HS6.5 | Water Level, Extent, Storage and Discharge from Remote Sensing and Assimilation in Hydrodynamic Models
EDI
Water Level, Extent, Storage and Discharge from Remote Sensing and Assimilation in Hydrodynamic Models
Co-organized by G3
Convener: Jérôme Benveniste | Co-conveners: Stefania Camici, Fernando Jaramillo, Karina Nielsen
Orals
| Fri, 19 Apr, 08:30–12:25 (CEST)
 
Room 2.23
Posters on site
| Attendance Fri, 19 Apr, 16:15–18:00 (CEST) | Display Fri, 19 Apr, 14:00–18:00
 
Hall A
Posters virtual
| Attendance Fri, 19 Apr, 14:00–15:45 (CEST) | Display Fri, 19 Apr, 08:30–18:00
 
vHall A
Orals |
Fri, 08:30
Fri, 16:15
Fri, 14:00
This session focuses on the hydrogeodetic measurement of water bodies such as rivers, lakes, floodplains and wetlands, groundwater and soil. The measurements relate to estimating water levels, extent, storage and discharge of water bodies through the combined use of remote sensing and in-situ measurements and their assimilation in hydrodynamic models.

Monitoring these resources plays a key role in assessing water resources, understanding water dynamics, characterising and mitigating water-related risks and enabling integrated management of water resources and aquatic ecosystems.

While in situ measurement networks play a central role in the monitoring effort, remote sensing techniques provide near real-time measurements and long homogeneous time series to study the impact of climate change from local to regional and global scales.

During the past three decades, a large number of satellites and sensors has been developed and launched, allowing to quantify and monitor the extent of open water bodies (passive and active microwave, optical), the water levels (radar and laser altimetry), the global water storage and its changes (variable gravity). River discharge, a key variable of hydrological dynamics, can be estimated by combining space/in situ observations and modelling, although still challenging with available spaceborne techniques. Interferometric Synthetic Aperture Radar (InSAR) is also commonly used to understand wetland connectivity, floodplain dynamics and surface water level changes, with more complex stacking processes to study the relationship between ground deformation and changes in groundwater, permafrost or soil moisture.

Traditional instruments contribute to long-term water level monitoring and provide baseline databases. Scientific applications of more complex technologies like Synthetic Aperture Radar (SAR) altimetry on CryoSat-2, Sentinel-3A/B and Sentinel-6 missions are maturing, including the Fully-Focused SAR technique offering very-high along-track resolution. The launched SWOT mission will open up many new hydrology-related opportunities when the data is calibrated, validated and released. We also receive submissions of preparation studies for Sentinel-3 Next Generation and CRISTAL and other proposed missions such as Guanlan, HY-2 and SmallSat constellations such as SMASH, and covering forecasting.

Orals: Fri, 19 Apr | Room 2.23

Chairpersons: Jérôme Benveniste, Karina Nielsen
Rivers Basins
08:30–08:40
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EGU24-8341
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Highlight
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On-site presentation
Angelica Tarpanelli, Francesco De Biasio, Karina Nielsen, Paolo Filippucci, Rosa Maria Cavalli, and Stefano Vignudelli

The alternation of extreme events is a source of great stress on the territory and forces us to adopt solutions to help mitigate their consequences. In this study, an attempt is made to exploit Earth Observation from space as a means to point out the interaction of inland waters and the coastal areas during hydrological extreme events, i.e. floods and droughts. During a flood event, large volume of water from the river reaches the coast, adding a considerable volume of freshwater. Conversely, during a drought event salt water from the sea enters inland causing severe damage to agriculture and the local population. With this study we attempt to investigate how the systems of sea and river interact during particularly intense events using satellite optical (Sentinel-2 and Sentinel-3) and altimeter (Sentinel-3, Cryosat-2, Icesat-2) sensor data. The area selected is the Po River delta (up to 200 km from the mouth), which in recent years has been exposed to severe events: in November 2019, the Po River was subject to a copious flood that had not occurred since 2000, while in the summer of 2022, it experienced the worst drought in the last 70 years.

The analysis aims at evaluating three fundamental aspects: 1) the ability of satellite altimetry to identify extreme events in the river; 2) the potential of satellite altimetry to detect salt wedge intrusion in the Po River delta; and 3) the potential correlation between the altimetry observations and optical imagery of the river’s plume along the Adriatic coast.

The analysis was conducted by analysing long time series (of about 10 years) for the first objective and by focusing on the drought event of 2022 and the flood events that occurred in the last 5 years for the other two objectives.

The results of the analysis confirm that the satellite observed the significant increase and decrease in water levels in correspondence of the extreme events. In addition, the analysis of the data at the virtual stations in the downstream part of the Po River, together with the data along the tracks crossing the plume closer to the mouth of the river, showed the interaction between the sea and the river. In particular, the temporal series of the river clearly highlight the influence of the sea water several km upstream the river (more than 40 km as reported in the news), probably related to the salt wedge intrusion, which has caused significant damage to agriculture and drinking water aquifers for a long time after the event. The study qualitatively shows that extreme hydrological events can also be captured in the open sea in this region.

The analysis illustrates the great potential of satellite sensors to monitor extreme events and the interaction of inland and coastal waters.

How to cite: Tarpanelli, A., De Biasio, F., Nielsen, K., Filippucci, P., Cavalli, R. M., and Vignudelli, S.: Understanding the interaction between inland waters and the coastal zone during extreme events over the Po Delta from space, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8341, https://doi.org/10.5194/egusphere-egu24-8341, 2024.

08:40–08:50
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EGU24-8949
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ECS
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On-site presentation
Frithjof Ehlers, Cornelis Slobbe, Martin Verlaan, and Marcel Kleinherenbrink

For almost 30 years radar altimeters provide water elevations of rivers and lakes only where a target intersects the satellite’s ground track, called Virtual Stations (VS). This way, the observations have both limited temporal and spatial resolution, because on one hand such intersections occur by chance and because on the other hand the repeat cycle of the orbit ranges from 10 to 35 days, depending on the mission.

Boy et al. [1] illustrated recently that river signals may also be captured when the river is located at cross-track distances of several kilometers, when utilizing high resolution SAR altimeter products (particularly fully-focused SAR [2], FFSAR). Therefore, the concept of the altimeter river measurements can be revisited completely. Based on the idea presented in [1], we developed a novel algorithm to calculate water surface elevations (WSE) of rivers within a ground swath of approximately 14 km width, and with along-track resolutions as fine as 10 m from the Sentinel-6 altimeter signal. All that is needed additionally to the FFSAR-processed signal is an a-priori river polygon or centerline to correct for non-zero cross-track distances.

Our algorithm can provide WSE along most parts of the river that fall within the swath, thus delivering densely sampled WSE profiles instead of a few point measurements over only the nadir crossings (VS). This marks a drastic improvement in the number of available WSE observations and opens completely new research possibilities, as water surface slopes and level changes due to rapids and dams can be studied directly. Essentially, these new Sentinel-6 WSE measurements resemble the river WSE product obtained with the recently launched SWOT mission (albeit with more limited coverage). As such, they can be exploited in similar manners to provide much additional information for hydrological research, e.g. for assimilation in hydrological models and more reliable estimation of river discharge.

We demonstrate and validate the new measurement approach and our algorithm over two rivers in France, the Creuse river and the Garonne river, showing biases that are typically on the order of +-4 cm and random errors on the order of 5 cm, both on 30 m along-track resolution. In our presentation, we will concentrate our attention on the new challenges of the method, including a sophisticated signal detection algorithm, the altered error budget of off-nadir WSE measurements and the limitations due to signal folding, clutter, lacking contrast and the complexity of the scene.

[1] Francois Boy et al. “Measuring longitudinal river profiles from Sentinel-6 Fully-Focused SAR mode”. In: Ocean Surface Topography Science Team (OSTST) meeting. Nov. 2023. doi: 10.24400/527896/a03-2023.3781.

[2] Alejandro Egido and Walter H. F. Smith. “Fully Focused SAR Altimetry: Theory and Applications”. In: IEEE Transactions on Geoscience and Remote Sensing 55.1 (Jan. 2017), pp. 392–406. doi: 10.1109/TGRS.2016.2607122.

How to cite: Ehlers, F., Slobbe, C., Verlaan, M., and Kleinherenbrink, M.: Looking beyond nadir: Measuring densely sampled river elevation profiles with the Sentinel-6 altimeter, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8949, https://doi.org/10.5194/egusphere-egu24-8949, 2024.

08:50–09:00
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EGU24-2399
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ECS
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On-site presentation
Haoyang Lyu and Fuqiang Tian

Satellite altimetry has emerged as a key alternative for inland water level measurement in addition to ground observations. Water surface slope (WSS) is one of the basic parameters of river morphology for discharge calculation. Estimation of WSS can also avoid systematic bias in satellite water levels relative to gauged data. A range of satellite data products are available to provide accurate river water level measurements and estimates of river WSS on a worldwide scale. Nonetheless, satellite-based observation of river water surface remains challenging in small rivers, such as the mountainous river reaches with narrow water surfaces. In this study, we examined the accuracy of the ICESat-2 ATL03 photon height data in estimating WSS over the mountainous river reach of Yongding River flowing across Hebei Province and Beijing City in northern China. With minimum along-track sampling interval of 0.7m, the ICESat-2 ATL03 data provided reliable estimation of WSS over narrow river reaches which are 50 to 100m wide. Satellite virtual stations were located mainly with a histogram-based statistical method, seeking for photon height that corresponds to the peak frequency. The twelve groups of satellite virtual stations chosen for river WSS estimation finally show an overall correlation coefficient of 0.96 in validation. Relative error of WSS estimation ranges from 0.13% to 14.51%. Findings of this study provide further implications for satellite-based river water surface measurement in small mountain river basins that lack of ground observation conditions, bringing in reliable estimation of key hydrological parameters based on satellite observation.

How to cite: Lyu, H. and Tian, F.: Satellite-based water surface slope in small mountain river, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2399, https://doi.org/10.5194/egusphere-egu24-2399, 2024.

