HS6.10 | Innovative technologies using remote sensing data for water management applications
PICO
Innovative technologies using remote sensing data for water management applications
Co-organized by ESSI4
Convener: Lluís Pesquer | Co-conveners: Ann van Griensven, Ioana Popescu
PICO
| Wed, 17 Apr, 08:30–10:15 (CEST)
 
PICO spot A
Wed, 08:30
Remote sensing products have a high potential to contribute to monitoring and modelling of water resources. Nevertheless, their use by water managers is still limited due to lack of quality, resolution, trust, accessibility, or experience.
In this session, we look for new developments that support the use of remote sensing data for water management applications from local to global scales. We are looking for research to increase the quality of remote sensing products, such as higher resolution mapping of land use and/or agricultural practices or improved assessments of river discharge, lake and reservoir volumes, groundwater resources, drought monitoring/modelling and its impact on water-stressed vegetation, as well as on irrigation volumes monitoring and modeling. We are interested in quality assessment of remote sensing products through uncertainty analysis or evaluations using alternative sources of data. We also welcome contributions using a combination of different techniques (physically based models or artificial intelligence techniques) or a combination of different sources of data (remote sensing and in situ) and different imagery types (satellite, airborne, drone). Finally, we wish to attract presentations on developments of user-friendly platforms providing smooth access to remote sensing data for water applications.
We are particularly interested in applications of remote sensing to determine the human water interactions and the climate change impacts on the whole water cycle.

Session assets

PICO: Wed, 17 Apr | PICO spot A

Chairperson: Lluís Pesquer
08:30–08:35
08:35–08:37
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PICOA.1
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EGU24-3849
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HS6.10
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ECS
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On-site presentation
Sandro Groth, Marc Wieland, Fabian Henkel, and Sandro Martinis
Remote sensing data has become an essential component of today's disaster management. Copernicus Sentinel-1/2 satellites are capable of providing high spatial and temporal resolution information that has proven to be effective in inundation mapping and other water management applications. In the recent years, DLR has developed a cloud-based, automated processing chain that uses convolutional neural networks (CNN) to extract surface water extent from SAR and multi-spectral images of Sentinel-1/2 satellites. Resulting water masks are aggregated to a reference water product that can be used to rapidly permanent water from temporary flooded and to analyze seasonal and long-term surface water dynamics. To unlock the full potential of the data and encourage community use, the technical barriers in access and usability have to be minimized. We approach this challenge by utilizing Spatio-temporal Asset Catalogs (STAC) to publish a global, open access collection of reference water products based on Sentinel-1/2 data. STAC enables users to easily search for matching datasets and load the data locally using open source tools. We further store data assets in the cloud-optimized GeoTiff (COG) format to improve processing efficiency and scalability. To give users a quick start, we publish a set of Jupyter Notebooks that demonstrate common use cases in the context of flood mapping such as the computation of flood duration, inundation time series analysis as well as the visualization of seasonal changes in water extent.

How to cite: Groth, S., Wieland, M., Henkel, F., and Martinis, S.: Global reference water information for flood monitoring: Increasing accessibility with STAC, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3849, https://doi.org/10.5194/egusphere-egu24-3849, 2024.

08:37–08:39
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PICOA.2
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EGU24-16008
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HS6.10
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On-site presentation
Diana Pascual Sanchez, Amanda Batlle, Eva Flo, Kaori Otsu, Ester Prat, Xavier Garcia, and Lluís Pesquer

The selection of a suitable spatial resolution for the inputs and outputs of many types of modelling is crucial in most of requirements analysis.

The easy and quick solution, the highest possible spatial resolution, is not always feasible, or at least, is not the best in terms of cost – benefit.

This work aims to analyse and assess the outputs of the water quantity SWAT (model (https://swat.tamu.edu/) at different spatial detail and fragmentation of HRUs (hydrologic response units) and the corresponding validation with remote sensing products at different spatial resolutions.

This work is being developed within the framework of the AquaINFRA project (https://aquainfra.eu/) which creates an EOSC-based (https://eosc-portal.eu/) research infrastructure for an integrated vision of the hydrosphere (inland + marine). Thus, our goal is to validate selected model outputs from inland and marine components in one of the project use cases.

