Flash floods and rainfall induced hydro-geomorphic hazards: from observation to forecasting and warning
Heavy precipitation events in small and medium size catchments can trigger flash floods, which are characterized by very short response times and high specific peak discharges, and often occur in ungauged basins. Under appropriate geomorphological conditions, such rainstorms also cause debris flows or shallow landslides mobilizing large amounts of unconsolidated material. Although significant progress has been made in the management of these different hazards and related risks, they remain poorly understood and their predictability is affected by large uncertainties, due to the fast evolution of triggering rainfall events, the lack of appropriate observations, the high variabilities and non-linearities in the physical processes, and the high variability and complexity of societal vulnerability.
This session aims to illustrate current advances in monitoring, understanding, modelling, and forecasting flash floods and associated geomorphic processes, and documenting and anticipating the societal impacts and social responses.
Contributions on the following scientific themes are more specifically expected:
- Development of new measurement techniques adapted to flash floods monitoring (including remote sensing data, weather radar, and lightning), and quantification of the associated uncertainties,
- Identification of processes leading to flash flood events and/or rainfall-induced geomorphic hazards from data analysis and/or modelling, and of their characteristic space-time scales
- Possible evolutions in hazard characteristics and frequency related to climate change.
- Development of short-range (0-6h) rainfall forecasting techniques adapted to heavy precipitation events, and representation of associated uncertainties
- Development of hydro-meteorological forecasting chains for predicting flash floods and/or rainfall-induced geomorphic hazards in gauged and ungauged basins
- Development of inundation mapping approaches specifically designed for an integration in flash floods forecasting chains.
- Use of new criteria such as specific “hydrological signatures” for model and forecast evaluation
- Observation, understanding and prediction of the societal vulnerability and social responses to flash floods and/or associated hydro-geomorphic hazards.
Drought and water scarcity: monitoring, modelling and forecasting to improve hydro-meteorological risk management
Drought and water scarcity are important issues in many regions of the Earth. While an increase in the severity and frequency of droughts can lead to water scarcity situations, particularly in regions that are already water-stressed, overexploitation of available water resources can exacerbate the consequences of droughts. In the worst case, this can lead to long-term environmental and socio-economic impacts. It is, therefore, necessary to improve both monitoring and sub-seasonal to seasonal forecasting for droughts and water availability and to develop innovative indicators and methodologies that translate the information provided into effective drought early warning and risk management. This session addresses statistical, remote sensing and physically-based techniques, aimed at monitoring, modelling and forecasting hydro-meteorological variables relevant to drought and/or water scarcity. These include, but are not limited to, precipitation, snow cover, soil moisture, streamflow, groundwater levels, and extreme temperatures. The development and implementation of drought indicators meaningful to decision-making processes, and ways of presenting and explaining them to water managers, policymakers and other stakeholders, are further issues that are addressed. The session aims to bring together scientists, practitioners and stakeholders in the fields of hydrology and meteorology, as well as in the field of water resources and/or risk management; interested in monitoring, modelling and forecasting drought and water scarcity, and in analyzing their interrelationships, hydrological impacts, and the feedbacks with society. Particularly welcome are applications and real-world case studies in regions subject to significant water stress, where the importance of drought warning, supported through state-of-the-art monitoring and forecasting of water resources availability is likely to become more important in the future. Contributors to the session are invited to submit papers to the Special Issue (SI) entitled "Recent advances in drought and water scarcity monitoring, modelling, and forecasting", to be published in the open-access journal Natural Hazard and Earth System Sciences (https://www.natural-hazards-and-earth-system-sciences.net/special_issues/schedule.html). Submission is open until 30 July 2020, for manuscripts that are not under consideration for publication elsewhere.
A tutorial video on "how to see and reply to comments on your display" is available for all participants at:
Ensemble and probabilistic hydro-meteorological forecasts: predictive uncertainty, verification and decision making
This session brings together scientists, forecasters, practitioners and stakeholders interested in exploring the use of ensemble hydro-meteorological forecast techniques in hydrological applications: e.g., flood control and warning, reservoir operation for hydropower and water supply, transportation, and agricultural management. It will address the understanding of sources of predictability and quantification and reduction of predictive uncertainty of hydrological extremes in deterministic and ensemble hydrological forecasting. Uncertainty estimation in operational forecasting systems is becoming a more common practice. However, a significant research challenge and central interest of this session is to understand the sources of predictability and development of approaches, methods and techniques to enhance predictability (e.g. accuracy, reliability etc.) and quantify and reduce predictive uncertainty in general. Ensemble data assimilation, NWP preprocessing, multi-model approaches or hydrological postprocessing can provide important ways of improving the quality (e.g. accuracy, reliability) and increasing the value (e.g. impact, usability) of deterministic and ensemble hydrological forecasts. The models involved with the methods for predictive uncertainty, data assimilation, post-processing and decision-making may include machine learning models, ANNs, catchment models, runoff routing models, groundwater models, coupled meteorological-hydrological models as well as combinations (multimodel) of these. Demonstrations of the sources of predictability and subsequent quantification and reduction in predictive uncertainty at different scales through improved representation of model process (physics, parameterization, numerical solution, data support and calibration) and error, forcing and initial state are of special interest to the session.
