HS6.3
Water Level, Storage and Discharge from Remote Sensing and Assimilation in Hydrodynamic Models

HS6.3

EDI
Water Level, Storage and Discharge from Remote Sensing and Assimilation in Hydrodynamic Models
Convener: Jérôme Benveniste | Co-conveners: Stefania Camici, Fernando Jaramillo, J.F. Crétaux
Presentations
| Fri, 27 May, 08:30–11:05 (CEST)
 
Room 2.17

Presentations: Fri, 27 May | Room 2.17

Rivers
08:30–08:37
|
EGU22-4110
|
ECS
|
On-site presentation
Daniel Scherer, Christian Schwatke, and Denise Dettmering

The water surface slope of rivers is an essential variable for estimating river discharge. It is also helpful as a correction applied to range measurements of satellite altimetry missions to derive water level time series at a virtual station. Still, only rough and mean estimates of water surface slope are obtainable using classical satellite altimetry because of its coarse time and space resolution.

Using the unique measurement geometry of ICESat-2 with six parallel laser beams, we derive instantaneous reach-scale water surface slope along and across the satellite's ground track. The method can be applied globally and provides extending insights into the time- and space-variability of the water surface slope of any river with increasing mission duration.

We compare the ICESat-2 water surface slope estimates with time-variable slopes derived from in-situ data from multiple gauging stations and with static datasets (e.g., from SWORD). We also show the possible performance gain at multiple virtual stations in the "Database for Hydrological Time Series of Inland Waters" (DAHITI, https://dahiti.dgfi.tum.de) applying the water surface slope estimates as a correction of the orbit-drift which can be a few kilometers for repeat missions such as Jason-2/3. However, the largest impacts are expected for non-repeat orbit missions such as CryoSat-2 or Saral (after July 2016).

How to cite: Scherer, D., Schwatke, C., and Dettmering, D.: Estimating Water Surface Slope of Rivers Using ICESat-2 Observations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4110, https://doi.org/10.5194/egusphere-egu22-4110, 2022.

08:37–08:44
|
EGU22-7475
|
Virtual presentation
Nico Sneeuw, Bo Wang, Jingyi Bao, Siqi Ke, and Mohammad Tourian

In order to determine river streamflow over poorly gauged basins, spaceborne techniques are widely used to obtain hydraulic parameters like river height variation (H), slope (S), river width (W), velocity (V) and river cross section (or bathymetry). Conventional radar altimetry can only provide water height. It is also difficult to measure the slope of a reach even from multi-mission altimetry, due to the problems of the simultaneity and intersatellite biases.

Laser altimetry with ICESat-2 enhances the opportunity to constrain river streamflow. The Advanced Topographic Laser Altimeter System (ATLAS) on the mission carries 3 pairs of laser transmitters (one strong and one weak beam in each pair) with photon-counting detectors. ATLAS emits 532-nm laser pulses (green light) at a 10 kHz repetition rate. It detects individual photons at 70 cm along-track separation for each shot on the earth’s surface with ~17 m diameter footprint. The very dense measurements can provide the height profile of the cross section. Since the laser penetrates water, it can potentially measure the river bathymetry, at least the nearshore part, depending on the turbidity of the water. With its off-nadir beams, the system is able to provide the slope of a reach.

In our study, we process the point cloud of the river cross section and extract the nearshore river bathymetry. The slope is determined by three strong beams of one track. We also analyze the maximum depth of bathymetry in different seasons and different turbidity conditions. The water height of each beam is obtained from measurement of the center of the river (centerline from SWOT River Database), and validated with measurements of the weak beams.

How to cite: Sneeuw, N., Wang, B., Bao, J., Ke, S., and Tourian, M.: Constraining river streamflow determination using bathymetry and slope from ICESat-2 satellite altimetry, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7475, https://doi.org/10.5194/egusphere-egu22-7475, 2022.

08:44–08:51
|
EGU22-7160
|
Presentation form not yet defined
|
Jérôme Benveniste and David Cotton and the HYDROCOASTAL Project Team

Introduction

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 waters, 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.

Objectives

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.

