This session concerns measurements and estimations of water levels, water extent, water storage and water discharge of surface water bodies such as rivers, lakes, floodplains and wetlands, through combined use of remote sensing and in situ measurements. Contributions that also cover aspects on assimilation of remote sensing together with in situ data within hydrodynamic models are welcome and encouraged.

The monitoring of river water levels, river discharges, water bodies extent, storage in lakes and reservoirs, and floodplain dynamics plays a key role in assessing water resources, understanding surface water dynamics, characterizing 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 is contributing in an increasing way, as they provide near real time measurements as well as long homogeneous time series to study the impact of climate change, over various scales from local to regional and global.

During the past twenty-nine years 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 space borne techniques.

Traditional instruments contribute to long-term water level monitoring and provide baseline databases. Scientific applications of more complex technologies like the SAR altimetry on CryoSat-2 and Sentinel-3A/B missions are maturing. The future SWOT mission, to be launched in 2021, will open up many new hydrology-related opportunities.

Convener: Jérôme Benveniste | Co-conveners: J.F. Crétaux, Ben Jarihani, Angelica Tarpanelli
| Attendance Tue, 05 May, 08:30–10:15 (CEST)

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Chat time: Tuesday, 5 May 2020, 08:30–10:15

Chairperson: Jérôme Benveniste, Angelica Tarpanelli, Jean-François Créteaux
D258 |
Kerstin Schulze, Jürgen Kusche, Olga Engels, Petra Döll, Somayeh Shadkam, and Christoph Niemann

Several applications, from water resource management to the prediction of extreme events, require a realistic representation of the global water cycle. Global hydrological models simulate continental water fluxes and individual storages. However, they poorly reproduce observations of discharge and total water storage anomalies (TWSA). To improve the realism of the simulations, TWSA derived from the Gravity Recovery and Climate Experiment (GRACE) mission are usually assimilated into hydrological models.
However, while assimilating GRACE-TWSA yields more realistic TWSA simulations, it is not clear how it affects the simulation of individual storages and fluxes. Therefore, assimilating discharge, in-situ or derived from satellite-altimetry, has been suggested to improve simulated discharge which is especially important for ungauged parts of basins.

In this study, we jointly assimilate GRACE-TWSA and discharge observations and, for the first time, simultaneously calibrate the model parameters in order to improve the simulation skills of the model beyond the observational time frame. For this, we couple the WaterGAP 2.2d model with the Parallel Data Assimilation Framework and apply an Ensemble Kalman Filter for the Mississippi River Basin from 2003 to 2016. Furthermore, we compare our results to single-data assimilation and validate them against discharge observations that were not used for calibration/assimilation. Additionally, we analyze the effect of the calibrated parameters on the model’s realism.

How to cite: Schulze, K., Kusche, J., Engels, O., Döll, P., Shadkam, S., and Niemann, C.: Effect of joint assimilation of GRACE and discharge observations on simulated water storages and fluxes, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13235, https://doi.org/10.5194/egusphere-egu2020-13235, 2020.

D259 |
Daniel Scherer, Christian Schwatke, and Denise Dettmering

Despite increasing interest in monitoring the global water cycle, the availability of in-situ discharge time series is decreasing. However, this lack of ground data can be compensated by using remote sensing techniques to observe river discharge.

In this contribution, a new approach for estimating the discharge of large rivers by combining various long-term remote sensing data with physical flow equations is presented. For this purpose, water levels derived from multi-mission satellite altimetry and water surface extents extracted from optical satellite images are used, both provided by DGFI-TUM’s “Database of Hydrological Time series of Inland Waters” (DAHITI, https://dahiti.dgfi.tum.de). The datasets are combined by fitting a hypsometric curve in order to describe the stage-width relation, which is then used to derive the water level for each acquisition epoch of the long-term multi-spectral remote sensing missions. In this way, the chance of detecting water level extremes is increased and a bathymetry can be estimated from water surface extent observations. Below the minimum hypsometric water level, the river bed elevation is estimated using an empirical width-to-depth relationship in order to determine the final cross-sectional geometry. The required flow gradient is computed based on a linear adjustment of river surface slope using all altimetry-observed water level differences between synchronous measurements at various virtual stations along the river. The roughness coefficient is set based on geomorphological features quantified by adjustment factors. These are chosen using remote sensing data and a literature decision guide.

Within this study, all parameters are estimated purely based on remote sensing data, without using any ground data. In-situ data is only used for the validation of the method at the Lower Mississippi River. It shows that the presented approach yields best results for uniform and straight river sections. The resulting normalized root mean square error for those targets varies between 10% to 35% and is comparable with other studies.

How to cite: Scherer, D., Schwatke, C., and Dettmering, D.: Estimation of River Discharge using Multi-Mission Satellite Altimetry and Optical Remote Sensing Imagery, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2650, https://doi.org/10.5194/egusphere-egu2020-2650, 2020.

D260 |
Jerome Monnier, Kevin Larnier, and Pierre-André Garambois

We present the Hierarchical Variational Discharge Inference (HiVDI) algorithm [1,2] and its capabilities to estimate the discharge and bathymetry of rivers from altimetry measurement, more particularly from the forthcoming SWOT space mission. The last version algorithm is based on hierarchical flow models and hybrid computational approaches : 1) a dedicated satellite-scale low-complexity model relating the discharge Q(x,t), the bathymetry b(x) and the friction parameter K [2]; 2) an advanced Variational Data Assimilation (VDA) formulation based on a relatively complete physics (Saint-Venant’s equations) [2,4] ; 3) deep neural networks based estimations obtained from recently enriched databases [1]. The resulting algorithm turns out to be robust and relatively accurate. Passed the assimilation of a hydrological cycle (~ 1 year variations, considered as a “learning period) the identified parameters (b(x), K) are identified; next given newly acquired satellite measurements, the low complexity model enables to estimate Q(x,t) in real-time [1,2].