09:00–09:10
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EGU24-242
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ECS
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Virtual presentation
Somil Swarnkar, Asari Sushma Surjibhai, Roshan Nath, Shobhit Singh, and Biswajit Patra

The measurement of surface water bodies plays a vital role in assessing the magnitude of floods and droughts despite their relatively little contribution to the overall hydrosphere on Earth's surface. The distribution and accessibility of water resources have been greatly impacted by global climate change and unsustainable human activities. These factors have resulted in heightened strain on surface water supplies, causing shortages that hinder both human consumption and socioeconomic progress. Therefore, the mapping and identification of surface water reserves are essential for achieving optimal utilization and sustainable management. Madhya Pradesh, henceforth referred to as MP, possesses a highly diverse range of geographical features within the Central Indian area. According to prior research, a total of thirty-six out of fifty-one districts within the state of Madhya Pradesh have seen significant hydrological drought conditions in recent years, mostly attributed to the scarcity of surface water resources. Despite the challenges faced in the MP area, there remains a lack of sufficient understanding of the long-term and seasonal variations in surface water dynamics within districts, as well as the overall availability and accessibility of surface water resources. Field-based observations of surface water bodies in regions with vast expanses, such as Madhya Pradesh (MP), pose considerable obstacles. However, the comprehension of spatiotemporal fluctuations in surface water can be enhanced with the utilization of remote sensing datasets for observations. Hence, to gain an understanding of the long-term fluctuations in surface water patterns in different regions of Madhya Pradesh, India, over a span of 38 years, we employed a publicly accessible global surface water dataset provided by the Joint Research Centre (JRC) of the European Commission. This dataset covers the time period from 1984 to 2021. Based on the results of our investigation, it is apparent that a disparity exists in the per capita accessibility of permanent water resources in the majority of MP districts, notably during periods of low precipitation, as well as in the per capita availability of seasonal water resources, particularly during months characterized by high levels of rainfall. While the monsoon period generally results in increased surface water availability, the Bundelkhand and Malwa Plateau regions experience severe shortages of surface water during dry periods, which, therefore, leads to the over-exploitation of groundwater resources. The implications of these findings are significant for the management of freshwater bodies in the Madhya Pradesh area, particularly in light of their depletion caused by climate change and human activities. Furthermore, these findings have broader implications for promoting sustainable development in the region.

How to cite: Swarnkar, S., Surjibhai, A. S., Nath, R., Singh, S., and Patra, B.: Assessment of Surface Water Dynamics between 1984-2021 in Madhya Pradesh, Central India, using Remotely Sensed Dataset, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-242, https://doi.org/10.5194/egusphere-egu24-242, 2024.

09:10–09:20
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EGU24-10856
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ECS
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On-site presentation
Francesco Leopardi, Luca Brocca, Carla Saltalippi, Jacopo Dari, Karina Nielsen, Nico Sneeuw, Mohammad J. Tourian, Marco Restano, Jérôme Benveniste, and Stefania Camici

River discharge monitoring is critical for many activities ranging from water resource management to flood risk reduction. Due to limitations of in situ stations (e.g. low station density, incomplete temporal coverage and delays in data access), river discharge is not always continuously monitored in time and space. This has led researchers and space agencies, among others, to develop new methods based on satellite observations for estimating river discharge.

In recent years, ESA has funded the SaTellite-based Runoff Evaluation And Mapping (STREAM) and STREAM-NEXT projects, which propose to use satellite observations of precipitation, soil moisture and terrestrial water storage within a simple and conceptually parsimonious model, STREAM, to estimate runoff.

The model, applied to five large basins in the world (Mississippi-Missouri basin, Amazon basin, Danube basin, Murray-Darling basin and Niger basin)  has demonstrated a high ability to estimate runoff and river discharge in both natural and non-natural basins with a high anthropogenic impact (i.e. in basins where flow is regulated by the presence of dams, reservoirs or floodplains along the river; or in heavily irrigated areas). In particular, the good results obtained paved the way for the application of the STREAM approach on a global scale. For this purpose, the STREAM-NEXT project will generalise the STREAM model to make it applicable to more than forty basins worldwide. Depending on the availability of in situ discharge data, the selected basins shall be grouped into calibration and validation clusters. The purpose is to use the basins into the calibration cluster to tune the parameters of the regionalized STREAM model and apply the regionalised model parameters to the validation cluster basins to estimate the accuracy of the STREAM model.  Additional satellite observations, such as altimetric water levels, will be used to estimate the water stored in the reservoirs; gravimetric data with different spatial/temporal resolutions will be explored to investigate the impact of these data on the model results.

Finally, a calibration procedure and a regionalisation approach will be developed to make the STREAM model applicable to non-calibrated basins.

Here we present the STREAM-NEXT project and some preliminary results related to the generalization of the STREAM model framework. Different basins with different climate, topography and level of anthropisation will be selected to demonstrate the suitability of the approach for a global scale application. 

How to cite: Leopardi, F., Brocca, L., Saltalippi, C., Dari, J., Nielsen, K., Sneeuw, N., Tourian, M. J., Restano, M., Benveniste, J., and Camici, S.: Toward a global scale runoff estimation through satellite observations: the STREAM model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10856, https://doi.org/10.5194/egusphere-egu24-10856, 2024.

09:20–09:30
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EGU24-13107
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On-site presentation
Himanshu Save, Mark Tamisiea, Nadege Pie, and Srinivas Bettadpur

The Gravity Recovery and Climate Experiment (GRACE) and its Follow-On (GRACE-FO) missions have provided near continuous and unique measurement of total water storage  (TWS) since 2002. These international partnership missions have provided valuable insights in the fields of Hydrology, Oceanography, Ocean dynamics, Cryosphere Sciences, Solid Earth etc. These data from GRACE/GRACE-FO have improved our understanding of the Earth’s water cycle since launch and have become indispensable for climate related studies.

The spatial resolution of the data products from GRACE/GRACE-FO are roughly 300km and they typically have a temporal resolution of a month. These products provide the unique measurement of the total water storage of the entire water column and can provide a constraint for hydrological and ocean models. Several studies have used GRACE products for assimilation into the hydrological models for improved assessment of the reality on the ground and for downscaling the information to a higher spatial resolution using data assimilation. This paper will introduce the techniques that improve of temporal resolution of GRACE/GRACE-FO products from month to shorter than 5 days. That includes production of global 5-day TWS solution and the daily TWS product from GRACE that is estimated as a “swath” over the daily satellite ground-track.  The paper will discuss the analysis results over hydrological and ocean basins and validate the higher frequency signals captured by this product.  The goal for the production of this higher temporal resolution GRACE/GRACE-FO product is to be able to use these signals with a latency of a few days for ingestion into machine learning algorithms for early flood detection applications and for daily assimilation into hydrological models at short latency.

How to cite: Save, H., Tamisiea, M., Pie, N., and Bettadpur, S.: Higher Temporal Resolution Global GRACE/GRACE-FO Total Water Storage Products for Assimilation in Hydrology Models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13107, https://doi.org/10.5194/egusphere-egu24-13107, 2024.

09:30–09:40
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EGU24-17133
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ECS
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On-site presentation
Isadora Rezende de Oliveira Silva, Pierre-Olivier Malaterre, Christophe Fatras, Hind Oubanas, Igor Gejadze, and Santiago Peña-Luque

Flooding has major economic, social, and environmental implications. Its modeling provides insights into potential risks and contributes to the protection of lives, natural resources, and infrastructure. In flood hazard assessment, the topography representation is a key factor as it dictates the water extent resulting from the simulations. In particular, for small and medium flood scenarios, it is imperative to have good knowledge of the modeled in-channel water height, especially for the river's bank full discharge. As these constitute the majority of flood events, the risk assessment is severely impacted by the quality of their estimates. However, the determination of the water profile can be a challenging task in data-sparse areas, as the bathymetry of the river channels is not well described in open-access digital elevation models (DEMs). Using the global coverage of remote sensing derived water levels and extents, this study builds towards a global estimation of river bathymetry. 
The methodology to achieve this can be divided into two parts, the correction of the river topography that can be directly observed by the sensors, above a minimum water level (the dry bathymetry), and the estimate of the part under the minimum observed water line (wet bathymetry). For the improvement of the dry bathymetry, the contours from water masks derived by optical sensors are projected in DEMs and a smooth profile is built from upstream to downstream. The wet bathymetry is calculated using hydraulic simulation and inverse problem methodologies. It requires as inputs the corrected dry bathymetry, observed water surface elevation and slope, and a prior discharge. The algorithm computes the flow using an integrated version of a modified Manning–Strickler’s equation and probability from beta distribution. It computes the roughness and the bottom depth of the section assuming a rectangular shape. 
Preliminary results are promising; a good agreement with in-situ discharge was achieved for the Po River (NSE > 0.8). It shows the potential and importance of accurate estimates of the river bathymetry for future flood monitoring and forecast.

How to cite: Rezende de Oliveira Silva, I., Malaterre, P.-O., Fatras, C., Oubanas, H., Gejadze, I., and Peña-Luque, S.: Global 1D river bathymetry estimation from remotely sensed observations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17133, https://doi.org/10.5194/egusphere-egu24-17133, 2024.

09:40–09:50
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EGU24-19761
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ECS
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Highlight
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On-site presentation
Debi Prasad Sahoo, Paolo Filippucci, Silvia Barbetta, Sylvain Biancamaria, Alice Andral, Laetitia Gal, and Angelica Tarpanelli

The implementation of action plans for sustainable water resources management requires daily river discharge time series at gauging stations, which are already decreasing in number worldwide. Although the development of remote sensing-based methods for river discharge estimation has proven its effectiveness worldwide, the temporal frequency especially at the daily scale for river discharge estimation is the most important research question to be explored. In this context, the study proposed a methodological framework to establish a virtual station (VS) where the information retrieved from the multi-mission satellites was merged using the non-parametric copula function for river discharge estimation. Here, in the first step, both passive (C/M) and active (altimeter) remote sensing signals can be integrated by deriving the joint probability distribution using the copula functions of the Archimedean family. Subsequently, the Frank copula was evaluated as the best-fit copula function as measured by the goodness-of-fit-test and subsequently selected for establishing the VS by merging the information. The proposed framework was tested on more than 10 rivers around the world. Here, MODIS from Aqua and Terra, Landsat series, and MSI from Sentinel-2 images were used for the C/M approach, whereas SARAL AltiKa, Sentinel-3 A and B, and Cryosat-2 mission altimeters were considered for water level retrieval. The established VSs along the river can be able to derive long near daily discharge time series while evaluating against the in situ discharge with reasonable accuracy measured by Nash-Sutcliffe efficiency, Root Mean Square Error, and Kling-Gupta efficiency. Conclusively, the establishment of this kind of VSs along the river can be able to derive missing discharge data records and long near-daily discharge time series along any world river which is one of the key variables for hydro climatological studies.  

