HS4.6 | Transforming observations and forecasts for management and policy action - the role of data services for the water related sectors
EDI
Transforming observations and forecasts for management and policy action - the role of data services for the water related sectors
Convener: Louise Crochemore | Co-conveners: Claudia Ruz VargasECSECS, Claudia FärberECSECS, Moritz Heinle, Louise ArnalECSECS, Tim aus der Beek, Charles Rougé
Orals
| Tue, 16 Apr, 16:15–17:59 (CEST)
 
Room 2.15
Posters on site
| Attendance Tue, 16 Apr, 10:45–12:30 (CEST) | Display Tue, 16 Apr, 08:30–12:30
 
Hall A
Orals |
Tue, 16:15
Tue, 10:45
Co-organized by ESSI2/GI2, co-sponsored by WMO

This session brings together HS4.6 “Improving hydro-climatic services for the water-related sectors: from S2S forecasting to climate projections, to management and policy” and HS1.2.4 “From observations to action: role of data services in hydrological research and management”.

We present a forum for discussing ideas, efforts and challenges in developing data products and hydro-climate services that aim to support water-related sectors. The session will bring together research scientists and operational managers in the fields of hydrology and climate and will showcase real-world applications of datasets, products, and services for research purposes and/or to tackle societal needs.
This session thus aligns with the goal of the Ninth Phase of the UNESCO Intergovernmental Hydrological Programme Strategic Plan (IHP IX; 2022 - 2029), which puts science, research and management into action for a water secure world. The contributions of this session will cover the following topics:

1. Data - observations, forecasts, projections:
- metadata, quality assurance,
- downscaling,
- advances in sub-seasonal, seasonal and decadal hydrological predictions,  
- seamless forecasting techniques and applications,
- data-driven and process-based approaches,
- extreme events prediction.
2. Databases and services:
- improvement in database services,
- operational hydro-climate products and services,
- tools and platforms for data exchange and exploration,
- collaborative and interoperable data platforms for better decision-making.
3. From data to action: role of data and climate services for societal needs
- data-driven studies and projects that aim to support decision-making and policy-making,
- studies showing the contribution of large data services to assessing water resources at national, regional and global scales,
- case studies demonstrating the benefits of operational observation networks to improve local, regional and global hydrological products and services,
- approaches integrating weather, climate and / or socio-economic information into decision-making frameworks,
- perspectives on forecast value for end users.

Orals: Tue, 16 Apr | Room 2.15

The oral presentations are given in a hybrid format supported by a Zoom meeting featuring on-site and virtual presentations. The button to access the Zoom meeting appears just before the time block starts.
Chairpersons: Claudia Ruz Vargas, Charles Rougé, Louise Arnal
16:15–16:20
16:20–16:23
16:23–16:33
|
EGU24-10766
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HS4.6
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solicited
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Highlight
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On-site presentation
Adina-Eliza Croitoru

The World Meteorological Organization (WMO) approved the Unified Data Policy in 2022, and different groups are working on its implementation under the coordination of the Commission for Observation, Infrastructure and Information Systems  (INFCOM), through the Focus Group on Data Exchange Policy. The WMO Research Board coordinates the actions on data exchange with the research sector through a dedicated Task Team: Task Team on Data Exchange with the Research Sector (TT DERS). The TT DERS’ main activities aim to accomplish section #4 of the WMO Resolution: Members should provide without charge access to all recommended data to public research and education communities for non-commercial activities. They consist of: i. Consultation with research communities dealing with weather, climate, water, cryosphere, and atmospheric composition on data exchange availability; ii. Involvement of research communities to monitor implementation and identify problems/opportunities; iii. Identification of case studies or use cases to be documented as best practice exemplars; iv. Outreach to National Meteorological and Hydrological Services on the benefits of data exchange with the research and academia.

Preliminary analysis revealed that:

  • Reasons for not sharing the data at the national/regional/international level are different from one region/country to another: lack of data, personnel to organise the data basis, or infrastructure to store/pre-process the database, national data sharing policy and regional geopolitical sensitivities;
  • There are big differences in approach for freely sharing the data in neighbouring countries;
  • Access to hydrological data is more critical than weather and climate data;
  • Access to observation data, especially for the old period for which they are not in a digital format, is quite limited.

WMO can support the Member States in exploring the possibility of integrating data from other sources when data are missing, developing the capacity and infrastructure, or providing recommendations to agree the national regulations with the WMO resolution.

In some situations, especially in developing countries, no particular reason for not sharing the data with the research community was identified. Thus, we assumed there was no will to share the data rather than objective limitations in doing it. Under these circumstances, the role of the WMO through the TT DERS and other bodies is to advocate for mutual benefit data sharing and finding those triggering factors (such as receiving funding, improvement of weather and hydrological forecast, better quality of climate/hydrological services) that could change the attitude, to encourage mutual exchanges of data and infrastructure, and make use of strategic communication.

How to cite: Croitoru, A.-E.: Data Exchange with the Research Sector - the World Meteorological Organization Perspective, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10766, https://doi.org/10.5194/egusphere-egu24-10766, 2024.

16:33–16:43
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EGU24-21980
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HS4.6
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On-site presentation
Stephan Dietrich, Matthias Zink, and Wouter Dorigo

In this study we refer how hydrological observation systems impact not only hydrological applications but also numerical weather prediction and climate re-analysis. The terrestrial water cycle, vital to the Earth system, intricately links with the atmosphere, biosphere, and human activities. Climate change intensifies water storage and flux changes, escalating the frequency and severity of water-related disasters. Water-related deaths have doubled in the last decade, with projections indicating a continued rise. Hydrological observations, unlike atmospheric weather data, lack systematic exchange of measurement. Existing datasets, mainly annual yearbook data, lack timeliness for operational numerical weather prediction and are only rarely accompanied by near real time data or satellite observations.

