S13

Dehesas are savanna-type ecosystems subject to regular water deficits; they have a complex structure with multiple vegetation layers that differ in phenology, physiology, and function, and each one contributes differently to turbulent exchanges and the radiative transfer budget. The combined differential functioning and characteristics of the vegetation components affect water dynamics, resulting in high spatio-temporal variability that creates distinct intra-ecosystem microclimates. This structure influences ecological processes, such as plant growth, and plays an important role in Dehesa resilience, making the system an efficient convector of sensible heat and keeping the canopy surface temperature within the survival range. Dehesas are productive landscapes, and one of their multiple uses, extensive livestock farming, requires an environment that is comfortable for animals and produces sufficient biomass yields. Thus, microclimate regulation and stability are key to maintaining the ecosystem’s profitability. We need to better understand the interactions between vegetation structure and dynamics, and microclimate delineation and regulation, at scales relevant to both farming management and the dominant mesoscale hydrologic regime. 

We evaluated the water use patterns estimated by different modeling approaches (FAO56, ALEXI - DisAlexi with STARFM, and SEBS) with different spatial resolutions (5 km to 10 m) of the herbaceous stratum and other typical vegetation patches (scrubs, humid areas, creek shore), which shape the distinctive dehesa microclimates. From a farm management viewpoint, we demonstrated the need for sufficient spatial and temporal resolution when evaluating water consumption. Higher spatial-temporal scales were crucial to determining the pasture drying cycle and can benefit the assessment of the nutritional quality for livestock feeding. In humid or denser areas that provide essential ecosystem services (e.g. shelter, soils with increased water retention) transpiration rates were higher throughout the year and often underestimated when using coarse data. Over mixed patches (grass and trees with low coverage fraction) products with coarse resolution (1 km to 5 km) reflected well the water use pattern. From a hydrological viewpoint, the 30 m resolution ET product may be useful to identify and delineate areas with different water storage capacities and runoff processes within the basin. Nevertheless, coarse resolutions are enough for regional managing purposes.

How to cite: Andreu, A., Carpintero, E., Muñoz-Gomez, M. J., Blazquez-Carrasco, A., and González-Dugo, M. P.: Scale influence in water resources management for heterogeneous oak-savanna distinctive microclimates., IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-499, https://doi.org/10.5194/iahs2022-499, 2022.

In the presence of uncertain initial conditions and model parameters coupled land surface model (LSM)- vegetation dynamic model (VDM) performance can be significantly improved by the assimilation of periodic observations of certain state variables, such as the soil moisture and normalized difference vegetation index (NDVI) as observed from satellite remote platforms.

The possibility to merge grass and tree NDVI observations and radar data with the model optimally for providing robust predictions of soil moisture and grass and tree leaf area index (LAI) in heterogenous ecosystems is demonstrated. We propose an assimilation approach that assimilates backscatter data from radar and NDVI from optical sensors through the Ensemble Kalman filter (EnKF) and provides a physics-based update of soil moisture and grass and tree LAI predicted by VDM. We used Sentinel 1 radar data for soil moisture, and Landsat 8 and Sentinel 2 optical data for NDVI.

This approach, as with other common assimilation approaches, may fail when key model parameters, e.g. the saturated hydraulic conductivity of LSM and the maintenance respiration coefficient (m_a) of VDM, are estimated poorly. This leads to biased model errors producing a violation of a main assumption (model errors with zero mean) of the EnKF. For overcoming this model bias an innovative assimilation approach was developed, which accepts this violation in the early model run-times and dynamically calibrates all the components of the model parameter ensembles as a function of the persistent bias in root zone soil moisture and LAI, allowing to remove the model bias, restore the fidelity to the EnKF requirements and reduce the model uncertainty.

The proposed multiscale assimilation approach was tested in a Sardinian field site, a typical Mediterranean ecosystem characterized by strong heterogeneity of the vegetation and water limited conditions. The site is also a case study of the SWATCH European Research Project, and in this field a micrometeorological eddy-covariance based tower is operating from 2003.

The effective impact of the proposed assimilation approach on the soil water budget, evapotranspiration and CO₂ uptake predictions in the heterogenous ecosystem is demonstrated.

How to cite: Gaspa, A. and Montaldo, N.: Multi Scale Assimilation of NDVI and radar data for soil moisture and Leaf Area Index Predictions in an Heterogeneous Mediterranean Ecosystem, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-648, https://doi.org/10.5194/iahs2022-648, 2022.

