Lakes and reservoirs monitoring is of sheer interest, as in-situ gauging station coverage is dwindling at a global scale. Water storage change show the impact of not only domestic consumption, low water maintenance in rivers or crop irrigation, but also the impact of climate change. In this frame, different approaches are explored in this study to be able to follow from space remote sensing data the lake storage change, either from water height or surface.

For the ESA CCI Lake Storage Change option, we focus on a few lakes distributed around the world to establish a methodology suitable at a global scale to different lake behaviors. In particular for highly varying water bodies, the automatic production and use of hypsometric curve is investigated This approach is yet suitable for volume variations only.

Complementary to this first approach, an image inpainting algorithm applied to digital elevation models around water bodies is developped to assess their total bathymetry (either for lake or reservoir) where the pixels to be reconstructed are the ones underneath the lake surface. The first results show encouraging estimations that may lead in the near future to the assessment of total water volume of lake and reservoir at a global scale. With the recent launch of SWOT that will provide an unprecedented coverage worldwide, the estimation of global water storage change has a bright future.

How to cite: Fatras, C., Andral, A., and Augot, J.: Assessing lake and reservoir storage change from remote sensing data at a global scale, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5527, https://doi.org/10.5194/egusphere-egu23-5527, 2023.

Dams are strategic tools for countries and their management of water resources. Within the Space for Climate Observatory (SCO) initiative and SWOT Downstream program, the StockWater project aims to put in place a system for monitoring water volume in dams. It is based on satellite data, and a specific processing system, thereby facilitating the work of the public authorities in this area.

Water resources monitoring, including surface and groundwater, is a vital issue for governments and public institutions. Water resources are essential for society and economic activity (drinking water, irrigation, hydroelectricity, industry, flood control) and for natural and water ecosystems.

Generally, reservoir stock information is collected and held by the local reservoir managers (public or private). Regional and national authorities might access this information with a certain latency, which depends on national water policies. Central authorities are then confronted with two issues: long latencies to retrieve water stock information and sparse or inexistent information about small reservoirs.

The project proposes a global solution to monitor reservoir stock volumes based on frequent satellite measurements. This solution is based on reservoir water extent monitoring by imaging satellites (Sentinel 1&2) based in the Surfwater processing chain, which integrates a multitemporal approach to improve water masks. Furthermore, StockWater innovation relies on reservoir estimation of Area/Elevation/Volume relationships just from a DEM, even when acquired after the reservoir construction. 

Recent total volume estimations from DEM estimations have been qualified on hundreds reservoirs in France, ranging from 10 to 10000 hectares, providing errors lower than 20% on 77% of the reservoirs. About the general system assesment, Filling rates estimates yield an error lower than 8% on 75% of the measurements.

New versions are evaluated on Spain, France, India and Brazil and deployed on  Burkina-Faso and Tunisia over more than 100 reservoirs. Results are available here: https://www.sco-stockwater.org/  This system will also easily allow volume estimations from Elevation measurements (altimeters Jason, Sentinel3 with limited coverage or SWOT globally).

StockWater project, led by CNES and developed with CS-Group and SERTIT,  holds a partnership initiative with CESBIO, GET, LISAH and FUNCEME/Université Pernambuco laboratories and their local partners in Tunisia, Laos, Burkina-Faso, Brazil and India. StockWater is open to new countries willing to participate in future project expansions.

How to cite: Peña Luque, S., Nicolas, G., Yesou, H., Ledauphin, T., Amzil, S., Maxant, J., Ferrant, S., Grippa, M., Ribeiro, A., Bailly, J. S., Molenat, J., Puech, F., and Reis, R.: Stockwater - Advances in reservoir stock monitoring from space, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12090, https://doi.org/10.5194/egusphere-egu23-12090, 2023.