Numerical results on numerous river datasets are analyzed in detail including for relatively complex flows and multi-satellite datasets [1,2,3].


[1] K. Larnier, J. Monnier. "Hybrid data assimilation - deep learning approaches to estimate rivers discharges from altimetry". Submitted.

[2] K. Larnier, J. Monnier, P.-A. Garambois, J. Verley. "River discharge and bathymetry estimations from SWOT altimetry measurements". Revised (nov. 2019).

[3] P.-A. Garambois, K. Larnier, J. Monnier, P. Finaud-Guyot, J. Verley, A. Montazem, S. Calmant. "Variational inference of effective channel and ungauged anabranching river discharge from multi-satellite water heights of different spatial sparsity". J. of Hydrology 2019.

[4] P. Brisset, J. Monnier, P.-A. Garambois, H. Roux. "On the assimilation of altimetry data in 1D Saint-Venant river models". Adv. Water Ress. 2018. 

[5] "DassFlow: Data Assimilation for Free Surface Flows", open-source computational software. INSA - IMT, CNRS, CNES, CS group. http://www.math.univ-toulouse.fr/DassFlow

How to cite: Monnier, J., Larnier, K., and Garambois, P.-A.: Discharge and bathymetry estimations of rivers from altimetry and datasets by hybrid computational methods, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9600, https://doi.org/10.5194/egusphere-egu2020-9600, 2020.

D261 |
Gennadii Donchyts, Dirk Eilander, Antonio Moreno-Rodenas, Maarten Pronk, Samapriya Roy, and Hessel Winsemius

Accurate and timely information on water storage changes in medium and small size reservoirs is needed for better water management and understanding of water dynamics on a global scale in general. While changes in surface water extent in these reservoirs can be monitored using satellite missions such as Landsat 8, Sentinel-1, and Sentinel-2, the information on water level and storage dynamics on a global scale is still missing. However, for most reservoirs, these storage changes can be estimated given that an accurate digital elevation model (DEM) is available for a dynamic part of the reservoir - the area covered between the minimum and maximum extents of the reservoir. In this research, we will investigate the applicability of data measured by the ICESat-2 lidar sensor and the off-nadir satellite imagery acquired by Planet’s SkySAT satellites and will evaluate how valuable these datasets are to estimate water storage changes in medium and small size reservoirs.

How to cite: Donchyts, G., Eilander, D., Moreno-Rodenas, A., Pronk, M., Roy, S., and Winsemius, H.: On the applicability of ICESat-2 and off-nadir SkySAT satellite datasets for the estimation of water storage in medium and small reservoirs at the global scale, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18116, https://doi.org/10.5194/egusphere-egu2020-18116, 2020.

D262 |
Carmela Cavallo, Maria Nicolina Papa, Giuseppe Ruello, Massimilano Gargiulo, Paolo Vezza, and Guillermo Palau Salvador

Freshwater environments have undergone important changes in recent years; the various pressures on land use, the effects of climate change and the over-exploitation of water resources are significantly affecting water resource availability and biodiversity in these fragile ecosystems. Constant monitoring of freshwater environments is crucial for their management and protection. This can be obtained by satellite remote sensing that is a powerful, cost-efficient and still under-exploited monitoring tool. The main idea of the research work is to investigate how different kind of satellite data can be exploited to achieve a better description of freshwater environments at adequate space scales and with high temporal resolution. The study-case is the Albufera wetland in Spain, one the most important protected areas in Europe for the presence of many migratory birds species. The Albufera Natural Park includes a lake surrounded by rice fields irrigated by periodic flooding that offer in some periods of the year suitable habitats for many species of birds and others water-related organisms, such as macroinvertebrates and fishes.

The continuous monitoring of the flooded area extension is a prerequisite to understand the link between the water presence and habitat availability. The study combines observation from multiple optical and synthetic aperture radar (SAR) sensors with spatial resolution between 3 and 30 m. Acquisitions from Landsat-8, Sentinel-2 satellites were used in the optical and infrared bands. The revisit time ranges between 5 and 10 days even if, in case of cloud cover, the revisit time increases consistently. An unsupervised classification method, based on the application of a threshold, was used, in particular multispectral indexes such as MNDWI, NDWI and NDVI were calculated. The NDWI and MNDWI indexes allowed to identify the presence of limpid and turbid water in the October-May period, while in the May-September period the NDVI was used to identify rice plants and therefore indirectly estimate the possible presence of water below the canopy.  In order to increase the time resolution, also in periods with frequent cloud presence, Sentinel-1A and 1B and COSMO-SkyMed SAR images were also used. The Sentinel-1 constellation operates in C band with time resolution of about 5 days; while COSMO-SkyMed operates in X bands with time resolution of about 10 days. The images were processed with both unsupervised and supervised classification methods. The information obtained from images processing were compared with very high-resolution (0.30 m and 0.50 m) satellite images and field measurements in order to validate and calibrate the classification method. The classification obtained with multispectral and SAR data were also cross-validated, providing very satisfactory results. Combination of different satellite data allowed for a significant increase of the temporal resolution of the observations, also in presence of cloud cover. The result of the study showed the dynamic of flooding-drying of the wetland and the flooding duration in different areas of the Albufera Park. This dataset is extremely useful for the optimization of wetland management and for further investigation on the link between flooding duration and habitat availability.

How to cite: Cavallo, C., Papa, M. N., Ruello, G., Gargiulo, M., Vezza, P., and Palau Salvador, G.: Continuous Monitoring of Albufera Wetland (Spain) by SAR and multispectral satellite data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-819, https://doi.org/10.5194/egusphere-egu2020-819, 2020.