Keywords: Remote Sensing, Virtual Station, Copula, Satellite merging, River Discharge, Altimeters

How to cite: Sahoo, D. P., Filippucci, P., Barbetta, S., Biancamaria, S., Andral, A., Gal, L., and Tarpanelli, A.: Establishment of Virtual Station based on Multi-mission Satellites for Near-daily River Discharge Observation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19761, https://doi.org/10.5194/egusphere-egu24-19761, 2024.

09:50–10:00
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EGU24-20523
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Highlight
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On-site presentation
Kevin Larnier, Pierre-André Garambois, Charlotte Emery, Laetitia Gal, and Adrien Paris

The SWOT mission (NASA, CNES, UK-SA, CSA) launched in December 2022 provides observations of surface of inland water bodies at unprecedented resolution and accuracy. Here we focus on the Level 2 river products (heights and widths at 200m scale) and we assess their usability in creating coupled hydrological-hydrodynamic simulations of large scale basin where in-situ data are sparse. First we use an automated toolchain that generates (i) the mesh and processed input data for the hydrological models SMASH [1] or MGB [2], (ii) the coupling points between the hydrological and the hydrodynamic models, (iii) the mesh for the hydrodynamic 1D model (DassFlow-1D [3]) using either SWOT Level2 river observations of water heights and widths or other EO missions (ICESat-2, Copernicus Sentinels).

Then we conduct experiments of data-assimilation of conventionnal altimetry missions (ICESat-2, Sentinel 3), in-situ level heights and SWOT Level 2 river heights in order to correct the unobserved quantities (channel bathymetry and friction coefficient) and the inflow discharges using advanced techniques taking into account correlated effects of control variables and simulated water surface properties. The accuracy obtained using this method is assessed by comparing with the sparse existing in-situ data and in terms of physical consistency of simulated flow signatures with some EO data selected for validation.

This methodology and the inference capabilities are illustrated on the Maroni basin (French Guyana) which is the first application of variational data assimilation over a multi-branch river network at basin scale. A large parameter vector composed of spatially distributed friction coefficient and channel bathymetry plus inflow/lateral hydrographs are successfully estimated at various spatio-temporal resolution given data cocktails of varying spatio-temporal densities and informative content.

 

[1] SMASH (Spatially distributed Modelling and ASsimilation for Hydrology) -

https://smash.recover.inrae.fr/

[2] https://www.ufrgs.br/lsh/mgb/what-is-mgb-iph/

[3] https://mathhydronum.insa-toulouse.fr/codes_presentation/pres_dassflow/

How to cite: Larnier, K., Garambois, P.-A., Emery, C., Gal, L., and Paris, A.: Coupled hydrological-hydrodynamic and data assimilation of the entire river network of the Maroni basin using SWOT river products and other EO missions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20523, https://doi.org/10.5194/egusphere-egu24-20523, 2024.

10:00–10:10
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EGU24-5830
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Highlight
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On-site presentation
Christian Schwatke, Daniel Scherer, and Denise Dettmering

For more than three decades classical satellite altimetry has been successfully used to monitor water levels of inland waters such as rivers, lakes and reservoirs. In December 2022, a new generation of altimeter mission called Surface Water and Ocean Topography (SWOT) was successfully launched. SWOT is equipped with a classical radar nadir altimeter comparable to Jason-3, but also with a new Ka-band Radar Interferometer (KaRIn). KaRIn uses the principle of SAR interferometry, which has the capability to monitor almost every inland water body worldwide because of its swath. 

In this contribution, we present a new approach to derive water level time series for lakes and rivers using the high-resolution SWOT pixel cloud dataset. This dataset allows us to monitor water levels of very small lakes (> 100m²). We use SWOT data measured on the fast sampling orbit (03/2023 – 07/2023, 1-day repeat cycle) and the science orbit (since 07/2023, 21-day repeat cycle). For quality assessment, the resulting water level time series will be validated with in-situ data. All water level time series will be freely available on the web portal of the "Database of Hydrological Time Series of Inland Waters" (DAHITI, https://dahiti.dgfi.tum.de). 

 

How to cite: Schwatke, C., Scherer, D., and Dettmering, D.: Estimation of water level time series for lakes and rivers using SWOT KaRIn measurements , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5830, https://doi.org/10.5194/egusphere-egu24-5830, 2024.

Coffee break
Chairpersons: Jérôme Benveniste, Karina Nielsen
10:45–10:55
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EGU24-8805
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On-site presentation
Megumi Watanabe and Dai Yamazaki

The current global water body maps offer an approximate resolution of 30 meters, contingent on available remote sensing data. However, to address the needs of advanced applications like global carbon cycle analysis and real-time flood predictions, a water body map with higher spatial resolution becomes imperative, especially for resolving smaller rivers. Traditional water extraction methods rely on water indexes that combine visible and infrared spectra. State-of-the-art remote sensing data, including aerial photography with spatial resolutions in the order of a few meters, often includes only the visible spectrum.

In response to this challenge, we have developed a water extraction method at an impressive 60cm resolution utilizing Bayesian inference based solely on the visible spectrum from aerial photography, without using the infrared spectrum. To enhance our methodology, we integrated references of water existence from a Landsat-based dataset called G1WBM and Open Street Map (OSM), along with a hydrography dataset (J-FlwDir) presumed to be linked to water bodies.

Our method successfully detected the main streams of the Tsurumi River and Tama River in Japan, including their previously unrecognized tributaries in the Landsat-based dataset. Notably, this study identified rivers with a width exceeding 10 meters. Furthermore, it contributed valuable area information for 37% of small rivers represented as "line" features in the OSM.

These findings underscore the effectiveness of our Bayesian water detection approach, which leverages hydrography data and existing water body maps to improve the spatial resolution of large-scale water mapping significantly. Notably, this improvement is achieved using remote sensing data that lacks infrared spectra, showcasing the potential of our method in advancing the accuracy and precision of global water mapping efforts.

How to cite: Watanabe, M. and Yamazaki, D.: A 60-cm Aerial Photography-based Water Body Mapping: Application to the Tama and Tsurumi Rivers in Japan, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8805, https://doi.org/10.5194/egusphere-egu24-8805, 2024.

Coffee Break
10:55–11:05
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EGU24-16275
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On-site presentation
Maxime Vayre, Julien Renou, Roger Fjortoft, and Nicolas Picot

The Surface Water and Ocean Topography (SWOT) mission, conducted by CNES and NASA was successfully launched on 16 December 2022. It aims at providing unprecedented 2D observations of the sea-surface height and mesoscale structures as well as water surface elevation, water stocks estimates and discharge over hydrological areas. A SAR-interferometry wide-swath altimeter, namely the Ka-band Radar Interferometer (KaRIn), is designed to cover two 50-km cross-track swaths. During its Calibration (Cal/Val) phase (January to July 2023), SWOT mission provided daily measurements for each swath due to its 1-day repeat cycle. While the spatial coverage during this phase is not as large as for the nominal phase with a 21-day repeat cycle, such short revisit time is relevant for Cal/Val purposes.  

 

The High Rate (HR) mode of KaRIn, dedicated to hydrology surfaces, provides HR SWOT products that are calibrated during the Cal/Val phase. The performance assessment of these SWOT observations can be achieved through the comparison against reference measurements. Although specific in-situ Cal/Val sites have been purposely designed for the validation of HR SWOT products on lakes and rivers, additional in-situ networks can also be useful, particularly if their spatial coverage allows a monitoring of lakes and rivers also observed by the SWOT mission. Such conditions are met for the French network (SCHAPI), providing several hundreds of georeferenced in-situ stations over rivers, and for the Swiss network (BAFU) which measures water surface elevation of main rivers and lakes. Moreover, the combination of measurements from current nadir altimetry missions (e.g. Sentinel-3, Sentinel-6 or ICESat-2) has also the potential to generate reference measurements on a large number of lakes and rivers. 

 

Our analysis will essentially propose a preliminary performance assessment of the distinct high-level HR SWOT products during the Cal/Val phase, using existing in-situ networks and measurements from Sentinel-3, Sentinel-6 and ICESat-2. We will first take advantage of hydrological areas being densely monitored below SWOT swaths, which are relevant to assess the quality and current limitations of the products.

How to cite: Vayre, M., Renou, J., Fjortoft, R., and Picot, N.: Cal/Val of HR SWOT products using in-situ networks and in-flight nadir altimetry missions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16275, https://doi.org/10.5194/egusphere-egu24-16275, 2024.

11:05–11:15
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EGU24-3426
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On-site presentation
|
Louise Yu, François Boy, Damien Desroches, and Alejandro Bohe

We wish to present the CNES contribution to the Sentinel-3 Next Generation - Topography (S3NG-T) project. In the wake of the SWOT mission, which pioneered the use of SAR interferometry for surface water altimetry, ESA is considering using this new approach for the successor to its current operational mission Sentinel-3 (S3), S3NG. Such so-called “swath altimetry” enables the access to two-dimensional features on water surfaces, much more directly than traditional Nadir altimetry (such as that used aboard S3) does, but it must also stand the test of accuracy requirements.

Scheduled to take flight in 2033, the altimetry component of S3NG, called S3NG-T, is wrapping up its development phase B1 wherein two consortia designed their proposal of a swath altimetry mission, and during which SWOT’s very promising first data released. A Mission Gate Review in early 2024 should lead to the definitive decision whether to adopt this new measurement technique for S3NG-T or not. Rich with the heritage of their contribution to SWOT and convinced of the potential of swath altimetry, the CNES teams bring a technical expertise to the S3NG table.

As such, we developed evolutions for Radarspy, our in-house simulator of swath altimeter data, in order to assess S3NG’s performances over oceans and inland waters. The swath altimetry instrument aboard S3NG-T, called SAOOH, differs from SWOT’s instrument mainly in its 3-meter baseline, its multiple receptors (four per swath – left or right – in order to flatten the gain pattern), and its interleaved observation pattern, where bursts of 128 Radar pulses are sent alternatively left and right. We wish to present the results of our simulations, which test SAOOH over scenes of various reflectivity, water content and topography. These simulations yield encouraging first results and let us see how some choices made in its on-board processing algorithm affect the random noise, the water detection performance and the point-target response.