Evapotranspiration (ET) is a major component of both the hydrological cycle and the surface energy balance. Furthermore, ET is strongly linked to the primary production of natural and cultivated vegetation covers. Therefore, obtaining spatialized estimates of ET is of paramount importance in Mediterranean areas, submitted to hot and dry summers, all the more so as climate change is expected to worsen the water deficit in this region. In the framework of the preparation of the TRISHNA satellite mission, the objective of this study was (1) to produce maps of ET over a small Mediterranean region from high resolution satellites, and (2) compare these satellite estimates with local measurements of ET from flux towers. The studied area was located in the Hérault river area, south of France. During the period 2013 - 2019, 63 clear sky Landsat 7 and 8 images were collected and processed, having spatial resolutions of 60 and 100 meters, respectively. Maps of ET were generated using the EVASPA processing tool (Gallego-Elvira, 2013), with various methods for estimating the soil heat flux and the evaporative fraction. These satellite estimates were compared to those measured by long term flux towers installed on three biomes representative of Mediterranean landscapes: Puechabon (Quercus ilex forest on a rocky soil), Larzac (grassland on a limestone plateau) and Roujan (vineyards in the plain). In the estimation of ET from satellite images, intermediate variables (albedo, net radiation, soil heat flux, surface temperature) were first compared to those measured locally, when available. Finally, instantaneous and daily satellite estimates of ET were compared to the local measurements. Depending on sites and EVAPSA methods, the RMSE of instantaneous estimates of ET ranged between 39 and 202 W.m^-2. The RMSE of daily estimates of ET ranged between 0.96 and 1.82 mm.day^-1. Future work will be conducted to analyze the effect of air temperature variations, induced by altitude variations, on ET satellite estimates. This will allow to extend this study to larger regions of southern France.

How to cite: Prévot, L., Mateo-Herrera, B., Jacob, F., Demarty, J., Limousin, J.-M., Ourcival, J.-M., Champollion, C., and Olioso, A.: Spatial assessment of surface-atmosphere fluxes in the Mediterranean region: synergy between satellite estimates and local observations, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-495, https://doi.org/10.5194/iahs2022-495, 2022.

We analyze NDVI time series of an irrigated area of 25 ha that was launched in 1984 in a sylvo-agro-pastoral mountainous of Northern Tunisia. The study area is under anthropic changes consisting in an increasing of pumping volumes for irrigation and spreading of arboriculture practices. The main occupation now is apple trees but earliest (unsuccessful) trials were within vineyards and pears. A series of Landsat 30 m resolution images for a 34-year period from the starting of the irrigation facilities in 1984 to 2017 is analyzed. The NDVI of June is considered. Sample statistics of every June map are identified to help monitoring the evolution of NDVI over the study area. In interpreting NDVI observations NDVI=0.2 and NDVI =0.5 are usually adopted as descriptors of respectively soil and full vegetation conditions. Results show that while the non-exceedance probability of the value NDVI equal 0.2 decreased through time, that corresponding to NDVI equal to 0.5 increased, well reflecting the growth of arboriculture through time. Furthermore, a log-normal probability distribution is adjusted to every NDVI map from 1984 to 2017. It is found a rising time trend in position and scale distribution parameters, signifying non stationary log normal distributions for NDVI. The position parameter of the log-normal distribution is found statistically related to the growing season rainfall with different regressions corresponding to two periods the one before and the one after 2010. The changing year 2010 represents phenological growth (10 years old for most trees) but also corresponds to a changing year with respect to the increase in irrigated volumes. Finally, homogeneity tests (Mann-Kendall, Pettit, Sen) were applied to test the time series of a subsample of pixels against temporal change in NDVI.  They result in rejecting homogeneity hypothesis at the confidence level of 5%. The most probable date of change was found 2005.  This year corresponds to 5 years old for apple plantations.  The study outlines the worth of NDVI information for monitoring anthropic changes in arboriculture systems. The next challenge is using this no stationary variable for sustainable water management in this case of a shared groundwater resource under pressure.

How to cite: Bargaoui, Z., Boussetta, I., Abid, N., and Ezzine, A.: Long-term NDVI series analysis for water management in irrigated areas, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-747, https://doi.org/10.5194/iahs2022-747, 2022.

The management of water resources requires the use of hydrometric data mainly from in situ stations. Despite the efforts made in setting up and maintaining a network of stations (hydrometric or meteorological), water resource managers face many problems (degradation of equipment during floods, vandalism, inaccessibility, etc. …) Satellite data can enrich the monitoring of water resources, whatever the objectives of this monitoring (strategic management of water resources, management of structures, forecasting of floods and low flows, etc.). These data have the advantage of covering the entire world at regular intervals, including difficult-to-access areas. Through several projects, various scientific and industrial partners (IRD, CNES, CLS, CNRS, CENEAU, etc.) have contributed to assess and enhance the use of satellite data for the management of water resources at the scale of a watershed (eg the Amazon) or a territory (Uganda). The objective is to deliver hydrological indicators resulting from the combination of hydrological models, in-situ data and satellite data. This article presents the current possibilities and limitations of using satellite data to optimize the monitoring of water resources. KEYWORDS: Coupling, satellite data, hydrological modeling, inter-comparison.

How to cite: Rocquelain, G., Legay, T., and Aubert, Y.: Use of spatial data for water resources monitoring, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-531, https://doi.org/10.5194/iahs2022-531, 2022.

There is a requirement of higher spatial resolution of soil moisture as the 9km resolution from SMAP is not adequate. In this work we use MODIS 1km and ECOSTRESS VIIRS 400m vegetation and surface temperature to downscale the SMAP 9km soil moisture to 1km and 400m. We have validated the global 1km soil moisture extensively using global networks. With the CyGNSS and SMAP missions, we have the ability to assimilate sub-daily soil moisture into land surface models. In this talk we will examine the performance of data assimilation of soil moisture using triple colocation.

How to cite: Lakshmi, V., Fang, B., and Kim, H.: Global downscaling and assimilation of soil moisture, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-482, https://doi.org/10.5194/iahs2022-482, 2022.