D263 |
Bart (A.J.) Wickel, Rene Colditz, Rainer Ressl, John Kucharski, and Sergio Salinas-Rodríguez

The main objective of this study was the evaluation of remote sensing methods that allow for extraction of metrics that link riparian flow regimes to hydro-periods (duration) and -patterns (extent) of wetland systems known to be of critical importance to migratory water fowl and shorebirds along the Pacific Flyway in Mexico. In this study we emphasized the use of freely available and easily accessible optical remote-sensing data and their processing using free and open-source tools. 

Through application of a set of common and well documented water and vegetation indices on the full Landsat 5 and Landsat 7 record spanning two decades, we created a data set that captures episodic, intra-annual and inter-annual variability in inundation for two contrasting wetland systems. For this study we focussed on the Marismas Nacionales wetland system along the Pacific coast and the Alvarado Lagoon system on the Gulf coast. A comparison of indices designed to extract vegetation and water characteristics from Landsat data (NDVI, EVI, NDWI, Tasseled Cap and MNDWI) led us to conclude that the Modified Normalized Difference Water Index (MNDWI) was most effective for identifying inundated areas while the Normalized Difference Vegetation Index (NDVI) worked best for identifying differences in vegetated areas. Our study also established that the high sensitivity to thresholds requires site specific optimization.

For the study we developed metrics to represent the hydro-pattern and hydro-periodicity of waterbodies in the study areas. The first method provides a metric for the intra-annual and inter-annual permanence of water bodies, while the second method quantifies recurrence of seasonal inundation. The Marismas Nacionales revealed a surprisingly strong and direct relationship between inundated area and gauge meassured discharge of the Rio San Pedro Mezquital. Annual and multi annual hydropatterns in this system are very strong and predictable, and primarily driven by large scale inundation of the delta of this river as it enters Marismas Nacionales. The relationship between discharge and inundated area was so string that the inundated area (up to several hundreds of sqare kilometers during peaks) remained correlated throught the full range of the hydrograph. For this system recurrent inundation patterns and their timing metrics were linked to specific ecosystem types and used to inform a bird conservation planning effort.

At the Laguna de Alvarado a very different dynamic was observed, where large scale inundation was less frequent, permanent water bodies were much more persistent in space, and the correlation between inundated area and discharge was much weaker. In this region persistent cloud cover was an issue and SAR based approached may be the only way to monitor inundation dynamics more consistently. Earlier studies by WIckel et al for other systems using PALSAR data for wetland systems in Colombia revealed other technical shortcomings of these kinds of data. A study by Colditz et al for wetland systems in Mexico revealed a strong potential of MODIS derived MNDWI data as well. We propose that future efforts explore the possibilties of aplications of combined (optical and SAR) products.

How to cite: Wickel, B. (A. J. )., Colditz, R., Ressl, R., Kucharski, J., and Salinas-Rodríguez, S.: Monitoring Hydroperiod and Hydropatterns of coastal wetland systems in Mexico using Landsat time series, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12991, https://doi.org/10.5194/egusphere-egu2020-12991, 2020.

D264 |
David Purnell, Natalya Gomez, William Minarik, and Gregory Langston

GNSS-Reflectometry (GNSS-R) is a promising new technique to monitor water levels due to easier and cheaper installation of instruments in remote environments compared to traditional acoustic sensors or pressure gauges. GNSS stations that have been used for reflectometry purposes thus far are designed for monitoring land motion and may cost more than 10,000 USD each. We have found that a low-cost GNSS antenna and receiver (10 USD) can be used to make equally precise water level measurements, with an RMSE of a few centimeters when compared to a collocated acoustic sensor. However, an RMSE of less than one centimeter is typical for water level sensors and this level of accuracy is desired for research purposes. Two of the dominant sources of error in GNSS-R measurements are the effects of random noise in the Signal-to-Noise Ratio (SNR) data and tropospheric delay. Modelling work suggests that these sources of error can be reduced by using multiple low-cost antennas in the same location. In light of this, we have installed an experimental setup of antennas at various locations along the Saint Lawrence River and Initial results show that multiple antennas can be used to provide more precise measurements than a single antenna. Our installations of multiple antennas are less than 5% of the cost of stations that have been used in previous GNSS-R literature. Hence this approach could be applied to install a dense network of water level sensors along rivers, lakes or coastlines at a relatively low cost. We expect that this approach could also be applied to GNSS-R soil moisture or snow depth measurements.

How to cite: Purnell, D., Gomez, N., Minarik, W., and Langston, G.: Precise ground-based GNSS-reflectometry water level measurements using multiple low-cost antennas, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11739, https://doi.org/10.5194/egusphere-egu2020-11739, 2020.

D265 |
Philippa Berry and Jerome Benveniste

The unique contribution of satellite radar altimetry to river monitoring is well understood, with ‘ altimeter virtual gauge’ heights increasingly ingested into river basin models. However, altimeters gather a wealth of additional information. Waveform shapes reflect underlying topographic variation, surface composition and roughness, and distribution of surface water within the footprint. Backscatter measurements allow soil surface moisture under the satellite track to be determined, using DRy EArth ModelS (DREAMS) crafted from multi-mission altimeter data and ground truth. Initially developed over desert areas, DREAMs are now being built over river basins to extend the scope of altimeter soil moisture measurement.