The WMO Task team EarthHydNet explores extending the Global Basic Observing Network (GBON) to incorporate hydrological observations, enhancing surface-based data for weather forecasts. The workshop addresses bridging observational gaps for Numerical Weather Prediction (NWP) and Climate Reanalysis, focusing on user requirements and data standardization. The study will focus on two aspects:

  • The status of the global terrestrial water observation architecture will be presented, showcasing the capacities and limitations of systematic observations, both in situ and via satellite remote sensing products.
  • Three hydrological observations—precipitation, snow, and soil moisture—have been identified as key to improving NWP in previous workshops. Soil moisture is directly influenced by rainfall patterns and vegetation systems, and it influences in turn both rainfall regimes and vegetation development. Unfortunately, soil moisture observations, coordinated under the International Soil Moisture Network (ISMN), are currently sparse in space and time, limiting climate change applications and NWP. Yet in situ observations are crucial because satellite products only provide information about the top few centimetres of the soil, and their capabilities are limited by dense vegetation.

The GBON expansion to terrestrial hydrological variables aligns with the WMO Earth System Approach, aiming to understand the planet as a whole system where atmospheric, oceanic, and terrestrial components are interconnected.

How to cite: Dietrich, S., Zink, M., and Dorigo, W.: Connecting different roles of globally systematic ground-based hydrological observations for Numerical Weather Prediction and Climate Reanalysis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21980, https://doi.org/10.5194/egusphere-egu24-21980, 2024.

16:43–16:53
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EGU24-19293
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HS4.6
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On-site presentation
Julia Kraatz, Katharina Demmich, Johannes Schnell, Benedikt Gräler, Stefano Bagli, and Paolo Mazzoli

Climate change is a pressing issue that affects countries and communities around the world. As global temperatures change intermittently, so do the occurrences and intensities of extreme weather events: which creates compounding, and sometimes simultaneous, instances of disasters. Thus, it is evident there is an urgent need for improved paradigms within the Disaster Risk Management (DRM) and climate change adaptation (CCA) domains, to promote better risk assessment, governance, communication, and systems which prevent, and respond, to disaster events. The DIRECTED project aims to facilitate disaster resilience among European societies, by creating a cohesive approach to Disaster Risk Reduction (DRR) and CCA strategies, and by promoting multi-risk thinking in relation to compounding events. The project will achieve this through integrated models, interoperable data, governance, cross-communication between actors, and the central platform, the Data Fabric. 

This presentation provides an overview of the technical requirements and analyses which will provide the foundation for the DIRECTED Data Fabric; the Data Fabric will serve as a federated spatial information system, capable of integrating diverse data sources and executing flood and risk modeling across institutions. The Data Fabric requires collaboration within the entire DIRECTED consortium, which will ensure interoperability, useability, and longevity of the platform. Technical requirements have been discussed with data providers and modelers to establish infrastructure which is capable of  visualizing flood and risk model outputs, as well as readily available climate data. Datasets involved range from custom file-based datasets to Spatial Data Infrastructures with Open API-based data access. Additionally, data mining activities have been carried out to produce flood forecasts based on a re-analysis of publicly available data from Copernicus: this has been done by accessing archives and calculating pixel-wise statistics, to spatially quantify upcoming forecasts and their potential severity. Significant challenges which have emerged include semantic interoperability, which encompasses aspects from data input and model parameterization, to the interpretation of model outputs. This presentation details how these challenges are addressed in DIRECTED, to create a cohesive and user-friendly spatial information system.

Based on an in depth requirement analysis of the RWLs, we will develop preliminary visualizations of climate services, which in turn will be hosted in the cloud-based Data Fabric. The co-creation and co-design approach thereby ensures end-users’ understanding of information in the platform, and the useability of the platform as a whole. 

Since a plethora of extreme weather patterns exist within the scope of DIRECTED (including, but not limited to: pluvial and fluvial flooding, droughts, wildfires, and erosion), the Data Fabric represents a significant step towards establishing a robust and adaptable spatial information system, capable of meeting evolving climate needs for geographically diverse stakeholders. 

How to cite: Kraatz, J., Demmich, K., Schnell, J., Gräler, B., Bagli, S., and Mazzoli, P.: A Federated Data Fabric to Enhance Disaster Resilience for Extreme Weather Events , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19293, https://doi.org/10.5194/egusphere-egu24-19293, 2024.

16:53–16:56
16:56–17:06
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EGU24-16596
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HS4.6
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On-site presentation
Jamie Hannaford and Katie Facer-Childs and the The Hydrological Outlooks Team

The UK Hydrological Outlook provides an insight into future hydrological conditions across the UK, through sub-seasonal to seasonal forecasts of river flows and groundwater levels. The Outlook was initiated during the 2012 drought, which subsequently terminated with severe flooding. That remarkable year saw simultaneous drought restrictions and flood warnings, underlining the pressing need to go beyond situation monitoring (where are we now?) towards seasonal forecasts (what is likely to happen next?). Motivated by these events, the UK Hydrological Outlook was first implemented in 2013 and now celebrates a decade of operational service. This decade has seen notable flood (e.g., 2013 – 2014, 2015 – 2016, 2019 – 2020) and drought episodes (e.g., 2018, 2022), with the Outlook now standing as a valuable source of evidence for users ranging from the news media to government departments. Over these years, it has evolved into a dependable and widely used tool, delivering essential insights to regulators, the water industry, and other stakeholders to inform their water resources management decisions. This collaborative effort, led by UKCEH and involving the Met Office, the British Geological Survey, and the UK Measuring Authorities, has matured into a sophisticated operational system, which includes information on uncertainties and user-friendly interactive visualisation options. This presentation showcases its origins, development, and growth, bringing together multiple research strands converging towards the current operational product. In addition, we include an overview of current and future developments.

How to cite: Hannaford, J. and Facer-Childs, K. and the The Hydrological Outlooks Team: Mapping a Decade of Seasonal Hydrological Forecasting: The UK Hydrological Outlook’s Journey , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16596, https://doi.org/10.5194/egusphere-egu24-16596, 2024.

17:06–17:16
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EGU24-13436
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HS4.6
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On-site presentation
Micha Werner and Clara Linés

Farmers in irrigated agriculture depend on the water allocated to them through the irrigation season. In drought years, when allocations may be curtailed, early information on water availability is of use to them in supporting their decisions on what to plant and when to plant. Seasonal forecasts provide such early information of water availability, and have been shown to be skilful, though effective lead times depend on the predictability of the local climate as well as the memory of the hydrological system. However, if the information provided is indeed useful, may depend on who uses it, the decisions they take, and the outcomes of those decisions.