Presentation

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. and Cotton, D. and the HYDROCOASTAL Project Team: Improving SAR Altimeter Processing over Inland Water - the ESA HYDROCOASTAL Project, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7160, https://doi.org/10.5194/egusphere-egu22-7160, 2022.

08:51–08:58
|
EGU22-1131
|
On-site presentation
|
Sophie Ricci, Charlotte Emery, and Andrea Piacentini

The upcoming Surface Water and Ocean Topography (SWOT) satellite mission will provide a global and high resolution measurement of river water surface elevation. This product issued from large swath interferometry altimetry will be combined with high fidelity hydrodynamics solvers thanks to data assimilation algorithms to allow for river discharge estimation and prediction. Before launch, the SWOT-HR hydrology simulator is used to produce synthetic SWOT observation at each overpass time, adding the radar measurement error, such as layover and thermal noise, to the water elevation issued from a golden run simulation. 

The ToolBoxSWOT is a chain of python scripts that formats the time varying hydrodynamic model outputs into a temporal sequence of water elevation raster data files used as inputs for SWOT-HR. A Digital Elevation Model, a geolocalized series of bathymetric profiles and river centerline are used to map the outputs of the hydrodynamic models onto the expected regular and high resolution 2D grid requested by SWOT-HR. The toolbox gathers strategies adapted to various levels of knowledge from well-known to unknown catchments. The ToolBoxSWOT is available on git and is provided with a container featuring the proper environment that embeds Python3, QGIS3, QGDAL and GRASS78. The ToolBoxSWOT was applied over a well-known reach of the Garonne river for which a DEM and gauge data are available as well as on the Brahmaputra river, using SWOT only-derived data. The toolbox allows the generation of various observation data sets available for the SWOT-hydrology community.

How to cite: Ricci, S., Emery, C., and Piacentini, A.: ToolBoxSWOT - A Python library dedicated to synthetical SWOT-like data for pre-launch river hydrodynamics studies., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1131, https://doi.org/10.5194/egusphere-egu22-1131, 2022.

08:58–09:05
|
EGU22-3900
|
ECS
|
Presentation form not yet defined
Victor Pellet, Filipe Aires, Dai Yamazaki, Adrien Paris, and Xudong Zhou

River discharge integrates many water-related processes over land, it is crucial for understanding inland water. Unfortunately, in situ measurements are very sparse at the global scale. This study presents a totally new approach for the mapping (i.e. spatially continuous estimate) of the river discharge based on satellite observation of hydrological variables and the water budget balance. First continuous satellite estimate of three water components (precipitation, evapotranspiration, and total water storage change) are corrected at basin scale using river discharge from a few gauge measurements. Secondly, the water budget is balanced at the grid level using flow direction for horizontal water exchange. This new approach is therefore based solely on satellite products and in situ measurements without the use of any dynamical model (except river map). The methodology is evaluated with the river dynamic model  CaMa-flood, altimetric water surface level (WSL) and surface water extent satellite estimate. While the spatial pattern of extreme events cannot be well represented only by in situ gauges information, our study shows the added value of the mapping to better describe these events. As hydrological application, our method can be used in synergy with all the altimetric stations to create discharge-WSL pair data, which will benefit to advanced applications.

How to cite: Pellet, V., Aires, F., Yamazaki, D., Paris, A., and Zhou, X.: A first continuous and distributed satellite-based mapping of river discharge over the Amazon, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3900, https://doi.org/10.5194/egusphere-egu22-3900, 2022.

09:05–09:12
|
EGU22-3513
|
ECS
|
Virtual presentation
|
Paolo Filippucci, Luca Brocca, Stefania Bonafoni, and Angelica Tarpanelli

River discharge is worldwide recognized as one of the variables whose knowledge is most needed in order to understand the evolution of climate, to assess the related risks and to develop mitigation and adaptation strategies. Notwithstanding this, river monitoring is still an open issue. The ground monitoring network is declining, with many rivers that are ungauged due to the difficulties of installing instruments on remote areas and the high costs of installation and maintenance of instruments. Furthermore, the absence of strategies for data sharing and the long latency in data dissemination worsen the situation, preventing the use of methods for natural hazard forecasting in many regions.