How to cite: Yu, L., Boy, F., Desroches, D., and Bohe, A.: Swath altimetry simulations with Radarspy, in preparation of Copernicus mission Sentinel-3 Next Generation - Topography, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3426, https://doi.org/10.5194/egusphere-egu24-3426, 2024.

11:15–11:25
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EGU24-16780
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Highlight
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On-site presentation
Sylvain Biancamaria, Stephane Calmant, Frederic Frappart, Pierre-Andre Garambois, Marielle Gosset, Manuela Grippa, Alexei Kouraev, Pierre-Olivier Malaterre, Simon Munier, Fabrice Papa, Herve Yesou, Thierry Amiot, Cecile Cheymol, and Sophie Le Gac

At global scale, there is still considerable uncertainty about the spatial and temporal variability of water storage and fluxes at the surface of continents. This is even more critical in the context of global climate change and the increasing human pressure on water resources. Despite this context, the following scientific questions remain difficult to answer, due to the coarse spatio-temporal resolution of current data: what is the global distribution of the heterogeneous change undergone by continental surface waters? What is the impact of anthropogenic pressure on water flows and stocks? What is the impact of these changes on the frequency and intensity of hydrological extremes (high and low waters)? To answer these questions, the Global Climate Observing System (GCOS) has identified river levels/discharges and lake/reservoir levels/volumes as essential climate variables, and recommends daily sampling (GCOS, 2022). Besides, extreme events, such as floods or droughts, cover a wide range of spatio-temporal scales. At present, water volume variations can only be observed by satellite at the coarsest scales (and are therefore of interest only for floods on the scale of the world's largest watersheds). The lack of observation of these events in basins with little or no in situ instrumentation is a major issue to understand, simulate and forecast these events. Observing these events globally, at least on a daily scale, would make it possible to quantify local flooding, thus greatly improving our knowledge of these events.

One of the main issue to tackle these questions is the still rather coarse temporal sampling of current satellite missions, particularly altimetry missions. To overcome it, we are proposing the SMall Altimetry Satellites for Hydrology (SMASH) mission. This is a constellation of around 10 compact nadir radar altimeters optimized to provide daily observations of water levels in rivers, lakes and reservoirs along the constellation tracks. The specifications of the SMASH mission are the following: daily temporal sampling, observe water bodies larger than 100 m x 100 m and rivers as narrow as 50 m, with an accuracy on water elevation ~10 cm, and should provide products in near-real time and over the long term (10 years) in open access (open science and FAIR principles).

Combining "high temporal frequency/low spatial frequency" measurements from the SMASH mission with "high spatial frequency/low temporal frequency" measurements from swath altimetry missions (current SWOT or futur Sentinel-3 Next Generation Topography missions) would cover unprecedented time and space scales and should open new fields of research.

How to cite: Biancamaria, S., Calmant, S., Frappart, F., Garambois, P.-A., Gosset, M., Grippa, M., Kouraev, A., Malaterre, P.-O., Munier, S., Papa, F., Yesou, H., Amiot, T., Cheymol, C., and Le Gac, S.: SMASH: a constellation of small altimetry satellites to monitor daily inland surface waters, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16780, https://doi.org/10.5194/egusphere-egu24-16780, 2024.

Lakes, Reservoirs, Groundwater, Floods and Wetlands
11:25–11:35
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EGU24-10929
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Highlight
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On-site presentation
Claire Pottier, Cécile Cazals, Marjorie Battude, Manon Delhoume, Jean-François Crétaux, and Roger Fjørtoft

The Surface Water and Ocean Topography (SWOT) satellite, launched on December 16th 2022, is a CNES and NASA joint project, in collaboration with the Canadian Space Agency (CSA) and the United Kingdom Space Agency (UKSA). SWOT represents a major breakthrough in space altimetry by using a new technical concept based on interferometric synthetic aperture radar (InSAR): in comparison with conventional altimetry, which provides point data along profiles at resolutions of tens or hundreds of kilometres, wide-swath altimetry provides a two-dimensional image with a horizontal resolution of the order of tens or hundreds of meters. Therefore, this mission will significantly improve both offshore and coastal ocean observation, while enabling global measurement also of the water levels (and their variations over time and space) of rivers, lakes and flood zones, with a repeat period of 21 days.

Over land, SWOT is planned to survey lakes with a surface area larger than 250 m by 250 m (objective: 100 m by 100 m). To do so, three main products are available to the user community. The pixel cloud (L2_HR_PIXC) product provides longitude, latitude, height, corrections and uncertainties for pixels classified as water and pixels in a buffer zone around these water bodies, as well as in systematically included areas (defined by an a priori water occurrence mask). The product specific to lakes (L2_HR_LakeSP) is computed from the pixel cloud for each water feature observed by SWOT and not assigned to a regular river. It consists of polygon shapefiles, delineating the lake boundary and providing the area and average height of each observed lake. A Prior Lake Database (PLD) allows to link the SWOT observations to known lakes and help monitoring them over time. The L2_HR_LakeAvg product aggregates L2_HR_LakeSP data over a 21-day cycle.

The validation of L2_HR_LakeSP water surface elevations is mainly based on existing gauge networks. It is a challenge to obtain reference height data that have an absolute accuracy well below what is required for the SWOT lake products we are validating (10 cm 1-sigma at the lake level). The validation of water surface areas relies on reference water masks obtained mainly from (Very-) High-Resolution optical or radar satellite images (Pléiades, Sentinel-2, Sentinel-1, RCM…), pre-processed so that comparisons can be made at the lake scale.

This presentation will first outline the lake processing and the Prior Lake Database. Then examples of products, preliminary accuracy assessments and associated Cal/Val activities will be presented.

How to cite: Pottier, C., Cazals, C., Battude, M., Delhoume, M., Crétaux, J.-F., and Fjørtoft, R.: SWOT lake processing and products, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10929, https://doi.org/10.5194/egusphere-egu24-10929, 2024.

11:35–11:45
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EGU24-5773
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On-site presentation
Carlos Yanez, François Boy, Gabriel Calassou, Jean-Alexis Daguzé, and Kassem Asfour

Remote sensing techniques are crucial for sustaining a continuous and global climate monitoring of inland waters. In particular, recent progress in satellite radar altimetry has enabled the observation of an increasing number of small and medium size lakes and reservoirs, even in complex topography. The arrival of nadir radar altimeters operating in Synthetic Aperture Radar (SAR) mode has considerably improved the resolution of the observations in the along-track direction, passing from several kilometers in conventional limited-pulse altimeters, to hundreds of meters in close-burst altimeters when applying unfocused SAR (UFSAR) processing and even to the theoretical limit of half the along track antenna length in open-burst altimeters that can totally exploit the Fully-Focused SAR (FFSAR) processing technique. Sentinel-6 is the first operational mission to operate in open-burst mode allowing this enhanced performance over inland waters [1]. Complementary to nadir radar altimetry, SWOT mission provides since the beginning of 2023 radar interferometry observations over wide-swaths that could entail great advances in hydrology [2].

The inversion methods to estimate geophysical parameters, such as Lake Water Level (LWL), from the backscattered altimetry signal are commonly called retrackers. These retrackers can be empirical, such as the widely used OCOG method or physically-based, that is to say, a background waveform model is derived from the theoretical knowledge of the microwave scattering process and then fitted to the real backscattered signal received on-board. Several retrackers of the second type have been developed for processing conventional pulse-limited radar observations, like the Brown-like models, and also for UFSAR observations in the case, for example, of the SAMOSA model. Nevertheless, no specific retracker for FFSAR observations has been developed yet. One of the limitations of analytical and numerical physical-based retrackers concerns the assumption that the radar footprint is completely covered by water. This assumption, that holds for large lakes, begins to degrade the accuracy on the retrieved geophysical parameters when monitoring smaller water bodies. For this reason, a retracker based on numerical simulations was proposed in 2021 adapted to UFSAR observations [3]. This latter model has the advantage of taking into account a prior knowledge of the lake contour and, in this way, only in-water areas of the radar footprint contributes to the simulated backscattered waveform. In this work, the derivation of a similar retracker taking into account the FFSAR processing particularities is presented. This results in the first retracking model specifically developed for FFSAR observations. Preliminary performance is assessed with a variety of lakes for which in-situ observations of LWL are available. Furthermore, a comparison with the recently delivered first products of the SWOT mission over lakes will be presented.

[1] Donlon, C.J., et al, 2021. The Copernicus Sentinel-6 mission: Enhanced continuity of satellite sea level measurements from space. Remote Sensing of Environment, 258, p.112395.

[2] Biancamaria, S., et al, 2016. The SWOT mission and its capabilities for land hydrology. Remote sensing and water resources, 117-147.

[3] Boy, F., et al, 2021. Improving Sentinel-3 SAR mode processing over lake using numerical simulations. IEEE Transactions on Geoscience and Remote Sensing, 60, pp.1-18.

How to cite: Yanez, C., Boy, F., Calassou, G., Daguzé, J.-A., and Asfour, K.: Performance assessment of Lake Water Level estimation from Sentinel-6 Fully-Focused SAR observations and comparison to SWOT mission, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5773, https://doi.org/10.5194/egusphere-egu24-5773, 2024.

11:45–11:55
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EGU24-4412
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ECS
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On-site presentation
Muharrem Hilmi Erkoç, Uğur Doğan, Büşra Keser, and Bülent Bayram

The aim of the study is to investigate the changes in the water level and surface area of Lake Iznik in Northwestern Anatolia, Türkiye, between 2014 and 2024. In this context, High-Resolution Satellite images provided by European Space Agency (ESA)'s Sentinel-2 are used to determine the lake surface area, and satellite altimetry data provided by Copernicus Land Service is utilized to determine the lake water level. Additionally, temperature and precipitation data from a meteorological station near the lake are obtained from the Turkish State Meteorological Service due to their significant impact on the lake's water level and surface area changes.

The estimated trend for the change in the water level from 2014 to 2024 is -23±1.9 cm/yr, and the change in the surface area trend is estimated as -1.2±0.2 km²/yr. The results indicate a decrease in both the lake's water level and surface area. Furthermore, Standardized Precipitation Index (SPI) and Standardized Precipitation-Evapotranspiration Index (SPIE) are calculated from precipitation and temperature data obtained from meteorological stations near the lake. These indices reveal a decrease in precipitation and an increase in temperatures in the Lake Iznik basin over the past 10 years.