Due to the limited resolution of freely available digital elevation models (DEM), DEM-derived river products cannot provide accurate flow lines in inland and lowland areas. Stream burning is used to improve the accuracy of extracted flowlines in addition to other flow routing algorithms (from LSDTopotools). Using the latest DEM products (Copernicus 30m DEM and FAB 30m DEM) and other remote sensing data (Sentinel-1, Sentinel-2), the framework is tested on different geomorphological features (meanders, branches) and weather conditions (with and without clouds). The results show a significant improvement in the accuracy of the flow lines compared with existing global hydrography products.

How to cite: Chen, Q., Mudd, S., Attal, M., and Hancock, S.: Extract an accurate river network, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7453, https://doi.org/10.5194/egusphere-egu23-7453, 2023.

This work presents the characterization and performance evaluation of the existing Copernicus EO products for the monitoring and modelling of water bodies dynamics purposes. This study is carried on the Water-ForCE project (https://waterforce.eu/). Water-ForCE (“Water scenarios For Copernicus Exploitation”) is an European H2020 project to develop a Roadmap for Copernicus Inland Water Services, aiming to better integrate the entire inland water cycle within the Copernicus Services.

From the requirements collected in the project through dedicated working group meetings and stakeholders’ consultation, a list of water quality and water quantity variables which are used in water modelling were identified. All of them were related to five types of modelling into water management: biogeochemical models, hydrodynamic models, river models, crop or pasture growth models and landscape water balance models. The availability of water related products in the Copernicus portfolio was analysed by checking and updating the previous water quantity and water quality project inventories.

This work studies their spatial coverage, data discovery and access, file data formats, validation reports, uncertainty indicators and spatial and temporal resolutions and their utility in the mentioned types of water modelling was analysed. Finally, recommendations on improvements of the existing Copernicus products were made based on the cross analysis between the existing features and user needs.

Main conclusions of the work point to the lack of bathymetry and evapotranspiration as well as some specific water quality products, the need of finer spatial resolution for chlorophyll-a in coastal zones and for soil moisture, surface water and snowmelt products, higher temporal resolution for river discharge and groundwater and water quality variables, the need of validation, increase the coherence between in-situ and remote sensing observations and to provide more quality and uncertainty information. Other demands are to uniform marine and lake products, a global (or at least Pan European) coverage for some local/regional products, improvements in data access and data delivery of new formats, and continuous and consistent long-term archives of vegetation and land cover products.

How to cite: Prat, E., Pesquer, L., Batlle, A., Spyrakos, E., and Thant, S.: Characterization of Copernicus EO products for water modelling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8586, https://doi.org/10.5194/egusphere-egu23-8586, 2023.

Drought within a short time, termed flash drought (FD), severely affects terrestrial ecosystems and water resources. Water use efficiency (WUE) is an essential parameter in understanding the relationship between the water and carbon cycles. However, little is known about the response of WUE to FDs in the Korea peninsula. Therefore, this study identified FD events in Korea using the evapotranspiration (ET) based Standardized Evaporative Stress Index (SESI) and rate of intensification (RI) using soil moisture at flux tower site. Results showed that FD events detected similar patterns in both SESI and RI. At the regional scale, we identified Korea FD frequency and duration via anomalies in SESI using MODerate Resolution Imaging Spectroradiometer (MODIS). Results showed that Korea suffered from 61.3% of FD events for 20 years. The regions with the most FD events were primarily found within the north and east, where the main landcover type is forest, and long FD events (over 30 days) were detected in the northeastern study region. In addition, the effects of FD events on WUE were different based on FD magnitude and landcover types. The changes in WUE response to moderate FD events were obviously driven by the GPP, and the WUE in cropland was observed the highest sensitivity to FD magnitude. To analyze FD impacts on cropland in detail, we focus on monitoring the crop response to FD using microwave remote sensing data such as Synthetic Aperture Radar (SAR) which will be helpful to detect FD effects on crops in a higher resolution.

How to cite: Kang, M. and Choi, M.: Assessing the impacts of flash drought on terrestrial ecosystem based on satellite data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12217, https://doi.org/10.5194/egusphere-egu23-12217, 2023.