This paper investigates  the potential contribution of these additional data to river basin analysis and modelling. 
The following key questions are addressed. 
1) How useful are the data encoded in complex waveform shapes? 
2) Can altimeter soil moisture estimates contribute to modelling in river basins?
A series of example river basins were chosen in different topographic and climate situations, including the Amazon, Orinoco, Nile, Niger and Congo basins, and wetlands including the Okavango delta.This paper presents outcomes from analysis of multi-mission altimetry, with ERS-1/2, Envisat, Topex, Jason-1/2, Cryosat-2 and Sentinel-3A/B, plus a database of over 86,000 river and lake timeseries.

The analysis outcomes demonstrate the value of altimeter soil surface moisture estimates, both as co-temporal and co-spatial data with inland water height measurements, and as an independent validation dataset to assess soil moisture estimates derived from other remote sensing techniques. The precise backscatter cross-calibration of altimeters on successive missions allows derivation of long soil moisture time series. The ability of nadir-pointing altimeters to penetrate vegetation canopy gives a unique perspective in rainforest areas, with information on underlying water height and extent as well as surface soil moisture. Waveform shape classification allows diverse information to be gleaned, particularly at the higher pulse repetition frequencies of the new generation of SAR Altimeters. In conclusion, satellite radar altimeters collect a wealth of information over river basins; this valuable resource is not yet fully exploited.

How to cite: Berry, P. and Benveniste, J.: Satellite Altimetry over River Basins - Beyond Water Heights, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3652, https://doi.org/10.5194/egusphere-egu2020-3652, 2020.

D266 |
Martina Wenzl, Marco Restano, and Jérôme Benveniste

The advent of SAR (delay-Doppler) altimetry allowed the production of data with a high spatial resolution (300 m along-track). Investigations in the inland water domain clearly benefited from SAR data and future processing strategies (e.g. the fully-focused SAR, FF-SAR) are expected to improve further the quantity of data points over water bodies of a reduced size.

The proposed work aims at investigating the quality of Sentinel-3 water level retrievals over three targets of different characteristics: the Ohio River, the Columbia River and the Great Salt Lake. Data are processed through the ESA G-POD SARvatore online and on-demand processing service for the exploitation of CryoSat-2 and Sentinel-3 data (https://gpod.eo.esa.int/services/SENTINEL3_SAR/) and obtained by using the SAMOSA2, SAMOSA+ & SAMOSA++ retrackers. The selected posting rate of measurements is 80 Hz to optimize the location of data points over the Ohio and Columbia River (an estimate every 80 m along-track), however a comparison with the 20 Hz posting rate is being made. Empirical retrackers outputs, available in the official 20 Hz Sentinel-3 LAN products, are also considered for comparison and water masks from (Pekel et al., 2016) are used to select data points acquired over water bodies.

The main goal of this study is to analyse the key parameters characterizing both the L1b SAR waveform and the retracking (e.g. the Pulse Peakiness, the Misfit…) to define a robust error characterization method that is expected to filter out an increased number of outliers. A validation exercise using in situ data will be presented to demonstrate that the proposed method leads to the definition of a reduced, highly reliable dataset, associated with a realistic error characterization model.

The study is expected to unlock possible synergies with SWOT and support the comparison of SAR estimates to FF-SAR estimates obtained at a comparable along-track resolution.

How to cite: Wenzl, M., Restano, M., and Benveniste, J.: A Robust Error Characterization Method for SAR Altimetry over the Inland Water Domain, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19031, https://doi.org/10.5194/egusphere-egu2020-19031, 2020.

D267 |
Jérôme Benveniste, Salvatore Dinardo, Giovanni Sabatino, Marco Restano, and Américo Ambrózio

The scope of this presentation is to feature the G-POD SARvatore service to users for the exploitation of CryoSat-2 and Sentinel-3 data, which was designed and developed by the Altimetry Team in the R&D division at ESA-ESRIN. The G-POD service coined SARvatore (SAR Versatile Altimetric Toolkit for Ocean Research & Exploitation) is a web platform that allows any scientist to process on-line, on-demand and with user-selectable configuration CryoSat-2 SAR/SARin and Sentinel-3 SAR data, from L1A (FBR) data products up to SAR/SARin Level-2 geophysical data products.
The G-POD graphical interface allows users to select a geographical area of interest within the time-frame related to the Cryosat-2 SAR/SARin FBR and Sentinel-3 L1A data products availability in the service catalogue. The processor prototype is versatile, allowing users to customize and to adapt the processing according to their specific requirements by setting a list of configurable options. Pre-defined processing configurations (Official CryoSat-2, Official Sentinel-3, Open Ocean, Coastal Zone, Inland Water (20Hz & 80Hz), Ice and Sea-Ice) are available. After the task submission, users can follow, in real time, the status of the processing. The output data products are generated in standard NetCDF format, therefore being compatible with the multi-mission “Broadview Radar Altimetry Toolbox” (BRAT, http://www.altimetry.info) and typical tools.
Initially, the processing was designed and optimized uniquely for open ocean studies. It was based on the SAMOSA model developed for the Sentinel-3 Ground Segment. However, since June 2015, the SAMOSA+ retracker is available as a dedicated retracker for coastal zone, inland water and sea-ice/ice-sheet. A new retracker (SAMOSA++) has been recently developed and will be made available in the future. The scope is to maximize the exploitation of CryoSat-2 and Sentinel-3 data over all surfaces providing user with specific processing options not available in the default processing chains.
Recent improvements include: 1) A Join & Share Forum to allow users to post questions and report issues (https://wiki.services.eoportal.org/tiki-custom_home.php); 2) A data repository to better support the growing Altimetry Community avoiding the redundant reprocessing of already processed data (https://wiki.services.eoportal.org/tiki-index.php?page=SARvatore+Data+Repository&highlight=repository); 3) A new function in the GUI allowing users to compute the geodetic distance between selected points on the map; 4) A new function in the GUI to filter the products search to a specific RON (Relative Orbit Number) and to a specific pass direction (Ascending or Descending). Furthermore, users will find in the folder SUM_RESDIR of the output data package a short summary report with information on the products that have not been processed and instructions on how to eventually try to re-process the missing data.
To respond to the request of hydrologists, and simulate data that a river gauge would provide, SARvatore  will soon include a post-processing service to convert water level estimates in L2 data to virtual station water level values,  which are typically required by hydrologists. Validation of SARvatore data over river targets will be presented to demonstrate the potential of both the SAMOSA+/++ retrackers and the innovative processing configurations not available in the default CryoSat-2 and Sentinel-3 processing chains.

