Displays

Understanding the current hydro-meteorological situation is critical to manage extreme events and water resources. The UK Water Resources Portal (UKWRP) has been developed to enable dynamic, interactive, real-time access to hydro-meteorological data, including catchment daily rainfall, real-time daily mean river flows, real-time soil moisture data from COSMOS-UK and standardised climate indices. Users can access and view data at the field, grid cell and catchment scale enabling holistic assessments of the hydro-meteorological status at a range of spatial scales. The portal offers a way of exploring the full range of river flow and rainfall variability, including comparing current conditions to those in the past, from droughts to floods. A variety of different plotting capabilities mean users can view and explore data in different ways depending on their requirements.

The UKWRP can be used alone or alongside other resources such as: the UK Hydrological Outlook seasonal forecasts, the Hydrological Summary for the UK and Environment Agency Water Situation Reports, to manage water resources, to plan and prepare for extreme events, and to understand and communicate their severity.  The UKWRP enables all water users, from farmers, to water companies to members of the general public to view and explore the data used by regulators to manage water supplies. Equalising access to data can be extremely powerful; for example in the case of farmers, it means they can easily view real time river flows in relation to conditions on their licence using the same data used by regulators to impose abstraction restrictions during a drought.

Here we present the stakeholder engagement story of how and why the UKWRP was developed, demonstrate the capability of the UKWRP to monitor the hydrological situation in real time, and present plans for its future development, such as the addition of more indicators and indices.

How to cite: Barker, L., Nash, G., Fry, M., Hannaford, J., and Tanguy, M.: Dynamic Real-time Hydrological Status Monitoring in the UK, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10038, https://doi.org/10.5194/egusphere-egu2020-10038, 2020.

This paper presents the research results related to the development of an intelligent system for monitoring and assessing the state of natural systems (PROSTOR), which was tested in the area from the city of Velikiy Ustyug to the city of Kotlas on the Northern Dvina River. It is one of the most vulnerable places in Russia to spring snow-melt and ice-jams induced floods.

The proposed automated flood forecasting technology is based on the concept of a multi-model description of complex natural objects implementing a mechanism of the selection and adaptation of parameters of the most adequate model for each specific situation. The computational core of PROSTOR is the two-dimensional hydrodynamic model STREAM_2D and its newer version STREAM_2D_CUDA based on the numerical solution of the shallow water equations with discontinuous bottom. Additional hydraulic resistance due to the ice roughness and decrease in the flow cross-section due to ice-caused congestion were taken into account for modeling the ice-jams water levels. The forecasting capabilities of the system are secured by the prediction of water levels at the gauging stations located upstream from Velikiy Ustyug basing either on neural networks, or by means of linking with the runoff formation model ECOMAG and using prognostic meteorological information.

The system was built with the use of a service-oriented architecture, that provides flexible interaction between software modules, implementing hydrodynamic and hydrological models; modules of collecting and processing of heterogeneous data, including data from gauging stations and remote sensing data; control modules, etc. All system components are realized as web services and can be geographically distributed and localized in various organizations, cities and countries. All results of the system implementation, including the results of flooded zones calculations, flow parameters there, as well as satellite images are available via the geoportal.

Models parameters were justified on the base of numerical experiments and simulations of the floods of 1980-2016 period, including more than 18 significant cases of ice-jamming. Grouping of model parameters according to the height of the ice-jam induced water levels suggested for the implementation of the hydrodynamic model incorporated into intelligent information system of river floods monitoring. Operational flood forecasting mode of the system was tested during 2017 – 2019 years under support of Russian Science Foundation project № 17-11-01254.

How to cite: Krylenko, I., Alabyan, A., Zelentsov, V., Belikov, V., Sazonov, A., Pimanov, I., and Potriasaev, S.: Ice-jam floods modeling in frameworks of intelligent system for river monitoring, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19688, https://doi.org/10.5194/egusphere-egu2020-19688, 2020.

Surface water flooding is caused by intense rainfall before it enters rivers or drainage systems. As the climate changes and urban populations grow, the number of people around the world at risk of surface water flooding increases. Although it may not be possible to prevent such flooding, reliable and timely flood forecasts can help improve preparedness and recovery. Unlike river and coastal flooding where flood forecasting methods are well established, surface water forecasting techniques that address the challenges around predicting the location, timing and impact of events are still in their infancy.

Over the past five years there has been a rapid development of convection permitting numerical weather prediction models and probabilistic forecasting. Combined with an increase in computational ability, this has meant that it is potentially feasible to develop operational surface water forecasting systems for urban areas. The ability to make flood risk management decisions based on such forecasts depends on an interdisciplinary understanding of their strengths and limitations.

In 2019, the Scottish Environment Protection Agency (SEPA) commissioned a systematic review of UK and international surface water forecasting capabilities to inform the development of forecasting capabilities for Scotland (Speight et al, 2019). As part of the review process a literature review of international examples of operational surface water forecasting tools was conducted alongside discussion with a number of industry experts and leading academics to incorporate emerging capabilities.

