HS6.3

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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