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.

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.

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.

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.

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.

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.

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.