The study area of the use case is located in the northwest Mediterranean region, specifically in the central Catalonia coast:  the Tordera river basin (898 km2) and the connected coastal neighbouring of its mouth. The inland landscape is mainly a heterogeneous mosaic of forests (upper river basin) and shrublands, croplands, and urban + industrial zones (lower river basin). This area shows an 800 mm mean annual precipitation and 13 ºC mean annual air temperature.

This study starts with the multiscale analysis of the evapotranspiration (ET): monthly time series (2011-2022) intercomparison between SWAT modelling outputs at different numbers of HRUs (range 261 - 1958) and the remote sensing ET products: from MODIS (500m) to Landsat (30m) and other modelling products, such as C3S (Copernicus Climate Change Service; 10km). 

Some of the outputs from the inland model are selected as inputs for the regional coastal model (MitGCM + BFM), where again, the most suitable spatial resolution is a key property for the integrated models.

(This project has received funding from the European Commission’s Horizon Europe Research and Innovation programme under grant agreement No 101094434).

How to cite: Pascual Sanchez, D., Batlle, A., Flo, E., Otsu, K., Prat, E., Garcia, X., and Pesquer, L.: Multiscale remote sensing assessment of water cycle modelling outputs  , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16008, https://doi.org/10.5194/egusphere-egu24-16008, 2024.

08:39–08:41
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PICOA.3
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EGU24-9069
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HS6.10
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On-site presentation
Ioana Popescu, Andreja Jonoski, Claudia Bertini, and Sajid Pareeth

European Union´s Earth Observation Programme Copernicus provides vast amounts of free and openly accessible global data from satellites and ground-based, airborne and seaborne measurement systems for six major thematic areas: land, marine, atmosphere, climate change, emergency, and security (the Copernicus services). Though water related issues are covered in each of the  Copernicus services, there is no explicit visibility for water related data and products.  The Horizon 2020 project Water-ForCE (Water scenarios for Copernicus Exploitation) developed a roadmap to better integrate data for the entire water cycle within the Copernicus services and available to address water-related issues. It addresses the data needs and requirements from the user community point of view; analyses the current disconnection between remote sensing and in-situ data; and looks on how remote sensing is used in the modelling of water problems.

One of the objectives of the project was to look at the current state of the art in modelling using Remote Sensing (RS) data for water quantity and quality for decision support and policy, with focus on Copernicus services. The analysis  focussed on three main pillars: EU institutions and their policies; the specific approaches by national policies in all EU countries and approaches at international level.

Moreover, the analysis looked at how the Copernicus data can be more effectively used in developing and delivering the upcoming versions of the directives.

The analysis pointed out that the reasons for slow uptake of RS data (including Copernicus) in water management are primarily due to the dynamic characteristics of the sector. Water is critical resource for different socio-economic activities and there are multiple aspects to the water management as in: water resources assessment, planning development and protection (both surface water and groundwater), public water supply, waste water treatment and disposal, management of water-related disasters such as floods and droughts, agricultural water use (irrigation and drainage), water for energy production, inland navigation, water-related ecosystem services, tourism and recreation (including bathing waters), etc.

The many and diverse water management aspects are also associated with many different water-related agencies, which are rather traditional in their approaches to using data (mainly in-situ), with little awareness of opportunities for RS and products that could be employed by EU institutions and agencies to systematically monitor the impacts and the implementation status of existing water related policies. A systematic approach is found in the area of climate change, where monthly reports produced by C3S are used as a monitoring tool. This approach can be implemented in the other Copernicus services as well.

This research work has been developed within the project WaterForCE, funded by European Union’s Horizon 2020 research and innovation programme under Grant Agreement Νο 101004186.

How to cite: Popescu, I., Jonoski, A., Bertini, C., and Pareeth, S.: Improving Copernicus data access and usability for water management , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9069, https://doi.org/10.5194/egusphere-egu24-9069, 2024.

08:41–08:43
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PICOA.4
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EGU24-7324
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HS6.10
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On-site presentation
Xian Wang and Yongqiang Zhang

Numerous datasets revealing locations and alterations of water bodies have been produced from field investigations and remote sensing imagery. However, measuring surface water changes with high resolution remains a challenge. Here, a high-precision random forest (RF) model constrained by the annual maximum remote sensing indices was developed. Validation result from visual inspection shows that the accuracy of the model has reached 99.46%. Based on the improved RF model, monthly surface water variations in the Yellow River Basin over the past 10 years were quantified at 30-meter resolution using Landsat 8 and Sentinel 2 satellite images. The variations of water bodies including when water was presented, where occurrence changed and what form changes took in terms of seasonality and persistence were obtained. It is found that between 2014 and 2023, there are evident variations of permanent water bodies including formation and disappearance of surface permanent water bodies in the Yellow River Basin. Further research can be conducted on the intricate impact of climate and human activity on water bodies using the high-resolution surface water dataset provided.

