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International audience; Continued Amazonian deforestation perturbs the surface turbulent fluxes which are important for building the conditions for the wet season onset in the southern Amazon. This work evaluates the impacts of tropical deforestation on the onset and development of the Amazonian rainy season using a weather typing approach. We use 19-year simulations (2001-2019) with the Regional Earth System Model from the Institute Pierre Simone Laplace (RegIPSL) with twin control/deforestation experiments. RegIPSL represents the dominant modes or the atmospheric circulation patterns (CPs) of the daily-to-decadal circulation variability in tropical South America, and the evolution of atmospheric and surface conditions along the dry-to-wet transition period. According to RegIPSL, forests and crops contribute differently to the onset. During the dry-to-wet transition period, croplands/grasslands present a stronger shallow convection driven by a higher atmospheric temperature. Large-scale subsidence suppresses low-level convection in the region and deep convection only persists over forests where the atmosphere presents more convective potential energy. After the onset and the establishment of large-scale rainfall structures, both land covers behave similarly in terms of surface fluxes. Deforestation decreases the frequency of the CP typically linked to the onset. Changes in the spatial structure and frequency of the wet season CPs reinforce the hypothesis of a deforestation-induced dry season lengthening. Variations in the CP frequency and characteristic rainfall have opposite effects on accumulated rainfall during the dry-to-wet transition period. Whereas alterations in frequency are associated with a regional circulation response, changes in the CP characteristic rainfall correspond to a local response to deforestation.

The understanding and prediction of the variability of the hydrological state of watersheds across the planet is growing, as water is a fundamental human need and therefore important for science and society. In the last 20 years, significant advances have been made toward hydrological modeling of large river basins, and also continental to global-scale land areas. Remote sensing has been widely used in hydrology because, in addition to being a clear advantage in regions with a poor monitoring network, it has proven to be suitable for use in global and continental hydrological applications.The estimation of river discharge is of paramount importance, as it is considered an aggregator of all water cycle processes in the basin. On the one hand, estimates of discharge solely from space remain limited because they are not the primary focus of current satellite missions. On the other hand, simulations of hydraulic variables have been performed with large-scale hydrologic and hydrodynamic models, but the accuracy of their estimates can be improved with recent techniques such as data assimilation (DA). DA techniques have been developed to use remotely sensed datasets to obtain the best estimate of the current state of a system by optimally combining observations and large-scale hydrological models. Recent studies have also demonstrated the advantages of assimilating several types of datasets at the same time, which can help to further constrain the model state variables to be more physically representative.Thus, the main objective of this research is to develop a proof-of-concept for estimating hydraulic variables such as discharge and water level by assimilating multiple remotely sensed datasets into a large-scale hydrologic and hydrodynamic model. Experiences on the assimilation of different mission datasets into a large-scale hydrological model are discussed, including radar altimetry-derived water level from JASON, ENVISAT and Sentinel missions, terrestrial water storage from GRACE mission, flooded area extent from SWAMPS database and soil moisture from the SMOS mission.To develop our proof-of-concept, the Amazon as the study area. We used the hydrologic-hydrodynamic MGB model and the Local Ensemble Kalman Filter as the DA method as it has been commonly used in hydrologic models. Different localization and multivariable assimilation techniques were implemented to improve the effectiveness of the DA.The results indicate that the multi-mission assimilation approach is able to smooth/average the improvement of the state variables of the model, such as discharge and water level anomaly, compared to the experiment of assimilating the mission datasets individually. This proof-of-concept allows us to spatialize the improvement of the dynamics of hydrological-hydrodynamic variables based on large-scale hydrologic modeling and DA from global remote sensing sources only, without requiring in-situ data. As our proof-of-concept is based on datasets globally available and a hydrologic-hydrodynamic model that can be applied almost everywhere, it is fully replicable in any region of the world and represents a great potential for regional to continental studies.

