharge that helped us to analyze the spatiotemporal dynamics of recent major CRB floods and droughts, and their teleconnection with El Niño Southern Oscillation (ENSO), the dominant driver of tropical precipitation.</p>
<p>To develp this dataset, we employ a large-scale hydrologic-hydrodynamic model (MGB) with lake storage dynamics representation and a data assimilation (DA) technique using in-situ discharge and water level and remote sensing observations from radar altimetry.</p>
<p>The performance of MGB is satisfactory, with KGE indices of 0.84 and 0.71 for calibration and validation, respectively. Incorporating lake representation significantly improves the simulations, from 0.3 to 0.63 for the Pearson correlation coefficient. Furthermore, DA shows a ~13% reduction in simulated discharge errors through cross-validation.</p>
<p>This final version of the product uses all in-situ and radar altimetry observations during the simulation period, after satisfactory performance was verified in the DA cross-validation process. The original simulation period was from 1981 but the first two years were removed to avoid inconsistencies due to model initial conditions.<br>
</p>
<p> </p>
<p>Please cite as:</p>
<p>Wongchuig, S.; Kitambo, B.; Papa, F.; Paris, A.; Fleischmann, A.; Gal, L.; Boucharel, J.; Paiva, R.; Oliveira, R.; Tshimanga, R. M.; Calmant, S. 2023. Improved modeling of Congo's hydrology for floods and droughts analysis and ENSO teleconnections.</p>We make available the daily discharge data set of the Hydrological Reanalysis for the Congo Basin in its v1.0 version, which correspond to the "HR_Congo_1983-2020.nc" and and its version in csv format "HR_Congo_1983-2020.nc" and "coordinates.csv" files.
The matrix of daily discharge contains 13880 columns (one per day, starting at 01Jan/1983) and 11580 rows (one per unit-catchment). These irregular unit-catchments have an average spatial resolution of ~15km.
Shapefiles containing the "Basin_boundary", "Centroids", "Drainage" and "Unitcatchments" are attached.
Authors:
Sly Wongchuig Correa
Benjamin Kitambo
Fabrice Papa
Adrien Paris
Ayan Santos Fleischmann
Laetitia Gal
Julien Boucharel
Rodrigo Cauduro Dias de Paiva
Romulo Jucá Oliveira
Raphael M. Tshimanga
Stéphane Calman
Étude de la ressource en eau en lien avec le climat dans le bassin du Congo : approche intégrée utilisant des données in situ, la télédétection et la modélisation hydrologique
No full textThe Congo River Basin (CRB) is the world's second largest watershed in terms of river discharge and drainage area, and therefore plays a significant role in the global water cycle and Earth's climate. Yet, its hydrodynamic and hydroclimatic characteristics remained relatively poorly known mainly due to its complexity and a lack of adequate in situ observations. To overcome this challenge and taking the advantage of the recent advances in earth system sciences, a robust approach based on a combination of in situ measurement, remote sensing techniques associated with numerical hydrological modeling is proposed for a comprehensive characterization of the CRB surface hydrology at a large scale. Firstly, the research intends to validate the remote sensing derived products over the CRB, in particular, radar altimetry Surface Water Height (SWH) and Surface Water Extent (SWE) from multi-satellite using in situ data, historical and current observations. Afterward, both satellite long-term records, satellite-derived SWH and SWE were then used to analyze the spatiotemporal dynamics of surface freshwater, for instance, the regional relative contribution to the flow at the most downstream gauge here Brazzaville/Kinshasa station characterized by a bimodal hydrological regime. Secondly, the two validated datasets were further combined, and in complement with topographic dataset to estimate and analyze the long-term spatiotemporal variations of Surface Water Storage (SWS) in rivers, lakes, floodplains, and wetlands across the entire CRB over the period 1992-2015 and characterized extremes events such as extensive droughts. These datasets ensured therefore an improved monitoring of the CRB hydrological variables from space, and a better evaluation of the impact of climate variability on water availability in the region. Thirdly, due to the temporal and spatial limitations of the currents remote sensing derived datasets, a hydrological-hydrodynamic model namely MGB has been proposed to better understand the hydrological processes and hydroclimatic characteristics in a more spatially and temporally distributed manner. For this, the run of MGB model built a unique long-term dataset over 1981 to 2020 at daily time scale across the entire CRB of different hydrological variables such as river discharge and water level that bridges the gap between the past in situ databases and current and future monitoring of the CRB. This thesis work contributes to a better characterization of the hydrological variability in the CRB and represents a step forward to a better fundamental understanding of the CRB and its hydro-climatic processes, bringing more opportunities for other river basins in Africa to improve the management of water resources.Le bassin du Congo, deuxième plus grand fleuve mondial en termes de superficie et d'apports d'eau douce à l'océan et jouant ainsi un rôle important dans le cycle global de l'eau et le climat, reste l'un des bassins fluviaux le moins étudié du monde. Ceci limite les connaissances de ses caractéristiques hydro-climatologiques et de la variabilité y associée. La densité peu élevée du réseau des stations hydrométéorologiques et sa complexité physiographique sont parmi les causes de cet état critique des connaissances sur le bassin du Congo. Pour mieux caractériser son