How to cite: Wang, X. and Zhang, Y.: High-resolution mapping of surface water dynamics using restricted random forest: A case study in the Yellow River Basin, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7324, https://doi.org/10.5194/egusphere-egu24-7324, 2024.

08:43–08:45
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PICOA.5
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EGU24-17631
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HS6.10
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On-site presentation
Ivette Serral, Philipp Bauer, Afroditi Kita, Kostas Vlachos, Marco Matera, Matteo Basile, Joan Masó, and Ioannis Manakos

In the era of global challenges and big Earth data computation it’s becoming increasingly important to have proper interoperable solutions for describing, cataloguing, finding, accessing, and distributing highly valuable datasets. The usability and reproducibility of data under FAIR and GEO Data Sharing and Data Management Principles, with accurate description of datasets in terms of semantics and uncertainty, can make data more valuable. EC is pushing Data Spaces as a tool to manage data and generate and provide knowledge ready to use for managers and decision makers.

The contribution presents a standard-based Data Space for automatically monitoring Water Quality specifically designed for European Lakes, based on remote sensing derived datasets, in-situ monitoring stations and web services. A web map browser gives access to water quality time series products (turbidity, Chl-a, floods, hydroperiod, etc) based on EO in Cloud Optimized GeoTIFF and in-situ observation stations connected using OGC STAplus standard. The map browser integrates the overall set of capabilities: data and metadata visualization, data analytics, quality indicators linked to the QualityML dictionary; semantic tagging of the Essential Water Variables; and OGC Geospatial User Feedback (GUF). The system is accessible through the OpenID-connect authentication standard which extends the OAuth 2.0 authorization protocol that allows different rights for different users to guarantee the preservation of data.

This approach has been developed and tested under the Horizon 2020 WQeMS - Copernicus Assisted Lake Water Quality Emergency Monitoring Service (nº 101004157). Some parts of the solution have been developed under the HORIZON-CL6 AD4GD - An Integrated, FAIR Approach for the Common European Data Space (nº 101061001) co-funded by the European Union, Switzerland and the United Kingdom.

How to cite: Serral, I., Bauer, P., Kita, A., Vlachos, K., Matera, M., Basile, M., Masó, J., and Manakos, I.: An innovative Drinking Water Data Space in times of water scarcity and extreme events: the WQeMS platform, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17631, https://doi.org/10.5194/egusphere-egu24-17631, 2024.

08:45–08:47
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PICOA.6
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EGU24-15142
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HS6.10
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Highlight
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On-site presentation
Stream Cold Water Patches identification and characterisation: acquisition and analysis approaches using Uncrewed Aerial Systems (UASs)-based imagery
(withdrawn)
Roser Casas-Mulet, Johannes Kuhn, Joachim Pander, and Juergen Geist
08:47–08:49
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PICOA.7
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EGU24-12818
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HS6.10
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ECS
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On-site presentation
Zhen Zhou, Laura Riis-Klinkvort, Emilie Ahrnkiel Jørgensen, Monica Coppo Frías, Peter Bauer-Gottwein, Alexander Rietz Vesterhauge, Daniel Haugård Olesen, Alexey Dobrovolskiy, Alexey Kadek, Niksa Orlic, Tomislav Grubesa, Henrik Grosen, Sune Nielsen, Daniel Wennerberg, Viktor Fagerström, Jenny Axén, and David Gustafsson

Compared to traditional river surface velocity measurement techniques such as in-situ point measurements with electromagnetic current meters, remote sensing techniques are attractive because measurements are fast, low cost and contactless. Based on Unmanned Aerial Systems (UAS) equipped with optical equipment (e.g., HD camera) and Doppler radar, surface velocity can be efficiently measured with high spatial resolution. UAS-borne Doppler radar is particularly attractive, because it is suitable for real-time velocity determination and has fewer limitations (no seeding of the flow required, no daylight required, works for both narrow and wide rivers).