Quantification of evapotranspiration is crucial for a sustainable management of scarce water resources. Surface energy balance models driven by remotely sensed surface temperatures observations enable to estimate total evapotranspiration and average (surface) water stress conditions. For improved agricultural water management as well as ecosystem health monitoring, it is also important to provide an estimate of evapotranspiration components, i.e. transpiration and soil evaporation, and target the water status of the plant. This is possible through the use of dual-source energy balance models because they solve separate energy budgets for the soil and vegetation. However, the dual-source models rely on specific assumptions on plant water stress to get both components out of the sole surface temperature information. Additional information are thus required, either specifically related to evaporation (such as surface water content, as it can be derived from active microwave information) or transpiration (such as physiological indices derived from specific optical bands). Present work evaluates the ability of the SPARSE dual-source energy balance model to compute not only total evapotranspiration, but also water stress and transpiration/evaporation components, exploiting the complementarities of multiple data sources, including those acquired at lower spatial resolution or from a different view angle. Flux datasets including available sapflow and lysimeter measurements acquired over rainfed and irrigated crops in temperate, Mediterranean and semi-arid regions are used to evaluate the retrieval performances of the evaporation and transpiration components. More than a systematic increase of retrieval performance, the main positive outcome of combining those different sources of data, as well as rightfully accounting for their specific signature (direction, resolution...), seems to be an increased robustness and a better realism of the subcomponents that are retrieved.

How to cite: Boulet, G., Mwangi, S., Olioso, A., Le Dantec, V., Merlin, O., Demarty, J., Malick, K., Rajasekaran, E., Karal Valiyaparambath, A., Rafi, Z., and Er-raki, S.: Advantages and opportunities in using multisensor remote sensing data for evapotranspiration retrieval as well as better partitioning between evaporation and transpiration, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-194, https://doi.org/10.5194/iahs2022-194, 2022.

Remote Sensing (RS) in the thermal infrared (TIR) provides useful information on evapotranspiration (ET). However, current satellites that are available for monitoring ET at a spatial resolution lower than 100 m have long revisit intervals (16 days for Landsat). Cloud occurrence also reduces the number of available images hindering the accuracy of continuous monitoring of ET (requiring interpolation between available ET estimations from RS data). Future satellite missions providing high spatial resolution data every 1 to 4 days are under study : TRISHNA by CNESand ISRO (France/India) and the Land Surface Temperature Monitoring mission (LSTM) by ESA/COPERNICUS.

We analyzed the impact of satellite revisit on the uncertainty in monitoring ET over Europe by considering combinations of climate, land use, revisit characteristics and errors in estimating ET from RS data. We analyzed a large range of crop types / soil / climate / revisit combinations by using synthetic data of ET as simulated using the ISBA-A-gs land surface model (while previous studies were based on flux tower measurements and considered only a limited range of situations). Revisit scenarios were defined from the orbital characteristics of TRISHNA and LSTM in comparison to nominal scenarios with revisit between 1 day and 16 days. We also introduced errors in ET estimation from RS data at the time of acquisition (depending on the surface energy balance model used to derive ET and on the accuracy of RS measurements).         

As expected, the uncertainty in monitoring ET increased significantly with the revisit period when cloud occurrence increased (Figure). However, the impact of cloud regime was lower at higher latitudes because the frequency of image acquisitions increased with the latitude. The impact of the uncertainty in estimating ET at the time of image acquisition was the main driver of the accuracy, in particular in southern Europe.

Figure: uncertainty in ET estimation (ΔET) depending on revisit, crop type, location and day of the year (DOY) as calculated for an error in estimating ET from RS data of 0.6 mm d^-1.

How to cite: Olioso, A., Carrière, S., Gamet, P., Delogu, E., Weiss, M., Guillevic, P., and Boulet, G.: Improving continuous monitoring of evapotranspiration with future thermal infrared missions, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-354, https://doi.org/10.5194/iahs2022-354, 2022.

Increasing availability of satellite remote sensing data triggered the use of hydrologically relevant satellite-based fluxes and variables towards improved modelling. Importance of innovative and satellite data sources for a better understanding of hydrologic processes has been highlighted in the IAHS scientific assembly’s 23 unsolved problems in hydrology. In this context, the present study investigates the use of satellite ET dataset form MODIS in the physically based semi-distributed model Soil and Water Assessment Tool (SWAT). The study area is Nith river watershed, located in Southern Ontario, Canada. We compare the potential of MODIS ET in improving the performance of SWAT at gauged and ungauged locations of Nith River watershed. Streamflow calibrated SWAT model is used as a benchmark model. The benchmark model results are compared with the MODIS ET only calibrated model results to understand the importance of satellite data in hydrologic model calibration. It is found that the calibration of SWAT model only using MODIS ET data resulted better or similar results to that of streamflow-based calibration. The results show that SWAT model improvement is highly dependent on the input data quality such as the precipitation data and land use data used in the initial model set up. The SWAT model calibrated using MODIS ET improved the soil moisture accounting and crop yield estimation. From our results we conclude that the satellite datasets can be a potential solution to parameter estimation in ungauged basins across the world.

How to cite: Jeyalakshmi, S., Bolisetti, T., and Balachandar, R.: Hydrologic model calibration using MODIS-ET data: The impact on predictions at gauged and ungauged locations, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-481, https://doi.org/10.5194/iahs2022-481, 2022.