hydrologie de surface à l'échelle du bassin, cette thèse s'est proposée de prendre avantage des récents développements et améliorations des techniques de télédétection ainsi que de la modélisation hydrologique en combinaison avec les données in situ. Dans un premier temps, la recherche a visé à valider les produits dérivés de la télédétection sur le bassin du Congo, en particulier, la hauteur d'eau de surface (SWH) provenant de l'altimétrie radar et les étendues d'eau de surface (SWE) provenant du jeu de données des étendues d'inondation global, en utilisant des données in situ des observations historiques et actuelles. Ensuite, Ces deux ensembles de données long terme, SWH et SWE ont été utilisés pour analyser la dynamique spatio-temporelle de l'eau douce de surface, par exemple, la contribution relative régionale des sous-bassins au débit de la station la plus en aval du bassin, Brazzaville/Kinshasa caractérisée par un régime hydrologique bimodal. Deuxièmement, les deux ensembles de données validés ont été combinés et complétés par des données topographiques pour estimer et analyser les variations spatio-temporelles à long terme du stockage des eaux de surface (SWS) dans les rivières, les lacs, les plaines d'inondation et les zones humides dans l'ensemble du bassin sur la période 1992-2015. Ces ensembles de données ont donc permis d'améliorer le suivi des variables hydrologiques du bassin du Congo depuis l'espace, et de mieux évaluer l'impact de la variabilité climatique sur la disponibilité de l'eau dans la région. Troisièmement, en raison des limitations temporelles et spatiales des ensembles de données dérivées de la télédétection précédemment mentionnés, un modèle hydrologique-hydrodynamique, à savoir MGB, a été proposé pour mieux comprendre les processus hydrologiques et les caractéristiques hydro-climatiques d'une manière plus spatialement et temporellement distribuée. Pour cela, l'exécution du modèle MGB a permis de construire un ensemble de données inédit à long terme de 1981 à 2020 à l'échelle de temps journalier sur l'ensemble du bassin du Congo de différentes variables hydrologiques telles que le débit des rivières et le niveau de l'eau, qui a comblé le fossé entre les bases de données in situ historiques et les observations contemporaines et future du bassin du Congo dans son ensemble. Ce travail de thèse contribue ainsi à une meilleure caractérisation de la variabilité hydrologique dans le bassin du Congo et représente une avancée majeure vers une meilleure compréhension du bassin du Congo et de ses processus hydro-climatiques, apportant de nouvelles opportunités pour d'autres bassins fluviaux en Afrique afin d'améliorer leur gestion des ressources en eau
Étude de la ressource en eau en lien avec le climat dans le bassin du Congo : approche intégrée utilisant des données in situ, la télédétection et la modélisation hydrologique
No full textThe Congo River Basin (CRB) is the world's second largest watershed in terms of river discharge and drainage area, and therefore plays a significant role in the global water cycle and Earth's climate. Yet, its hydrodynamic and hydroclimatic characteristics remained relatively poorly known mainly due to its complexity and a lack of adequate in situ observations. To overcome this challenge and taking the advantage of the recent advances in earth system sciences, a robust approach based on a combination of in situ measurement, remote sensing techniques associated with numerical hydrological modeling is proposed for a comprehensive characterization of the CRB surface hydrology at a large scale. Firstly, the research intends to validate the remote sensing derived products over the CRB, in particular, radar altimetry Surface Water Height (SWH) and Surface Water Extent (SWE) from multi-satellite using in situ data, historical and current observations. Afterward, both satellite long-term records, satellite-derived SWH and SWE were then used to analyze the spatiotemporal dynamics of surface freshwater, for instance, the regional relative contribution to the flow at the most downstream gauge here Brazzaville/Kinshasa station characterized by a bimodal hydrological regime. Secondly, the two validated datasets were further combined, and in complement with topographic dataset to estimate and analyze the long-term spatiotemporal variations of Surface Water Storage (SWS) in rivers, lakes, floodplains, and wetlands across the entire CRB over the period 1992-2015 and characterized extremes events such as extensive droughts. These datasets ensured therefore an improved monitoring of the CRB hydrological variables from space, and a better evaluation of the impact of climate variability on water availability in the region. Thirdly, due to the temporal and spatial limitations of the currents remote sensing derived datasets, a hydrological-hydrodynamic model namely MGB has been proposed to better understand the hydrological processes and hydroclimatic characteristics in a more spatially and temporally distributed manner. For this, the run of MGB model built a unique long-term dataset over 1981 to 2020 at daily time scale across the entire CRB of different hydrological variables such as river discharge and water level that bridges the gap between the past in situ databases and current and future monitoring of the CRB. This thesis work contributes to a better characterization of the hydrological variability in the CRB and represents a step forward to a better fundamental understanding of the CRB and its hydro-climatic processes, bringing more opportunities for other river basins in Africa to improve the management of water resources.Le bassin du Congo, deuxième plus grand fleuve mondial en termes de superficie et d'apports d'eau douce à l'océan et