In this paper, videos from a UAS RGB video camera were analysed using both Particle Image Velocimetry (PIV) and Space-Time Image Velocimetry (STIV) techniques. Furthermore, we recorded full waveform signal data using a 24 GHz continuous wave Doppler radar (e.g., Geolux RSS-2-300) at multiple waypoints across the river. Different from previous processing methods, which only considered the processed velocity from Doppler radar itself, we propose an algorithm for picking the correct river surface velocity from the raw data. The algorithm fits two alternative models to the raw data average amplitude curve to derive the correct river surface velocity: a Gaussian one peak model, or a Gaussian two peaks model.

Results indicate that river flow velocity and drone-induced propwash velocity can be found in the river’s lower flow velocity portions (i.e., surface velocity between 30 cm/s and 80 cm/s), while the drone-induced velocity can be neglected in fast and highly turbulent flows (i.e., surface velocity > 80 cm/s). To verify the river flow velocity derived from Doppler radar, a mean PIV value within the footprint of the Doppler radar at each waypoint was calculated. Finally, quantitative comparisons of electromagnetic velocity sensor data (OTT MF Pro) with STIV, mean PIV and Doppler radar revealed that UAS-borne Doppler radar could reliably measure the river flow velocity.

How to cite: Zhou, Z., Riis-Klinkvort, L., Ahrnkiel Jørgensen, E., Coppo Frías, M., Bauer-Gottwein, P., Rietz Vesterhauge, A., Haugård Olesen, D., Dobrovolskiy, A., Kadek, A., Orlic, N., Grubesa, T., Grosen, H., Nielsen, S., Wennerberg, D., Fagerström, V., Axén, J., and Gustafsson, D.: Measuring river surface velocity using UAS-borne Doppler radar, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12818, https://doi.org/10.5194/egusphere-egu24-12818, 2024.

08:49–08:51
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PICOA.8
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EGU24-525
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HS6.10
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ECS
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Highlight
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On-site presentation
Dominik Brétt and Jan Pacina

Cutting-edge techniques for analyzing archival aerial survey images are crucial in enhancing terrain analysis and landscape modeling. This research study is centered on examining the effects of different image parameters, namely contrast, brightness, and smoothing, on the terrain model creation process. The goal is to determine the ideal combination of these factors that will optimize the quality of the produced models and improve their precision for water management applications.

The methodology includes collecting and preparing the images (by cropping) and conducting experiments to adjust the image parameters before creating terrain models to identify the best combination of adjustments.

The study's results reveal a significant influence of modified parameters on the final terrain models. Boosting contrast or brightness in images can enhance model intricacy, but excessive tweaking of these parameters may decrease accuracy. Image smoothing has emerged as a crucial component in noise reduction and obtaining smoother terrain models.

How to cite: Brétt, D. and Pacina, J.: Landscape Modeling: Impact of Image Parameters in Processing Historical Aerial Surveys for Water Management, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-525, https://doi.org/10.5194/egusphere-egu24-525, 2024.

08:51–08:53
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PICOA.9
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EGU24-19391
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HS6.10
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On-site presentation
Enhancing Capabilities for Integrated Onshore and Offshore Freshened Groundwater Modeling: A Case Study in the Maltese Islands
(withdrawn)
Adam Gauci, Ariel Thomas, and Joel Azzopardi
08:53–08:55
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PICOA.10
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EGU24-16336
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HS6.10
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ECS
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On-site presentation
Riddick Kakati, Matteo Dall’Amico, Marco Toffolon, Federico Di Paolo, Stefano Tasin, and Sebastiano Piccolroaz

In water resources management, predictive services are essential to support sustainable planning and operations over a range of time scales, from the short term (days) to the medium term (seasons) to the long term (years to decades). Current forecasting tools mainly address water availability (i.e., quantity), with limited practical applications for water quality. Within the framework of the project called “Strumenti di monitoraggio e previsionali sullo stato di QUalità delle Acque Superficiali” (SQUAS; founded by CARITRO Foundation, Italy; website: https://sites.google.com/unitn.it/hydrosquas), we aim to fill this gap, which is particularly relevant in view of the ongoing transformation of water resources due to rapidly changing climatic conditions. More specifically, we aim to 1) increase the accessibility of tools for diagnosing and predicting surface water quality for use by local authorities and managers of surface water resources, such as agricultural consortia, hydroelectric plant operators, municipal companies, and public entities, and 2) improve the ability of these entities to plan and manage water resources efficiently and sustainably. Anchored in a multidisciplinary approach, the project integrates physical-based modelling used to forecast key water quality parameters with satellite remote sensing data for monitoring purposes. As for the modelling component, the project will be based on the widely used air2water and air2stream models for water temperature prediction in lakes and rivers. Central to the project is the revision, improvement and extension of these models by including water quality variables (e.g., turbidity, dissolved oxygen) and by integrating them into a state-of-the-art web-based Geographic Information System (GIS) platform. The web-GIS platform will not only allow to forecast future conditions based on the above models but also allow for real-time monitoring of water quality. Its Python fast-api based interface will provide a user-friendly GUI for the user interaction, using any web browser. The speed of computation of the forecasting models will be ensured by efficient Cython-based functions. The intuitive interface of the web-GIS platform will appeal to a wide range of users, from policy makers and water resource managers to academic researchers, facilitating informed decision-making and sustainable management practices. An interactive presentation of the web-GIS tool will be given during the session.