The estimation of spatialized Evapotranspiration (ET) is of great interest to tackle various scientific issues in hydrology. It is particularly relevant in Sahelian Regions where there is a spatial scarcity of in-situ ET measurements, despite having key issues in water management and food safety. Nowadays, many ET products are available with different temporal and spatial resolutions. These products use a wide range of methods: empirical ET equations, Land Surface Models (LSM), energy balance models, in-situ measurements upscaling, neural networks or data fusion approaches. In this study, a quite exhaustive review of available ET products has been performed over Sahel. We have evaluated the potential of 18 ET products, both at local scale, using eddy-covariance measurements and a LSM calibrated on sahelian ecosystems, and at mesoscale, using the ALMIP2 models ensemble (20 LSM). Results show that only few products succeed at representing realistic temporal and spatial repartition of ET in sahelian context. The wide variety of temporal and spatial resolutions is also limiting the applications of such products, especially in eco-hydrology or agricultural management. Alternative methods are thus proposed to provide more reliable ET estimation over sahelian region by combining multi-sources ET estimations and satellite data. First, derived at kilometer scale using the EVASPA S-SEBI Sahel (E3S) contextual approach and MODIS Thermal Infrared (TIR) data on clear sky days. Then days with clouds have been filled with a data fusion algorithm. This algorithm uses ET estimates from the Amsterdam Methodology (GLEAM); which proved to have one of the best temporal representation of ET in our study area; and/or a simple parametric model (PAMEAS). Annual and seasonal ET estimates have been compared with the existing products on sahelian areas in Niger and Mali, and will be soon in Senegal too in the frame of the EVAP’EAU project (ICIREWARD Unesco Center). The E3S model and temporal interpolation techniques for daily ET estimations are also investigated in the mission group of the TRISHNA satellite mission (CNES-ISRO) which will provide TIR data and products with high spatial (60m) and temporal (3 days) resolution.

How to cite: Etchanchu, J., Demarty, J., Dezetter, A., Olioso, A., Allies, A., Marlio, F., Bodian, A., Boulet, G., Diop, L., Issoufou, H. B.-A., Mainassara, I., Ogilvie, A., Barral, H., Chazarin, J.-P., Oï, M., and Cappelaere, B.: Assess evapotranspiration over Sahelian regions: Review and evaluation of available products and alternative methods., IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-121, https://doi.org/10.5194/iahs2022-121, 2022.

Actual evapotranspiration (AET) is a key component of the energy balance and hydrological regime of catchments. Estimating actual evapotranspiration (AET) in agricultural semi-arid regions is important for crop yield and drought assessment. The SEBS model, a physically-based model of energy balance, and the BBH-model, a conceptual water balance model, are used to estimate AET at the 10-day scale in Northern Tunisia using in situ and remote sensing data. Their estimates were compared to those obtained from a satellite product LSA SAF, based on the soil-vegetation-atmosphere model TESSEL. Comparisons are performed at two spatial scales: at the level of the pixel, and aggregating pixels from the same watershed. Eight gauged watersheds were considered with an area varying between 52 and 416 km². The spatial and temporal study of the coefficient of variation of AET indicates that the AET is coherently related to the spatial and temporal variation of ecosystems. Results indicate that the summer and autumn seasons are the most unstable period and the south part is the most unstable area. The comparison of AET-LSA SAF within AET-SEBS estimations results in R² under 0.6 at the pixel scale and R² varying from 0.2 to 0.5 at the basin scale. The SEBS model estimations overvalue those of LSA SAF, with an MAE = 20 mm 10-day^-1 for almost basin. The comparison of AET-LSA SAF and AET-BBH at the basin scale shows an acceptable coefficient of determination (R² = 0.6) at the level of basins situated in the north part of the study area. By cons, a nonsignificant R² was obtained at the level of the basin in the south. The MAE is about 6.5 mm 10-day^-1 with a general overestimation of AET-BBH comparing to AET-LSA SAF. A good coefficient of determination (R²=0.7) was found when comparing the AET-SEBS and AET-BBH estimations for the basin situated in the south part. The MAE = 16 mm 10-day^-1 and the RMSE = 18 mm 10-day^-1 with an overestimation of AET-SEBS comparing to AET-BBH. These results are encouraging and may help stakeholders to have a range of AET estimations using three different sources and approaches.

How to cite: Abid, N., Bargaoui, Z., and Mannaerts, C. M.: Comparison of Actual Evapotranspiration assessment by satellite-based models (SEBS/ LSA SAF) and hydrological modeling (BBH), IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-733, https://doi.org/10.5194/iahs2022-733, 2022.