 In this framework, over the last few decades, satellite data have been used to support the ground network information thanks to the strong growth in technologies, data processing and applications that fostered their use for the water cycle monitoring. In particular, considering the daily river discharge measurement, the recent advances in near-infrared (NIR) satellite sensors encouraged their use for the river discharge estimation, due to their frequent revisit time and wide spatial coverage. Therefore, passive remote sensing data from multiple sensors such as MODIS, MERIS and OLCI (spatial resolution of about 250 - 300 m) have been used to develop a non-linear regression model to estimate the river flow in medium-sized catchments (around 100’000 km2). The model is based on the different behavior in the NIR band between a calibration pixel C, selected over land, and a measurement pixel M, selected over the river boundaries. The ratio of the two pixels is indeed well correlated with in situ river discharge, but the methodology still needs to calibrate the pixel locations by using observed data, limiting the usefulness of the methodology to the gauged areas.

More recently, Sentinel-2 satellites of the European Union’s Earth observation COPERNICUS programme, foster the monitoring of narrow rivers (< 150 m wide) thanks to the high spatial resolution (10 m). The high resolution enables to better identify the main geographical features (e.g., water, vegetation, urban area, river boundaries) and to better monitor the effect of several factors (vegetation and sediments) in the river discharge estimation. An important contribution has been found in the sediment component, affecting the reliable reproduction of high flow due to the high reflectance of turbid water sensed by the satellite. For this reason, the original formulation for the estimation of river discharge has been modified and tested over several rivers worldwide to assess its influence in different environments.

Here, we show the results of the analysis applying the new approach for the estimation of the river discharge to both Sentinel-2 and MODIS data, in order to evaluate the advantages of the use of high spatial resolution information. Furthermore, results and limitations of the uncalibrated version of the algorithm are also shown underling the possibility to use the methodology over ungauged rivers, where the absence of observed data prevents the applicability of the classical satellite methods for river discharge estimation. 

How to cite: Filippucci, P., Brocca, L., Bonafoni, S., and Tarpanelli, A.: River Discharge estimation from optical satellite data: latest advances using NIR sensors, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3513, https://doi.org/10.5194/egusphere-egu22-3513, 2022.

Lakes/Reservoirs
09:12–09:19
|
EGU22-12647
|
Virtual presentation
Gennadii Donchyts, Hessel Winsemius, Tjalling de Jong, Antonio Moreno-Rodenas, and Maarten Pronk

Small and medium-sized reservoirs play an important role in water systems that help cope with climate variability. Although reservoirs and dams are criticized for their negative social and environmental impacts by reducing natural flow variability and obstructing river connections, they are also recognized as important for social and economic development and climate change adaptation. These reservoirs are crucial to the well-being of many societies worldwide, but regular monitoring records of their water dynamics are mostly missing. Multiple studies exist which look into the quantification of water stored in the reservoirs behind these dams. Still, very few studies focus on small and medium-sized reservoirs globally. In this research, we present the current status of the research focusing on the derivation of storage for small and medium-sized reservoirs. We use multi-annual multi-sensor satellite data with up to daily observation frequency, combined with cloud analytics, derive dynamics and storage of small (10-100ha) to medium-sized (>100ha) artificial water reservoirs globally. We derive storage by combining multiple datasets such as water occurrence, surface water area dynamics observed from space, and several elevation datasets available globally such as ALOS, NASADEM, EU-DEM. We evaluate the applicability of ICESat-2 and GEDI LiDAR sensors to estimate water storage in these reservoirs, perform validation for more than 700 reservoirs globally, and assess the applicability of these datasets to monitor water storage for more than 70 000 reservoirs globally.

How to cite: Donchyts, G., Winsemius, H., de Jong, T., Moreno-Rodenas, A., and Pronk, M.: Deriving storage of small and medium-sized reservoirs with elevation datasets and medium-resolution satellite imagery, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12647, https://doi.org/10.5194/egusphere-egu22-12647, 2022.