Here, we explore seasonal forecasts in supporting decisions farmers take in an area with irrigated agriculture in the Ebro basin in Spain. We develop a simple decision model, considering the preferences farmers have and the crop choices they make depending on if water is expected to be abundant or if it is expected to be scarce. The model also considers the interconnected water allocation decision by operators of the reservoir feeding the irrigation area, and the expectation they have of the balance between supply and demand to the end of season. Demand is informed by the (expected) choices farmers make, while supply is predicted using bias corrected ECMWF System 5 seasonal forecasts and a simple hydrological model.

To understand to whom the forecast is useful, we consider farmers with differing levels of technical capability, which allows them to plant either one or two crops per season; as well as with differing levels of risk averseness. Decisions informed by the seasonal forecasts for each of these farmer types are then compared to those made using perfect information, and to those made using current allocation practice. We then evaluate (relative) benefit through simulating the outcome of the forecast decisions using the observed climate and a crop model to predict yield, and the market price versus investment costs of crops planted to predict net profits.

Results show that seasonal predictions of water availability in the area are skilful, attributed largely to catchment memory, though skill varies; with poorer skill early in the season and around the spring snowmelt. The decision timelines through the season vary per farmer type. Risk averse farmers with less technical capability take key decisions earlier in the season. While forecasts are potentially more useful to those early decisions, we find these are also more sensitive to uncertainty in the forecast. The more technical farmers take decisions late in the season, where skill is higher. They can then rely more on the information provided, though the added value of the forecast to them is lower. These results show that not all are served equally by seasonal forecast information. Some stand to benefit more than others, depending on the decisions they make, and when they take these.

How to cite: Werner, M. and Linés, C.: Seasonal forecasts to support cropping decisions: To whom are these useful, and when?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13436, https://doi.org/10.5194/egusphere-egu24-13436, 2024.

17:16–17:26
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EGU24-14096
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HS4.6
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On-site presentation
Steve Burian, Ryan Johnson, Danyal Aziz, Courtenay Strong, Paul Brooks, Margaret Wolf, Logan Jamison, Luke Stone, Laura Briefer, Jesse Stewart, Tracie Kirkham, and Tamara Prue

Western United States municipal water system management requires estimates of system performance with sufficient lead time to identify and mitigate potential vulnerabilities. Given their dependence on winter snowpack and the resulting timing of surface water availability contrasting that of peak water demand, there is a need to deliver earlier estimates of system performance to increase the lead time for decision-making. Addressing this need, we develop a long to short-term water systems operations workflow that provides operators with estimates of performance with up to a ten-month lead time and demonstrate the workflow using the Salt Lake City Department of Public Utilities in Northern Utah, United States. The workflow leverages teleconnections with global climate signals to estimate the precipitation, surface water yield, and demand for up to a ten-month forecast horizon. We use the estimates of supply and demand to drive a water systems model that provides a range of likely reservoir levels, groundwater withdrawal volumes, and volume of out-of-district water needs to determine potential system vulnerabilities needed to evaluate and develop mitigation measures.

How to cite: Burian, S., Johnson, R., Aziz, D., Strong, C., Brooks, P., Wolf, M., Jamison, L., Stone, L., Briefer, L., Stewart, J., Kirkham, T., and Prue, T.: Extending the Decision-Making Lead time of Municipal Water System Management Using Teleconnections to Support Supply and Demand Estimates, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14096, https://doi.org/10.5194/egusphere-egu24-14096, 2024.

17:26–17:29
17:29–17:39
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EGU24-1895
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HS4.6
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ECS
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solicited
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On-site presentation
Sandra Margrit Hauswirth, Marc F.P. Bierkens, Vincent Beijk, and Niko Wanders

Extreme events like droughts and floods can have significant impact on the environment and society. As a result effective water management strategies are necessary to limit and mitigate these impacts. In the past decade, the Netherlands has experienced several extreme drought events, raising increasing interest in adapting water management practices, traditionally focused on floods, to address droughts more directly and effectively.

Machine learning techniques have previously been tested for the same region in terms of seasonal forecasting1  and projections2 under different warming scenarios3, showing the additional benefit of these techniques in downscaling large-scale input data to local-scale relevant information for water managers.

This recent work aims to go a step further to explore water management options for drought mitigation by incorporating machine learning in a framework of hydrological simulations, water management scenarios and impact functions. By incorporating the insights gained from previous work, a closer focus is given on human aspects and its impact on local drought management.

We developed a Multi-Target Long Short-Term Memory (LSTM) model which facilitates the exploration of different water management options. An essential finding is that taking proactive actions earlier can further limit drought impacts and help to mitigate long recovery periods that would have been observed otherwise. With the Multi-LSTM water management model we can potentially reduce drought impact by 3-5% for the droughts in 2003, 2015 and 2018. As a results, this work yields valuable insights for operational water management and potential improvements in water management strategies with machine learning techniques to effectively address future drought events.

1) Hauswirth, S. M., Bierkens, M. F. P., Beijk, V., and Wanders, N.: The suitability of a seasonal ensemble hybrid framework including data-driven approaches for hydrological forecasting, Hydrol. Earth Syst. Sci., 27, 501–517, https://doi.org/10.5194/hess-27-501-2023, 2023.

2) Hauswirth SM, van der Wiel K, Bierkens MFP, Beijk V and Wanders N (2023) Simulating hydrological extremes for different warming levels–combining large scale climate ensembles with local observation based machine learning models. Front. Water 5:1108108. doi: 10.3389/frwa.2023.1108108

3) Van der Wiel, K., Wanders, N., Selten, F. M., & Bierkens, M. F. P. (2019). Added value of large ensemble simulations for assessing extreme river discharge in a 2 °C warmer world. Geophysical Research Letters, 46, 2093– 2102.