The Congo River Basin (CRB), located in the central region of Africa, is of particular importance for regional and global climate and carbon studies. Being the second largest river basin after the Amazon, it is also the one with the most free-flowing rivers. However, despite these important characteristics, it has not attracted as much attention among the scientific communities as the Amazon Basin or other large tropical rivers in the world. Because of the lack of comprehensive and maintained in situ data networks over time, large-scale monitoring of hydroclimatic variables has not been properly conducted. In this context, near real-time observations of the CRB, such as water surface elevation (WSE) and river discharge, as well as understanding the impacts of climate change in a spatiotemporally distributed manner across the basin present a major challenge. In the last few years, however, the scientific community, supported by the leading operational organism in the CRB (the CICOS), has worked on applying innovative tools, from hydrological and hydrodynamic modeling to the use of space data, to improve this monitoring and understanding of hydrological processes.Our work illustrates how space Earth Observation (EO) datasets used jointly with a hydrological model improve both near-real-time monitoring and past-period revisiting (from 1980). First, we built and validated an extensive database on long-term time series of water levels (WL) from satellite altimetry using a comprehensive unprecedented in situ database (root mean square error varying between 10 cm to 75 cm). Crossing this database with the Global Inundation Extent from Multi-Satellites (GIEMS) database, we analyzed the normal behavior of surface water in the CRB, and worked towards understanding the genesis of recent extreme events. The observations permitted to highlight the different travel time of waters from one to three months depending on its origin, and to discriminate the relative contribution of southern and northern sub-basins to the first and second peaks at the outlet of the basin Kinshasa/Brazzaville station. These datasets are then used to calibrate/validate the setting of a large-scale hydrologic and hydrodynamic model, the MGB model, in which lakes representation parameters are tuned using all the aforementioned databases and the long term CHIRPS precipitation product. In terms of discharge estimates, the model run resulted in an average KGE efficiency index value of 0.84 and 0.71 for the calibration (2001-2020) and validation (1981-2000) periods respectively.When included within a scheduler, this model run validated by space EO datasets now permits the inference of discharge and depths all over the basin in real-time. In addition, data assimilation techniques applied to ingest remote sensing datasets, into the MGB model, improves such real-time estimates. Long term modeling also provides a new look and understanding on recent hydrological extreme events that occurred in the CRB, and permits analyzing the impact of recent global and regional climate change on freshwater in one of the most free-flowing watersheds.

Water fluxes in the Amazon River floodplain affect hydrodynamic and ecological processes from local to global scales. These fluxes remain poorly understood due to difficult access and limited data in the Amazon basin. In this study, we characterize the hydrodynamics of eight floodplain units of the central Amazon River (40'000 km2) using the 2D hydraulic model HEC-RAS. Remote sensing data, such as floodplain topography estimated by Landsat images, water surface elevation from altimetry, and surface water extent products, were used for model validation. High resolution modeling improved the representation of river and floodplain discharge, water surface elevation (77 cm accuracy) and flood extent (~80% - high water period, ~52% -low water period). The floodplain is organized in units of about 80 km with upstream inflow and downstream outflow. These gross flows are much larger than the net flows with values of up to 20% of the Amazon River discharge and a residence time around 6 days during floods (several months during low water period). Water extent does not a have strong interannual variability during floods as the volume stored in the floodplain, possibly due to topographic constrains. Significant flood extent and volume hysteresis, as well as active flow and storage zones on the floodplain, highlight the complexity of floodplain hydrodynamics. Extreme floods strongly impact the onset and duration of the flood of up to 2 months and, consequently, on the period of high connectivity with the river. These findings are important for understanding carbon and sediment fluxes, and the effects of climate change on water fluxes and riparian communities.

The vast Amazon wetlands support multiple social-ecological systems basin-wide, and influence global water and carbon cycles. Recent environmental changes related to climate and infrastructure expansion have altered their rhythmicity and dynamics in many aspects, and understanding their impacts on hydrological variables such as river and floodplain water levels and discharges, inundation extent and storage, is urgent. The combination of new hydrodynamic modeling approaches with in situ and multisatellite data provides great opportunities to fostering this research agenda.Here, we present an unprecedented analysis of the Amazon’s surface water dynamics, its status and long-term trends, as well as perspectives with in situ and satellite data. We analyze inundation extent, water levels and river-floodplain interactions based on in situ (water levels across rivers and floodplains) and satellite data (radar altimetry, optical imagery, passive microwave and L-band SAR), as well as hydrodynamic modeling (MGB and CaMa-Flood models). Firstly, we present the outcomes of a recent intercomparison project where 29 inundation datasets were compared across the basin (Fleischmann et al., 2022; WebGIS at ). While a higher agreement was observed along the Amazon river floodplain, major discrepancies occurred for interfluvial wetlands, stressing the need of pursuing optimal merging techniques to improve local to large-scale inundation estimates. By looking at the dynamic inundation datasets, we were able to analyze long-term inundation trends, revealing a major increase of 26% in the maximum annual inundation across the Amazon River system since 1980, associated with longer flood duration and higher river-floodplain connectivity over multiple areas.While changes in regional-scale hydroclimatic processes have led to the intensification of the Amazon’s hydrological cycle, local geomorphological processes are able to largely alter river-floodplain interactions. To investigate it, we used long-term optical data from the Global Surface Water dataset to assess changes along the Amazon River channels and the associated erosion/sedimentation processes. Our results evidence major changes along the river over the last decades, and a mapping of the impacts on 238 riparian communities shows that 21% have been largely affected by bank erosion, damaging several properties, while 19% have been affected by sedimentation, impairing transportation and reducing access to the river waters.Finally, we present the first outcomes of a new floodplain hydrology monitoring network in the Mamirauá region of Central Amazon, which includes the widest floodplain reach of the Amazon. The network under development is the first of its kind in the Amazon, and aims at improving our understanding of river-floodplain dynamics through the optimal combination of in situ (more than 25 in situ water level loggers, several weather stations, among others) and satellite data, especially from current SAR altimetry missions such as Sentinel-3A/B and Sentinel6 and the forthcoming wide-swath SWOT mission. The presented results provide an important step towards a broad understanding of the Amazon surface water dynamics, from basin to local scales, and the sustainable use of the region’s river and wetland resources.