The session welcomes new experiments and practical applications showing successful experiences, as well as problems and failures encountered in the use of uncertain forecasts and ensemble hydro-meteorological forecasting systems. Case studies dealing with different users, temporal and spatial scales, forecast ranges, hydrological and climatic regimes are welcome.
The session is part of the HEPEX international initiative: www.hepex.org
Operational forecasting and warning systems for natural hazards: challenges and innovation
This interactive session aims to bridge the gap between science and practice in operational forecasting for different water-related natural hazards. Operational (early) warning systems are the result of progress and innovations in the science of forecasting. New opportunities have risen in physically based modelling, coupling meteorological and hydrological forecasts, ensemble forecasting and real time control. Often, the sharing of knowledge and experience about developments are limited to the particular field (e.g. flood forecasting or landslide warnings) for which the operational system is used.
The focus of this session will be on bringing the expertise from different fields together as well as exploring differences, similarities, problems and solutions between forecasting systems for varying natural hazards. Real-world case studies of system implementations - configured at local, regional and national scales - will be presented, including trans-boundary issues. An operational warning system can include, for example, monitoring of data, analysing data, making forecasts, giving warning signals and suggesting response measures.
Contributions are welcome from both scientists and practitioners who are involved in developing operational forecasting and/or management systems for water-related natural or man-made hazards, such as flood, drought, tsunami, landslide, hurricane, hydropower, pollution etc.
From sub-seasonal forecasting to climate projections: predicting hydrologic extremes and improving water management
Many water management sectors are already having to cope with extreme weather events, climate variability and change. In this context, predictions on sub-seasonal, seasonal to decadal timescales (i.e. horizons ranging from months to a decade) are an emerging and essential part of hydrological forecasting. By providing science-based and user-specific information on potential impacts of extreme events, operational hydro-meteorological services are invaluable to a range of water sectors such as transport, energy, agriculture, forestry, health, insurance, tourism and infrastructure.
This session aims to cover the advances in climate and hydro-meteorological forecasting, and their implications on forecasting extreme events for improved water management. It welcomes, without being restricted to, presentations on:
- Making use of climate data for hydrological modelling (downscaling, bias correction, temporal disaggregation, spatial interpolation and other technical challenges),
- Methods to improve forecasting of hydrological extremes,
- Improved representations of hydrological extremes in a future climate,
- Seamless forecasting, including downscaling and statistical post- and pre-processing,
- Propagation of climate model uncertainty to hydrological models and impact assessment,
- Lessons learnt from forecasting and managing present day extreme conditions,
- Operational hydro-meteorological (sub-seasonal to decadal) forecasting systems and climate services,
- Effective methods to link stakeholder interests and scientific expertise (e.g. service co-generation).
The session will bring together research scientists and operational managers in the fields of hydrology, meteorology and climate, with the aim of sharing experiences and initiating discussions on this emerging topic. We encourage presentations from initiatives such as the H2020 IMPREX, BINGO, S2S4E and CLARA projects, and from WWRP/WCRP S2S projects that utilise the recently established S2S project database, and all hydrological relevant applications.
Welcome to HS4.6 at #shareEGU20!
This session aims to cover the advances in climate and hydro-meteorological forecasting, and their implications on forecasting extreme events for improved water management. We thank the authors for their valuable contributions to this session. We have a range of brilliant displays, which cover a range of forecast lead times, case study areas and applications.
The displays for the session have been grouped into two categories: Research Studies and Operational & Applied Studies, with each display having a 5 min slot for discussion.
We will start the session at 10:45 CET on Thursday 07 May. The display times listed below may change a bit last minute, but this is the schedule we will try to stick to.
We hope you will enjoy the session!
--- HS4.6 session co-conveners
Welcome and opening remarks
D252: EGU2020-17646 - Spatial and temporal patterns in seasonal forecast skill based on river flow persistence in Irish catchments
Daire Quinn et al.
D253: EGU2020-9149 - Seasonal streamflow forecasting - Which are the drivers controlling the forecast quality?
Ilias Pechlivanidis et al.