How to cite: Hauswirth, S. M., Bierkens, M. F. P., Beijk, V., and Wanders, N.: Exploring Water Management Strategies for Mitigating Local Drought Impacts in the Netherlands using Data-Driven methods previously used for Simulations to Projections, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1895, https://doi.org/10.5194/egusphere-egu24-1895, 2024.

17:39–17:49
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EGU24-16043
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HS4.6
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Highlight
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On-site presentation
Jonghun Kam, Seunghui Choi, and Anqi Liu

A severe drought causes catastrophic economic losses, resulting in mental degradation/deterioration. While drought monitoring has been focused on detecting and characterizing an emerging drought (physical system-focused), artificial intelligence with big data from social monitoring provides a unique opportunity to investigate sentimental alterations of the public along the drought propagations and explore their triggers (social system-focused). This study examines the potential of an AI technique, Natural Language Processing (NLP), in monitoring sentimental alterations of the public. This study is a case study of the recent Korea drought, leveraging X (formerly, twitter) and Google Trends data. In this study, we evaluate the seasonal-to-seasonal predictability of drought measures in the southwest region of the Korean Peninsula for the 2022/23 period and analyze spatiotemporal changes in media and public interest in drought phenomena during the 2022/23 drought period through newspapers and social media. Initially, to understand the predictability of drought measures in March 2023, we evaluate the predictability of drought measures based on probabilistic and deterministic seasonal-to-seasonal forecasts for the 2022/23 Korea drought. Subsequently, using drought-related articles in newspapers and Twitter data from the 2022 to 2023, we utilize natural language processing and text mining technologies to detect and monitor the topic and emotional alternation of the titles of news article and public, respectively, regarding the 2022/23 drought. The results of this study indicate that the predictability of drought measures in May 2023 is skillful at the sub-seasonal scale but limited at the seasonal scale. Statistical forecasts provide crucial information on precipitation needed for drought recovery through weekly precipitation forecasts, aiding in assessing the drought condition. The public interest in the 2022/23 drought shows spatiotemporal differences based on the drought-affected areas and drought stages. Especially in April 2023, when a severe drought occurred in the southwestern Korean region, an increase in the number of newspaper articles with negative titles was observed, and negative emotions were detected from the social media data. This study provides an insight about the role of AI in developing the next-generation drought monitoring system.  

How to cite: Kam, J., Choi, S., and Liu, A.: Next Generation Drought Monitoring: Forecasting to Emotion-Focused Coping, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16043, https://doi.org/10.5194/egusphere-egu24-16043, 2024.

17:49–17:59
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EGU24-19945
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HS4.6
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On-site presentation
Manuel Pulido-Velazquez, Dariana Avila-Velasquez, Hector Macian-Sorribes, Juan Manuel Carricondo-Anton, Carlos Antonio Echeverria, Felix Frances, Alberto Garcia-Prats, Francisco Martinez-Capel, Marta Garcia-Molla, Miguel Angel Jimenez-Bello, Fernando Martinez-Alzamora, Ivan Gerardo Lagos-Castro, and Juan Manzano-Juarez

Forecast-informed decision-making has been proven to improve water management. However, the practical implementation of such systems need to account for a wide range of processes and variables with the proper spatiotemporal resolution at the regional and local levels (meteorological, hydrological, agronomic, reservoir management and ecosystems). Furthermore, forecasts need to cover all the relevant temporal scales, from short-term to subseasonal to seasonal, to ensure an integrated approach including from quick emergency responses to strategic operational decisions.

In this regard, the project "Integrated Water and Environmental Forecasting System (WATER4CAST)" develops an innovative visual decision support system (VDSS) to enable forecast-informed decision-making in the Jucar River Basin (Spain) covering the above processes (https://water4cast-app.upv.es/). The VDSS offers short-term (15 days), subseasonal (8 weeks) and seasonal (6-7 months) forecasts. It includes meteorological (temperature, precipitation, solar radiation, wind), hydrological (streamflow, soil moisture, reservoir inflows), agronomic (potential evapotranspiration, irrigation needs), environmental (habitat for native fish species) and water resource management variables (stored volumes, reservoir releases) and indicators (drought and fire risk). Short-term meteorological forecasts come from the NOAA GFS, while subseasonal predictions are obtained from the NOAA CFS. On the contrary, a multi-model approach is adopted to acknowledge uncertainty in seasonal forecasts, employing predictions from the Copernicus Climate Change Service (C3S). All raw forecasts are post-processed to correct biases, ensuring their fit to the local climatic patterns of the Jucar River Basin using artificial intelligence (fuzzy logic). Hydrological forecasts are provided by the fully-distributed eco-hydrological model TETIS, properly calibrated and validated for the Jucar. Agronomic forecasts rely on FAO56 agronomic models are tailored to the irrigated areas of the Jucar with the support of remote sensing. Ecosystem forecasts employ fish habitat models that relate streamflows to suitable habitat of native species. Finally, reservoir operation forecasts are provided by a water resource management model whose operating rules are defined using fuzzy rule-based systems.

The VDSS consists of two parts: a public part and a private part available to specific users on request for selected variables considered sensible. The VDSS was co-developed with the users of stakeholders of the Jucar River Basin to ensure they account for their needs.

Acknowledgement: This work has been carried out with funding from the project “INtegrated FORecasting System for Water and the Environment (WATER4CAST)”, funded by the Program for the promotion of scientific research, technological development and innovation in the Valencian Community for research groups of excellence, PROMETEO 2021 (ref: PROMETEO/2021/074), from the Ministry of Innovation, Universities, Science and Digital Society. Generalitat Valenciana. Recognition of the University Teacher Training (FPU) grant from the Ministry of Universities (FPU20/0749). 

How to cite: Pulido-Velazquez, M., Avila-Velasquez, D., Macian-Sorribes, H., Carricondo-Anton, J. M., Echeverria, C. A., Frances, F., Garcia-Prats, A., Martinez-Capel, F., Garcia-Molla, M., Jimenez-Bello, M. A., Martinez-Alzamora, F., Lagos-Castro, I. G., and Manzano-Juarez, J.: WATER4CAST- integrated Forecasting System for Water and the Environment, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19945, https://doi.org/10.5194/egusphere-egu24-19945, 2024.