This PICO will summarise the three approaches to surface water forecasting identified as part of this review; empirical based rainfall scenarios, hydrological forecasts linked to pre-simulated impact scenarios, and, real time hydrodynamic simulation. International examples of each type of approach will be presented along with discussion of their ability to meet the varying needs of decision makers. It will conclude by identifying ‘grand interdisciplinary challenges’ that still need to be addressed to provide effective solutions for reliable and timely surface water forecasts. For example although the emergence of new meteorological and hydrological capabilities is promising there is a scientific limit to the predictability of convective rainfall. To overcome this challenge re-thinking of the established role of flood forecasting is needed alongside developing interdisciplinary solutions for communicating uncertainty, making the best use of all available data and increasing preparedness.

Speight, L., Cranston, M., Kelly, L. and White, C.J. (2019) Towards improved surface water flood forecasts for Scotland: A review of UK and international operational and emerging capabilities for the Scottish Environment Protection Agency. University of Strathclyde, Glasgow, pp 1-63, doi:10.17868/69416 Available online at https://strathprints.strath.ac.uk/69416/

How to cite: Speight, L., Cranston, M., Kelly, L., and White, C.: Reviewing operational and near operational progress in surface water flood forecasting for urban areas, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20391, https://doi.org/10.5194/egusphere-egu2020-20391, 2020.

Verification provides the answer to the question "How good is my forecast?". Knowing the quality of a forecasting system provides a necessary baseline for improvement of that quality. It also contributes to forecast informed decision making, as verification provides a baseline estimate of residual uncertainties. 

To monitor the quality of the forecasts produced by Deltares Global Flow Forecasting system, a prototype of an operational forecast verification system was developed. The verification system comprises various components including the Ensemble Verification System (EVS), the Deltares OpenArchive and the Delft-FEWS forecast production system. Relevant verification metrics are computed by the EVS, which are subsequently stored and displayed in the forecasting system. This will allow for robust, automated forecast verification, and the usage of this information during the real-time forecasting process.  

Future work on the system will include a post-processing routine which will cast the verification information in a format suitable for publication to both existing and prospective GLOFFIS clients. Over the years, the system's outcomes are expected to provide a significant contribution to the quality of the GLOFFIS forecasts. 

In parallel, the system is being applied to various other operational hydrological forecasting systems around the globe.

How to cite: Boumis, G., Twigt, D., and Verkade, J.: Development of an operational forecast verification system, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22343, https://doi.org/10.5194/egusphere-egu2020-22343, 2020.

Flooding is a rapidly growing concern in West Africa. Several floods have occurred in recent years with severe consequences including loss of lives and damaged infrastructure. Flooding is also projected to increase with climate change. Access to operational forecasts is a critical component in addressing these challenges. This study presents results from our joint efforts to co-design, co-adapt, and co-operate a short- and medium-term operational hydrological forecasting and alert pilot system for West Africa, within the FANFAR project (www.fanfar.eu).

The system has been co-developed through a cycle of workshops, training sessions, and expert exchanges involving representatives from hydrological services, emergency management agencies, river basin organisations, and expert agencies in 17 countries in West and Central Africa. Multi-criteria decision analysis was employed to clarify and prioritize system objectives and configurations. We found that the most highly prioritized objectives were: high accuracy, clear flood risk information, reliable access, and timely production and distribution of the information. Our agile development approach also provided ample opportunities to focus development efforts on the most highly prioritized components, and incorporate stakeholder feedback in the development process.

The system is built on an ICT cloud platform that employs a daily forecasting chain including meteorological reanalysis and forecasting, data assimilation of gauge observations and satellite altimetry, hydrological initialisation and forecasting, flood alert derivation, and distribution through e-mail, SMS, web visualisation and API. The system is designed to enable multiple configurations and integration of several information sources (e.g. different hydrological models, observations, flood hazard thresholds etc.). We present the system configurations, stakeholder-driven adaptations, challenges, and current forecast performance. To our knowledge, the FANFAR system constitutes a significant advancement toward the vision of achieving efficient flood management in West Africa.

How to cite: Andersson, J., Ali, A., Arheimer, B., Crochemore, L., Gbobaniyi, B., Gustafsson, D., Hamatan, M., Kuller, M., Lienert, J., Machefer, M., Magashi, U., Mathot, E., Minoungou, B., Naranjo, A., Ndayizigiye, T., Pacini, F., Silva Pinto, F., Santos, L., and Shuaib, A.: Flood forecasting and alerts in West Africa − experiences from co-developing a pre-operational system at regional scale, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7660, https://doi.org/10.5194/egusphere-egu2020-7660, 2020.

Within the framework of the project “Strengthening Disaster Management Capacity of Women in Guyana and Dominica”, the National Flood Early Warning System (NFEWS) for Guyana is currently under development. The technical component of the system aims at implementing an operational flood forecasting modelling chain linking meteorological, hydrological and inundation models to provide timely early warnings and predicted flood scenarios, allowing the decision maker to take prevention actions and reduce the impacts of the forecasted event.