jouant ainsi un rôle important dans le cycle global de l'eau et le climat, reste l'un des bassins fluviaux le moins étudié du monde. Ceci limite les connaissances de ses caractéristiques hydro-climatologiques et de la variabilité y associée. La densité peu élevée du réseau des stations hydrométéorologiques et sa complexité physiographique sont parmi les causes de cet état critique des connaissances sur le bassin du Congo. Pour mieux caractériser son hydrologie de surface à l'échelle du bassin, cette thèse s'est proposée de prendre avantage des récents développements et améliorations des techniques de télédétection ainsi que de la modélisation hydrologique en combinaison avec les données in situ. Dans un premier temps, la recherche a visé à valider les produits dérivés de la télédétection sur le bassin du Congo, en particulier, la hauteur d'eau de surface (SWH) provenant de l'altimétrie radar et les étendues d'eau de surface (SWE) provenant du jeu de données des étendues d'inondation global, en utilisant des données in situ des observations historiques et actuelles. Ensuite, Ces deux ensembles de données long terme, SWH et SWE ont été utilisés pour analyser la dynamique spatio-temporelle de l'eau douce de surface, par exemple, la contribution relative régionale des sous-bassins au débit de la station la plus en aval du bassin, Brazzaville/Kinshasa caractérisée par un régime hydrologique bimodal. Deuxièmement, les deux ensembles de données validés ont été combinés et complétés par des données topographiques pour estimer et analyser les variations spatio-temporelles à long terme du stockage des eaux de surface (SWS) dans les rivières, les lacs, les plaines d'inondation et les zones humides dans l'ensemble du bassin sur la période 1992-2015. Ces ensembles de données ont donc permis d'améliorer le suivi des variables hydrologiques du bassin du Congo depuis l'espace, et de mieux évaluer l'impact de la variabilité climatique sur la disponibilité de l'eau dans la région. Troisièmement, en raison des limitations temporelles et spatiales des ensembles de données dérivées de la télédétection précédemment mentionnés, un modèle hydrologique-hydrodynamique, à savoir MGB, a été proposé pour mieux comprendre les processus hydrologiques et les caractéristiques hydro-climatiques d'une manière plus spatialement et temporellement distribuée. Pour cela, l'exécution du modèle MGB a permis de construire un ensemble de données inédit à long terme de 1981 à 2020 à l'échelle de temps journalier sur l'ensemble du bassin du Congo de différentes variables hydrologiques telles que le débit des rivières et le niveau de l'eau, qui a comblé le fossé entre les bases de données in situ historiques et les observations contemporaines et future du bassin du Congo dans son ensemble. Ce travail de thèse contribue ainsi à une meilleure caractérisation de la variabilité hydrologique dans le bassin du Congo et représente une avancée majeure vers une meilleure compréhension du bassin du Congo et de ses processus hydro-climatiques, apportant de nouvelles opportunités pour d'autres bassins fluviaux en Afrique afin d'améliorer leur gestion des ressources en eau
Study of water resources in relation with climate in the Congo River Basin : integrated approach using in situ observations, remote sensing, and hydrological modeling
No full textLe bassin du Congo, deuxième plus grand fleuve mondial en termes de superficie et d'apports d'eau douce à l'océan et jouant ainsi un rôle important dans le cycle global de l'eau et le climat, reste l'un des bassins fluviaux le moins étudié du monde. Ceci limite les connaissances de ses caractéristiques hydro-climatologiques et de la variabilité y associée. La densité peu élevée du réseau des stations hydrométéorologiques et sa complexité physiographique sont parmi les causes de cet état critique des connaissances sur le bassin du Congo. Pour mieux caractériser son hydrologie de surface à l'échelle du bassin, cette thèse s'est proposée de prendre avantage des récents développements et améliorations des techniques de télédétection ainsi que de la modélisation hydrologique en combinaison avec les données in situ. Dans un premier temps, la recherche a visé à valider les produits dérivés de la télédétection sur le bassin du Congo, en particulier, la hauteur d'eau de surface (SWH) provenant de l'altimétrie radar et les étendues d'eau de surface (SWE) provenant du jeu de données des étendues d'inondation global, en utilisant des données in situ des observations historiques et actuelles. Ensuite, Ces deux ensembles de données long terme, SWH et SWE ont été utilisés pour analyser la dynamique spatio-temporelle de l'eau douce de surface, par exemple, la contribution relative régionale des sous-bassins au débit de la station la plus en aval du bassin, Brazzaville/Kinshasa caractérisée par un régime hydrologique bimodal. Deuxièmement, les deux ensembles de données validés ont été combinés et complétés par des données topographiques pour estimer et analyser les variations spatio-temporelles à long terme du stockage des eaux de surface (SWS) dans les rivières, les lacs, les plaines d'inondation et les zones humides dans l'ensemble du bassin sur la période 1992-2015. Ces ensembles de données ont donc permis d'améliorer le suivi des variables hydrologiques du bassin du Congo depuis l'espace, et de mieux évaluer l'impact de la variabilité climatique sur la disponibilité de l'eau dans la région. Troisièmement, en raison des limitations temporelles et spatiales des ensembles de données dérivées de la télédétection précédemment mentionnés, un modèle hydrologique-hydrodynamique, à savoir MGB, a été proposé pour mieux comprendre les processus hydrologiques et les caractéristiques hydro-climatiques d'une