Consequently, it is observed that the changes in the water level and surface of Lake Iznik are influenced by climate change, and hence,necessary measures need to be taken for the conservation and sustainable use of the lake.

How to cite: Erkoç, M. H., Doğan, U., Keser, B., and Bayram, B.: Effect of Climate Change on Water Level and Surface Area of Lake Iznik (NW Türkiye) , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4412, https://doi.org/10.5194/egusphere-egu24-4412, 2024.

11:55–12:05
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EGU24-3273
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On-site presentation
Groundwater storage change monitoring in North China Plain based on GNSS and satellite gravity
(withdrawn)
Wei Wang, Chuanyin Zhang, Qiang Yang, Tingting Zhao, and Tao Jiang
12:05–12:15
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EGU24-17653
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On-site presentation
Sophie Ricci, Thanh Huy Nguyen, Andrea Piacentini, Raquel Rodriguez-Suquet, Santiago Pena-Luque, Quentin Bonassies, Christophe Fatras, Brian Astifan, Raymond Davis, Michael Durand, and Stephen Coss

Flooding is represented with a 2D hydrodynamic model over a reach of the Ohio river between Cannelton and Newburgh locks and dams. The geometry of the river and floodplain was provided by the National Oceanic and Atmospheric Administration (NOAA), based on U.S. Army Corps of Engineers (USACE) survey channel data merged with United States Geological Survey (USGS) LiDAR in the overbank regions. The description of hydraulic structures from USACE and in-situ water depth measurements from USGS stations were also used. Working from the 1D HEC-RAS model from Ohio University that covers a much larger area, the friction for our 2D local model was set uniformly over the floodplain. These values were further calibrated to 45 m1/3s-1 over the river bed and 17 m1/3s-1 with in-situ water depth measurements from USGS stations at Cannelton, Owensboro, and Newburgh over high flows periods in 2022 and 2023. 

The performance of the model was first assessed for the significant flooding event in February 2018, with RMSEs of the order of a few tens of centimeters. Remote-sensing (RS) products provided by satellite missions such as Sentinel-1 SAR, Sentinel-2 optical and Landsat-8 optical imagery undoubtedly offer opportunities to improve our ability to monitor and forecast flooding. For this study, the performance of the TELEMAC-2D (www.opentelemac.org) Ohio model was improved with the joint assimilation of in-situ and remote-sensing data within an EnKF framework that accommodates 2D RS-derived observations alongside with in-situ water level time-series. The RS-derived flood extent maps are expressed in terms of wet surface ratios (WSR) in selected subdomains of the floodplain. The assimilation of in-situ data reduces the RMSE to tenths of a centimeter. Ongoing work on the assimilation of WSR aims at improving the dynamic of the floodplain.  This 2D Ohio model will serve as a demonstrative test case for the FloodDAM-DT (https://www.spaceclimateobservatory.org/flooddam-dt) prototype dedicated to flood detection, mapping, prediction and risk assessment.

How to cite: Ricci, S., Nguyen, T. H., Piacentini, A., Rodriguez-Suquet, R., Pena-Luque, S., Bonassies, Q., Fatras, C., Astifan, B., Davis, R., Durand, M., and Coss, S.: Merits of Data Assimilation on Improving Flood Forecasting - A case study of Ohio Cannelton-Newburgh, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17653, https://doi.org/10.5194/egusphere-egu24-17653, 2024.

12:15–12:25
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EGU24-10126
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ECS
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On-site presentation
Juliette Bernard, Catherine Prigent, Carlos Jimenez, Marielle Saunois, Frederic Frappart, Cassandra Normandin, Pierre Zeiger, Shushi Peng, Yi Xi, Etienne Fluet-Chouinard, and Zhen Zhang

Wetlands and inundated areas cover only a few percent of the Earth's surface. However, they play an important role in freshwater regulation, biodiversity, and climate. In particular, a significant proportion of atmospheric methane is emitted from these areas [1]. There is therefore a need for data that can reliably capture surface water interannual variability over the past decades. 

The Global Inundation Extent from Multi-Satellites (GIEMS-2) [2] is based on microwave remote sensing data (SSM/I and SSMIS). It provides a 0.25° global monthly estimate of inundated and saturated areas and has been extended to 2020 to cover three decades (1992-2020). 

First, an evaluation of GIEMS-2 together with other products is presented. Key findings include consistent spatial patterns, seasonal cycles and time series anomalies observed by GIEMS-2 with the other observational datasets studied (MODIS-derived surface water, CYGNSS-derived surface water, river discharge). This highlights the interest of such a product for the calibration of hydrological models, as has been achieved for example by Xi et al. (2022) for TOPMODEL [3]. 

In a second part, the use of GIEMS-2 for the estimation of methane emissions from wetlands and inundated areas is discussed. GIEMS-2 has been processed with other data sources to derive a dynamic map of wetlands (including peatlands), open water (lakes, rivers, reservoirs) and rice paddies. This comprehensive product allows a consistent view of the area between the different classes, limiting problems of double counting and miss counting. This new database can then be used to constrain the extent of the water surface in models estimating methane flux rates, in order to study the influence of surface water changes on interannual variations in methane emissions.

 

[1] Marielle Saunois et al. “The Global Methane Budget 2000–2017”. In: Earth System Science Data 12.3 (July 2020), pp. 1561–1623. doi: 10.5194/essd-
12-1561-2020. url: https://doi.org/10.5194/essd-12-1561-2020.
[2] C. Prigent, C. Jimenez, and P. Bousquet. “Satellite-Derived Global Surface Water Extent and Dynamics Over the Last 25 Years (GIEMS-2)”. In: Jour-
nal of Geophysical Research: Atmospheres 125.3 (Feb. 2020). doi: 10.1029/2019jd030711. url: https://doi.org/10.1029/2019jd030711.
[3] Yi Xi et al. “Gridded Maps of Wetlands Dynamics over Mid-Low Latitudes for 1980–2020 Based on TOPMODEL”. In: Scientific Data 9.1 (June 2022), p. 347. issn: 2052-4463. doi: 10.1038/s41597-022-01460-w

How to cite: Bernard, J., Prigent, C., Jimenez, C., Saunois, M., Frappart, F., Normandin, C., Zeiger, P., Peng, S., Xi, Y., Fluet-Chouinard, E., and Zhang, Z.: Development of a global and dynamic map of wetland and inundated areas based on microwave remote sensing product (GIEMS-2) over 1992-2020, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10126, https://doi.org/10.5194/egusphere-egu24-10126, 2024.

Posters on site: Fri, 19 Apr, 16:15–18:00 | Hall A

Display time: Fri, 19 Apr, 14:00–Fri, 19 Apr, 18:00
Chairpersons: Jérôme Benveniste, Karina Nielsen
A.49
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EGU24-3091
Philippa Berry and Jerome Benveniste

The contribution of  satellite radar altimetry to river monitoring is well-established, with data forming  valuable inputs to river models.
Surface soil moisture can also be determined from altimetry using DRy EArth Models (DREAMs) which model the response of a completely dry surface to Ku band nadir illumination. New DREAMs over Africa now cover more than 70% of the continent, encompassing more than 30 river basins including the Congo, Niger, Okavango, Zambezi and Volta.  

It was decided to fly multi-mission altimetry over these river DREAMs to assess the potential of this technique to contribute to studies in river basins. As a detailed DREAM exists for the Amazon basin, this was also included. Envisat, ERS-1/2, Jason-1/2, CryoSat-2 and Sentinel-3A altimeter data were utilised in this study, together with a database of over 86000 graded altimeter River and Lake height time series. Soil moisture estimates were generated and validated.

Summative conclusions: the highest data retrieval rate over river DREAMs is found over ‘river’ and ‘wetland’ pixels, with lower percentages over ‘soil’ pixels where soil moisture estimates can be generated. This is an expected outcome, as targeting ‘soil’ pixels will select for rougher topography. 
Within the constraints of satellite orbit and repeat period, data can be successfully gathered over the majority of these overflown DREAM surfaces. It is also clear that very detailed DREAM models, at least 10 arc seconds resolution, are required to capture the intricate structure in river basins. It is noted that many tributaries are below the current 10 arc seconds spatial resolution of the DREAMs, and are classified with their surrounding terrain as wetland pixels.
ERS-2 and Envisat performed best; Sentinel-3A OLTC mask is found to preclude monitoring of almost all ‘soil’ pixels, except those adjacent to the largest rivers.
The ability of nadir-pointing altimeters to penetrate vegetation canopy gives a unique perspective in rainforest areas. Amazon soil moisture time series in the lower Amazon are seen to correlate to river height variations: in the upper Amazon basin the annual rainfall signature is dominant.
Over much of the river DREAMs, along-track time series of soil moisture can be generated at the spatial resolution of the underlying DREAM, currently 10 arc seconds.  The major constraint, as with altimeter height measurements, is the spatio-temporal sampling, so use is envisaged in combination with other remote sensed and in-situ data.  However, DREAMing provides a valuable independent dataset which can be used to validate soil moisture estimates from other techniques.

How to cite: Berry, P. and Benveniste, J.: DREAMing in River Basins, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3091, https://doi.org/10.5194/egusphere-egu24-3091, 2024.

A.50
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EGU24-14002
Jérôme Benveniste, Marco Restano, Salvatore Dinardo, Christopher Buchhaupt, Michele Scagliola, Marcello Passaro, Luciana Fenoglio-Marc, Américo Ambrózio, and Carla Orrù

This presentation updates on the ESA Altimetry Virtual Lab for the exploitation of CryoSat-2 (CS-2), Sentinel-3 (S-3) and Sentinel-6 Michael Freilich (S-6MF) data from L1A (FBR) data products up to SAR/SARin L2 geophysical data products. The following on-line & on-demand state-of-the-art research  algorithm services compose the portfolio:

  • The ESA-ESRIN SARvatore service for CS-2 and S-3 services, which allow users to customise the processing at L1b & L2 (a list of configurable options for, e.g., SAMOSA+/++ and ALES+ SAR retrackers, not yet available in the ESA Ground Segment).
  • The ESA SAMPY (Cryo-TEMPO project) for CryoSat-2, which appends the SAMOSA+ retracker output to official CryoSat-2 Level-2 GOP products.
  • The TUDaBo SAR-RDSAR (TU Darmstadt–U Bonn SAR-Reduced SAR) for CS-2 and S-3, which allows users to generate reduced SAR, unfocused SAR & LRMC data, with configurable L1b & L2 processing options and retrackers (BMLE3, SINC2, TALES, SINCS, SINCS OV).
  • The TU München ALES+ SAR for CS-2 and S-3, which allows users to process official L1b data and produces L2 products by applying the empirical ALES+ SAR subwaveform retracker, including a dedicated Sea State Bias solution.
  • The Aresys Fully-Focused SAR for CS-2 & S-3, to produce L1b products with configurable options and appending the ALES+ FFSAR output.