09:19–09:26
|
EGU22-2927
|
ECS
|
On-site presentation
Amirhossein Ahrari, Epari Ritesh Patro, Mahdi Akbari, Björn Klöve, and Ali Torabi Haghaighi

Lakes' water levels have a dynamic behavior, and their variations are an essential subject for water resources research and management. These variations have a wide range of time scales, from short-term (daily) to long-term (yearly) scales. However, access to hydrological data is limited due to scarce observation stations, fragmented data holdings, and low data quality in developing countries. Satellite altimeters are considered the main source of water level estimation among remote sensing data. Although many seas and oceans are covered by altimetry satellites, currently, they have a huge gap in covering inland lakes. Accordingly, we proposed an alternative approach to estimate shallow lakes' water levels using typical optical imageries and digital elevation models. The water level is estimated based on the Area Elevation Model (AEM) approach, using MODIS surface reflectance product, ALOS DSM and Landsat JRC product as inputs to the model. The AEM helps extract the water level time series based on the information about water area obtained from satellite products using various spectral indices (NDWI_GNIR, NDVI, NDWI_RSWIR and MNDWI). The methodology was applied to eight shallow lakes in Iran using Google Earth Engine (GEE) platform. These lakes are located across the arid and semi-arid regions of the Persian Plateau, Iran. The lakes' water level in these regions is declining, and there is a great need for taking important measures by regional authorities for sustainable water management. Spectral indices and the effect of satellite resolutions were evaluated. Overall, this methodology can be the alternative approach for water level estimation for lakes with minimum or no ground observation and altimetry coverage.

How to cite: Ahrari, A., Patro, E. R., Akbari, M., Klöve, B., and Torabi Haghaighi, A.: Shallow Lakes Water Level Estimation using Satellite Optical Imagery and Digital Elevation Models Over the Persian Plateau, Iran, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2927, https://doi.org/10.5194/egusphere-egu22-2927, 2022.

09:26–09:33
|
EGU22-10411
|
On-site presentation
Albrecht Weerts, Pieter Hazenberg, Bart van Osnabrugge, and Willem van Verseveld

In many places of the world, reservoirs play an important role in relation to water security, flood risk, agriculture production, hydropower, hydropower potential, and environmental flows. By limiting the amount of water flowing out of the reservoir, reservoirs control flooding downstream, but they can also increase downstream runoff during drought.  

Detailed information about reservoir management (e.g. inflow, volume and outflow operations) is generally unknown or only available to the local control authority. As a result, large-scale information on reservoir dynamics is currently unknown. Recently, reservoir volume dynamics have been estimated from satellite observations based on reservoir surface area estimates. While Earth observation (EO) has the potential to monitor water from space and fill this gap, temporal resolution of these datasets generally varies between 3-7 days without direct information on reservoir inflow and outflow. Hydrological model reanalysis provides a complementary data source. Using cloud computing infrastructure and a high resolution distributed hydrological model wflow_sbm, we present a novel dataset of historical daily reservoir variations for 3236 headwater reservoirs across the globe for the period 1970-2020. Results derived with wflow_sbm model forced with various forcing sources based on observations and reanalysis (ERA5, EOBS, CHIRPS, NLDAS, BOM, MSWEP) are compared with: 1) measured discharge observations, 2) in situ reservoir elevation and volume measurement, and 3) volume estimates derived using satellite observations. Overall good comparisons between the hydrological model and the different measurement sources are observed, although considerable variations are observed. During the presentations we will zoom in on some of the large-scale changes in reservoir dynamics as observed in South America and Africa and how these potentially impact society.

 

How to cite: Weerts, A., Hazenberg, P., van Osnabrugge, B., and van Verseveld, W.: Long-term dynamics of reservoirs across the globe, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10411, https://doi.org/10.5194/egusphere-egu22-10411, 2022.

09:33–09:40
|
EGU22-5716
|
ECS
|
Virtual presentation
|
Tabea Donauer, Silvan Ragettli, Peter Molnar, Ron Delnoije, and Tobias Siegfried

Water resources in the African Sahel Region are under increasing pressure due to climatic changes, population growth and land degradation. Often, societies rely on surface water from lakes and rivers to sustain their lives and livelihoods. It is therefore essential to monitor and understand the dynamics of these water bodies to assess past, present, and future water resource changes.