manière plus spatialement et temporellement distribuée. Pour cela, l'exécution du modèle MGB a permis de construire un ensemble de données inédit à long terme de 1981 à 2020 à l'échelle de temps journalier sur l'ensemble du bassin du Congo de différentes variables hydrologiques telles que le débit des rivières et le niveau de l'eau, qui a comblé le fossé entre les bases de données in situ historiques et les observations contemporaines et future du bassin du Congo dans son ensemble. Ce travail de thèse contribue ainsi à une meilleure caractérisation de la variabilité hydrologique dans le bassin du Congo et représente une avancée majeure vers une meilleure compréhension du bassin du Congo et de ses processus hydro-climatiques, apportant de nouvelles opportunités pour d'autres bassins fluviaux en Afrique afin d'améliorer leur gestion des ressources en eau.The Congo River Basin (CRB) is the world's second largest watershed in terms of river discharge and drainage area, and therefore plays a significant role in the global water cycle and Earth's climate. Yet, its hydrodynamic and hydroclimatic characteristics remained relatively poorly known mainly due to its complexity and a lack of adequate in situ observations. To overcome this challenge and taking the advantage of the recent advances in earth system sciences, a robust approach based on a combination of in situ measurement, remote sensing techniques associated with numerical hydrological modeling is proposed for a comprehensive characterization of the CRB surface hydrology at a large scale. Firstly, the research intends to validate the remote sensing derived products over the CRB, in particular, radar altimetry Surface Water Height (SWH) and Surface Water Extent (SWE) from multi-satellite using in situ data, historical and current observations. Afterward, both satellite long-term records, satellite-derived SWH and SWE were then used to analyze the spatiotemporal dynamics of surface freshwater, for instance, the regional relative contribution to the flow at the most downstream gauge here Brazzaville/Kinshasa station characterized by a bimodal hydrological regime. Secondly, the two validated datasets were further combined, and in complement with topographic dataset to estimate and analyze the long-term spatiotemporal variations of Surface Water Storage (SWS) in rivers, lakes, floodplains, and wetlands across the entire CRB over the period 1992-2015 and characterized extremes events such as extensive droughts. These datasets ensured therefore an improved monitoring of the CRB hydrological variables from space, and a better evaluation of the impact of climate variability on water availability in the region. Thirdly, due to the temporal and spatial limitations of the currents remote sensing derived datasets, a hydrological-hydrodynamic model namely MGB has been proposed to better understand the hydrological processes and hydroclimatic characteristics in a more spatially and temporally distributed manner. For this, the run of MGB model built a unique long-term dataset over 1981 to 2020 at daily time scale across the entire CRB of different hydrological variables such as river discharge and water level that bridges the gap between the past in situ databases and current and future monitoring of the CRB. This thesis work contributes to a better characterization of the hydrological variability in the CRB and represents a step forward to a better fundamental understanding of the CRB and its hydro-climatic processes, bringing more opportunities for other river basins in Africa to improve the management of water resources
Étude de la ressource en eau en lien avec le climat dans le bassin du Congo : approche intégrée utilisant des données in situ, la télédétection et la modélisation hydrologique
No full textThe Congo River Basin (CRB) is the world's second largest watershed in terms of river discharge and drainage area, and therefore plays a significant role in the global water cycle and Earth's climate. Yet, its hydrodynamic and hydroclimatic characteristics remained relatively poorly known mainly due to its complexity and a lack of adequate in situ observations. To overcome this challenge and taking the advantage of the recent advances in earth system sciences, a robust approach based on a combination of in situ measurement, remote sensing techniques associated with numerical hydrological modeling is proposed for a comprehensive characterization of the CRB surface hydrology at a large scale. Firstly, the research intends to validate the remote sensing derived products over the CRB, in particular, radar altimetry Surface Water Height (SWH) and Surface Water Extent (SWE) from multi-satellite using in situ data, historical and current observations. Afterward, both satellite long-term records, satellite-derived SWH and SWE were then used to analyze the spatiotemporal dynamics of surface freshwater, for instance, the regional relative contribution to the flow at the most downstream gauge here Brazzaville/Kinshasa station characterized by a bimodal hydrological regime. Secondly, the two validated datasets were further combined, and in complement with topographic dataset to estimate and analyze the long-term spatiotemporal variations of Surface Water Storage (SWS) in rivers, lakes, floodplains, and wetlands across the entire CRB over the period 1992-2015 and characterized extremes events such as extensive droughts. These datasets ensured therefore an improved monitoring of the CRB hydrological variables from space, and a better evaluation of the impact of climate variability on water availability in the region. Thirdly, due to the temporal and spatial limitations of the currents remote sensing derived datasets, a hydrological-hydrodynamic