Extensive floodplains throughout the Amazon basin support important ecosystem services and influence global water and carbon cycles. A recent change in the hydroclimatic regime of the region, with increased rainfall in the northern portions of the basin, has produced record-breaking high water levels on the Amazon River mainstem. Yet, the implications for the magnitude and duration of floodplain inundation across the basin remain unknown. Here we leverage state-of-the-art hydrological models, supported by in-situ and remote sensing observations, to show that the maximum annual inundation extent along the central Amazon increased by 26% since 1980. We further reveal increased flood duration and greater connectivity among open water areas in multiple Amazon floodplain regions. These changes in the hydrological regime of the world’s largest river system have major implications for ecology and biogeochemistry, and require rapid adaptation by vulnerable populations living along Amazonian rivers., Environmental Research Letters, 18 (3), ISSN:1748-9326, ISSN:1748-9318

Una de las zoonosis de gran impacto es la Leishmaniasis visceral causada por el parásito protozoario Leishmania infantum, transmitida por el vector hembra hematófaga infectada denominada Lutzomyia longipalpis siendo el principal reservorio el perro doméstico, considerada una patología tropical olvidada en 98 países a nivel mundial especialmente en Colombia desde sus primeros casos en humanos y caninos. El objetivo del trabajo fue desarrollar una revisión sistemática de literatura acerca del comportamiento epidemiológico de Leishmaniasis Visceral con el transcurso de los años en Colombia, utilizando tres bases de datos reconocidas (Google Académico, PubMed y Dialnet) así como el gestor de referencias bibliográficas Mendeley y otras fuentes bibliográficas, inicialmente se encontraron 180.610 documentos de los cuales 21.673 duplicados se eliminaron, dejando 158.937 estudios restantes, posteriormente se realizó una tercera selección de 11.127 artículos y por último 126 artículos seleccionados abarcan las temáticas de interés. La distribución de los casos de la enfermedad van desde el Valle del río magdalena como Huila, Tolima hasta los departamentos de Sucre y Córdoba con una alta frecuencia, además se resalta que en los caninos los datos sobre la ocurrencia no son actualizados por la falta de estudios. En conclusión, la Leishmaniasis visceral se considera un problema epidemiológico y de Salud pública debido al contacto entre el principal reservorio y el hombre. En Colombia a través de los años se ha encontrado que los datos sobre los reportes en los humanos se ha desarrollado sin interrupciones en las zonas de mayor riesgo (Sucre, Córdoba, Santander, Tolima, Huila) mientras que en los caninos la búsqueda y notificación no ha sido específica e incluso incompleta presentándose limitaciones en el momento de determinar la ocurrencia actual de la enfermedad, por lo tanto es fundamental fortalecer los sistemas de vigilancia para generar información verídica sobre los casos dependiendo directamente del conocimiento de la LV por parte de los veterinarios y entes responsables.

The 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 Multi-Satellite (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), Multi-Error-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 ∼101±23 km3. The Middle Congo sub-basin shows a higher mean annual amplitude (∼71±15 km3). 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 (∼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 satellite-derived 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).

Analyzing historical droughts is essential to improve the assessment of future hydrological risks and to understand the effects of climate variability on streamflow. However, prolonged and consistent hydrological time series are scarce in the Brazilian savanna region. This study aimed to analyze the performance of climate reanalysis products in precipitation estimation, hydrological modeling, and historical drought analysis in a Brazilian savanna basin. For this purpose, precipitation data from the twentieth-century atmospheric model ensemble (ERA-20CM) and the land component of the fifth generation of European ReAnalysis (ERA5-Land) with bias correction were used. The weather variables were obtained from the Climatic Research Unit (CRU) and the hydrological modeling was performed using the Soil and Water Assessment Tool (SWAT). The Standardized Streamflow Index (SSI) was used to calculate hydrological drought in the basin. Overall, ERA5-Land performed satisfactorily in precipitation estimation, mainly on the monthly time scale, hydrological modeling, and drought prediction. Since ERA-20CM showed unsatisfactory values for the performance statistics in all analyses, the hydrologic drought (1950 to 2018) was performed with ERA5-Land. The results showed both an increase in the number of dry months and a decrease in wet months in recent decades.