How to cite: Kakati, R., Dall’Amico, M., Toffolon, M., Di Paolo, F., Tasin, S., and Piccolroaz, S.: A monitoring-forecasting tool for advancing surface water quality management in lakes, reservoirs and major rivers , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16336, https://doi.org/10.5194/egusphere-egu24-16336, 2024.

08:55–08:57
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PICOA.11
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EGU24-13584
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HS6.10
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ECS
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On-site presentation
Mir Talas Mahammad Diganta, Md Galal Uddin, Tomasz Dabrowski, and Agnieszka I. Olbert

Among water quality (WQ) indicators, Chlorophyll-a (CHL) plays a pivotal role in assessing algal biomass production in aquatic ecosystems, serving as a crucial parameter for monitoring aquatic health and eutrophication events. Satellite remote sensing (RS) techniques offer an extended spatio-temporal coverage compared to conventional methods, making them valuable for CHL estimation, especially in optically complex waters like coastal and inland areas. However, the retrieval of CHL using RS in such environments poses challenges, and selecting the appropriate algorithm is one such challenge.

In this study, Sentinel-3 OLCI images were employed to estimate CHL levels in Cork Harbour, Ireland. Twenty widely used CHL retrieval algorithms, ranging from traditional blue-green band-based ocean color algorithms (e.g., OC4, OC5, OC6) to two-band and three-band NIR-red algorithms, were applied to water leaving reflectance data obtained from the Case 2 Regional CoastColour atmospheric correction algorithms. Additionally, in-situ CHL concentration data from 32 monitoring sites within Cork Harbour were used for validation.

The results revealed that three-band algorithms based on NIR-red bands (specifically B12/(B08-B11)) exhibited a higher sensitivity (R2 = 0.77) to in-situ CHL measurements, outperforming other CHL algorithms with superior performance (RMSE = 0.28 mg/m3, MAPE = 28.6%, MAE = 0.28 mg/m3, and MBE = - 0.00 mg/m3). Furthermore, the study demonstrated the potential of Sentinel-3 OLCI satellite images for CHL retrieval in Irish coastal waters. These findings offer valuable insights into optimizing CHL retrieval from remotely sensed data, potentially enhancing traditional monitoring programs by addressing existing limitations.

Keywords: Coastal and transitional water quality, chlorophyll-a, retrieval algorithms; remote sensing, atmospheric correction.

How to cite: Diganta, M. T. M., Uddin, M. G., Dabrowski, T., and Olbert, A. I.: Comparing the performance of Chlorophyll-a retrieval models in coastal and transitional waters , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13584, https://doi.org/10.5194/egusphere-egu24-13584, 2024.

08:57–08:59
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EGU24-7285
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HS6.10
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ECS
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Virtual presentation
Mayank Bajpai, Devyan Mishra, Shishir Gaur, and Ojas Srivastava

This study delves into the intricate challenges associated with employing Large Scale Particle Image Velocimetry (LS PIV) in river systems affected by anthropogenic contamination and algae, with a specific focus on the Varuna River Basin. The presence of pollutants and the proliferation of algae pose unique obstacles to the accurate assessment of flow dynamics and sediment transport using LS PIV technology.
In our investigation, we utilize a setup consisting of Jetson Nano with a camera. The setup is checked for feasibility for two cameras: i) GoPro Hero 7 camera  and ii) a Sony IMX219-200 Camera. The integration of these technologies allows for real-time observations with Real Time Messaging Protocol (RTMP), providing a dynamic perspective on the impact of contaminants and algae on the LS PIV measurements. Through this detailed case study, we scrutinize the complexities arising from the interplay of contaminants and algal growth, examining their effect on the data captured by our setup. Lastly, a holistic comparison of both the setups is done. The findings contribute valuable insights for researchers and practitioners working on water quality assessment and river management strategies in regions facing similar challenges.