How to cite: Benveniste, J., Dinardo, S., Sabatino, G., Restano, M., and Ambrózio, A.: SAR and SARin Altimetry Processing on Demand for Cryosat-2 and Sentinel-3 at ESA G-POD, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11691, https://doi.org/10.5194/egusphere-egu2020-11691, 2020.

D268 |
Angelica Tarpanelli, Karina Nielsen, Paolo Filippucci, Rossella Belloni, Stefania Camici, Luca Brocca, Tommaso Moramarco, Marco Restano, and Jérôme Benveniste

RIDESAT - RIver flow monitoring and Discharge Estimation by integrating multiple SATellite data, is an ESA-funded Permanent Open Call project aimed to develop a new methodology for estimating river discharge through the combination of radar altimeter, optical and thermal satellite sensors. The combination of multi-sensor measurements can provide significant advantages over single sensors contributing to improve the quality of the final products also in terms of spatial and temporal coverage.

The methodology developed in the project includes two phases. First, the single-instrument products (altimeter, optical and thermal sensors) are independently processed to generate a dataset of proxies of hydraulic variables strongly linked with river discharge (e.g. water level, flow velocity, width). Successively, these proxies are implemented as integrated techniques for the final estimation of the river discharge.

To test the ability of the approach to retrieve river discharge at global scale, 20 pilot sites are selected all over the world, based on the availability of in-situ measurements and the climatic characteristics of the basins. The availability of large datasets of in situ measurements is used for: 1) the validation of single-instrument products and the river discharge product; 2) the evaluation of the uncertainty attributed to the combination process; 3) the evaluation of the limitation of the procedure.

How to cite: Tarpanelli, A., Nielsen, K., Filippucci, P., Belloni, R., Camici, S., Brocca, L., Moramarco, T., Restano, M., and Benveniste, J.: Monitoring of river discharge through the combination of multiple satellite data: RIDESAT project, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18567, https://doi.org/10.5194/egusphere-egu2020-18567, 2020.

D269 |
Rossella Belloni, Stefania Camici, and Angelica Tarpanelli

In view of recent dramatic floods and drought events, the detection of trends in the frequency and magnitude of long time series of flood data is of scientific interest and practical importance. It is essential in many fields, from climate change impact assessment to water resources management, from flood forecasting to drought monitoring, for the planning of future water resources and flood protection systems. 
To detect long-term changes in river discharge a dense, in space and time, network of monitoring stations is required. However, ground hydro-meteorological monitoring networks are often missing or inadequate in many parts of the world and the global supply of the available river discharge data is often restricted, preventing to identify trends over large areas.  
The most direct method of deriving such information on a global scale involves satellite earth observation. Over the last two decades, the growing availability of satellite sensors, and the results so far obtained in the estimation of river discharge from the monitoring of the water level through satellite radar altimetry has fostered the interest on this subject.  
Therefore, in the attempt to overcome the lack of long continuous observed time series, in this study satellite altimetry water level data are used to set-up a consistent, continuous and up-to-date daily discharge dataset for different sites across the world. Satellite-derived water levels provided by publicly available datasets (Podaac, Dahiti, River& Lake, Hydroweb and Theia) are used along with available ground observed river discharges to estimate rating curves. Once validated, the rating curves are used to fill and extrapolate discharge data over the whole period of altimetry water level observations. The advantage of using water level observations provided by the various datasets allowed to obtain discharge time series with improved spatio-temporal coverages and resolutions, enabling to extend the study on a global scale and to efficiently perform the analysis even for small to medium-sized basins.  
Long continuous discharge time series so obtained are used to perform a global trend analysis on extreme flood and drought events. Specifically, annual maximum discharge and peak-over threshold values are extracted from the simulated daily discharge time series, as proxy variables of independent flood events. For flood and drought events, a trend analysis is carried out to identify changes in the frequency and magnitude of extreme events through the Mann-Kendall (M-K) test and a linear regression model between time and the flood magnitude.  
The analysis has permitted to identify areas of the world prone to floods and drought, so that appropriate actions for disaster risk mitigation and continuous improvement in disaster preparedness, response, and recovery practices can be adopted. 

How to cite: Belloni, R., Camici, S., and Tarpanelli, A.: Discharge estimation and monitoring extreme events by satellite altimetry, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8020, https://doi.org/10.5194/egusphere-egu2020-8020, 2020.

D270 |
Liguang Jiang, Youjiang Shen, Dedi Liu, Henrik Madsen, and Peter Bauer-Gottwein

Satellite radar altimetry has been widely used in hydrological studies, such as monitoring of lakes and reservoirs, retrieving water level and discharge of rivers, calibration of river models, etc. Sentinel-3 SAR altimeter delivers data at three levels of latency, i.e. near real-time (less than 3 hours after data acquisition), slow time critical (within 48 hours after data acquisition), and non-time critical (typically one month after data acquisition). However, most studies use final products, i.e. non-time critical products of altimetry data for inland water monitoring or hydrological simulations. So far, to the best of our knowledge, no study has been exploiting the value of near-real time satellite altimetry data for hydrological research.