We analyze the observed relationship between sea surface temperatures (SSTs) over the Atlantic Ocean and the normalized difference vegetation index (NDVI) in the Orinoco and Amazon basins. Monthly correlations between anomalies of NDVI and SSTs are computed for different regions of the Atlantic Ocean. We also use a mixture of observations and reanalysis products to analyze lagged correlations. Our results show that during August–September (i.e., the dry-to-wet transition season), changes in NDVI in the central Amazon and the so-called Arc of Deforestation are associated with precedent changes in the SSTs of the tropical North Atlantic (TNA) and the Caribbean (CABN) during March–June. Anomalous warming of the CABN and TNA generates changes in surface winds and atmospheric moisture transport in the region, decreasing precipitation, with consequent decreases of soil moisture, moisture recycling, and NDVI. An increase in TNA and CABN SSTs during March–June is also associated with an increase of NDVI over the northern Orinoco during June (i.e., the wet season). Unlike in the southern Amazon, precipitation and soil moisture in the Orinoco basin do not exhibit significant changes associated with SSTs. By contrast, atmospheric moisture recycling and transport increase with warmer SSTs in the TNA. Therefore, for the Orinoco, the link between SSTs and NDVI appears to be related not to changes in precipitation but to changes in moisture recycling. However, the causality between these changes needs to be further explored. These findings highlight the contrasting responses of the Amazon and Orinoco basins to Atlantic temperatures and the dominant role of atmospheric moisture transport linking these responses.

This study provides a spatio-temporal analysis of the great floods that occurred in South America in 1983 using hydrometeorological data and outputs from a continental-scale hydrological-hydrodynam...

Satellite observations offer invaluable insights into hydrological processes and environmental change in the Amazon.

The Amazon River basin harbors some of the world's largest wetland complexes, which are of major importance for biodiversity, the water cycle and climate, and human activities. Accurate estimates of inundation extent and its variations across spatial and temporal scales are therefore fundamental to understand and manage the basin's resources. More than fifty inundation estimates have been generated for this region, yet major differences exist among the datasets, and a comprehensive assessment of them is lacking. Here we present an intercomparison of 29 inundation datasets for the Amazon basin, based on remote sensing only, hydrological modeling, or multi-source datasets, with 18 covering the lowland Amazon basin (elevation 1000 km2) is 323,700 km2. The highest spatial agreement is observed for floodplains dominated by open water such as along the lower Amazon River, whereas intermediate agreement is found along major vegetated floodplains fringing larger rivers (e.g., Amazon mainstem floodplain). Especially large disagreements exist among estimates for interfluvial wetlands (Llanos de Moxos, Pacaya-Samiria, Negro, Roraima), where inundation tends to be shallower and more variable in time. Our data intercomparison helps identify the current major knowledge gaps regarding inundation mapping in the Amazon and their implications for multiple applications. In the context of forthcoming hydrology-oriented satellite missions, we make recommendations for future developments of inundation estimates in the Amazon and present a WebGIS application (https://amazon-inundation.herokuapp.com/) we developed to provide user-friendly visualization and data acquisition of current Amazon inundation datasets. Ministry of Education (MOE) Nanyang Technological University The work was part of the SABERES project financed by the BNPParibas Foundation as part of its “Climate & Biodiversity Initiative” program 2019. A.S.F. was supported by CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnologico, ´ Brazil) [grant number 141161/2017-5]. F.P., J.F.F., M.P.B. and F.A. received support from CNES (SWOT-ST project SWOT for SOUTH AMERICA, ID: 6018-4500066497). F.P. and M.P.B. also received support from CNES (SWOT-ST project SWOT Wetlands Hydrology Monitoring). F.P. is supported by the IRD Groupement De Recherche International (GDRI) SCaHyLab. J.M.M. received support from NASA IDS grant NNX17AK49G and the US National Science Foundation (Division of Environmental Biology, grant 1753856). E.P. acknowledges Nanyang Technological University (SUG-NAP EP3/19) and Ministry of Education of Singapore (AcRF Tier1 RT 06/19 and AcRF Tier2 RT 11/ 21). A.F.R. acknowledges the Research Foundation of Sao ˜ Paulo (FAPESP, grant #2019/24049-5). S.W. has been supported by the French AMANECER-MOPGA project funded by ANR and IRD (ref. ANR18-MPGA-0008). M.C. received funding from NASA IDS grant NNX17AK49G. The SWAF dataset development was financed by the CATDS and the SWOT-AVAL programs by CNES.