How to cite: Bajpai, M., Mishra, D., Gaur, S., and Srivastava, O.: Investigating the Complexities of LS PIV in River Contaminations: A case study of Varuna River Basin, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7285, https://doi.org/10.5194/egusphere-egu24-7285, 2024.

08:59–09:01
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EGU24-18504
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HS6.10
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ECS
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Virtual presentation
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Abhinav Galodha, Sanya Anees, Brejesh Lall, and Shaikh Ziauddin Ahammad

India is ranked 120th among 122 countries globally in WaterAid’s water quality index. Regular water quality monitoring is essential to determine which inland water bodies are experiencing depreciating water quality. Long-term trends have been obtained using satellite remote sensing, necessitating multiple image analysis. The computational burden of processing numerous satellite images can be reduced using Google Earth Engine’s (GEE) cloud computing capabilities. Thorough research was conducted to determine the global spatio-temporal and biochemical factors impacting surface water quality (WQ). The public availability of geospatial datasets and free access to cloud-based geo-computing platforms such as GEE are widely used for spatio-temporal mapping, global surface water monitoring, water quality parametric variation, and real-time forecasting. Many researchers have recently focused on improving data mining and machine learning algorithms to accurately deal with image classification and predictive problems for crop identification and monitoring. In the present study, we propose to define band spectral ratios, spectral band equations, and empirical models for water quality parameters. A few neural network models will be used to (i) query and pre-process satellite earth observations that coincide with the study area, (ii) extract the spectra, and (iii) use spectral band wavelength charts, time-series charts, spatial-distribution maps, and the development of an online dashboard application to visualize the results graphically upon using integrated Landsat (8,9), Sentinel-2A/B and PlanetScope satellite data for the pre-monsoon and post-monsoon seasons in 2023 in the pan-India region. MODIS-TERRA provides LST spatial-temporal monitoring. In this, we have assessed and compared the performance of CART, SVM, and RF ML algorithms. We found that RF outperforms CART and SVM algorithms in the GEE platform with PlanetScope data (80.71% overall accuracy (OA) with Kappa 0.89) and also with the integration of PlanetScope and Sentinel-2A/B data (OA = 85.53%, Kappa 0.91). But CART outperforms RF and SVM algorithms with Sentiel-2A/B data (OA = 81.59%, Kappa 0.85). The SAM technique, spectral feature fitting, continuum band removal, and other band-spectral ratio techniques are employed for quantitative hyperspectral data analysis. Specifically, the performance metrics of XGBoost and SGD for both Chl-a (R^2 = 0.818) and Turbidity (R^2 = 0.815) models exhibited robust accuracy. We have also developed a Google API-based JavaScript code that can be tested under complex coastal shores, challenging inundation and variable climatic conditions. Our method provides the end-to-end cloud computing workflow shown in this research, considering cost and computational efficiency for timely information delivery.

Keywords: Classification and Regression Trees (CART), Chlorophyll-a (conc), Color dissolved organic matter (CDOM), Composite water management index (CWMI), Environmental Mapping and Analysis Program (EnMAP), Land Surface Temperature (LST), Machine Learning (ML), Random Forest (RF), Spectral Angle Mapper (SAM), Stochastic Gradient Descent (SGD), Support Vector Machine (SVM), Total Suspended Solids (TSS), Water Quality (WQ), XGBoost (Extreme Gradient Boosting).

How to cite: Galodha, A., Anees, S., Lall, B., and Ahammad, S. Z.: Google’s cloud computing platform performance assessment of spatio-temporal and estimation for global water quality analysis and surface water mapping using integrated satellite data of MODIS (TERRA), Landsat- (8, 9), Sentinel‑2A/B and Planets, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18504, https://doi.org/10.5194/egusphere-egu24-18504, 2024.