We present a new basis for measuring river discharge in ungauged catchments worldwide, which is a prerequisite for any flood or drought mitigation system to be effective. Surrogate runoff (SR) is created from remotely sensed data to make up for the absence of in-situ streamflow. Because of its widespread availability and global coverage, SR derived from remotely sensed data offers an attractive streamflow alternative.
Specifically, the satellite-derived measurement-calibration ratio (MC ratio, also known as C/M ratio) is an appealing option because of its positive correlation with the observed streamflow and its physical property to detect floods. However, challenges in using the C/M ratio to predict streamflow dynamics have been identified because of its limited penetration skill and assumptions in the SR calculation. A signal with a longer wavelength is a possible alternative with better penetration, but the key assumptions for deriving SR are hard to satisfy with a coarser signal. Thus, a new approach to making an SR is required to use a longer wavelength sensor, such as the L-band microwave, which allows advanced data quality. The proposed SR formulation in our study alternates or reduces assumptions in SR calculation to use a coarse grid. The improved performance of the new SR is presented for 467 Australian Hydrologic Reference Stations, which can be considered free from anthropogenic effects and have distinct attributes. Results show significant enhancements in the Pearson linear correlation (R) between SR and in-situ streamflow: 44% of the study areas show R higher than 0.4 with the new approach, whereas only 13% of the study areas show R higher than 0.4 with the previous approach (C/M ratio). Overall, SR is dramatically improved by using the newly designed SR with the L-band microwave signal.

How to cite: Yoon, H. N., Marshall, L., Sharma, A., and Kim, S.: A novel streamflow estimation using L-band passive microwave, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-315, https://doi.org/10.5194/iahs2022-315, 2022.

Spatial altimetry allows to complete in-situ hydrometric data through the establishment of "virtual stations", at the crossing of the satellite ground track with a watercourse. Elevation measurements of water bodies and rivers are available on the Hydroweb-NG website (http://hydroweb.theia-land.fr). The SWOT satellite, scheduled for launch mid november 2022 by the French Centre National d'Etudes Spatiales (CNES) and the U.S. National Aeronautics and Space Administration (NASA), should further improve accuracy, thanks to innovative technology. Other types of multi-sensor space data are also useful in hydrology. A project to support the International Commission of the Congo-Ubangi-Sangha river basin (CICOS) developed since 2016 with funding from the French Development Agency (AFD) has promoted space hydrology through a group of French institutions supporting CICOS. Various activities have been developed including the development of a spatial database, comparison with in-situ data and the development of an operational Hydrological Information System within CICOS, integrating both spatial and in-situ data.
In the framework of the Space Hydrology Group and the CICOS support project, an innovative methodology has been developed to estimate flows from currently available satellite data (Envisat, Jason, Sentinel, etc.), transforming altitudes into flows at virtual stations. These satellite data can be complemented by global databases (width databases with GWD-LR or Sword, mean flow databases with WBM, or Digital Terrain Model databases with SRTM Mission), as well as in-situ data on the studied area (2 hydrological stations in Kinshasa East on the Congo and Bangui on the Ubangi river). This methodology, tested on these 2 rivers, allowed the generation of a hydraulic model of the Saint-Venant 1D type, allowing the generation of Q(Z) calibration curves at any point of the river. Powered by satellite altimetry data, these calibration curves provide flow rates. A web-based interface has been developed providing this information in real-time. The comparision of the bathymetry obtained with this method with ADCP measurements, the analysis of the rating curve at two in-situ stations, and the time delay of the hydraulic model, proved to be very satisfactory, having into consideration all the hypothesis made in this methodology.

How to cite: Malaterre, P. O., Brachet, C., Guzanga, G. G., Tondo, B. L., Andral, A., Dorchies, D., and Chouet, M.: River discharge estimation from satellite observations. Application in the Congo river basin, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-111, https://doi.org/10.5194/iahs2022-111, 2022.

Land Surface Models are key tools to study the continental water cycle and can be used to better understand the main hydrological processes and their sensitivity to climate change. Yet, they are subject to potentially large errors, especially over ungauged basins where they cannot be calibrated or validated. The Surface Water and Ocean Topography (SWOT) satellite mission will provide unprecedented measurements of water elevation for all rivers wider than 100 m worldwide. Many recent studies focused on the assimilation of such observations into global hydrologic models, including ISBA-CTRIP developed at Météo-France, and they have demonstrated its added value. The SWOT mission will also provide discharge estimations derived from observed water elevation, river width and slope. The algorithms require ancillary data, such as the roughness coefficient, which needs to be estimated empirically at the global scale, potentially resulting in large errors in the discharge estimation. Yet, it is still unclear whether assimilating discharge instead of water level (or water level anomalies) would lead to better performances in terms of simulated discharge along the river network. In this study, we extended the assimilation of water elevation to river discharge into the CTRIP river routing model. We used the new version of the model at a 1/12 degree spatial resolution, which is more compatible to the resolution of the SWOT discharge product (reach length of about 10 km). The Congo river basin is chosen as a test case. SWOT-like river elevations and discharges are constructed by adding realistic errors to elevations and discharge provided by an independent river routing model, MGB. Also, a realistic satellite orbit is used to provide times and locations of available SWOT observations. The impact of observation errors on the assimilation is analysed, as well as the propagation of discharge corrections through the river network. Finally, model performances using discharge assimilation are compared to those using water level assimilation.

How to cite: Munier, S., Boone, A., Biancamaria, S., and Le Moigne, P.: Assimilation of SWOT discharge versus water level into CTRIP-12D over the Congo Basin, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-657, https://doi.org/10.5194/iahs2022-657, 2022.