model namely MGB has been proposed to better understand the hydrological processes and hydroclimatic characteristics in a more spatially and temporally distributed manner. For this, the run of MGB model built a unique long-term dataset over 1981 to 2020 at daily time scale across the entire CRB of different hydrological variables such as river discharge and water level that bridges the gap between the past in situ databases and current and future monitoring of the CRB. This thesis work contributes to a better characterization of the hydrological variability in the CRB and represents a step forward to a better fundamental understanding of the CRB and its hydro-climatic processes, bringing more opportunities for other river basins in Africa to improve the management of water resources.Le bassin du Congo, deuxième plus grand fleuve mondial en termes de superficie et d'apports d'eau douce à l'océan et jouant ainsi un rôle important dans le cycle global de l'eau et le climat, reste l'un des bassins fluviaux le moins étudié du monde. Ceci limite les connaissances de ses caractéristiques hydro-climatologiques et de la variabilité y associée. La densité peu élevée du réseau des stations hydrométéorologiques et sa complexité physiographique sont parmi les causes de cet état critique des connaissances sur le bassin du Congo. Pour mieux caractériser son hydrologie de surface à l'échelle du bassin, cette thèse s'est proposée de prendre avantage des récents développements et améliorations des techniques de télédétection ainsi que de la modélisation hydrologique en combinaison avec les données in situ. Dans un premier temps, la recherche a visé à valider les produits dérivés de la télédétection sur le bassin du Congo, en particulier, la hauteur d'eau de surface (SWH) provenant de l'altimétrie radar et les étendues d'eau de surface (SWE) provenant du jeu de données des étendues d'inondation global, en utilisant des données in situ des observations historiques et actuelles. Ensuite, Ces deux ensembles de données long terme, SWH et SWE ont été utilisés pour analyser la dynamique spatio-temporelle de l'eau douce de surface, par exemple, la contribution relative régionale des sous-bassins au débit de la station la plus en aval du bassin, Brazzaville/Kinshasa caractérisée par un régime hydrologique bimodal. Deuxièmement, les deux ensembles de données validés ont été combinés et complétés par des données topographiques pour estimer et analyser les variations spatio-temporelles à long terme du stockage des eaux de surface (SWS) dans les rivières, les lacs, les plaines d'inondation et les zones humides dans l'ensemble du bassin sur la période 1992-2015. Ces ensembles de données ont donc permis d'améliorer le suivi des variables hydrologiques du bassin du Congo depuis l'espace, et de mieux évaluer l'impact de la variabilité climatique sur la disponibilité de l'eau dans la région. Troisièmement, en raison des limitations temporelles et spatiales des ensembles de données dérivées de la télédétection précédemment mentionnés, un modèle hydrologique-hydrodynamique, à savoir MGB, a été proposé pour mieux comprendre les processus hydrologiques et les caractéristiques hydro-climatiques d'une manière plus spatialement et temporellement distribuée. Pour cela, l'exécution du modèle MGB a permis de construire un ensemble de données inédit à long terme de 1981 à 2020 à l'échelle de temps journalier sur l'ensemble du bassin du Congo de différentes variables hydrologiques telles que le débit des rivières et le niveau de l'eau, qui a comblé le fossé entre les bases de données in situ historiques et les observations contemporaines et future du bassin du Congo dans son ensemble. Ce travail de thèse contribue ainsi à une meilleure caractérisation de la variabilité hydrologique dans le bassin du Congo et représente une avancée majeure vers une meilleure compréhension du bassin du Congo et de ses processus hydro-climatiques, apportant de nouvelles opportunités pour d'autres bassins fluviaux en Afrique afin d'améliorer leur gestion des ressources en eau
Hydrological Reanalysis for the Congo River Basin (1983-2020)
No full text<p>The Congo River basin (CRB), the world's second-largest river system, is subject to extreme hydrological events that strongly impact its ecosystems and population. Here we present an unprecedented 38-year (1983-2020) hydrological reanalysis of CRB daily discharge that helped us to analyze the spatiotemporal dynamics of recent major CRB floods and droughts, and their teleconnection with El Niño Southern Oscillation (ENSO), the dominant driver of tropical precipitation.</p>
<p>To develp this dataset, we employ a large-scale hydrologic-hydrodynamic model (MGB) with lake storage dynamics representation and a data assimilation (DA) technique using in-situ discharge and water level and remote sensing observations from radar altimetry.</p>
<p>The performance of MGB is satisfactory, with KGE indices of 0.84 and 0.71 for calibration and validation, respectively. Incorporating lake representation significantly improves the simulations, from 0.3 to 0.63 for the Pearson correlation coefficient. Furthermore, DA shows a ~13% reduction in simulated discharge errors through cross-validation.</p>
<p>This final version of the product uses all in-situ and radar altimetry observations during the simulation period, after satisfactory performance was verified in the DA cross-validation process. The original simulation period was from 1981 but the first two years were removed to avoid inconsistencies due to model initial conditions.<br>
</p>We make available the daily discharge data set of the Hydrological Reanalysis for the Congo Basin in its v1.0 version, which correspond to the HR_Congo_1983-2020.nc file.