We analyze the characteristics of atmospheric variations over tropical South America using the pattern recognition framework of weather typing or atmospheric circulation patterns (CPs). During 1979-2020, nine CPs are defined in the region, using a k-means algorithm based on daily unfiltered 850 hPa winds over 0035°N-30°S, 90°W-30°W. CPs are primarily interpreted as stages of the annual cycle of the low-level circulation. We identified three “winter” CPs (CP7, CP8 and CP9), three “summer” CPs (CP3, CP4 and CP5) and three “transitional” CPs (CP1, CP2 and CP6). Significant long-term changes are detected during the dry-to-wet transition season (July-October) over south tropical South America (STSA). One of the wintertime patterns (CP9) increases from 20% in the 1980s to 35% in the last decade while the “transitional” CP2 decreases from 13% to 7%. CP9 is characterized by enhancement of the South American Low-Level Jet and increasing atmospheric subsidence over STSA. CP2 is characterized by southerly cold-air incursions and anomalous convective activity over STSA. The years characterized by high (low) frequency of CP9 (CP2) during the dry-to-wet transition season are associated with a delayed South American Monsoon onset and anomalous dry conditions over STSA. Consistently, a higher frequency of CP9 intensifies the fire season over STSA (1999-2020). Over the Brazilian states of Maranhão, Tocantins, Goiás and São Paulo, the seasonal frequency of CP9 explains around 35%-44% of the interannual variations of fire counts.

Adequate and accurate hydrological data is necessary to manage water resources, which are critical in developing countries where such information is limited. In recent years, global reanalysis datasets have been developed to provide this information in climatic fields and, more recently, in hydrologic fields. Nevertheless, these latest efforts have been limited to temporal coverage (∼30 years) and mostly simplified hydraulic scheme routing in rivers, which can be inadequate for regional and long term scale objectives. In this article, a dataset (called hydrological reanalysis across the 20th century [HRXX] in the Amazon Basin) was developed as a case study spanning back to the year 1900 through the use of: 1) a large-scale hydrologic-hydrodynamic model (MGB) forced by a long-term climatic reanalysis of rainfall (ERA-20CM) with bias removed; and 2) a data assimilation (DA) technique coupled with a localization method (LEnKF) to use several ground observations of daily discharge within a radius of influence. Several tests were assessed to find the best bias removal method, the optimal radius of influence for the localization method and the final HRXX dataset. A total of 114 hydrological ground observations of daily information were used for assimilation and validation purposes, and several statistics indexes were employed to assess their performance. Results indicate that both bias correction and the DA with localization method greatly improved the simulations. Overestimations of the peaks in the open-loop (OL) (free run) simulation, mainly in the southern and northern regions of the Amazon Basin, were removed, and recession timing in the east-central region, as seen at the Obidos gauge station, were corrected. An average performance of ∼0.6 and ∼0.7 of the Nash–Sutcliffe and Kling–Gupta indexes was reached, even when only a few of the longest ground observations were used, which can be representative of the oldest periods (since ∼1930). To assess extreme events, the Pearson correlation coefficient was used for maximum and minimum annual water level anomaly values, reaching 0.6 and 0.7, respectively, at the Manaus gauge station, which is remarkable considering that the analysis covers approximately 110 years. Considering the results of this case study and the global coverage of rainfall datasets, this methodology can be transferred to other regions in order to better estimate and create a hydrological reanalysis that adequately represents the hydrologic and hydraulic spatio-temporal fields.

The relationship between multiple hydroclimatic variables and vegetation conditions in the upper Madeira Basin (southwestern Amazon) has been analyzed. Vegetative dynamics are characterized using N...

The relationship between multiple hydroclimatic variables and vegetation conditions in the upper Madeira Basin (southwestern Amazon) has been analyzed. Vegetative dynamics are characterized using NDVI dataset as an indicator of the photosynthetic capacities of vegetation. Hydroclimatic variability is analyzed using satellite-based precipitation datasets, observed river discharge, and satellite measurements of terrestrial water storage (TWS). Our results show that the vegetation in the Basin varies from energy- to water-limited. During the peak of the wet season (January-February), rainfall, discharge, and TWS are negatively correlated with NDVI (r=-0.48 to -0.65), suggesting that during this period the vegetation is mainly energy-dependent. Outside this period, these correlations are positive (r=0.55 to 0.9), suggesting that vegetation depends mainly on water availability. This higher water dependence is more noticeable during the vegetation dry season (VDS; June-October). Considering the predominant land cover types, differences in the hydroclimate-NDVI relationship are observed. Evergreen forests remain energy-limited during the beginning of the VDS, but they become water-dependent almost at the end. Savannas show a different behavior, where water dependence occurs months before the onset of the VDS. On the other hand, unlike the other variables, the TWS better explains the NDVI in evergreen forests during the VDS (r=0.7 to 0.85). This is probably because evergreen forests are more dependent on deep soil water. A spatial analysis between hydroclimatic variables and the NDVI shows the predominance of positive correlations in most of the basin. However, specific areas do not show significant correlations. The weak relationship in these areas is explained by two factors i) very wet conditions during most of the year in the "rainfall hotspot" regions, where the vegetation is not water-limited, and ii) recent land-use changes (deforestation) that break the natural response in the hydroclimate-vegetation system. These findings provide new evidence on the impacts of the land cover changes on the natural relationship between vegetation and hydroclimatic variability, which is particularly relevant given the increasing rates of deforestation in this region during recent years.