Despite being the second-largest watershed with significant impacts on the global water cycle, the Congo River Basin’s (CRB) hydroclimatology remains among the least studied worldwide due to the insufficient in situ observations. To better characterize CRB surface hydrology and the variability of its different components at large scale, we jointly used a trove of large records of in situ and satellite-derived observations, specifically, Surface Water Level (SWL) from radar altimetry (a total of ~2,300 virtual stations) and Surface Water Extent (SWE) from the Global Inundation Extent from Multi-Satellite (GIEMS) dataset. A good performance is found between SWL and in situ water height at different locations, with root mean square error varying from 10 cm for Sentinel-3A to 75 cm for European Remote Sensing-2.  The assessment of SWE also agreed relatively well over a ~25-year period with in situ discharge from sub-basin to basin scale. SWL annual amplitude exhibits large spatial variability across the basin, with Northern sub-basins varying more than 5 m while the central and the southern sub-basins vary in smaller proportions (1.5 to 4.5 m). Furthermore, SWL and SWE help capture the water travel time across the basin that varies from 0 to 3 months and the regional relative contribution to the flow at Brazzaville station characterized by a bimodal hydrological regime. Northern sub-basins and the cuvette centrale contribute much to the large peak in December-January while the southern sub-basins contribute to both peaks. We further combine these two datasets to estimate the quantity of the variability of Surface Water Storage (SWS) using two methods, one method used hypsometric curves approach combining topographic data and SWE, and the second method used SWL variation and SWE. SWS in rivers, lakes, floodplains, and wetlands of the CRB is estimated over the period 1992–2015. The CRB SWS shows an annual amplitude ranging between ~74 km³ and ~112 km³. Moreover, the combination of SWS and the annual variations of GRACE/GRACE-FO-derived terrestrial water storage permits us to estimate the long-term variation of groundwater storage. Our results provide a better understanding of the hydrological variables in the CRB and their link with climate.

How to cite: Kitambo, B., Papa, F., Paris, A., Tshimanga, R., Calmant, S., and Frappart, F.: Long-term satellite-derived observations unrevealed the spatio-temporal hydrological variations in the Congo River Basin, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-644, https://doi.org/10.5194/iahs2022-644, 2022.

The Surface Water and Ocean Topography CNES and NASA mission, planned for launch in late 2022, will provide a global mapping of the water surface elevation, width and slope of rivers wider than 100m worldwide. The estimation of discharge using solely the SWOT-type observations together with the existing satellite data has received a noticeable attention recently. The attempts to solve this problem have expectedly confirmed that it is ill posed and additional data could be needed, such as the estimates of the mean, minimum and maximum discharge from the available global scale hydrological databases. However, taking into account the accuracy of such estimates and the issue of their relevance to the study period, the problem remains challenging. We suggest a new estimation method, designed specially to reduce the solution bias. It combines Bayesian and Variational approaches to improve the algorithm robustness and stability. In this method the likelihood function is computed, allowing a useful analysis of equifinality often encountered in discharge estimation problems for ungauged basins. The algorithm is designed for global and/or basin-scale applications given the multi-level structure of the methodology. The latter involves different levels of complexity in terms of the representation of the flow dynamics and therefore different computational requirements. Here, we investigate discharge and bathymetry estimation under strong uncertainties from SWOT simulations and their combination with optical (Landsat and Sentinel 2) and altimetry (Jason, ENVISAT, Sentinel 3) data. The global application involves a Python low-cost algorithm that will be run globally through the Confluence platform implemented by UMass with the SWOT Science Team.

Keywords: Data assimilation, Bayesian estimation, remote sensing, SWOT, hydraulic modelling, discharge estimation, altimetry, optical imagery, rivers

How to cite: Oubanas, H., Gejadze, I., and Malaterre, P.-O.: River discharge estimation from SWOT satellite using Hybrid Bayesian-Variational method, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-54, https://doi.org/10.5194/iahs2022-54, 2022.

In this work, we evaluate the contribution of radar altimetry in the analysis of water level variations in Lake Buyo, located in the southwest of Côte d'Ivoire. Lake Buyo is a major hydroelectric dam and plays an important role in economic, social and environmental terms. It is characterised by periods of high and low water which affect certain economic activities such as electricity production and fishing. Water level fluctuations in Lake Buyo also influence ecological processes and represent a marker of climate change in the region.

The altimetry data considered in this study come from the Sentinel-3A satellite, more precisely from tracks n° 016 and n° 743 from orbits 8 and 372 respectively, which cover the area of interest. These level 2 data are available on the CTOH platform. They have been corrected from atmospheric and geophysical effects to make them operational. The calculation of the water level is based on the range measured by the altimeter and the sum of these corrections.

The results indicate that the Buyo lake is intensively recharged from June to September. The time between December and May represents the drying period of the lake. Furthermore, the analysis of inter-annual variations shows that 2016 has the highest peak in the study period. From 2016 to 2020, the maximum water level heights show a decreasing trend with estimated values of 200.98 m; 200.55 m; 200.53 m; 200.05 m; 198.36 m. The trend in the water level of the lake is therefore constantly decreasing.

Although these results have not yet been validated in the field, they constitute a very important preliminary database for monitoring Lake Buyo. Indeed, recent studies have evaluated the performance of several radar altimetry missions, including Sentinel-3A and Sentinel-3B for continental water level surveys.