The matrix of daily discharge contains 13880 columns (one per day, starting at 01Jan/1983) and 11580 rows (one per unit-catchment).
Authors:
Sly Wongchuig Correa
Benjamin Kitambo
Fabrice Papa
Adrien Paris
Ayan Santos Fleischmann
Laetitia Gal
Julien Boucharel
Rodrigo Cauduro Dias de Paiva
Romulo Jucá Oliveira
Raphael M. Tshimanga
Stéphane Calman
A long-term monthly surface water storage dataset for the Congo basin from 1992 to 2015
No full textInternational audienceThe spatio-temporal variation of surface water storage (SWS) in the Congo River basin (CRB), the second-largest watershed in the world, remains widely unknown. In this study, satellite-derived observations are combined to estimate SWS dynamics at the CRB and sub-basin scales over 1992-2015. Two methods are employed. The first one combines surface water extent (SWE) from the Global Inundation Extent from MultiSatellite (GIEMS-2) dataset and the long-term satellite-derived surface water height from multi-mission radar altimetry. The second one, based on the hypsometric curve approach, combines SWE from GIEMS-2 with topographic data from four global digital elevation models (DEMs), namely the Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Advanced Land Observing Satellite (ALOS), MultiError-Removed Improved Terrain (MERIT), and Forest And Buildings removed Copernicus DEM (FABDEM). The results provide SWS variations at monthly time steps from 1992 to 2015 characterized by a strong seasonal and interannual variability with an annual mean amplitude of similar to 101 +/- 23 km(3). The Middle Congo sub-basin shows a higher mean annual amplitude ( similar to 71 +/- 15 km(3)). The comparison of SWS derived from the two methods and four DEMs shows an overall fair agreement. The SWS estimates are assessed against satellite precipitation data and in situ river discharge and, in general, a relatively fair agreement is found between the three hydrological variables at the basin and sub-basin scales (linear correlation coefficient > 0 :5). We further characterize the spatial distribution of the major drought that occurred across the basin at the end of 2005 and in early 2006. The SWS estimates clearly reveal the widespread spatial distribution of this severe event ( similar to 40% deficit as compared to their long-term average), in accordance with the large negative anomaly observed in precipitation over that period. This new SWS long-term dataset over the Congo River basin is an unprecedented new source of information for improving our comprehension of hydrological and biogeochemical cycles in the basin. As the datasets used in our study are available globally, our study opens opportunities to further develop satellitederived SWS estimates at the global scale. The dataset of the CRB's SWS and the related Python code to run the reproducibility of the hypsometric curve approach dataset of SWS are respectively available for download at https://doi.org/10.5281/zenodo. 7299823 and https://doi.org/10.5281/zenodo.8011607 (Kitambo et al., 2022b, 2023)
Rating curves based on satellite altimetry and in-situ discharge data
No full text<h1>Context: </h1>
<p>The ESA river discharge Climate Change Initiative (CCI) project is a precursor study. It aims to derive long term climate data records (at least over 20-years) of river discharge for some selected river basins (and some locations in the river network) using satellite remote sensing observations (altimetry and multispectral images) and ancillary data. It aims to provide a proof-of-concept for the feasibility for a potential River Discharge ECV product to meet the requirements for the <a href="https://gcos.wmo.int/en/essential-climate-variables/rivers/" target="_blank" rel="noopener">Global Climate Observing System</a>. This project covers precursor activities towards the production of data products that address the GCOS-defined requirements for the River Discharge ECV.</p>
<h1>Data description :</h1>
<p>Just as in-situ stage measurements can be used to gauge river discharge, altimetry-derived water surface elevation (WSE) can serve as an alternative means of estimating river discharge when discharge time series data is available. Several methodologies have been documented for deriving discharge time series from multimission altimetry observations and supplementary data (Biancamaria et al., 2024). At least two approaches will be used, depending on the available in situ discharge and altimetry water surface elevation (WSE) time series:</p>
<p>⋅ <strong><em>Method 1</em>: </strong>The preferred approach relies on the altimetry water surface elevation time series and in situ discharge time series to create a rating curve (RC) characterized by a power relationship between these two variables following a Bayesian approach (Rantz et al., 1982). However, this method necessitates a significant overlap period between discharge data and radar altimetry measurements (e.g., Biancamaria et al., 2011; Papa et al., 2012), or it requires the assumption that the rating curve remains valid and consistent when discharge data is only available prior to the altimetry observation period.</p>