Here we use satellite imagery and altimetry to determine water level and storage changes in small water bodies across the African Sahel. The method consists of detecting the ever-shifting edge of lakes and rivers in Landsat and Sentinel-2 optical imagery and assigning heights to shoreline points using altimetric data from ICESat-2satellite. This so-called “waterline method” assumes that the water-land boundary can be regarded as a contour line that connects points of equal elevation. We present novel extension of the waterline method which also allows to identify bathymetry changes over time from shoreline position observations. By tracking the temporal changes of surface water contour shapes, we can quantitatively analyse erosion and deposition processes. Past reservoir capacity changes and water storage variations are thus retrieved from optical remote sensing data, which are available over much longer periods of time and at higher revisiting frequenciesthan altimetry data.

The operational implementation of the method offers access to the water levels and storage variations of more than 300 water bodies in 10 Sahelian countries over the period 2000-2021. The identified spatio-temporal trends reveal fascinatingly heterogeneous patterns of drying and wetting across the Sahelian zone. Wet-season water level data reveal increasing trends over the last 20 years from West to East. Dry-season water availability then depends to a large degree on storage capacity.

Finally, we use the method for a detailed attribution analysis to identify drivers of change at Lac Wégnia, a designated RAMSAR site in Mali. The lake is characterized by an alarming decrease of dry-season surface water extent over the last 20 years. We recognize silting at the tributaries to the lake, but overall, erosion processes are dominant and threaten the persistence of the lake because of continuous backward erosion at the outlet of the lake. This explains the decreasing trend in water levels even for the wet-season, in spite of positive rainfall patterns.

How to cite: Donauer, T., Ragettli, S., Molnar, P., Delnoije, R., and Siegfried, T.: Unraveling the hydrology of water bodies in the African Sahel Region using continental scale remote sensing, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5716, https://doi.org/10.5194/egusphere-egu22-5716, 2022.

Models and Earth Observations
09:40–09:47
|
EGU22-9234
|
Presentation form not yet defined
Stefania Camici, Angelica Tarpanelli, Luca Brocca, Christian Massari, Karina Nielsen, Nico Sneeuw, Mohammad J. Tourian, Shuang Yi, Marco Restano, and Jérôme Benveniste

River discharge monitoring is crucial for many activities ranging from the management of water resources to flood risk mitigation. Due to the limitations of the in situ stations (e.g., low station density, incomplete temporal coverage as well as delays in data access), the river discharge is not always continuously monitored in time and in space. This prompted researchers and space agencies, among others, in developing new methods based on satellite observations for the river discharge estimation.

In the last decade, ESA has funded the SaTellite based Runoff Evaluation And Mapping and River Discharge Estimation (STREAMRIDE) project, which proposes the combination of two innovative and complementary approaches, STREAM and RIDESAT, for estimating river discharge. The innovative aspect of the two approaches is an almost exclusive use of satellite data. In particular, precipitation, soil moisture and terrestrial water storage observations are used within a simple and conceptual parsimonious approach (STREAM) to estimate runoff, whereas altimeter and Near InfraRed (NIR) sensors are jointly exploited to derive river discharge within RIDESAT. By modelling different processes that act at the basin or at local scale, the combination of STREAM and RIDESAT is able to provide less than 3-day temporal resolution river discharge estimates in many large rivers of the world (e.g., Mississippi, Amazon, Danube, Po), where the single approaches fail. Indeed, even if both the approaches demonstrated high capability to estimate accurate river discharge at multiple cross sections, they are not optimal under certain conditions such as in presence of densely vegetated and mountainous areas or in non-natural basins with high anthropogenic impact (i.e., in basin where the flow is regulated by the presence of dams, reservoirs or floodplains along the river; or in highly irrigated areas).

Here, we present some new advancements of both STREAM and RIDESAT approaches which help to overcome the limitations encountered. In particular, specific modules (e.g., reservoir or irrigation modules for STREAM approach) as well as algorithm retrieval improvements (e.g., to take into account the sediment and the vegetation for RIDESAT algorithm) were implemented. Furthermore, in order to exploit the complementarity of the two approaches, the two river discharge estimates were also integrated within a simple data integration framework and evaluated over sites located on the Amazon and Mississippi river basins. Results demonstrated the added-value of a complementary river discharge estimate with respect to a stand-alone estimate.