09:01–09:03
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PICOA.13
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EGU24-7795
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HS6.10
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On-site presentation
Ana Andreu, Rafael Pimentel, Eva Contreras, Raquel Gómez-Beas, Cristina Aguilar, Javier Aparicio, Francisco Herrera, Noe Rodriguez-Fernandez, and María José Polo

Significant progress has been achieved since the 2013 implementation of ecological flow rates due to the Water Framework Directive in Spain (WFD). Nevertheless, certain shortcomings exist, such as adequately monitoring compliance and analyzing the ecological response post-implementation. This is especially evident in areas characterized by complex meteorology, with extended periods of drought, as observed in regions affected by the Mediterranean climate. Moreover, it is crucial to combine minimum flows with pollution issues, whether anthropogenic or natural, to attain the good ecological status of water bodies.

Our study aims to address three distinct questions: 1) How does implementing various environmental flow regimes impact the levels of hydrological alteration in terms of water quality and riparian vegetation downstream of the reservoirs? 2) How can we use remotely sensed information to complement existing water monitoring networks to assess changes in water quality and riparian vegetation? 3) What is the required spatiotemporal resolution needed to monitor these alterations?  

The pilot reservoir to conduct this study is within the Guadalquivir River Basin (southern Spain). This basin has relevant problems of reservoir silting and water pollution arising from high erosion and human intervention rates. To assess the evolution of water quality, quantity, and vegetation state, we evaluate different indexes derived from high, medium, and low spatial resolution VIS/NIR satellite images, with temporal resolution ranging from daily to biweekly. The analysis spans the period from 2018 to 2023, and we correlate remotely sensed information with ground data series of reservoir inlet and outlet flows, volume, and water level, provided by the regional government's Automatic Hydrological Information System (SAIH), but also with water quality data provided by the regional government’s DMA network (WFD approach). This also allowed for evaluating the relationship between flow regimes and the estimated water and vegetation parameters.

Higher spatiotemporal scales proved crucial in studying changes in riparian vegetation, capturing the natural characteristics of Mediterranean riversides, which are not very wide and exhibit marked seasonal patterns. Due to the homogeneous land use of the basin, coarse-resolution indexes accurately reflect nearby vegetation patterns, serving as a proxy for the basin's ecological status. For water quality indexes, a spatial resolution of meters becomes necessary because, in this reservoir, invasive species proliferation and clogging levels are low. The lower resolution water index aligns with water level fluctuations, allowing us to use this information for longer-term analysis. Our ultimate goal is to provide effective metrics based on observations and simulations, accessible in quasi-real time, to support operational decision-making processes. We will apply the methodology to different reservoirs of the Guadalquivir River's upper, middle, and lower areas.

This work has been funded by the project TED2021-130937A-I00, ENFLOW-MED “Incorporating climate variability and water quality aspects in the implementation of environmental flows in Mediterranean catchments” with the economic collaboration of MCIN/AEI/10.13039/501100011033 and European Union “NextGenerationEU”/Plan de Recuperación

How to cite: Andreu, A., Pimentel, R., Contreras, E., Gómez-Beas, R., Aguilar, C., Aparicio, J., Herrera, F., Rodriguez-Fernandez, N., and Polo, M. J.: Remote Sensing Utility for Water Quality and Riparian Vegetation Monitoring in Mediterranean Reservoirs: Impact of Ecological Flow Regimes Management, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7795, https://doi.org/10.5194/egusphere-egu24-7795, 2024.

09:03–09:05
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PICOA.14
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EGU24-20369
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HS6.10
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ECS
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On-site presentation
Irene Biliani and Ierotheos Zacharias

Coastal waters face growing challenges from population growth, urban expansion, and alterations in hydrologic flows caused by climate change, affecting their quantity and seasonal patterns. Long-term observations of chlorophyll-a in aquatic environments, can show seasonal variability. Remote sensing analysis of chlorophyll-a seasonal variability is a significant approach for understanding the interactive dynamics of climate change, providing information for mitigation methods and trophic state. This study examines long-time series of Surface Reflectance datasets in selected sampling stations at coastal waters, proposes logical corrections after statistical analysis, and evaluates chlorophyll-a seasonal variability. The derived chlorophyll-a bloom phase was consistent with the limited field measurements. In addition, the results indicated that sampling stations of higher depth present better accuracy in evaluating seasonal phytoplankton blooms

How to cite: Biliani, I. and Zacharias, I.: Monitoring coastal eutrophication with remote sensing analysis. Seasonal variability of chlorophyll-a , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20369, https://doi.org/10.5194/egusphere-egu24-20369, 2024.

09:05–10:15