Posters on site: Tue, 16 Apr, 10:45–12:30 | Hall A

The posters scheduled for on-site presentation are only visible in the poster hall in Vienna. If authors uploaded their presentation files, these files are linked from the abstracts below, but only on the day of the poster session.
Display time: Tue, 16 Apr 08:30–Tue, 16 Apr 12:30
Chairpersons: Louise Crochemore, Claudia Färber, Tim aus der Beek
A.53
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EGU24-14183
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HS4.6
Andy Wood, Joshua Sturtevant, Katie Van Werkhoven, Matthew Denno, and Terri Hogue

The US NOAA Cooperative Institute for Research to Operations in Hydrology (CIROH) is a consortium of several dozen US institutions (academic, private, non-profit) that collectively partner with the US National Water Center (NWC) to conduct research to advance operational hydrologic forecasting services.  This presention describes the new CIROH Hydrologic Prediction Testbed (CHPT), a community-oriented initiative to establish rigorous, quantitative intercomparison and benchmarking of US operational hydrologic forecasts, and particularly the multiple elements – models, methods, datasets – involved in producing them.  The Testbed’s overarching goal is to address the problematic lack of coherence of research into fundamental challenges and needs for operational prediction systems, which is a significant impediment to intercomparison, benchmarking, and synergistic learning across diverse investments into forecasting research and development. Hundreds of localized, one-off studies are published, yet few of the resulting potential advances ever become operational. The CHPT promotes a benchmark-oriented paradigm through facilitating the use of multiple community-based experimental protocols with standardized evaluation tools, targeting different forecasting and forecasting sub-component objectives. Examples of forecasting sub-components include the models, model parameterizations (e.g., glacier physics, channel routing), input datasets, and techniques (e.g., data assimilation, post-processing, ensemble methods), across time scales from nowcasting to multi-season prediction. This paradigm is essential to produce a consistent intercomparison and evaluation of innovations arising from distinct forecast-related research projects across the community.  This in turn supports a rational assessment of the potential gains of each innovation against current operational baseline capabilities. As it matures, the CHPT will enable the US to quantify and track the current performance of its hydrologic forecasting capabilities – for the first time – enabling evidence-driven decisions regarding the adoption of new forecast elements into operational practice. The core concepts of CHPT are generalizable to forecasting research and development activities in countries and communities globally.  

How to cite: Wood, A., Sturtevant, J., Van Werkhoven, K., Denno, M., and Hogue, T.: Creating a community testbed to strengthen the research to operations pathway for hydrologic prediction in the US, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14183, https://doi.org/10.5194/egusphere-egu24-14183, 2024.

A.54
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EGU24-20673
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HS4.6
The data value chain-Connecting the dots from data to impact 
(withdrawn)
Luna Bharati
A.55
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EGU24-3995
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HS4.6
Yongshin Lee, Andres Peñuela, Francesca Pianosi, and Miguel Rico-Ramirez

Given the escalating uncertainty in water resources management attributed to climate change, the significance of reliable flow forecasts becomes increasingly crucial. Recent technological advancements have brought attention to seasonal weather forecasts, which provide predictions of weather variables for the next several months. Accordingly, numerous studies have investigated the skill of Seasonal Flow Forecasts (SFFs) forced by seasonal weather forecasts, in various regions and countries. Our previous work on the skill of SFFs across South Korea demonstrated that SFFs generally outperform Ensemble Streamflow Prediction (ESP) up to 3 months ahead and exhibit notably higher skill during the wet season in abnormally dry years.

This study builds upon our earlier research with the objective of evaluating the value of SFFs for reservoir operations for drought management. This analysis is conducted for two pivotal reservoirs, Soyanggang and Chungju, which serve as the major water sources for the metropolitan area, including the capital city, Seoul. For the severe drought period from July 2014 to June 2016, we used model simulation to compare different reservoir operation models: the Simple Conjunctive Operation (SCO) model, forced by either a worst-case or low inflow scenario (similarly to what currently done in reality) and the Forecast-informed Conjunctive Operation (FCO) model, forced by either ESP or SFFs. Multi-objective evolutionary algorithm is utilised to optimise release scheduling with two objective functions: securing storage volume at the end of the hydrological year and minimizing supply deficit over the entire year. We explore the impact of using different reservoir operation models, different forecasting lead times, as well as different ways to determine a ‘best compromise’ solution between the two competing objectives.

How to cite: Lee, Y., Peñuela, A., Pianosi, F., and Rico-Ramirez, M.: Assessing the value of seasonal flow forecasts in reservoir operations for drought management in South Korea, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3995, https://doi.org/10.5194/egusphere-egu24-3995, 2024.

A.56
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EGU24-12675
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HS4.6
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ECS
Simon Moulds, Louise Slater, Louise Arnal, and Andrew Wood

Streamflow forecasts months ahead are an important component of flood risk management. Hybrid methods that predict seasonal streamflow quantiles using ML/AI models driven by climate model outputs are currently underexplored, yet have some important advantages over traditional approaches based on hydrological models. For example, they are computational efficient, can incorporate a wide variety of input data, and may avoid the need for spatial downscaling and/or bias correction. Here we develop a hybrid subseasonal to seasonal streamflow forecasting system to predict the monthly maximum daily streamflow up to four months ahead. We train a machine learning model on dynamical precipitation and temperature forecasts from a large ensemble from the Copernicus Climate Change Service (C3S). We show that multi-site ML models trained on pooled catchment data together with static catchment attributes are significantly more skilful compared to single-site ML models that are trained on data from each catchment individually. Overall, we find 99.8% of stations show positive skill relative to climatology in the first month after initialization, 90.7% in the second month, 57.9% in the third month and 35% in the fourth month.

How to cite: Moulds, S., Slater, L., Arnal, L., and Wood, A.: Skilful probabilistic forecasts of UK floods months ahead using a hybrid approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12675, https://doi.org/10.5194/egusphere-egu24-12675, 2024.