How to cite: Camici, S., Tarpanelli, A., Brocca, L., Massari, C., Nielsen, K., Sneeuw, N., Tourian, M. J., Yi, S., Restano, M., and Benveniste, J.: Satellite observations for runoff and river discharge estimation: STREAMRIDE approach, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9234, https://doi.org/10.5194/egusphere-egu22-9234, 2022.

09:47–09:54
|
EGU22-3716
|
On-site presentation
Brian Thomas

Terrestrial water response to climate-induced acceleration of the hydrologic cycle underpins water management challenges, whereby water management in an increasingly uncertain climate must adapt to ensure sufficient resource availability to sustain global ecosystems while meeting societal water needs. Although hydrologic cycle intensification is expected through increased evaporation and precipitation, the unequal redistribution of water fluxes over terrestrial land remains unclear. Studies investigating hydroclimatic sensitivity of runoff assumed long-term steady-state basin storage conditions where P=ET+Q.  Steady-state assumptions neglect the role of groundwater, lakes and reservoirs as vital management resources.  Although hydrologic models have been used to measure sensitivity of basin responses attributed to climate, an assessment of observation-based data which captures temporal changes in basin storage can provide valuable insights to understand fundamental changes in water storage due to hydroclimatic factors.  Here, a sensitivity analysis using the Gravity Recovery and Climate Experiment (GRACE) satellite observations combined with auxiliary hydroclimate variables is investigated.  GRACE captures changes in water stores that result due to water management schemes to offset short-term (i.e., monthly) and long-term (i.e., annual) water deficits in addition to water budget changes driven by P and ET.  Accessible water (AW) represents the combination of groundwater and surface water storage anomalies derived from GRACE, water stores deemed accessible to fulfill water demands.  Results document the role of seasonality and storage potential with respect to hydroclimate elasticity.  Findings reveal that >1 billion people live in basins with elasticity magnitudes >10, meaning that small shifts in P-ET will be magnified 10-fold with respect to AW.

How to cite: Thomas, B.: Hydroclimate Elasticity of Accessible Water, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3716, https://doi.org/10.5194/egusphere-egu22-3716, 2022.

09:54–10:00
Coffee break
10:20–10:27
|
EGU22-9755
|
Presentation form not yet defined
Shirin Moradi, David Mengen, Harry Vereecken, and Carsten Montzka

Climate change affects the Earth system at all levels (IPCC et al., 2007). The Monitoring and prediction of droughts and flood events, agricultural production, and analysis of energy and water will continue to gain importance, accordingly. Especially agricultural systems are of the main affected by rising temperatures, extreme precipitation events, and droughts, all of which can lead to crop failures (Lobell et al., 2011). Approximately 40% of the world's crop production comes from irrigated agriculture (Vereecken et al., 2009), the future expansion of which will continue to provide adequate food for the population. However, efficient irrigation must be ensured to prevent unnecessary groundwater depletion (Richey et al., 2015). To increase efficiency and safeguard yields, novel technologies need to be developed for innovative, real-time water management strategies that will allow farmers to make management decisions at the right time (OECD, 2010). Predicting the overall water supply and its components (e.g., soil water content and groundwater) for plants growth and at each growing stage would assure a sustainable irrigation. Therefore, the aim of this study is to predict the root zone soil water content which is one of the main components of the total water supply for plant growth. For this purpose, spaceborne remote sensing data from C- and L-band Synthetic Aperture Radar will be used. These data provide valuable information about the surface soil moisture only. But by integration into a hydrologic model in a data assimilation framework the soil moisture of the root zone as well as the groundwater recharge can be estimated to identify the actual irrigation requirements and resources. 

How to cite: Moradi, S., Mengen, D., Vereecken, H., and Montzka, C.: Integrating satellite remote sensing data and hydrological models by data assimilation for a near real time estimation of the soil water content at local scale., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9755, https://doi.org/10.5194/egusphere-egu22-9755, 2022.