The short-term prediction of soil moisture variation is a decisive indicator of irrigation scheduling and crop management in agriculture. Traditional soil water dynamic models require complex descriptions of water movement and multiple parameters to calibrate for specific fields, which limit the model’s capability of generalization. Machine learning methods based on large sample datasets can automatically learn the most accurate way of predicting soil moisture with numerous related input variables. However, it could be time consuming in training and model optimization to improve performance. Combining the advantages of both methods, we designed a new soil moisture prediction neural network guided by the water transport driving mechanism. The water balance principle is used to limit the training process with remote sensing-based field-scale evapotranspiration, meteorological rainfall and primary soil water changes calculated from a simplified soil water model. By adding the physics layer to neural network, the demand for large datasets and the requirements of training and optimization are reduced. The prediction of soil moisture is at a half-monthly scale, and we tested the model during the winter wheat growing period. The results show that it requires less training capability to achieve high accuracy. Physics-guided neural networks could act as a better framework for parameter prediction in further researches.

How to cite: Ma, Z., Wu, B., Chang, S., Yan, N., and Zhu, W.: Developing a physics-guided neural network to predict soil moisture with remote sensing evapotranspiration and weather forecasting, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10597, https://doi.org/10.5194/egusphere-egu23-10597, 2023.

The application of remote sensing can aid the decision makers and the researchers in the field of water resources for the effective monitoring of water quality in a water sparse region.  The monitoring of water quality in a wetland dominated by the heterogeneous biomass becomes more intricate. This research study was carried out in Loktak Lake, a Ramsar site nestled in the Indo-Myanmar range between the time intervals February 2022 to December 2022. In order to carry out this study, high and very high resolution multispectral satellite imageries were used. The physical water quality parameters namely electrical conductivity, total suspended solids, pH, turbidity, and nitrates were considered for the assessment. The results of this study clearly indicate a strong correlation between the field-measured parameters and reflectance. The prediction algorithms were generally the best fit to derive the water quality parameters. The model performance indices indicates good performance of the model with correlation coefficient greater than 0.80. The outcomes of this study emphasize the use of high and very high multi-spectral satellite imageries for the monitoring of water bodies with complex dynamics.

How to cite: Oinam, B., Anand, V., Sana, R. N., and Wieprecht, S.: Satellite remote sensing based approach for water quality monitoring in a data sparse region, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15535, https://doi.org/10.5194/egusphere-egu23-15535, 2023.

In agriculture, water and fertilizer are two limiting elements of plant growth. Indeed, the lack or the excess of one of them disturbs the yields in terms of quality and quantity. Optimal irrigation/fertilization and precisely dosed nutrient supply allow fast growing plants to reach their full potential, offering much larger and better quality yields. The use of remote sensing through satellites images becomes necessary in the case of a large area. To manage properly the use of water and fertilizers in a region it is necessary to know the spatial distribution of crops. So first, we have to discriminate crops. Next the control of doses and plant growth rate must be performed.

In this paper we present a tool for smart management of the water irrigation and fertilizer using remote sensing data and mathematical algorithms by considering crops as a dynamical system.

We give some mathematical algorithms to discriminate dynamical systems (crops) and after we consider the problem of detection of the impact of irrigation and fertilization on the crop through spectral signatures. For this, we consider the problem of detecting the effects of nitrogen and irrigation on the mint  by spectroscopy and we compare the obtained results with other obtained measures for rosemary  without fertilizer. For our case study, we choose potted mint as a plant that grows very fast and we apply our spectral measurement protocol to answer the following problem: Can we detect the effect of water and nitrogen by observing the growth of a given crop using the spectrometer? The results will be used for our tool to manage irrigation and fertilizer.

Keywords: irrigation, fertilizer, crop growth, remote sensing, dynamical systems

This work is the result of a research project: Alkhawarizmi/2020/11: Tool for intelligent management of irrigation water and forest heritage, funded by MESRSFC, CNRST and ADD, Morocco

How to cite: Taia, H., Wozniak, E., Bernoussi, A. S., and Amharref, M.: Toward a Smart Tool for Irrigation Systems management Using remote sensing, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9469, https://doi.org/10.5194/egusphere-egu23-9469, 2023.