In this study, we first investigate data quality of Sentinel-3 near real-time data against non-time critical product and in-situ data over the Han River in China. Then, we assimilate these data into a 1-D hydrodynamic model, i.e. MIKE Hydro River, to exploit the near-real time altimetry dataset for hydrological forecasting. Specifically, we use the Ensemble Kalman Filter to assimilate altimetry-derived water surface elevation data into MIKE Hydro River model. The model state variable that is updated is the water level defined on the numerical grid of the 1D hydrodynamic model. Observation error estimates are generated from the standard deviations of water levels at each virtual station. Applying this operational forecasting system retrospectively over historical periods, the effect of updating water level at multiple virtual stations on forecast performance is investigated.

Through this study, we gain new knowledge about near real-time altimetry products for hydrological studies. This will be informative for both the hydrology community and satellite data providers.

How to cite: Jiang, L., Shen, Y., Liu, D., Madsen, H., and Bauer-Gottwein, P.: Assimilation of near real-time radar altimetry data into a hydrodynamic model for streamflow and water level forecasting, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-15219, https://doi.org/10.5194/egusphere-egu2020-15219, 2020.

D271 |
Andrea Scozzari, Stefano Vignudelli, Mohamed Elsahabi, Neama Galal, Marwa Khairy, and Abdelazim Negm

It is currently well known that a combination of stressors, such as climate change, human activities and new infrastructures might influence the storage capacity of strategic surface water reservoirs at a global level.

The Nasser Lake is the biggest and most important lake in Egypt, located in the southern part of the Nile River in Upper Egypt. The expected impact of the Grand Ethiopian Renaissance Dam (GERD) on the future availability of the Nile water, together with the significant and rapid water level variations and sedimentation processes, make the Nasser Lake a particularly challenging place to be monitored in the next years.

This work describes a preliminary study on the possible usage of the imaging radiometer SLSTR (Sea and Land Surface Temperature Radiometer) onboard Sentinel-3 for estimating water coverage extent in inland water contexts, in synergy with radar altimetry measurements provided by the SRAL (Synthetic aperture Radar ALtimeter) instrument. In particular, this work wants to exploit the simultaneous acquisition offered by SRAL and SLSTR instruments hosted by the Sentinel-3A/B platform.

We introduce an alternative technique to the classical calculation of the whole water extent based on high-resolution imagery, essentially intended for the application to wide-swath short-revisit sensors. The proposed approach starts from the hypothesis that a much-reduced subset of pixels may carry enough information for assessing the status of the observed water body by estimating the water coverage percent within each single pixel. Such an assumption can rely only on the radiometric performance of the instrument, SLSTR in this case.

The timeseries of water levels by the SRAL instrument were obtained by using the 20 Hz product generated by the SARvatore processor run on the ESA GPOD (Grid Processing On Demand) platform. A timeseries derived from SLSTR measurements has been generated by a simple feature extraction technique, based on the selection of pixels exhibiting the highest variability of the collected radiance. As expected, this subset essentially identifies particular spots on the coastlines of the target, as a consequence of its morphological characteristics.

Preliminary results show a promising relationship between the timeseries generated by the two independent measurements and between the available in situ data as well. Under the hypothesis of a time-invariant system (i.e., characterised by no significant morphological changes), once an area-level-volume relationship is identified, volume estimations can be inferred by either altimetric or radiometric measurements per se.

Thus, the simultaneous observation by the two instruments represents a relevant opportunity for cross-validating the acquired data. Moreover, the approximation experimented in this work gives the perspective of a very light computational process for expedite water storage estimations in large surface reservoirs, provided that the natural system is fully identified on the basis of ground-truth data.

How to cite: Scozzari, A., Vignudelli, S., Elsahabi, M., Galal, N., Khairy, M., and Negm, A.: Synergy between optical imaging radiometry and radar altimetry for inland waters: an experience with Sentinel-3 on the Nasser Lake , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18804, https://doi.org/10.5194/egusphere-egu2020-18804, 2020.

D272 |
Léo Pujol, Pierre-André Garambois, Pascal Finaud-Guyot, Jérôme Monnier, Robert Mosé, Kevin Larnier, Sylvain Biancamaria, Daniel Medeiros Moreira, Adrien Paris, and Stéphane Calmant

With the upcoming SWOT satellite mission, which should provide spatially dense river surface elevations, widths and slopes observations globally, comes the need to pertinently use such data into hydrodynamic models, from the reach to hydrographic network scales. Based on the HiVDI (Hierarchical Variational Discharge Inversion) modeling strategy ([1,2], DassFlow software1), this work tackles the forward and inverse modeling capabilities of distributed channel parameters and inflows (in the 1D Saint-Venant model) from multisatellite observations of river surface. Several synthetic cases are designed to study fluvial and torrential flows signatures and assess the inference capabilities of model parameters (inflows, bathymetry, friction) given different observation patterns. Accurate inferences of both inflows and distributed channel parameters (bathymetry-friction) is achievable even with a minimum spatial observability between inflows. A sensitivity analysis of the inferences to prior hydraulic parameter values and to regularization parameters is performed. Next a real case is studied: 871km of the Negro river (Amazon basin) including complex multichannel reaches, 21 tributaries and backwater controls from major confluences. An effective modeling approach is proposed using (i) WS elevations from ENVISAT observations and dense in situ GPS flow lines, (ii) average river top widths from optical imagery, (iii) upstream and lateral flows from the MGB large-scale hydrological model [3]. The calibrated effective hydraulic model closely fits satellite altimetry observations of WS signatures and contains real-like spatial variabilities and flood wave propagations (frequential features analyzed with identifiability maps [2]). Synthetic SWOT observations are generated from the simulated flowlines and the identifiability of model parameters (579 bathymetry points, 17 friction patches and 22 upstream and lateral hydrographs) is tested using the HiVDI computational inverse method and given hydraulically coherent prior guesses and regularization parameter values. Inferences of channel parameters carried out on this fine hydraulic model applied at large scale give satisfying results considering the challenging inverse problems solved globally in space and time, even with noisy SWOT data. Inferences of spatially distributed temporal parameters (lateral inflows) give satisfying results as well, with even small scale hydrograph variations being infered accurately.