D254: EGU2020-18796 - Sensitivity of seasonal hydrological predictability sources to catchment properties
Maria Stergiadi et al.
D255: EGU2020-1533 - Analysis and prediction of hydrological extreme conditions for a small headwater catchment in a German lower mountain range
Lisa Hennig et al.
D257: EGU2020-9321 - Sensitivity analysis of MOHID-Land model. Calibration and validation of Ulla river watershed.
Ana Oliveira et al.
D260: EGU2020-2167 - Modelling runoff generation of a small catchment in the context of climate change by using an ensemble of different climate model outputs and bias correction methods
Kai Sonntag et al.
Open discussion and short break (if time allows)
Operational & Applied Studies:
D261: EGU2020-9773 - A Real-time Ensemble Hydrological Forecasting System over Germany at Sub-seasonal to Seasonal Time Range
Husain Najafi et al.
D262: EGU2020-20290 - Towards improved disaster preparedness and climate proofing in semi-arid regions: development of an operational seasonal forecasting system
Christof Lorenz et al.
D263: EGU2020-5494 - Using seasonal forecast for energy production: SHYMAT climate service, a small hydropower management and assessment tool
Eva Contreras Arribas et al.
D264: EGU2020-5550 - How seasonal forecast can improve the water planning in multipurpose reservoirs: ROAT climate service, a reservoir operation assessment tool
Javier Herrero Lantarón et al.
D265: EGU2020-15853 - SMHI Aqua: a new co-generated hydro-climate service to enable sustainable freshwater management
Carolina Cantone et al.
D266: EGU2020-9006 - Using seasonal forecast information to strengthen resilience and improve food security in Niger River Basin
Bernard Minoungou et al.
Open discussion and HS4.6 closing remarks
Advances in statistical post-processing, blending and verification of deterministic and ensemble forecasts
Statistical post-processing techniques for weather, climate, and hydrological forecasts are powerful approaches to compensate for effects of errors in model structure or initial conditions, and to calibrate inaccurately dispersed ensembles. These techniques are now an integral part of many forecasting suites and are used in many end-user applications such as wind energy production or flood warning systems. Many of these techniques are flourishing in the statistical, meteorological, climatological, hydrological, and engineering communities. The methods range in complexity from simple bias correction up to very sophisticated distribution-adjusting techniques that take into account correlations among the prognostic variables.
At the same time, a lot of efforts are put in combining multiple forecasting sources in order to get reliable and seamless forecasts on time ranges from minutes to weeks. Such blending techniques are currently developed in many meteorological centers.
In this session, we invite papers dealing with both theoretical developments in statistical post-processing and evaluation of their performances in different practical applications oriented toward environmental predictions, papers dealing with the problem of combining or blending different types of forecasts in order to improve reliability from very short to long time scales.
Reducing the impacts of natural hazards through forecast-based action: from early warning to early action
The Sendai Framework for disaster risk reduction (SFDRR) and its seventh global target recognizes that increased efforts are required to develop risk-informed and impact-based multi-hazard early warning systems. Despite significant advances in disaster forecasting and warning technology, it remains challenging to produce useful forecasts and warnings that are understood and used to trigger early actions. Overcoming these challenges requires understanding of the reliability of forecast tools and implementation barriers in combination with the development of new risk-informed processes. It also requires a commitment to create and share risk and impact data and to co-produce impact-based forecasting models and services. To deal with the problem of coming into action in response to imperfect forecasts, novel science-based concepts have recently emerged. As an example, Forecast-based Financing and Impact-based Multi-Hazard Early Warning Systems are currently being implemented operationally by both governmental and non-governmental organisations in several countries as a result of increasing international effort by several organizations such as the WMO, World Bank, IFRC and UNDRR to reduce disaster losses and ensuring reaching the objectives of SFDRR. This session aims to showcase lessons learnt and best practices on impact-based multi-hazards early warning system from the perspective of both the knowledge producers and users. It presents novel methods to translate forecast of various climate-related and geohazards into an impact-based forecast. The session addresses the role of humanitarian agencies, scientists and communities at risk in creating standard operating procedures for economically feasible actions and reflects on the influence of forecast uncertainty across different time scales in decision-making. Moreover, it provides an overview of state-of-the-art methods, such as using Artificial Intelligence, big data and space applications, and presents innovative ways of addressing the difficulties in implementing forecast-based actions. We invite submissions on the development and use of operational impact-based forecast systems for early action; developing cost-efficient portfolios of early actions for climate/geo-related impact preparedness such as cash-transfer for droughts, weather-based insurance for floods; assessments on the types and costs of possible forecast-based disaster risk management actions; practical applications of impact forecasts.