A.57
|
EGU24-13863
|
HS4.6
|
ECS
|
William Turner, Shraddhanand Shukla, and Greg Husak

The Famine Early Warning Systems Network (FEWS NET) and its partners use timely and spatially focused monitoring products to guide humanitarian assistance and secure livelihoods in some of the world’s most food-insecure populations. Subseasonal predictions bridge the gap between coarse, seasonal climate predictions and finer-scale, medium-range weather forecasts, providing insight for in-season agricultural decision making, including planting dates, labor allocation, and loan and fertilizer investments. In adverse conditions, they can increase the lead-time for necessary intervention (e.g., food or monetary aid); and in favorable conditions can provide opportunities for increased investment and subsequent improved agricultural productivity. Accuracy in forecasting both each scenario can enhance loss mitigation and increase capital, providing opportunities for long-term resilience building and poverty reduction. Collectively, this work aims to improve upon and contribute to early warning systems in semiarid African rainfed agricultural zones for the purpose of improving food security and livelihoods.

Here we report on an ongoing project to assess the efficacy of the NMME Experimental Subseasonal Precipitation Forecasts (SubX) for use in a regional water balance model—the Water Requirement Satisfaction Index (WRSI)in Sub-Saharan Africa. While SubX has been shown to be effective for hydrologic monitoring in India and eastern South America, and for predicting extreme events, including droughts and floods in the US and South Korea, assessments of the accuracy and certainty of SubX is quite limited over Africa. We begin to fill this gap by assessing the viability of SubX for use in WRSI deterministic and probabilistic forecasts in rainfed agricultural areas of east, southern, and west Africa, from 1999-2016. We briefly explore the regional characteristics and inter-model variability in forecast skill.

How to cite: Turner, W., Shukla, S., and Husak, G.: NMME Experimental Subseasonal Precipitation Forecasts (SubX) provide enhanced predictions of end-of-season water balance over Sub-Saharan Africa, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13863, https://doi.org/10.5194/egusphere-egu24-13863, 2024.

A.58
|
EGU24-13321
|
HS4.6
|
ECS
Aron Widforss, Liss Marie Andreassen, Yngve Are Antonsen, Stefan Blumentrath, Niklas Fossli Gjersø, Sjur Anders Kolberg, Karsten Müller, Nils Kristian Orthe, Tuomo Saloranta, Solveig Havstad Winsvold, and Rune Verpe Engeset

The Norwegian Water Resources and Energy Directorate (NVE) is responsible for operating the Varsom forecasting service issuing forecasts covering a number of natural hazards in Norway, including flooding, avalanches and lake ice coverage. Additionally, NVE is monitoring glacier lakes that have a risk for glacier lake outburst floods (GLOFs). This responsibility requires multi-modal data gathering, ranging from permanent hydrological stations and field observers digging snow pits, to analysis of country-wide satellite-borne radar and optical imaging.

In this study we demonstrate how satellite remote sensing allow us to detect hazardous events and monitor hydrological conditions. Our infrastructure utilizes publicly available satellite data from Copernicus. We process the data  through a central platform built on Apache Airflow for job monitoring, GRASS and Actinia for spatial processing and Docker for parallelization. Using this platform we have built a number of products that identifies and digitizes objects, as well as time series analysis of various hydrological data. The platform can output these products to existing public datasets, like the Norwegian national landslide and avalanche database, as well as to purpose-built solutions, like our time-series database, which will make spatially aggregated time series publicly available.

Examples of using satellite data operationally include automated detection of avalanches making it possible to validate the details of a published avalanche bulletin of a high avalanche danger episode in the spring of 2023. Snow coverage monitoring of important watersheds during the same period allowed us to get confidence in the validity of our models and resulting assessment of the risk of melting season floodings.

In addition to use cases where satellite observations give us complementary information, we use the data in areas where there is few or no other available source of information. The best example of this is the monitoring of formation and drainage of glacier lakes, where optical imaging and manual digitizing has been the go-to solution for a long time. We are now developing automated products using Sentinel-1 and Sentinel-2 data to aid mapping and monitoring

The satellite products produced at NVE provide richer information of snow, ice and hydrologic condtitions. Products are being made publicly available.

How to cite: Widforss, A., Andreassen, L. M., Are Antonsen, Y., Blumentrath, S., Fossli Gjersø, N., Kolberg, S. A., Müller, K., Orthe, N. K., Saloranta, T., Havstad Winsvold, S., and Verpe Engeset, R.: Using satellite remote sensing to evaluate and calibrate hydrological monitoring in Norway, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13321, https://doi.org/10.5194/egusphere-egu24-13321, 2024.

A.59
|
EGU24-17382
|
HS4.6
Dirk Diederen
Make the invisible visible!
With Groundwater Global Foundation we offer a mobile app with public registration, which can be used by anyone.
Utilizing speaker and microphone, this mobile app turns anyone's smartphone into a groundwater measurement device.
The measurement is based on acoustic resonance and currently works down to thirty meters deep, providing large coverage world-wide.
Hand measurements such as these may be used as a first basis for groundwater management, or may complement fully automatic systems with the occasional calibration.

The mobile app works in internet connected regions, as all recordings are centrally collected in a single database in the cloud.
Recordings are analysed on a webserver, after which the groundwater depth is returned to the mobile app.
One unique aspect that this system has to offer is that it allows for citizens and water professionals to operate jointly.
Respecting privacy, citizens' measurements are private by default.
However, citizens can choose explicitly to unlock their locations, making them publicly visible and thereby turning themselves into citizen scientists.

Datasets are available for download from the database API (csv, xlsx, json) in a few different ways.
The system works with open data in the sense that everyone can download their own measurements.
Water professionals, including academic professionals, can download their own measurements from the API by project(s).
Interestingly, we provide the option that water professionals can download citizen science data by region, where they simply have to provide us with a shapefile for the region/area of interest.

https://groundwater.global/
https://app.groundwater.global/

How to cite: Diederen, D.: The Groundwater Global App - global groundwater measurements with the smartphone, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17382, https://doi.org/10.5194/egusphere-egu24-17382, 2024.