<p>⋅ <em><strong>Method 2:</strong></em> The final option, in cases where there is no temporal overlap between in-situ or simulated discharge and water surface elevation data, assumes that the validity and stability of the rating curve persist across the various time periods covered by the two datasets. Both of these time periods should be sufficiently long to encompass a wide range of events. With this assumption, Tourian et al. (2013, 2017) introduced a method for calculating the rating curve, not based on the time series of discharge and water surface elevation, but on the distribution of their quantiles. This method has been adopted by a limited number of recent studies (e.g., Belloni et al., 2021). However, it’s important to note that this methodology naturally introduces higher errors when compared to the preferred approach. For this reason, this methodology will be validated over some stations with various hydrological dynamics and satisfying previous methods (overlap period exists between WSE and Q).</p>
<h1>Approaches to derive Rating Curve (RC) :</h1>
<h2>Bayesian Approach :</h2>
<p>The Bayesian method is a robust statistical approach used for constructing a rating curve, frequently applied in the field of hydrology when the goal is to estimate unknown parameters from observed data, while taking into consideration the associated uncertainty in these estimates. </p>
<p>According to this, the estimation of the rating curve using the Bayesian method involves several steps:</p>
<ul>
<li>The initial step entails defining a probabilistic model that describes the relationship between observed data and the parameters we aim to estimate. In many hydrological applications, the relationship between discharge data (Q) and water surface elevation data (WSE) is often expressed as a power function:</li>
</ul>
<p><em> Q = a⋅(WSE-z</em><em>0</em><em>)</em><sup><em>b</em></sup></p>
<p>Here, <em>a, z0</em> and <em>b</em> are the parameters of the rating curve. <em>a,</em> is a scaling coefficient governing the magnitude of the Q-WSE relationship, <em>b,</em> characterizes the nature of this relationship, and <em>z0</em>, represents the height of the free surface above the reference point, corresponding to the river bottom's altitude. The power relationship is especially pertinent due to its consistency with numerous hydrodynamic phenomena. The exponent b within the equation allows for the representation of distinctive flow characteristics, including factors like roughness and channel geometry. Moreover, it offers adaptability in modelling to accommodate variations in flow characteristics, whether they are turbulent or laminar. This relationship, despite its mathematical simplicity, facilitates the fine-tuning of model adjustments in accordance with observed data (Chow, 1959).</p>
<ul>
<li>The second step involves the use of prior normal distributions, reflecting our prior knowledge about these parameters. These distributions can either be informative or uninformative, depending on our level of knowledge. The limits and ranges for a, z0 and b can vary depending on the specific context of the study, the dataset used, and the characteristics of the river or channel being analysed.</li>
</ul>
<p><u>- Coefficient “a”</u>: adjustment parameter for the rating curve representing the scaling factor for discharge. Its value can significantly fluctuate based on various factors such as the characteristics of the river or channel, hydraulic conditions, and other influencing factors. Consequently, "a" must be non-negative and constrained within a sensible range specific to the system under study. Following the Manning equation, “a” must be equal to W/n*S<sup>1/2</sup> (Chow et al., 1988) where W is the river’s width (m), n the Manning’s roughness coefficient and S the slope (m/m). Given the considerable variability in river width and slope across different stations, a feasible range for this coefficient can be considered as:</p>
<p> a ∈ [0; 3000]</p>
<p><u>- Coefficient “b”</u>: adjustment parameter representing the exponent of the rating curve and indicating the hydraulic condition of the study site. Like "a," this value must comply with physical constraints and cannot be negative. Following the Manning equation, “b” must be equal to 5/3 for reference hydraulic condition (Rantz et al., 1982). To accommodate the variability in system characteristics across sites, the following range values can be considered for this coefficient:</p>
<p> b ∈ [0; 5]</p>