Linking the Amazonian deforestation to changes in the hydrological cycle remains a puzzling question, addressed here through the use of recent global databases analyzing the relations between key hydro-climate variables (Precipitation (P), potential and actual evapotranspiration (PET and AET, respectively)), the surface water-energy balance and indices of forest cover change (regional forest loss ratio -RFL and regional non-forest vegetation ratio -RNF) for Southern Amazon (south of 8°S) and over the 1981-2018 period. The Southern Amazon constitutes a peculiar region due to specific climatic characteristics and shows a higher significant deforestation rate in comparison with the Northern Amazon. We further subdivided the study region into three subregions called Southern Bolivian Amazon (15° S‒21° S, 57° W‒70° W), Southern Peruvian Amazon (8° S‒15° S, 77° W‒65° W) and Southern Brazilian Amazon (8° S‒15° S, 65° W‒50° W). The surface water-energy balance is analyzed using a pixel-based Budyko-like theoretical framework approach, which discriminates energy-limited regions from water-limited regions. Southern Bolivian Amazon is shown to have undergone the strongest forest transition, becoming water-limited in conjunction with high forest loss. In this region, there is a significant relation between RFL values above 40%, P decrease, PET increases and AET decrease. These results suggest that areas with RNF values higher than 40% are prone to shift from an energy-limited to a water-limited state and remain trapped in this new state. Regions further north remain energy-limited due to minor P changes and even though significant increases in PET and decreases in AET are observed, associated with deforestation (high values RFL). This is typically the case in the ‘Arc of Deforestation’. In the Southern Bolivian Amazon, land use transition is associated with much larger changes from closed forest to a low-tree cover state as compared to regions further north - by at least a factor three as a proportion of area. Our findings indicate a clear link between hydro-climatic changes and deforestation, providing a new perspective on their spatial variability on a regional scale. This research is part of the French AMANECER-MOPGA project.

Estimating discharges is a major challenge in water resources management, and techniques such as data assimilation (DA) can be used to improve these estimates. This study assessed applicati...

International audience; As the largest river basin on Earth, the Amazon is of major importance to the world's climate and water resources. Over the past decades, advances in satellite-based remote sensing (RS) have brought our understanding of its terrestrial water cycle and the associated hydrological processes to a new era. Here, we review major studies and the various techniques using satellite RS in the Amazon. We show how RS played a major role in supporting new research and key findings regarding the Amazon water cycle, and how the region became a laboratory for groundbreaking investigations of new satellite retrievals and analyses. At the basin-scale, the understanding of several hydrological processes was only possible with the advent of RS observations, such as the characterization of "rainfall hotspots" in the Andes-Amazon transition, evapotranspiration rates, and variations of surface waters and groundwater storage. These results strongly contribute to the recent advances of hydrological models and to our new understanding of the Amazon water budget and aquatic environments. In the context of upcoming hydrology-oriented satellite missions, which will offer the opportunity for new synergies and new observations with finer space-time resolution, this review aims to guide future research agenda toward integrated monitoring and understanding of the Amazon water from space. Integrated multidisciplinary studies, fostered by international collaborations, set up future directions to tackle the great challenges the Amazon is currently facing, from climate change to increased anthropogenic pressure.