Keywords: Radar altimetry, Sentinel-3A, CTOH, Lake Buyo, Water level, Hydroelectric dam, Ivory Coast

How to cite: Oularé, S., Koffi Kouamé, F., Armel Kouassi Komenan, C., Deh Kouakou, S., and Therrien, R.: Monitoring seasonal and interannual water level variability using sentinel-3 radar altimetry data: Application to Lake Buyo from 2016 to 2020., IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-268, https://doi.org/10.5194/iahs2022-268, 2022.

The wetlands are essential for ecology and environmental regulation but also for the economy and social service functions. However, the natural hydrology of this kind of system is often modified by the agricultural drainage of the upstream areas. Remote sensing and GIS methods are currently emerging as an alternative to traditional methods like field survey (usually laborious, time consuming and expensive) to analyse the effects of the anthropogenic activities in the water bodies.

In this paper, the Palos lagoon (Southwest Spain), a Ramsar site whose surrounding lands have been intensively modified by petrochemical industry and greenhouse strawberry crops, was taken as a research object. The Global Surface Water (GSWE) online machine, combined with bathymetric and historical meteorological data, were used to spatially quantify water surface during the period 1984-2020. This allowed us, through a water balance approach on a monthly basis, the estimation of water inputs and outputs to analyse the hydrological changes in terms of seasonality and persistence.

The results show greater fluctuations with seasonal changes marked by the climatic regime during the first two decades of the study period. From 2000, linked to a large increase of the greenhouse strawberry crops, the surface water remained stable around 80% of the maximum lagoon extension, which allowed the use of the last twenty years to describe the current state in terms of water balance. On one hand, water inputs exceed outputs from October to March, raising the lagoon water level and increasing the water surface from 80 to 92% of the maximum extension (drying period). On the other hand, water outputs exceed inputs from April to June, when water surface changes from 85 to 76% (wetting period), a percentage which remains from July to September (balance period). This approach was verified with field measurements in the main control points.

This highlights that remote sensing products, combined with local information, can be used to successfully evaluate hydrological behaviour in those places where historical hydrological data series are not available. These kinds of methods and tools can provide the knowledge to support better informed water-management decision-making.

How to cite: Contreras, E., Pimentel, R., Aguilar, C., Aparicio, J., and Polo, M. J.: Monitoring of the wetland hydrology using remote sensing and historical data series: The case of Palos lagoon (Southwestern Spain), IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-671, https://doi.org/10.5194/iahs2022-671, 2022.

With growing urbanisation and climate change, flooding is likely to become even more frequent and severe.  Therefore, it is essential to constantly monitor water level changes at a large scale. Synthetic Aperture Radar (SAR) images are often used for flood mapping as they allow a rather straightforward detection of water bodies, at almost all weather and illumination conditions. As the number of satellite observations and global digital elevation models (DEMs) are becoming more available, this mapping approach is gaining in popularity.

In this study, we propose three different approaches to retrieve water level maps based on the combination of satellite and topographic data, hereby referred to as the global Height Above Nearest Drainage (HAND), the local HAND and the local DEM methods.

The three approaches are based on the optimization of a threshold applied on the topography (HAND or DEM) data enabling a best fit with the SAR-extracted flood map. The optimized threshold values provide at the same time the normalized water levels (with respect to the drainage network). The water depth map is thus computed from the difference between the water level and the DEM. The global HAND method applies a single optimized threshold to the HAND map, over the entire area of interest. The local HAND method is based on the same concept but optimizes and applies the HAND threshold value locally, using a sliding window. The local DEM thresholding method employs the same principle as the second method but directly on the DEM.

We evaluate these methods using hydraulic model simulation results and ground truth data, and we carry out several experiments using various SAR images (Envisat, TerraSAR-X and Sentinel-1) and topographic datasets (SRTM, CopDEM and LiDAR).

The best results are obtained while combining a high-resolution image (e.g:TerraSAR-X) with: 1) a high-resolution dataset (e.g: LiDAR DEM), using the local DEM approach, or 2) a coarse-resolution dataset (e.g: Srtm DEM), using the global HAND approach. RMSDs on the derived water depth maps reach respectively 0.52m and 0.93m.

How to cite: Ayoub, V., Delenne, C., Pelich, R.-M., Chini, M., Matgen, P., and Hostache, R.: Imaging flood depth from space: a method based on the fusion of topography and synthetic aperture radar data, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-570, https://doi.org/10.5194/iahs2022-570, 2022.

HYDROCOASTAL is a two-year project funded by ESA, with the objective to maximise exploitation of SAR and SARin altimeter measurements in the coastal zone and inland water, by evaluating and implementing new approaches to process SAR and SARin data from CryoSat-2, and SAR altimeter data from Sentinel-3A and Sentinel-3B. Optical data from Sentinel-2 MSI and Sentinel-3 OLCI instruments will also be used in generating River Discharge products. New SAR and SARin processing algorithms for the coastal zone and inland waters will be developed and implemented and evaluated through an initial Test Data Set for selected regions. From the results of this evaluation a processing scheme will be implemented to generate global coastal zone and river discharge data sets. A series of case studies will assess these products in terms of their scientific impacts. All the produced data sets will be available on request to external researchers, and full descriptions of the processing algorithms will be provided.