A.60
|
EGU24-206
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HS4.6
|
ECS
Kaori Otsu, Lluís Pesquer, and Xavier Garcia

The increasing acquisition of observations in hydrological research is a critical factor to accelerate FAIR and open data sharing across relevant scientific communities.  Conventionally, research infrastructures have developed in silos that are specific to the domains and/or countries. With the ambition of tackling this fragmentation, the establishment of European Open Science Cloud (EOSC) is in progress to federate multidisciplinary research infrastructures by ‘Enabling an operational, open and FAIR EOSC ecosystem (INFRAEOSC)’ projects.  One of them, AquaINFRA (https://aquainfra.eu), has been funded to protect oceans, seas, coastal and inland waters, in support of the EU Mission 'Restore our Ocean and Waters' by 2030.

AquaINFRA will develop a research infrastructure equipped with FAIR multi-disciplinary data and services, allowing seamless data discovery and processing through the AquaINFRA Interaction Platform (AIP) to support freshwater and marine scientists and integrate with EOSC seamlessly. More specifically, the AIP will include a data discovery and access service as well as spatio-temporal models in Virtual Research Environments (VREs) to provide the optimal environment for the global hydrosphere research through interoperability with external infrastructures such as the European Digital Twin of the Ocean (EDITO).

Regional studies will highlight the Mediterranean case in Tordera and Maresme, Catalonia, with a VRE workflow to evaluate the marine ecological state impacted by land catchment interactions in a system of the hydrological cycle.  Based on the methodology of coupling freshwater and marine models using different types of data (e.g. in-situ, remote sensing, socio-economic) and services (e.g. Copernicus, EMODnet) from local, regional, national to European sources, we conclude with the challenges and opportunities for enabling a FAIR research environment among the interested regional stakeholders including scientists and decision-makers to provide feedback to the EOSC Partnership.

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

How to cite: Otsu, K., Pesquer, L., and Garcia, X.: Key role of AquaINFRA Interactive Platform integrated in blue research infrastructures, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-206, https://doi.org/10.5194/egusphere-egu24-206, 2024.

A.61
|
EGU24-11504
|
HS4.6
|
Catherine Sefton, Stephen Turner, Amit Kumar, Gayatri Suman, Isabella Tindall, Katie Muchan, Grant Kennedy, Glenda Tudor-Ward, Gary Galbraith, and Jamie Hannaford

High quality and trustworthy data on peak river flows are fundamental for assessing, monitoring, estimating and managing flood events.  In the UK, a national data service provides open access to peak flow data (annual maximum and peaks-over-threshold) with supporting metadata at more than 900 gauging locations.  A collaborative programme of work involves the four main Measurement Authorities (MAs) and the National River Flow Archive (NRFA) which is based at the UK Centre for Ecology & Hydrology (UKCEH).  This partnership and the channelling of peak flow data for the UK through one organisation also promotes sharing of best practice.  Once the data have been generated, they undergo checks by each of the MAs before being sent to UKCEH who undertake a number of complementary automatic and manual quality control checks.  These include the consistency of the data with stage-discharge ratings, the continuity of the digital and pre-digital periods, and the suitability of the data for flood estimation purposes.  Queries that arise during this process are resolved in close collaboration with the MAs.

The data are updated and released annually.  The most recent water year is added to the dataset, and a rolling programme of period-of-record review for a percentage of sites each year captures data reprocessing, newly available data from digitisation and other improvements that have come to light since the initial submission of the data to the central repository.  Following each annual cycle, the data are released in a number of accessible formats, including files which can be loaded directly into the UK’s industry standard Flood Estimation Software (WINFAP), as well as being added to the NRFA website and API.  Each gauging station has a webpage with a wealth of associated metadata and context to aid the community in using the data.

There are however challenges remaining.  These include event independence, concerned with the criteria used to derive peaks-over-threshold data, consolidation of stage-discharge ratings between the MAs and UKCEH, and further digitisation of pre-digital data where physical charts exist.  Running through all of these threads is the quantification of uncertainty in high flow measurement and the challenges in how to communicate this to users.  Initiatives are addressing some of these, but more is needed to ensure that reliable long records are available for reproducible flood estimation and trend analysis.

How to cite: Sefton, C., Turner, S., Kumar, A., Suman, G., Tindall, I., Muchan, K., Kennedy, G., Tudor-Ward, G., Galbraith, G., and Hannaford, J.: Collaborative provision of a national peak flow data service, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11504, https://doi.org/10.5194/egusphere-egu24-11504, 2024.

A.62
|
EGU24-12157
|
HS4.6
David Schäfer, Peter Lünenschloß, Bert Palm, Lennart Schmidt, Thomas Schnicke, Corinna Rebmann, Karsten Rinke, and Jan Bumberger

Global and regional hydrological databases, as well as domain-agnostic repositories, play a crucial role in advancing scientific research and decision-making processes. With new and existing data infrastructures such as TERENO and eLTER, as well as governmental monitoring initiatives, efforts to enhance the size, capabilities, and accessibility of these services are underway. However, a key challenge persists across large-scale data collections - the need for rigorous harmonization of diverse data from various sources. 

This challenge extends beyond obvious considerations like numerical precision and date formats, encompassing more nuanced aspects such as data quality and its representation. Hydrological time series data, often acquired from remote sensors and monitoring stations, are susceptible to errors arising from sensor malfunctions, anomalies, and environmental fluctuations. Unchecked, these inaccuracies can lead to erroneous results and compromise decision-making processes. 

Addressing this critical issue, the System for Automated Quality Control - SaQC emerges as a pioneering solution, offering a comprehensive tool/framework for automated and customizable quality control and processing of time series data. SaQC empowers researchers and practitioners in the hydrological sciences, providing a convenient and efficient means to identify and rectify data anomalies. In addition to a large body of built-in routines and algorithms, the framework's extensibility allows users to implement custom quality check routines and schemes, tailoring the quality control process to specific research objectives and the evolving needs of data services. 

This presentation delves into the core principles of SaQC, showcasing its flexibility in handling diverse data types and adapting to various hydrological monitoring scenarios. Through real-world examples of fully automatized quality control and data processing workflows, we highlight the benefits of SaQC in enhancing data integrity, reducing manual intervention, and expediting the analysis pipeline. SaQC not only identifies anomalies but also provides a systematic and transparent approach to data quality assurance, contributing to the overall reliability of hydrological datasets. 