10:27–10:34
|
EGU22-8505
|
ECS
|
On-site presentation
|
Benjamin Kitambo, Fabrice Papa, Adrien Paris, Raphael Tshimanga, Stéphane Calmant, and Frédéric Frappart

Despite being the second-largest watershed and tropical forest worldwide, with significant impacts on the global water cycle and in regulating Earth’s climate, the Congo River Basin’s (CRB) hydroclimatology remains among the least studied worldwide due to the lack of 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 from multi- satellite missions and in situ water height of historical and contemporary observations at different locations. The root mean square error varies from 10 cm for Sentinel-3A to 75 cm for European Remote Sensing-2.  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). The assessment of SWE also agreed relatively well over a ~25-year period with in situ discharge from sub-basin to basin scale. At the basin scale, SWE shows that cuvette centrale is flooded at its maximum in October/November. The northern part of the basin reaches its maximum in September/October, and the southern eastern one in January/February. 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 variability of Surface Water Storage (SWS) in rivers, lakes, floodplains, and wetlands across the entire basin over the period 1992–2015. The CRB SWS shows an annual amplitude varying 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 sub-surface water storage. The use of these new long-term satellite-derived observations are an invaluable source of information for hydrological modeling and will allow to properly characterize and reproduce the hydro-climate variability of the CRB, and a better representation of local and regional hydrological processes. These results ensure therefore an improved monitoring of CRB hydrological variables from space, and open new perspectives towards a better evaluation of the impact of climate variability on water availability in the region.

How to cite: Kitambo, B., Papa, F., Paris, A., Tshimanga, R., Calmant, S., and Frappart, F.: Large-scale spatio-temporal variability of the Congo Basin surface hydrologic components from space, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8505, https://doi.org/10.5194/egusphere-egu22-8505, 2022.

10:34–10:41
|
EGU22-4817
|
ECS
|
Presentation form not yet defined
|
Dung Trung Vu, Thanh Duc Dang, and Stefano Galelli

Over the past three decades, large-scale hydrological models have gained popularity due to the need to support water resources management at the regional and continental scales. One of the most challenging tasks for developing such models is the availability of data. The presence of human-water interactions, especially reservoir operations, can influence the model parameterization, while measured discharge and/or water levels along the rivers are necessary to the calibration purpose. However, such information is often unavailable. In particular, data on reservoir storage or river discharge are often not measured or shared between the riparian countries of transboundary rivers. A potential solution for this challenging task lies in satellite observations. Specifically, reservoir storage/release and river discharge/water level can be inferred from satellite images (Landsat/Sentinel-1/2) and/or altimetry data (Jason/Sentinel-3). In this study, we take advantage of remote-sensed data to improve the accuracy of a hydrological-water management model (VIC-Res) setup for the northern portion of the Mekong River Basin. Our modeling framework combines VIC-Res with an automated calibration procedure (based on a multi-objective evolutionary algorithm) that explicitly accounts for key water management decisions—inferred from satellite data—occurring within the basin. Results show that the use of such data largely improves the performance and reliability of the model.

How to cite: Vu, D. T., Dang, T. D., and Galelli, S.: Improving the reliability of large-scale hydrological models with satellite observations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4817, https://doi.org/10.5194/egusphere-egu22-4817, 2022.

Hydrological Forecasting
10:41–10:48
|
EGU22-7925
|
Presentation form not yet defined
Vanessa Pedinotti, Rémi Jugier, Adrien Paris, Laura Sourp, Laetitia Gal, Marielle Gosset, Nicolas Picot, Gilles Larnicol, Sylvain Biancamaria, Denis Blumstein, Bachir Tanimoun, and Kone Soungalo

The main motivation for this study is to evaluate the use of real time observations from different sources for hydrological forecasting. The advent of new satellite missions providing high-resolution observations of continental waters has raised the question of how to use them, especially in conjunction with models. At the same time, the multiplication of extreme events such as flash floods points to the need for tools that can help anticipate such disasters. To do so, it is necessary to set up a forecasting system that is generic enough to be used with different types of data and to be applied to different basins. It is in this perspective that a platform named HYdrological Forecasting system with Altimetry Assimilation (HYFAA) was implemented, which encompasses the MGB large scale hydrological model and an EnKF module that corrects model states and parameters whenever observations are available. As a preliminary study towards operationnability, the platform was tested in offline mode, in the framework of Observing Systems Simulation Experiments (OSSEs). Discharge estimates from three different observing systems were generated, namely in-situ streamflow measurement stations, Hydroweb radar altimetry, and the future SWOT interferometry mission. In this study, we chose to assimilate these data separately in order to analyze the capacity of the system to adapt itself to different orbital characteristics, especially coverage and repetitivity. This also allows us to quantify the contribution of SWOT. The MGB model, developed within the large-scale hydrology research group of the University of Rio Grande do Sul (Brazil), is a physically based and distributed hydrological model, which was coupled to an externalized Ensemble Kalman Filter (EnKF) to give corrected estimates of the model state variables and parameters.