International audience; Global estimates of river dynamics are needed in order to manage water resources, mainly in developing countries where in-situ observation is limited. Remote sensors such as nadir altimeters can complement ground data. Current altimeters miss however a large number of continental surface water bodies. This issue will be largely resolved by the future Surface Water and Ocean Topography (SWOT) mission, thanks to its wide swath altimeter. SWOT will provide almost globally two-dimensional water elevation maps for rivers over 100 m wide and water bodies over 250 m × 250 m. During this research, we investigated the potential of SWOT to correct hydrological models on a global/continental scale, through data assimilation. For this purpose, an Observing System Simulation Experiment (OSSE), also known as “twin experiment”, has been implemented. Model forcings and parameters were perturbed to jointly achieve global hydrological models (GHMs) uncertainties, which is the expected scenario in which the SWOT community will mainly evaluate the future SWOT data. SWOT-like observations of water surface elevation (WSE), flooded water extent (FWE), and/or SWOT derived discharge (Q) were used to correct modelled Q, WSE and FWE from a large-scale hydrological and hydrodynamic model (MGB – portuguese acronym of “Modelo de Grandes Bacias”), using a Ensemble Kalman filter (EnKF). The results indicate that SWOT products could largely improve hydrological simulations on a global and continental scale. SWOT-like discharge can reduce ~40% of model errors in daily discharge. Furthermore, when anomalies of the WSE DA approach were implemented, the error reduction was even greater for all state variables compared to the absolute WSE DA, achieving average error reduction values of about ~30% compared to ~24%. Finally, the simultaneous DA of all the SWOT-like variables together reduces errors from ~14% to ~22% compared to the average of assimilating only one variable.

Large evidences support the strong impacts on rainfall amount and the increasing of dry-season length on the Amazonian forest. All of these effects are usually attributed to large scale atmospheric circulation and to land cover changes as part of anthropogenic effects. In this research we assess statistical and modeling approaches to investigate the interaction between changes in forest cover and hydroclimate processes on a regional and local scale.Henceforth, the deforestation areas and climatic indexes for the southern Amazon basin (south of 14°S) were evaluated. The deforestation map was estimated for the 1992-2018 period, based on global land cover maps at 300 m of spatial resolution produced by the European Space Agency (ESA) Climate Change Initiative (CCI) by using several remote sensing datasets. The CHIRPS rainfall dataset (P) for the 1981-2018 period was used to estimate the dry day frequency (DDF, P10mm). In addition, the mean actual seasonal evapotranspiration (AET) was GLEAM and ET-Amazon evapotranspiration datasets for the 1980-2018 and 2003-2013 periods respectively. In order to determine the local and the regional climatic effect for each pixel of the climatic index (DDF, WDF and AET), the deforestation fraction was estimated considering different spatial radii of influence (20 to 50 km).The first results indicate a particular pattern in the southern Bolivian Amazon where two groups of areas were identified, considering the common period of analysis (1992-2018). One of them shows a significant relationship between increasing trend of DDF and decreasing trend of WDF while deforestation fraction is high, what mainly occurs during the wet season. In addition, this region is clearly placed in areas with values of deforestation fraction above ~30%, a closest value to the usually estimated Amazon Tipping Point (~40%). Below this value, the second group is also located in regions with positive trends of DDF and negative trends of WDF. This region has probably a strongest link with the large-scale climate.Considering these preliminary results, the statistical approaches developed in this research could give some insights about the interactions between forest change and the regional hydro climatology, which might improve the understanding of this interaction based on large-scale hydrological modeling.

Projections of climate change are essential to guide sustainable development plans in the tropical Andean countries such as Peru. This study assessed the projections of precipitation and potential evaporation, rain erosive potential, and precipitation concentration in the Mantaro River Basin, in the Peruvian Andes, which is important for agriculture and energy production in Peru. We assumed the Intergovernmental Panel on Climate Change (IPCC) A1B greenhouse gas emission scenario and simulated the global climate change by the HadCM3 global climate model. Due to the steepness of the mountain slopes and the narrowness of the river valley, this study uses the downscaling of the global model simulations by the regional Eta model down to 20-km resolution. The downscaling projections show decrease in the monthly precipitation with respect to the baseline period, especially during the rainy season, between February and April, until the end of the 21st century. Meanwhile, a progressive increase in the monthly evaporation from the baseline period is projected. The Modified Fournier Index (MFI) shows a statistically significant downward trend in the Mantaro River Basin, which suggests a possible reduction in the rain erosive potential. The Precipitation Concentration Index (PCI) shows a statistically significant increasing trend, which indicates increasingly more irregular temporal distribution of precipitation towards the end of the century. The results of this study allow us to conclude that there should be a gradual increase in water deficit and precipitation concentration. Both changes can be negative for agriculture, power generation, and water supply in the Mantaro River Basin in Peru.

Soil degradation by water erosion has been accelerated by human activities. This process is aggravated in the Andes region due to steep slopes, sparse vegetation cover, and sporadic but high intensity rainfall, which together with a shallow soil depth, increases soil erosion risk. The objective of this study was to analyze the soil erosion risk, associated with A1B climate change scenario over the twenty-first century, for the Mantaro River basin (MRB), Peruvian Andes. The temporal analyses revealed maintenance of current soil erosion risk along the twenty-first century in almost all the MRB, whose current risk is either “very severe” or “extremely severe”. At the sub-basin level, for those located in the center and northern MRB, progressive increases were observed in the average erosion rate by the end of this century, increasing the soil erosion risk. In sub-basins under greater influence of the Andes, this risk was classified as “moderate” and remained this way throughout the century, despite the increase in rainfall erosive potential simulated for these. In annual terms, there was a significant trend of decreasing rainfall erosivity and increasing the concentration of rainfall simulated based on A1B climate change scenario. Because the A1B scenario affects rainfall erosivity mainly during the rainy season, this causes a risk to the environmental sustainability and future agricultural activities.