 

Lennart Schmidt, David Schäfer, Juliane Geller, Peter Lünenschloss, Bert Palm, Karsten Rinke, Corinna Rebmann, Michael Rode, Jan Bumberger, System for automated Quality Control (SaQC) to enable traceable and reproducible data streams in environmental science, Environmental Modelling & Software, Volume 169, 2023, 105809, ISSN 1364-8152, https://doi.org/10.1016/j.envsoft.2023.105809. 

How to cite: Schäfer, D., Lünenschloß, P., Palm, B., Schmidt, L., Schnicke, T., Rebmann, C., Rinke, K., and Bumberger, J.: SaQC: Empowering Hydrological Data Integrity through Automated Quality Control  , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12157, https://doi.org/10.5194/egusphere-egu24-12157, 2024.

A.63
|
EGU24-21922
|
HS4.6
|
ECS
Zora Leoni Schirmeister, Markus Ziese, Elke Rustemeier, Peter Finger, Astrid Heller, Raphaele Schulze, Magdalena Zepperitz, Siegfried Fränkling, Bruno Heller, and Jan Nicolas Breidenbach

Since its founding in 1989, the Global Precipitation Climatology Centre (GPCC) has been producing global precipitation analyses based on land surface in-situ measurements. This year the GPCC marks its 35th anniversary. During these years the precipitation database has been continuously expanded and includes a high station density and large temporal coverage. Due to the semi-automatic quality control routinely performed on the incoming station data, the GPCC database has a very high quality. Today, the GPCC holds data from more than 126,000 stations, about three quarters of them having long time series.

The core of the analyses is formed by data from the global meteorological and hydrological services, which provided their records to the GPCC, as well as national meteorological and hydrological services from all over the world. In addition, the GPCC receives SYNOP and CLIMAT reports via the WMO-GTS. These form a supplement for the high quality precipitation analyses and the basis for the near real-time evaluations.

Quality control activities include cross-referencing stations from different sources, flagging of data errors, and correcting temporally or spatially offset data. This data then forms the basis for the following interpolation and product generation.

In near real time, the 'First Guess Monthly', 'First Guess Daily', 'Monitoring Product', ‘Provisional Daily Precipitation Analysis’ and the 'GPCC Drought Index' are generated. These are based on WMO-GTS data and monthly data generated by the CPC (NOAA).

With a 2-3 year update cycle, the high quality data products are generated with intensive quality control and built on the entire GPCC data base. These non-real time products consist of the 'Full Data Monthly', 'Full Data Daily', 'Climatology', and 'HOMPRA-Europe' and are now available in the 2022 version.

All gridded datasets presented in this paper are freely available in netcdf format on the GPCC website https://gpcc.dwd.de and referenced by a digital object identifier (DOI). The site also provides an overview of all datasets, as well as a detailed description and further references for each dataset.

How to cite: Schirmeister, Z. L., Ziese, M., Rustemeier, E., Finger, P., Heller, A., Schulze, R., Zepperitz, M., Fränkling, S., Heller, B., and Breidenbach, J. N.: Overview of the gridded daily and monthly precipitation data sets provided by the Global Precipitation Climatology Centre (GPCC), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21922, https://doi.org/10.5194/egusphere-egu24-21922, 2024.

A.64
|
EGU24-18770
|
HS4.6
|
ECS
WHOS: a Catalyst to Earth Systems Data access in Climate Change Adaptation and Implementation of Early Warning Systems for All
(withdrawn)
Washington Otieno, Enrico Boldrini, Johanna Korhonen, Dominique Berod, Yirgalem Gebremichael, and Peter Wasswa
A.65
|
EGU24-13225
|
HS4.6
|
ECS
Mehrad Rahimpour Asenjan, Francois Brissette, Jean-Luc Martel, and Richard Arsenault

The use of Multi-model ensembles (MMEs) has become crucial in assessing future climate change impacts and uncertainties. These ensembles leverage simulations from various global climate models (GCMs). While the traditional "model democracy" method, where equal weights are assigned to all models, has succeeded in reproducing the mean state of historical climate, it faces challenges in hydrological impact studies. Two key criticisms prompt the investigation of model democracy: the diverse performance of GCMs across different variables and locations, and the assumption of independence among ensemble members. Shared modules and features in climate models may introduce common biases, affecting confidence in projection uncertainty and potentially increasing uncertainties in climate change predictions. To address these challenges, diverse weighting approaches are explored, assigning varying weights to GCMs based on their performance in diagnostic metrics. While equal weighting is a common approach, unequal-weighting methods aim for a more reliable ensemble mean or constrained uncertainty.

This study assesses five weighting schemes—equal weighting, random weighting, skill-based weighting, the representation of annual cycle (RAC), and Reliability Ensemble Averaging (REA)—in hydrological impact evaluations. We utilized data from A set of 22 CMIP6 GMCs, coupled with a lumped hydrological model, and one bias correction method across 3107 North American catchments during the 1971-2000 and 2071-2100 periods. To understand how weighting methods influence streamflow bias in future periods, we used a "pseudo-reality" method, which involves comparing the bias between the weighted mean of climate models and a selected model used as a reference dataset. Through multiple iterations considering climate variables and geographic regions, this research aims to uncover the complex interactions between weighting schemes and their implications for hydrological assessments.

Our findings indicate that the performance of equal weighting and other weighting methods are similar in cases where bias correction has been applied. Bias correction is commonly used in climate change impact assessments due to the inherent inaccuracies in climate models, and in such cases equal weighting approach would provide adequate results for climate change impact assessment studies. For scenarios without bias correction, applying unequal weights provides improved simulation performance and reliability. The findings of this study contribute valuable insights to the broader landscape of climate change impact studies, emphasizing the importance of tailored weighting strategies in enhancing the reliability of hydrological assessments.

How to cite: Rahimpour Asenjan, M., Brissette, F., Martel, J.-L., and Arsenault, R.: Evaluating the Sensitivity of Hydrological Impacts to Different Climate Model Weighting Strategies, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13225, https://doi.org/10.5194/egusphere-egu24-13225, 2024.