HYFAA is run on the Niger river basin over a reanalysis period and its performance against a control ensemble simulation (without data assimilation) is assessed to quantify the impact of assimilating observations from the different observing systems. The results show that data assimilation leads to significant improvements of NRMSE and KGE of the simulated discharge, everywhere on the basin and regardless of the observation system considered. Moreover, it is shown that the correction of the hydrodynamic parameters helps to improve the performance of the assimilation, in particular when observations are dense in space, probably due to the concomitant correction of forcing biases. The assimilation of SWOT data combined with a selection method provides the best correction of the discharge on the river itself as well as on its tributaries, giving promising perspectives for the prediction of flash floods. We therefore discuss limits and prospects for application in the framework of Observing System Experiments (using real observations).

How to cite: Pedinotti, V., Jugier, R., Paris, A., Sourp, L., Gal, L., Gosset, M., Picot, N., Larnicol, G., Biancamaria, S., Blumstein, D., Tanimoun, B., and Soungalo, K.: A generic hydrological forecasting system using existing and future altimetry assimilation: an OSSE study over the Niger basin, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7925, https://doi.org/10.5194/egusphere-egu22-7925, 2022.

10:48–10:55
|
EGU22-736
|
ECS
|
Virtual presentation
|
Claudia Canedo Rosso, Jafet C.M. Andersson, David Gustafsson, Mohammed Hamatan, and Mélissande Machefer

Water level information is highly sought-after by operational hydrologists and emergency managers to improve flood management in, for example, West Africa (Lienert et al 2020). A main constraint of large-scale flood forecasting systems is an inability to convert streamflow volumes to water level at specific locations. Accurately representing water levels – and hence potential impacts of peak flows on local scale – is possible through detailed field work and hydraulic simulations (e.g. Massazza et al 2020). However, large-scale implementation of such approaches is typically constrained by lack of detailed topographic data. In this study we therefore develop a pragmatic method to estimate water levels using rating curves created through a combination of ground-based (in-situ) hydrometric gauge observations, hydrological simulations, and satellite altimetry data. Specifically, rating curves are created based on simulated discharge from HYPE models and 305 in-situ discharge observations from 1980 to 2020, in addition to 42 in-situ and 558 virtual water level stations (i.e. locations where Sentinel-3 missions intersect large rivers) from 2018 to 2020. The rating curves were estimated by fitting a conventional power-law equation. For the in-situ data this could be done directly from the two variables. These were, however, very scarce. We therefore exploited the EO-based virtual stations to be able to predict water levels at many more locations. To this end, rating curves were estimated using simulated discharge together with EO-based water level data at the virtual station locations. The inverted rating curve equation was subsequently used to transform simulated discharge to water level. The water levels estimated from simulated discharge were finally compared with the in-situ and virtual altimetry stations using accuracy performance metrics such as Nash-Sutcliffe efficiency (NSE) and Kling-Gupta efficiency (KGE). Furthermore, we examine and compare the rating curve uncertainty obtained from different data sources (in-situ, modelled and satellite data). This pragmatic methodology can be used in operational hydrology, specifically flood forecasting, to render forecasts more relevant at local scale and hence enable better flood risk management.

How to cite: Canedo Rosso, C., Andersson, J. C. M., Gustafsson, D., Hamatan, M., and Machefer, M.: Combining satellite altimetry, in-situ observations, and models to improve hydrological forecasting in West Africa, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-736, https://doi.org/10.5194/egusphere-egu22-736, 2022.

10:55–11:05