These services will be extended with the following new services:

  • Appending the SAMOSA+ retracker output in all services.
  • The Aresys FF-SAR service for S-6MF
  • The CLS SMAP S-3 FF-SAR processor extended to process S-6MF
  • The UBonn FF-SAR Omega-Kappa processor for S-3 and S-6MF
  • An upgrade of the TUDaBo SAR-RDSAR extended to S-6MF with new ocean and coastal retrackers.
  • The ESA-ESTEC/isardSAT L1 S-6MF Ground Prototype Processor.
  • SAR services updated to process S-6MF

Output products are generated in netCDF, therefore compatible with the multi-mission “Broadview Radar Altimetry Toolbox” (BRAT, http://www.altimetry.info).

The ESA Altimetry Virtual Lab, a community space for simplified services and knowledge-sharing, is hosted on the EarthConsole® (https://earthconsole.eu), supported by the ESA Network of Resources. This service has more than 120 users and sponsored so far more than 500 CPU years, leading to more than 30 publications and 3 PhD theses.

Brochure at https://earthconsole.eu/knowledge-base/. Info at altimetry.info@esa.int.

How to cite: Benveniste, J., Restano, M., Dinardo, S., Buchhaupt, C., Scagliola, M., Passaro, M., Fenoglio-Marc, L., Ambrózio, A., and Orrù, C.: Synthetic Aperture Radar Altimetry Processing on Demand at ESA’s Altimetry Virtual Lab, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14002, https://doi.org/10.5194/egusphere-egu24-14002, 2024.

A.51
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EGU24-18194
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ECS
Malik Boussaroque, Fernando Niño, Adrien Paris, and Stéphane Calmant

In the context of climate change, it is imperative to have a global understanding of water resources and their use. Long-term water level time series play an essential role in analyzing long-term trends, detecting inter-annual variations, understanding seasonal cycles and estimating long-term hydrological changes. Unfortunately, long time series from in situ stations are seldom available at the global scale, and particularly in remote areas such as tropical forests. Altimetry is emerging as an effective alternative.

We have developed an innovative processing method to optimize the use of historical altimetry missions in low-resolution mode (LRM) and generate extended time series. This retracker uses a physical model to search for sinc-squared patterns in echoes. Adapted to the specific features of each altimeter, it enables the processing of clipped waveforms, a common phenomenon for narrow rivers. This is particularly relevant for Poseidon altimeters, where conventional retracking methods, such as Offset Center of Gravity (OCOG), struggle to produce accurate results with such clipped echoes.

Applying this retracker, we generated time series of water levels along the Mana River in French Guiana, using data from the Jason family missions. These results illustrate the influence exerted on the water cycle by the construction of a run-of-river hydroelectric power plant near Saut Maman Valentin.

 

Keywords – Altimetry, Jason, Topex/Poseidon, Low Resolution Mode Altimetry, Inland Water Altimetry, River

How to cite: Boussaroque, M., Niño, F., Paris, A., and Calmant, S.: TITRE: ARARAS (Algorithm for Radar Altimetry Retracking on speculAr waveformS), application over the Mana river for long-term multimission Time Series, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18194, https://doi.org/10.5194/egusphere-egu24-18194, 2024.

A.52
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EGU24-15163
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ECS
Shahin Khalili, Mohammad J. Tourian, Omid Elmi, Johannes Engels, and Nico Sneeuw

This study deals with the identification and retrieval of anomalous waveforms generated in Level 1B processing chain of satellite altimetry over coastal areas and inland water bodies. Efficient identification of anomalous waveforms greatly improves the retracking performance, leading to the generation of precise water level time series that serve as vital inputs for hydrological studies. Abnormal behaviour in waveforms may be an indication of environmental changes, instrument malfunctions or other critical factors. To find anomalous waveforms, our framework utilizes an unsupervised machine learning technique. We categorise different parameters of the satellite's altimeter like AGC parameter, tracker range and features related to shape of waveforms for instance waveform’s skewness, number and location of peaks and so on for each sample in the dataset. Then we identify abnormal waveforms using a two-step density distribution probability analysis.

The secondary purpose of this study is proposing a robust strategy to retrieve abnormal waveforms in the level 1B SAR processing chain. This step is vital for narrow rivers and small inland water bodies, in which low number of measurements on related cycle cause missing hydrology data. In contrast to previous studies focusing solely on investigating L2 waveforms to determine precise retracking gates for multipeak and noisy waveforms, we propose an additional step in the L1B processing chain, specifically tailored to coastal and inland waters, enabling the retrieval of abnormal waveforms. In both fully focused and unfocused SAR processing, the final waveform is formed through the combination of various beam looks from the altimeter during fixed illumination time in stacks to the desired point on the surface, certain looks in the stack may exhibit undesirable patterns due to variations in environmental characterization, antenna footprint, and sidelobe gain. The proposed methods will mitigate the presence of undesirable waveforms in the stack prior to the generation of the final waveforms.

We apply the proposed methodology for Sentinel 3A and 3B datasets over different inland waters and validated our results against in-situ data. The results demonstrate that the water level time series, obtained by regenerated waveforms have significantly improved. The results show the potential of our proposed framework for detecting and retrieving anomalous waveforms leading to robust water level estimates from satellite altimetry data.

How to cite: Khalili, S., Tourian, M. J., Elmi, O., Engels, J., and Sneeuw, N.: Fault tolerant approach to regenerate Level 1B SAR altimetry waveforms for enhancing Level 2 retrackers performance, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15163, https://doi.org/10.5194/egusphere-egu24-15163, 2024.

A.53
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EGU24-15263
Florian Wery, François Boy, Sophie Le Gac, Alexandre Homerin, Malik Boussaroque, and Jeremie Aublanc

Over the last decade, there has been a burgeoning interest in altimetry measurements for inland waters, with a focus on comprehensive studies of water levels in lakes, reservoirs, and rivers on a global scale. This research is crucial for the hydrology community to accurately assess the Earth's freshwater resources. Significant advancements have been achieved in enhancing altimeters' capacity to obtain high-quality measurements over inland waters.

The Open-Loop Tracking Command (OLTC) stands out as a noteworthy development in altimeter on-board tracking modes. Its effectiveness has been proven through successful implementation in previous missions and is now designated as the operational mode for current missions, including Sentinel-3, Sentinel-6, and SWOT nadir.

Over the past decade, OLTC data, crucial in tracking inland water bodies from radar altimetry satellites, has undergone substantial refinement. Originally developed for Jason-2, new missions as Jason-3, Sentinel-3A&B, Sentinel-3B, Sentinel-6, SWOT nadir have been incorporated. Algorithms and procedures to compute location and elevation of inland waters targets (rivers, lakes, reservoirs) have also been largely improved. The number of hydrological targets has increased fivefold with an acquisition success rate which is now close to 90%. Presently, each mission tracks between 30,000 to 70,000 hydrological targets.

Despite modifications to a software developed 15 years ago, ongoing advancements and the necessity for covering  land ice surfaces in preparation of upcoming S3C&D missionshave prompted the development of a new software. In addition, the availability of new input data provided by the SWOT mission (water mask and elevation) required also to revise the current software to make their usage efficient. Work is currently underway to establish a new OLTC platform, named AltiGIS. The platform is designed with three primary objectives: facilitate collaboration, enhance data generation validity, and broaden dissemination through the use of DevOps practices. The presentation aims at harvesting new user needs but will also cover both the undergoing software development and roadmap.

How to cite: Wery, F., Boy, F., Le Gac, S., Homerin, A., Boussaroque, M., and Aublanc, J.: New Open-Loop Tracking Command (OLTC) platform : AltiGIS, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15263, https://doi.org/10.5194/egusphere-egu24-15263, 2024.

A.54
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EGU24-146
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ECS
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Highlight
Girish Patidar, Jayaluxmi Indu, and Subhankar Karmakar

Even though altimetry has redefined our understanding of global rivers and lakes, the sparse temporal sampling of altimeters is often a cause of concern for many applications. This study explores the efficiency of temporal sampling offered by the recently launched Surface Water and Ocean Topography (SWOT) mission for hydrological applications over India. In particular, two research hypotheses are being investigated, namely a). Potential of SWOT data for enhancing flood mapping capabilities across India and b) Impact of SWOT-based discharges for calibrating a hydrological model calibration. Toward answering the first hypothesis, we considered a hypothetical launch date for SWOT, generating overpass data based on the mission's spatiotemporal orbital configuration. These overpass data were then compared with flood-affected areas identified in the Indian Flood Inventory (IFI) data to assess SWOT's potential for flood mapping. Results show that the spatio-temporal resolution of SWOT facilitates the monitoring of diverse proportions of Indian districts based on the cycle. More specifically, 0.67%, 15.79%, 29.24%, 45.54%, and 8.06% of Indian districts have one, two, three, four, and more than four observations per SWOT cycle (~21 days), respectively. To evaluate the second hypothesis, namely, the feasibility of SWOT discharge in hydrological model calibration, we created proxy-SWOT data by sampling in-situ data in accordance with the SWOT orbit configuration. Subsequently, errors were introduced into the in-situ gauge data based on recommendations from the SWOT science team. Results are presented over selected case study region of the Mahanadi river basin in India.

How to cite: Patidar, G., Indu, J., and Karmakar, S.: Improving Flood Mapping Capabilities and Hydrological Model Calibration in India through the Surface Water and Ocean Topography (SWOT) Mission, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-146, https://doi.org/10.5194/egusphere-egu24-146, 2024.