The scientific objectives of HYDROCOASTAL are to enhance our understanding of interactions between the inland water and coastal zone, between the coastal zone and the open ocean, and the small-scale processes that govern these interactions. Also, the project aims to improve our capability to characterize the variation at different time scales of inland water storage, exchanges with the ocean and the impact on regional sea-level changes.

The technical objectives are to develop and evaluate new SAR and SARin altimetry processing techniques in support of the scientific objectives, including stack processing, and filtering, and retracking. Also, an improved Wet Troposphere Correction will be developed and evaluated.

The presentation will describe the different SAR altimeter processing algorithms that are being evaluated in the first phase of the project, and present results from the evaluation of the initial test data set. It will focus particularly on the performance of the new algorithms over inland water.

How to cite: Benveniste, J., Cotton, D., and Team, H.: SAR Altimetry Processing Over the Coastal Zone and Inland Water  - the ESA HYDROCOASTAL Project, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-541, https://doi.org/10.5194/iahs2022-541, 2022.

How to cite: Orieschnig, C. A., Belaud, G., Venot, J. P., and Massuel, S.: Retro- and forecasting inundation extents for trend analysis in the Cambodian Mekong Delta - a new method combining Sentinel-1 and 2 and local water level measurements, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-205, https://doi.org/10.5194/iahs2022-205, 2022.

A long-term rainfall dataset with high spatial and temporal resolution is an indispensable resource for climatological studies. This information is crucial for water resource management. Among available rainfall products, SM2RAIN datasets estimate rainfall from satellite soil moisture observation through the so-called “bottom-up” approach. Previous research has indicated the high performance of rainfall estimation of SM2RAIN products over different parts of the globe. SM2RAIN-CCI and SM2RAIN-ASCAT are two rainfall products that estimate rainfall at 0.25° and 0.1° spatial and daily temporal resolution for the period of 1998-2015 and 2007-2020 on a global scale, respectively. The goal of this study is to design the long-term climatological datasets with 0.25° spatial and monthly temporal resolution for the period from 1998 to 2020 by merging these two SM2RAIN products for spatiotemporal investigation of rainfall over the United States of America as a case study. Moreover, the spatiotemporal analysis results of the resulting product are compared with other rainfall products based on ground observations and reanalysis, such as the Global Precipitation Climatology Project (GPCP) and ERA5. The results show a good agreement of the developed SM2RAIN-based monthly rainfall dataset with respect to GPCP and ERA5 and pave the way to build a global scale dataset based on satellite soil moisture data through SM2RAIN.

How to cite: Mosaffa, H., Filippucci, P., Massari, C., Ciabatta, L., and Brocca, L.: Long-term climatological SM2RAIN datasets for rainfall spatiotemporal analysis, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-526, https://doi.org/10.5194/iahs2022-526, 2022.

Effects of climate change and variability in West African countries are heightening the vulnerability of local populations, which heavily rely on agriculture and natural resources through ecosystem services. Developing effective water management strategies for mitigation of these impacts requires knowledge of weather, especially rainfall, built upon continuous long-term records (in both time and space). Yet, most West African countries are poorly gauged, with a low density of reliable gauging stations, hampering applications such as water planning and weather forecasting. Recently, some daily global precipitation products have been developed, providing rainfall estimates derived from soil moisture observations using the innovative SM2RAIN (Soil Moisture to Rain) bottom-up inversion algorithm, hence treating the soil as a natural rain gauge. Since these products are gridded, they also provide continuous spatial information regarding rainfall. In this study, the accuracy of such three typical SM2RAIN products (SM2RAIN-CCI, GPM+SM2RAIN, SM2RAIN-ASCAT) at depicting rainfall estimates in Burkina Faso (West Africa, area of 272,200 km²) at 10 synoptic stations over the period 2007-2017 (9 years), at the daily, dekadal (10-days accumulation), monthly and annual timescales. The results reveal that at the daily timescale, all products performance is poor to moderate (KGE: 0.18 to 0.36). At higher time scales, however, both products performed satisfactorily to very good (dekadal: KGE: 0.61 to 0.79; monthly: KGE: 0.63 to 0.91; annual: KGE: 0.44 to 0.81), with SM2RAIN-CCI being consistently superior. Overall, SM2RAIN-CCI presented the lowest volumetric hit and miss bias at all stations, whereas SM2RAIN-GPM presented the lowest false bias. Also, SM2RAIN-CCI featured the highest ability at picturing the timing of occurrence of daily rainfall events (probability of detection, false alarm ratio, threat score) for various thresholds in the range of 0 to 25 mm. Finally, the ability of these products at picturing observed rainfall extremes have been evaluated through various ETCCDI climate indices, at which SM2RAIN-CCI and SM2RAIN-GPM presented equal performance, SM2RAIN-ASCAT being less good. These results provide a quantitative assessment of the SM2RAIN approach in the context of Burkina Faso and might help in the selection of an optimal product for further applications.

Keywords: ASCAT, Burkina Faso, CCI, GPM, rainfall, Sahel, SM2RAIN.

How to cite: Yonaba, R., Belemtougri, A., Tazen, F., Mounirou, L. A., Koïta, M., Karambiri, H., and Yacouba, H.: Assessing the accuracy of SM2RAIN (Soil Moisture to Rainfall) products in poorly gauged countries: the case of Burkina Faso in the West African Sahel., IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-263, https://doi.org/10.5194/iahs2022-263, 2022.