In this paper, we present a methodology to map inland water in tropical areas under dense vegetation at high spatial and temporal resolution using multi-source remote sensing data. A new inundation product (SWAF-HR) is presented. It is characterized by a high spatial resolution (30', 1 km) and high temporal resolution (3 days). The SWAF-HR product is estimated over the Amazon basin for the 2010-2016 period. It is based on a downscaling procedure and the synergistic use of: (1) water surface fraction at coarse spatial resolution from an L-band passive microwave sensor (Soil Moisture and Ocean Salinity - SMOS), (2) Global Surface Water Occurrence from Landsat (GSWO) and (3) the Digital Elevation Model (DEM) Multi-Error-Removed-Improved-Terrain (MERIT) based on the Shuttle Radar Topography Mission (SRTM). Thanks to the high capability of L-band microwave emission to reveal surface water under all-weather conditions and beneath the vegetation, the inundated area extent estimated by the SWAP-HR product is always larger than GSWO estimates obtained by the optical sensor (Landsat). SWAF-HR data is compared to ESA CCI and IGBP land covers, two SAR images and flooded areas over the Purus basin computed by the MGB-IPH model simulation. The results show the coherence of spatial and temporal dynamics of the SWAF-HR data. We show that the flooded area of the Branco River floodplain in Roraima (Brazil) varies from 0.2 x 10(4) to 2.7 x 10(4) km(2) whereas the extent of the Bolivian floodplain (Llanos de Moxos) inundation ranges between 0.8 x 10(4) and 8.1 x 10(4) km(2) during 2010-2016. The flooded area in the Branco floodplain gradually decreased from 2010 to 2015 but in 2016, the flooded area has increased during the rainy season. During 2010-2016, the minimum of the inundated surface extent was reached during 2015-2016 reflecting to a drought event related to ENSO. The most important uncertainties of the DEM are located over tropical areas but this information is essential in the downscaling procedure. Therefore, we investigate the impact of the choice of the DEM for the downscaling procedure. It is found that the choice of the DEM introduces 5% of error in the instantaneous water surface extent estimate but can reach up to 10% in the flood probability estimations over seven years. This new SWAF-HR product will be helpful for the understanding of the water, carbon and biogeochemical cycles of the Amazon.

River floodplains and reservoirs interact throughout a basin drainage network, defining a coupled human-water system with multiple feedbacks. Recent modeling developments have aimed to improve the representation of such processes at regional to continental scales. However, most large-scale hydrological models adopt simplified lumped reservoir schemes, where an offline routine is run with inflows estimated by the model, with limited consideration of the complementarity between floodplains and reservoirs on altering the hydrological regime at regional scale. This paper presents a novel approach that fully couples river-floodplain-reservoir hydrodynamic and hydrological models, significantly improving the representation of reservoir dynamics and operation in the river-floodplain-reservoir continuum at large scale and across multiple dam cascades. The model is applied to the Parana River Basin with explicit simulation of 31 large dams and river hydraulic variables at basin scale. Three types of reservoir bathymetry representation are compared, from lumped to distributed methods, combined with three reservoir operation schemes and varying degrees of input data requirement within two parameterization scenarios (global and regional setups). The operation schemes were more relevant than the reservoir bathymetry representation to estimate downstream flows and water levels. While the data-driven operation scheme, based on linear regressions between observed water levels and dam outflows, provided the best estimates of both active storage and discharges, the more generic operation reasonably estimated discharges and peak attenuation, albeit not as accurately for active storage. The global parameterization of reservoir operation resulted in poorer performance compared to the regional-based one, but it satisfactorily modeled discharge and peak attenuation. Regarding the reservoir bathymetry representation, a basin scale comparison of the lumped and distributed schemes indicated the inability of the former to represent backwater effects. This was further corroborated by validating the longitudinal water level profile of Itaipu dam with ICESat satellite altimetry data. Finally, the model was used to show the complementarity between floodplains and reservoirs on attenuating floods at regional scale. Large scale models should move beyond offline coupling strategies, and include regional-based, data-driven reservoir operation schemes together with a distributed representation of reservoir bathymetry into river-floodplain hydraulic schemes. This will largely improve the estimation of river discharges, water levels and flood storage, and thus the model ability to represent the regional scale river-floodplain-reservoir continuum.