A.55
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EGU24-15723
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ECS
Hakan Uyanik, Jiaming Chen, Luciana Fenoglio, and Jürgen Kusche

Water level and lake extent are estimated from combined in-situ, Sentinel-3 and Sentinel-6 nadir altimeters and SWOT-altimetry. Accuracy and precision of the various techniques are compared, the goal is the validation of the new SWOT data in the river Rhine and in Swiss lakes.

 

The main challenge is the interference among multiple water surfaces which contaminate the signal. Fully-focus and Unfocused SAR and individual echoes processed data have a different sensitivity to the signal coming from non-nadir targets. For nadir-altimeters the accuracy and precision of water level depend on the frequency selected for the low level processing. The accuracy ranges from 10-30 cm and depends on the location. The precision is of few centimeters at 80-140 Hz and decreases with increasing frequency selected in low level processing.

 

SWOT derived parameters are validated against the nadir derived equivalent. A more accurate river slope parameter is expected from the SWOT high spatial resolution data. Water extent is another new parameter from SWOT, which is used to derive river discharge and water storage change. In rivers, Sentinel-3A pass 156, that is parallel to the river centerline for about 30 km, is a test area for a direct comparison of water height, slope and discharge parameters from nadir-altimeters and SWOT.

 

In lakes, SWOT water level and water extent are validated against in-situ lake bathymetry, water area extent from Sentinel-1 and Sentinel-2 satellite imagery and water level from nadir-altimeters. In its 21-day phase, SWOT is used to monitor storage change of lakes and reservoirs.

How to cite: Uyanik, H., Chen, J., Fenoglio, L., and Kusche, J.: Monitoring water level and lake extent change with nadir-altimeters and SWOT, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15723, https://doi.org/10.5194/egusphere-egu24-15723, 2024.

A.56
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EGU24-2603
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ECS
Jongmin Park, Yangwan Kim, and Kijin Park

Intensification of climate change and extreme climate conditions around the world caused an increase in the frequency and intensity of water disasters (e.g., drought and flood). Particularly, the Korean Peninsula underwent a significant amount of rainfall during the summer monsoon, along with the increased number of typhoons passing through. In early August 2020, heavy rainfall occurred across the southern part of the Korean Peninsula (e.g., Jeolla and Chungcheong provinces), which resulted in the loss of life and properties. Accordingly, there is a continuous need to establish flood system monitoring over a wide region.

Accordingly, various studies have utilized different types of satellite imagery (e.g., optical, synthetic aperture radar [SAR], LiDAR) for flood inundation mapping. For example, optical satellite imagery (e.g., MODIS, Landsat, Sentinel-2/3) has been widely utilized for flood mapping, while it has limitations with regard to weather conditions. Synthetic Aperture Radar (SAR) imagery has been brought as an alternative as it is not hindered by weather conditions and has relatively high spatial resolution. Therefore, this study utilizes Sentinel-1 C-band backscatter (from 01/2016 to 12/2022) provided by the European Space Agency (ESA) to estimate the inland water body storage as well as water level at Naju Lake located in the Yeongsan River basin, South Korea.

 Prior to estimating the water body storage and water level, two threshold-based methods (i.e., Otsu threshold method, k-mean clustering) were used to distinguish water and no-water pixels based on the bimodal histogram of Sentinel-1 C-band backscatter. The validation of the water body area is conducted by comparing against optical image-based modified normalized difference water index calculated from the harmonized Landsat sentinel-2 (HLS) imagery. The overall evaluation confirmed that the accuracy of the water body area with k-mean clustering (0.8) showed better performance than that from the Otsu threshold method. Especially, the water body area from the Otsu threshold method showed a clear overestimation during the monsoon period. Afterwards, we established support vector regression (SVR) with the number of water pixels and ground-based water storage datasets. Estimation of water storage with SVR showed similar trend with observed water storage with the coefficient of determination (R2) of 0.92, while estimated water storage showed slight underestimation (bias = -899 m3).

Overall, Sentinel-1 C-band backscatter showed the capability to capture the inland water body as well as the volume of the inland lake. Even though there are several limitations (e.g., sensitivity toward vegetation, coarse revisit frequency) in the context of near real-time flood monitoring, it still has value in monitoring the spatio-temporal behavior of inland water body.

Acknowledgement: This work was supported by Korea Environment Industry & Technology Institute(KEITI) through R&D Program for Innovative Flood Protection Technologies against Climate Crisis Program(or Project), funded by Korea Ministry of Environment(MOE)(RS-2023-00218873)

How to cite: Park, J., Kim, Y., and Park, K.: Estimating lake water storage and water level using Sentinel-1 C-band SAR, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2603, https://doi.org/10.5194/egusphere-egu24-2603, 2024.

A.57
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EGU24-6828
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ECS
Weixiao Han, Chunlin Huang, Weizhen Wang, Jinliang Hou, Gabriela Schaepman-Strub, Juan Gu, Yanfei Peng, Ying Zhang, and Peng Dou

The source region of the Yellow River is one of the main regions of the Asian water tower, lakes storing standing or slowly flowing water that provides essential ecosystem services of fresh water and food supply, waterbird habitat, cycling of pollutants and nutrients, and recreational services. Lakes are also key components of biogeochemical processes and regulate climate through cycling of carbon. Thus the estimation of trends and variability of the lake surface water storage is very critical, the direct human activities (damming and water consuption) and the natural factors (precipitation, runoff, temperature and potential evaporation) is gradually changing this environmentally sensitive region, especially the glacier retreat and permafrost thawing partially drive alpine lake expansion.

The objective is mainly estimating the trends and variability of lake surface water storage using the deep learning module and long-term multi-source remote sensing data from the source region of the Yellow River. Optical remote sensing time-series images (Landsat 5-9, MODIS, and Sentinel-2) are employed to generate high-resolution, complete and closed lake surface shorelines and areas based on the Deep Convolutional Generative Adversarial Networks (DCGAN) deep learning method. Additionally, radar altimeters (GFO, T/P, Jason-1/2/3, Sentinel-6, ERS-2, Envisat, Cryosat-2, Saral/AltiKa, Sentinel 3/SRAL, ICESat, and ICESat-2) are utilized to recover lake water levels through the application of the Spatial-Temporal Graph Neural Networks (ST-GAN) deep learning method, providing insights into the long-term changes in lake water surface storage from 1992 to 2022. The study aims to assess the contributions of human activities and natural factors, and provids the valuable guidelines for water resource management.

How to cite: Han, W., Huang, C., Wang, W., Hou, J., Schaepman-Strub, G., Gu, J., Peng, Y., Zhang, Y., and Dou, P.: Trends and variability of lake surface water storage in the source region of the Yellow River based on deep learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6828, https://doi.org/10.5194/egusphere-egu24-6828, 2024.

A.58
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EGU24-2637
Liwei Chang, Lei Cheng, Lu Zhang, and Pan Liu

A comprehensive understanding of renewable water resources, including surface water and groundwater, is crucial for human sustenance, societal advancement, and ecosystem well-being at both local and global levels. Remote sensing technology offers an opportunity to rapidly and conveniently monitor inland water resources on a large scale. This study presents a framework for modeling water storage changes by integrating data from multiple satellites. Specifically, the GRACE and GRACE-FO gravity satellites are utilized to observe changes in terrestrial water storage (TWS), while the Landsat multispectral and ICESat / ICESat-2 altimetry satellites are employed to simulate changes in surface water storage (SWS). Groundwater changes are calculated by subtracting SWS and soil moisture storage (SM) from TWS, with SM data obtained from GLDAS 2.1. The innovation of this framework lies in the improved simulation of surface water, facilitated by the fine resolution of ICESat-2, enabling the establishment of an area-elevation relationship for very small water bodies (< 1 km2). This framework does not account for variations in river channel storage, making it suitable for regions where river discharge can be disregarded. The framework is applied to four provinces or cities in the North China Plain, where water scarcity constrains the demand of drinking water, irrigation, and environment. The study reveals a decrease in TWS from 2002 to 2020 in the study area. Although surface water increased following the operation of the Middle Route of the South-to-North Water Diversion Project in December 2014, groundwater continued to decline until 2020 and remained stable from 2020 to 2022. This study represents the first use of 4-year ICESat-2 data to monitor water bodies of all sizes (from <1 km2 to >100 km2). Leveraging the exceptional capability of ICESat-2 data in modeling small water bodies, this study advances the prospect of achieving a comprehensive simulation of inland water resources.

How to cite: Chang, L., Cheng, L., Zhang, L., and Liu, P.: A framework for surface water and groundwater modeling by multiple satellites., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2637, https://doi.org/10.5194/egusphere-egu24-2637, 2024.

A.59
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EGU24-1621
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ECS
Taejun Park, Ki-Weon Seo, and Dongryeol Ryu

Accelerating groundwater depletion, driven by climate change and growing groundwater extraction for irrigation, has increased the need for accurate monitoring of this indispensable resource. Traditional methods, such as in-situ water table observations and pumping tests, have proven valuable for continued monitoring of groundwater availability and aquifer characteristics but are limited in assessing groundwater variations at a larger basin scale. In contrast, the Gravity Recovery and Climate Experiment (GRACE) offers a method to estimate basin-scale groundwater changes, although its estimates encompass not only groundwater in the aquifer but also surface water (e.g., lakes, rivers) and soil moisture in the vadose zone. To delineate groundwater variations accurately from GRACE observations, additional data sources are necessary.

In this study, we use the European Space Agency’s Climate Change Initiative for Soil Moisture (ESA CCI SM) in the surface layer (top 0-2cm), which is extrapolated to the profile moisture content for the entire root zone (0-120cm). Utilizing the estimated profile soil moisture, we derive groundwater variations in the southern Victoria region of Australia by subtracting the ESA CCI SM derived soil moisture component from GRACE observations. The estimated groundwater variations agree well with the groundwater mass changes estimated from in-situ observations. This study presents an approach that integrates GRACE observations with profile soil moisture estimates derived from the ESA CCI SM product to assess groundwater variations. The validation against in-situ data indicates that satellite observations of soil moisture and gravity changes can provide robust estimation of basin-scale variations in both profile soil moisture and groundwater.

How to cite: Park, T., Seo, K.-W., and Ryu, D.: Groundwater storage change in Victoria, Australia observed by GRACE and ESA CCI soil moisture products, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1621, https://doi.org/10.5194/egusphere-egu24-1621, 2024.