This study brings insights in:

  1. the hydraulic visibility of multiple inflows hydrographs signature at large scale with SWOT;

  2. the simultaneous identifiability of spatially distributed channel parameters and inflows by assimilation of satellite altimetry data;

  3. the need to further taylor and scale hydrodynamic models and assimilation methods to improve potential information feedbacks to hydrological modules in integrated chains.


[1] Larnier, Monnier, Garambois, Verley. (2019) River discharge and bathymetry estimations from SWOT altimetry measurements.

[2] Brisset, Monnier, Garambois, Roux. (2018) On the assimilation of altimetric data in 1d Saint-Venant river flow models. AWR, doi: 10.1016/j.advwatres.2018.06.004.

[3] Paiva, Buarque, Collischonn, et al. Large-scale hydrologic and hydrodynamic modeling of the amazon river basin. WRR, doi: 10.1002/wrcr.20067.



How to cite: Pujol, L., Garambois, P.-A., Finaud-Guyot, P., Monnier, J., Mosé, R., Larnier, K., Biancamaria, S., Moreira, D. M., Paris, A., and Calmant, S.: Estimation of Inflows and Effective Channel from Satellite Observations: From Local to Hydrographic Network Scale, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3614, https://doi.org/10.5194/egusphere-egu2020-3614, 2020.

D273 |
Alice César Fassoni-Andrade, Rodrigo Cauduro Dias de Paiva, Claudio Clemente Faria Barbosa, Evlyn Marcia Leão de Moraes Novo, Conrado de Moraes Rudorff, and Ayan Santos Fleischmann

Terrain elevation data are essential for land management, navigation, and earth science applications. Digital elevation models (DEMs) can be created for land as well as underwater surfaces, and remote sensing advancements have led to the increase in the availability of a range of DEMs over the land. However, the generation of underwater DEMs usually requires the shorelines delineation of the water body, and in regions with many lakes, such approach have high processing costs. Currently, there is no systematic mapping of lakes and channels bathymetry of large and complex wetlands using remote sensing data.

We present here the first high-resolution topographic mapping (30 m) of the central Amazon floodplain (~1100 km extension of the Amazon River) using a new method based on water surface elevations and a flood-frequency map derived from Landsat images. Validation using field bathymetric surveys presented a Root Mean Square Error (RMSE) of 1.30 m in floodplain elevations and Pearson’s correlation coefficient of 0.73. These results indicate adequate spatial representation over a large complex floodplain geomorphology and important improvements relative to the SRTM (RMSE of 3.55 and Pearson’s coefficient of 0.22). The method can be applied to temporarily flooded regions, with the advantage of not requiring lake delimitation. Finally, this method provides synergism with the forthcoming satellite SWOT mission for advancements in hydrological, ecological and geomorphological studies of floodplain as the projected increase in availability of surface water elevation data will enhance its applicability and yield unprecedented opportunities to create new datasets of floodplain DEMs and lakes storage volumes.

How to cite: Fassoni-Andrade, A. C., Paiva, R. C. D. D., Barbosa, C. C. F., Novo, E. M. L. D. M., Rudorff, C. D. M., and Fleischmann, A. S.: High-resolution mapping of lake and floodplain topography from space, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-142, https://doi.org/10.5194/egusphere-egu2020-142, 2020.

D274 |
Eulogio Pardo-Iguzquiza, David Pulido-Velazquez, Antonio-Juan Collados-Lara, and Leticia Baena-Ruiz

Wetlands protect and improve water quality, store floods, maintain surface water during dry periods and provide valuable habitats for wildlife. However, wetlands may be very sensitive to climate change and appropriate monitoring works and modelling activities are needed in order to design sustainable management strategies. In this work we aim to analyze the dynamics of the Lagunas de Ruidera wetland (Spain) for the period 1984−2015. We applied the supervised classification method to LANDSAT satellite images (missions 5, 7 and 8) with a spatial resolution of 30 m and a temporal resolution of around 16 days. In this case study two different water bodies in terms of surface reflectance have been detected. Both zones have been considered for the calibration of the water detection algorithm through a non-steady threshold. We have also analysed daily surface reflectance data from MODIS (MOD09GQ) to complete the temporal dynamic of the wetland. We obtained some correlations between surface reflectance of LANDSAT and MODIS but the efficiency to detect water surfaces of the second is considerably lower due to its 250 m spatial resolution. The results show a minimum and a maximum wetland surface of around 2.7 and 6.3 km² for the considered period. We have also analysed the relationship of the wetland surface with precipitation and aquifer discharge (obtained from a groundwater flow model). For the mean year at monthly scale, the maximum correlation between the wetland surface and precipitation is obtained for a lag of one month. The wetland surface has a similar monthly trend to the aquifer discharge and the maximum correlation is obtained without lag.

This research has been partially supported by the SIGLO-AN project (RTI2018-101397-B-I00) from the Spanish Ministry of Science, Innovation and Universities (Programa Estatal de I+D+I orientada a los Retos de la Sociedad) and by the GeoE.171.008-TACTIC project from GeoERA organization funded by European Union’s Horizon 2020 research and innovation program.