<p><u>- Coefficient “z0”</u>: offset or the elevation at which discharge begins. It should be within the range of elevations relevant to your study. For this <em>reason, the value</em> cannot exceed the minimum value of water surface elevation (WSE) and the range value need to consider of the variability in term of water depth over the sites. A feasible range for this coefficient can be considered as:</p>
<p> z0 ∈ [min(WSE)-30; min(WSE)]</p>
<ul>
<li>The final step involves parameter estimation. The posterior distribution of the parameters yields probabilistic estimates of the rating curve parameters in the form of mean values (optimal values) and credibility intervals (95th percentiles). This accounts for the uncertainty associated with these parameters and is achieved through Markov Chain Monte Carlo (MCMC) sampling from the posterior distribution. Two commonly employed MCMC algorithms are "NUTS" (No-U-Turn Sampler) and "Metropolis-Hastings." The Metropolis-Hasting sampler "MH" algorithm, which is relatively simple and efficient where a balance between exploration and exploitation is desired. This algorithm can be adapted to sample from discrete state spaces.</li>
</ul>
<h2>Quantile approach : </h2>
<p>The Quantile approach employs statistical modelling using quantile functions to create a rating curve, eliminating the necessity for overlapping measurements. This algorithmic method enables the estimation of river discharge using satellite altimetry, even in instances where there are no in situ measurements within the altimeter's timeframe. This approach has undergone application and validation in diverse river basins spanning different climatic zones, such as the Amazon, Brahmaputra, Danube, Niger, and Ob (Tourian et al., 2013).</p>
<p>Assuming a stationary flow behaviour and no modification in the river bathymetry both at the altimetry virtual station and at the in-situ gage, this approach ensures the utilization of historical in situ data in current applications. This method computes the quantile functions of the altimetry water surface elevation on one hand and of the discharge time series on the other hand. Then a scatter plot of these in-situ discharge quantiles versus altimetry water surface elevation quantiles is computed to establish the rating curve using the bayesian approach described previously.</p>
<h1>File description :</h1>
<table>
<tbody>
<tr>
<td><strong>Column name</strong></td>
<td><strong>Description</strong></td>
</tr>
<tr>
<td>basin-station</td>
<td>Basin name in capital letters and Station name in capital letters separated by "_" and where spaces have been replaced by "-".</td>
</tr>
<tr>
<td>lon</td>
<td>Longitude in decimal degrees [-180,180] with 4 decimals - corresponding to the insitu discharge station.</td>
</tr>
<tr>
<td>lat</td>
<td>Latitude in decimal degrees [-90,90] with 4 decimals – corresponding to the insitu discharge station.</td>
</tr>
<tr>
<td>a</td>
<td>Adjustment parameter for the rating curve representing the scaling factor for discharge. Number with 3 decimals.</td>
</tr>
<tr>
<td>b</td>
<td>Adjustment parameter representing the exponent of the RC and indicating the hydraulic condition of the study site. Number with 3 decimals.</td>
</tr>
<tr>
<td>z0</td>
<td>Offset of the elevation at which discharge begins. Number with 3 decimals.</td>
</tr>
<tr>
<td>a_sd</td>
<td>Standard deviation of the coefficient "a". Number with 3 decimals.</td>
</tr>
<tr>
<td>b_sd</td>
<td>Standard deviation of the coefficient "b". Number with 3 decimals.</td>
</tr>
<tr>
<td>z0_sd</td>
<td>Standard deviation of the coefficient "z0". Number with 3 decimals.</td>
</tr>
<tr>
<td>period</td>
<td>Period used to compute the rating curve under the format %Y-%m-%d where the start and the end dates are separated by ":"</td>
</tr>
<tr>
<td>nb</td>
<td>Number of overlap dates to compute the rating curve.</td>
</tr>
<tr>
<td>Methodology</td>
<td>Methodology used to compute the rating curve. The first part describes the approach used to compute the RC and the second part, separated by “_”, describes the algorithm used. To avoid any issue for the reader the spaces have been replaced by “-”. At the end 2 approaches has been used: “Overlap-approach” or “Quantile-approach” and 2 algorithms: “Bayesian-algorithm” or “Multiple-algorithms” designed for Arctic rivers experiencing frozen periods. </td>
</tr>
<tr>
<td>Source</td>
<td>In-situ data sources to compute the rating curve. If multiple sources has been used, the sources are separate by "/"</td>
</tr>
</tbody>
</table>
<p>---------</p>
<p><em>THE DATASET IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR </em><em>IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,</em><br><em>FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE </em><em>AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER </em><em>LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, </em><em>OUT OF OR IN CONNECTION WITH THE DATASET OR THE USE OR OTHER DEALINGS IN THE </em><em>DATASET.</em></p>