A.60
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EGU24-15510
Shinhyeon Cho, Seongkeun Cho, Yeji Kim, HyunOk Kim, and Minha Choi

Sandbars on the riparian are ecologically important as they provide and protect habitats for organisms and act as natural septic tanks to filter and purify pollutants. In recent years, the role of sandbars in water pollution in rivers has been highlighted, and monitoring of the riparian is required. Sandbars are common in the lower reaches of deltas and at downstream of rivers, especially where the river is wide, and the flow velocity is relatively slow so that remote sensing can be used effectively. Synthetic Aperture Radar (SAR) imagery is an effective tool for spatial monitoring of the riparian because it provides high resolution and can detect regardless of weather conditions. In recent years, research has been conducted to use SAR imagery with AI to improve accuracy of detecting both riparian and sandbars. In this study, we utilized Sentinel-1 SAR (VV, VH polarized backscatter coefficient imagery), Sentinel-2 optical imagery Normalized Difference Water Index (NDWI), and Normalized Difference Vegetation Index (NDVI) data to identify changes of riparian and sandbars using AI-based clustering techniques. The confusion matrix is performed to validate the performance of deep learning techniques and waterbody detection. Technological advances in remote sensing will improve the data resolution of SAR and optical imagery, allowing detailed features to be observed. In further study is expected to improve the monitoring and management of sandbars on the riparian as monitoring technology advances.

Keywords: Riparian, sandbars, Water body detection, Sentinel-1, Sentinel-2, Deep learning

Acknowledgement
This work was supported by the “Development of Application Technologies and Supporting System for Microsatellite Constellation”project through the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. 2021M1A3A4A11032019). This research was supported by the BK21 FOUR (Fostering Outstanding Universities for Research) funded by the Ministry of Education (MOE, Korea) and National Research Foundation of Korea (NRF). This work is financially supported by Korea Ministry of Land, Infrastructure and Transport (MOLIT) as 「Innovative Talent Education Program for Smart City」

How to cite: Cho, S., Cho, S., Kim, Y., Kim, H., and Choi, M.: Riparian monitoring using SAR image-based water body detection technique, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15510, https://doi.org/10.5194/egusphere-egu24-15510, 2024.

A.61
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EGU24-15514
Konstantinos Panousis, Konstantinos M. Andreadis, Andreas Langousis, Nikolaos Th. Fourniotis, and Christoforos Pappas

Understanding how the combined effects of hydroclimatic variability and anthropogenic interventions shape lake reservoirs is crucial for sustainable water resources management as well as for numerous ecosystem services. In the present study, we focus on the interplay between two lakes in Western Greece that are part of the Natura 2000 network of protected areas, namely the Trichonida – Lysimachia lake system. Lake Trichonida is the largest natural lake in Greece and is connected to the substantially smaller lake Lysimachia through an open channel. The two lakes, together with the connecting channel, constitute a couple system. The channel regulates the flow from Trichonida to Lysimachia lake based on irrigation needs (summer time) and peak flows in the main river corridor (winter-time discharge of Acheloos river). The spatial variability in the extent of the two lakes was quantified at the seasonal, annual, and decadal time scales with remote sensing spectral indices, compiling a wealth of Earth observations. Moreover, water level data from satellite altimetry and ground measurements were combined to characterize water level fluctuations in each lake and their cross-correlation. Gridded data of key meteorological variables (air temperature, precipitation, etc.) as well as drought indices were used to characterize the hydroclimatic variability in the watersheds associated with the two examined lakes. The combined used of ground measurements together with multivariate Earth observations offers new insights into the spatiotemporal dynamics of the coupled Trichonida – Lysimachia lake system that could support and guide sustainable water resource management in the area under environmental change.

How to cite: Panousis, K., Andreadis, K. M., Langousis, A., Fourniotis, N. Th., and Pappas, C.: Remotely-sensed spatiotemporal dynamics of the coupled Trichonida – Lysimachia lake system in Western Greece at the seasonal, annual, and decadal time scale, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15514, https://doi.org/10.5194/egusphere-egu24-15514, 2024.

Posters virtual: Fri, 19 Apr, 14:00–15:45 | vHall A

Display time: Fri, 19 Apr, 08:30–Fri, 19 Apr, 18:00
Chairpersons: Jérôme Benveniste, Karina Nielsen
vA.5
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EGU24-11731
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ECS
Manu K Soman and Indu Jayaluxmi

Comprehensive and accurate quantification of inland surface water dynamics is vital to our understanding of terrestrial water cycle. Declining trend in availability of in-situ gauge stations elevates a need to switch to alternate measurement sources. In this context, satellite radar altimetric observations of Water Surface Elevations (WSE) offer vast possibilities, especially in poorly gauged basins.  The potential of altimetry is expected to escalate with the availability of high-resolution point cloud measurements of surface waters from the recently launched Surface Water and Ocean Topography (SWOT) mission. In the proposed research, we evaluate the potential of node averaged vector product of river WSE from SWOT to improve discharge estimation through assimilated hydrodynamic modelling over an entire river basin in India. The study uses proxy SWOT river products generated using an Observing System Simulation Experiment (OSSE), the CNES Large Scale SWOT Hydrology Simulator (Elmer et al., 2020; Nair et al., 2022) and RiverObs software. Here, we use the state-of-the-art CaMa-Flood (Catchment-based Macro-scale Floodplain Model) hydrodynamic model (Yamazaki et al., 2011) and the Local Ensemble Transform Kalman Filter (LETKF) assimilation algorithm (Hunt et al., 2007) with a physically based empirical localization approach (Revel et al., 2019). Normalized assimilation approach is adopted to handle the bias between modelled WSE and observed WSE from SWOT. The integration of SWOT altimetric observations in river modelling presents a promising avenue, considering its unprecedented spatiotemporal resolution and accuracy. The research addresses the challenges associated with the terrestrial water cycle, acknowledging the limitations of hydrodynamic modelling and uncertain space-borne observations. Results provide valuable insights into the potential of node averaged products of WSE from the SWOT mission in enhancing discharge estimation in the context of Indian river systems. The study is highly beneficial to sparsely gauged or ungauged basins, which are very common in India.

How to cite: K Soman, M. and Jayaluxmi, I.: Assimilation of high-resolution node averaged water surface elevations from the SWOT mission towards improve discharge estimates., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11731, https://doi.org/10.5194/egusphere-egu24-11731, 2024.

vA.6
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EGU24-16879
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ECS
Ankit Sharma, Mukund Narayanan, and Idhayachandhiran Ilampooranan

Traditional methods of mapping inland water bodies involve labor-intensive and manual data labeling, limiting their scalability to a larger extent. This study introduces a novel approach: self-supervised machine learning (SSML) for mapping inland water bodies. SSML is a training method where a model learns from data without the need for explicit human-labeled annotations. Using this technique, the study mapped inland water bodies in Pudukkottai, India, using LANDSAT-8 imagery from 2021. The training data for SSML were derived from two spectral indices: the Normalized Difference Vegetation Index and the Modified Normalized Difference Water Index. These indices were used to establish a threshold for automatically generating pseudo labels for two categories: water and non-water. This pseudo-labeled dataset was then utilized to train various machine learning models, including random forest, support vector machine, classification and regression tree, and gradient boosting. The accuracy of the final classified map was assessed using a spatial agreement test, which measures the degree of agreement of the classified map in relation to a reference dataset. The spatial agreement test used the Joint Research Commission (JRC) water map of 2021 as the reference dataset. The final inland water body map, derived from the SSML approach, demonstrated a high spatial agreement of 91% with the JRC water map. Among the SSML models, the random forest model outperformed others due to its ensemble nature. Compared to traditionally supervised classifiers (trained with 137 water points and 74 non-water points), the SSML models exhibited superior performance with a spatial agreement of 91%, significantly higher than the 67% achieved by the supervised model. This study is the first to demonstrate the application of SSML for mapping inland water bodies, offering an efficient and cost-effective alternative to traditional manual labeling. This approach holds significant potential for advancing remote sensing applications, particularly in regions where obtaining ground truth labeling is costly or impractical.

How to cite: Sharma, A., Narayanan, M., and Ilampooranan, I.: Advancing Inland Water Body Mapping with Self-Supervised Machine Learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16879, https://doi.org/10.5194/egusphere-egu24-16879, 2024.

vA.7
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EGU24-4660
Mohammad Hosein Kachoue, Mahdieh Goli, Ali Bakhtiari, Mohsen Sedghi, Mahdi Hosseinipoor, and Farkhondeh Khorashadi Zadeh

Dams are essential for effectively managing water resources as they serve multiple vital purposes, such as water storage, flood control, hydroelectric power generation, recreation and tourism and ecosystem regulation. The rising number of reservoirs, prompted by population growth, and the urgency for climate change adaptation and mitigation strategies emphasize the importance of developing efficient methods for calculating reservoirs water balance. In this study, we propose a remote sensing-based method for water balance analysis, aiming to facilitate the monitoring and management processes of water storage. The Karun (IV) reservoir, a dam situated in the southwestern region of Iran, is selected as the case study for this research. A conceptual rainfall-runoff model is employed to simulate the daily inflow rate of the reservoir by utilizing hydrological data obtained through remote sensing techniques. This data includes various parameters such as precipitation, evaporation and transpiration, soil moisture, vegetation, and land use. Moreover, a sensitivity analysis of model parameters is conducted to assess the significance of each parameter and simplify the model for future applications. Reservoir water evaporation is estimated by utilizing the reservoir area of the dam, which is obtained from the NDWI water index and the evaporation rate extracted from the WAPOR dataset. Then, altimetry data and reservoir area data are utilized to calculate changes in water storage. Finally, the water balance equation incorporating the calculated balance elements above is applied to determine the daily output of the dam reservoir. This study showcases the utilization of remote sensing data in estimating the output of the Karun (IV) reservoir. The accuracy of the proposed method is verified through comparisons with field data, making it a valuable tool for reservoirs where field data collection is costly or challenging.

How to cite: Kachoue, M. H., Goli, M., Bakhtiari, A., Sedghi, M., Hosseinipoor, M., and Khorashadi Zadeh, F.: Water Balance Analysis for Reservoirs through Remote Sensing: A Case Study of the Karun (IV) Reservoir in Iran., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4660, https://doi.org/10.5194/egusphere-egu24-4660, 2024.