How to cite: Pardo-Iguzquiza, E., Pulido-Velazquez, D., Collados-Lara, A.-J., and Baena-Ruiz, L.: Mapping the dynamics of the Lagunas de Ruidera wetland (Spain) using remote sensing, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8098, https://doi.org/10.5194/egusphere-egu2020-8098, 2020.

D275 |
Peirong Lin, Ming Pan, Eric Wood, Dongmei Feng, Colin Gleason, Craig Brinkerhoff, Xiao Yang, and Tamlin Pavelsky

One important goal of the Surface Water and Ocean Topography (SWOT) satellite mission is to estimate global river discharges from observations of river width, height, and slope. While a range of algorithms have been developed and intercompared for SWOT (e.g., Durand et al. 2016), our understanding of the algorithm accuracy has been confined to tens of rivers globally, due to the limited SWOT-like observations currently collected from hydraulic model outputs.

To scale up the assessment of SWOT discharge algorithms, this study will first collect discharge observations at thousands of global gauges whose river widths are wider than 50 m (i.e., observable by SWOT), to provide the most comprehensive observations to evaluate discharge estimations. Then at those gauges, all available Landsat images from 2010 to 2017 (8 years) will be collected to extract river widths with an automatic Google Earth Engine tool called RivWidthCloud (Yang et al. 2019). The extracted river width time series (temporally intermittent) will provide SWOT-like observations, which can be used to derive discharge using the Bayesian AMHG-Manning (BAM) algorithm (Hagemann et al. 2017; Feng et al. 2019). The prior discharge information needed by the BAM algorithm will come from an updated global discharge modeling database (Lin et al. 2019). These datasets, collectively, will provide a critical assessment of the SWOT discharge algorithms.

This study is expected to provide the first geographically explicit assessment of the BAM algorithm at thousands of global locations, and the insights gained may also help the global hydrologic modeling community with their data assimilation efforts.

How to cite: Lin, P., Pan, M., Wood, E., Feng, D., Gleason, C., Brinkerhoff, C., Yang, X., and Pavelsky, T.: Scaling up the assessment of the SWOT discharge inversion algorithm to thousands of gauges globall, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12483, https://doi.org/10.5194/egusphere-egu2020-12483, 2020.

D276 |
Anne Jost, Thomas Verbeke, Catherine Ottlé, Nicolas Flipo, Shuaitao Wang, François Colleoni, Anthony Bernus, and Agnès Rivière

Gravel pit lakes created by sand and gravel mining are structural landscape elements in alluvial plains. As they create openings through the aquifer, they provide direct access to groundwater and make it possible for remote sensing to offer an integrated vision of water resources at the alluvial plain scale. Indeed the Surface Water and Ocean Topography (SWOT) satellite mission will soon map near weekly the surface water elevation of reservoirs with areal extent greater than 6 ha. Using the CNES large scale simulator, we evaluated the SWOT water level errors on gravel pits in La Bassée alluvial plain, in the middle reach of the Seine River (France). Despite their rather small size, the elevation accuracy over the artificial lakes should be centimetric. Using as input in-situ lake level measurements, SWOT-like outputs were generated. The full exploitation of these data requires a modelling tool for gravel pit lake simulation, which includes groundwater interaction. We also present the development of our lake module, imbedded in the CaWaQS platform, its validation using benchmark test problems and numerical experiments illustrating its performance in simulating gravel pit lake stage fluctuations.

How to cite: Jost, A., Verbeke, T., Ottlé, C., Flipo, N., Wang, S., Colleoni, F., Bernus, A., and Rivière, A.: Modelling lake-groundwater interactions in preparation of future SWOT mission: the case of the gravel pit lakes in the Seine River alluvial plain, France., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11618, https://doi.org/10.5194/egusphere-egu2020-11618, 2020.

D277 |
Chung-Chieh Huang, Hong-Ru Lin, Jyun-Lin Chen, Shao-Yang Huang, Jet-Chau Wen, Jen-Feng P Yeh, and Ben Jarihani

         Since the successful launch of the Gravity Recovery and Climate Experiment (GRACE) on March 17th, 2002, a number of scientists have adopted satellite gravimetry for the detection of variations on terrestrial water storage (TWS). Use of high-precision GRACE gravimetry presents advantages in hydrogeologic studies, such as providing accurate estimates of currents and gravity fields. Many studies have proven that the high-precision GRACE gravimetry can observe large-scale (over 50,000 km2) variations in groundwater storage (GWS). However, relatively few studies conducted using satellite gravimetry have focused on scales smaller than 5,000 km2.

        The purpose of this study is to investigate the potential for using GRACE gravimetry to observe small-scale variations in GWS specifically, this paper presents a case study of the Zhoushui River alluvial fan (~2,560 km2) in central Taiwan as an example of how well GRACE data compare to field-based data for ascertaining small-scale variations in GWS. Field measurements of groundwater level in 52 observation wells (2002-2017) were used to analyze variations in GWS. Results of this field-based analysis were compared to results obtained using the GWS data (2002-2017) obtained by GRACE gravimetry. This comparison allowed us to evaluate the similarities and differences in both methods as well as to prove the feasibility of using GRACE gravimetry in small-scale regions. Results of our comparative analysis indicate that water resources in small watershed can be successfully managed using gravimetric data collected by GRACE satellite.


Keywords: Groundwater storage, GRACE, Watershed

How to cite: Huang, C.-C., Lin, H.-R., Chen, J.-L., Huang, S.-Y., Wen, J.-C., Yeh, J.-F. P., and Jarihani, B.: Detect groundwater storage in an island watershed by GRACE gravimetry, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1827, https://doi.org/10.5194/egusphere-egu2020-1827, 2020.