The objective is to implement, together with the Hydromet Service of Guyana, a technical tool able to provide daily forecasts of extreme flood events 1 to seven 7 days in advance, up to the local scale of inundation maps for selected locations.

The forecasting chain implemented is composed of five (5) main components: i) the weather forecasts, using the limited area WRF model executed twice a day at Hydromet; ii) observational inputs preparation, in particular rain maps through conditional merging between local ground stations and satellite information; iii) rainfall downscaling in several equiprobable scenarios using RAINFARM stochastic model; iv) the distributed hydrological model CONTINUUM, able to estimate river discharge and soil moisture conditions from the meteorological inputs (observation and forecasts), and v)the hydraulic model HYDRA-2D, that using a simplification of the shallow water equations allows fast and reliable inundation mapping.

At four (4) selected locations, corresponding to relevant flood-prone communities in Guyana, an innovative coupling between the hydrological and the inundation models allows to trigger an operational execution of several hydraulic simulations, resulting in real-time probabilistic forecast of inundation maps. The outflow volumes, derived from CONTINUUM hydrological routing, for different rainfall scenarios are used as inflow inputs for HYDRA-2D. Scalability between hydrological (1.5km) and hydraulic (12m) scales has been achieved through detailed field data collection, that was also used, together with local knowledge, to calibrate the inundation model.

Through the complete flood forecasting chain set up for Guyana, probability of exceeding significant water depths can be provided in advance to involved stakeholders, triggering early actions and thus enhancing flood resilience at the local scale.

The hydrological component of the forecasting chain has been implemented at the national level for the whole country, at a feasible spatial and temporal resolution based on a balance between input data availability and expected response time for civil defense activities.

Being developed using an open source model, as for all the other elements of the forecasting system, the hydraulic modelling component can be, in future, extended and replicated in other areas of interests.

How to cite: Masoero, A., Hodzic, I., Allen, C., Libertino, A., Giusti, A., Pignone, F., Dell'Oro, L., Gabellani, S., and Cummings, G.: Building the Flood Early Warning System in Guyana at the National scale, with real-time forecast of inundated areas for selected flood prone communities., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16807, https://doi.org/10.5194/egusphere-egu2020-16807, 2020.

Deltares operates the Global Fluvial Flood Forecasting System (GLOFFIS) is a real-time fluvial forecasting system with global coverage. At any location in the world, for both the recent past and the near future, the system produces estimates of various hydrological parameters. 

The continued investment in GLOFFIS is justified by various reasons. Primarily, there is an R&D rationale. Any operational system that runs in near real-time poses high requirements to the availability of input data and the runtime of the models used. This problem is augmented when applying the system to the global scale: both model domains and data volumes become significantly larger. Also, data originates from a wide variety of sources. Runtimes, however, cannot be significantly larger hence this poses additional requirements to the efficiency of models used. Solving these issues requires a considerable R&D effort. The resulting developments tend to be useful for the ‘local’ systems we develop and maintain for our clients. An additional rationale is found in the increased demand for global forecasts – notably from a client base that is not able or willing to operate forecasting systems themselves. 

At its core, GLOFFIS operates a set of hydrological models that, jointly, cover the entire earth’s land. The models are forced by meteorological data – pertaining to both the recent past and the near future. The models produce estimates of various hydrological parameters such as soil moisture content, surface water runoff and streamflow rates. Future versions of GLOFFIS will include hydrodynamic models, allowing to produce estimates of water level in addition to streamflow rates. Also, future versions will include seasonal forecasts, i.e. forecasts going out several weeks if not months. In addition to real-time data, the system enables the production of long-term timeseries. 

In terms of the infrastructure of the system, GLOFFIS is based on the wflow framework for hydrological modelling which is embedded within a the Delft-FEWS forecast production system. Neither of these require any licensing fees and the wflow framework is available through an open source license. The Delft-FEWS system is used for many operational flood forecasting systems including those of the US National Weather Service, the English Environment Agency, the Bureau of Meteorology and many other national forecasting agencies. Wflow is a distributed modelling framework specifically designed to accommodate multiple model schematization types and data assimilation techniques. For GLOFFIS, we opted for the physically based wflow_sbm that uses kinematic wave routing for surface and subsurface flow. So-called pedotransfer functions that translate input base maps to model parameter values ensure that the models require little calibration.

How to cite: den Toom, M., Verkade, J., Weerts, A., and Schotmeijer, G.-J.: Development of the Deltares global fluvial flood forecast system, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22344, https://doi.org/10.5194/egusphere-egu2020-22344, 2020.

An operational Impact-based Forecast System (IBFS) has been established in Barbados through a series of implementation workshops, training sessions, and decision support application development.  UCAR/COMET collaborated with local partners and stakeholders including the Barbados Meteorological Service (BMS), Barbados Department of Emergency Management, (DEM), and the Caribbean Institute of Meteorology and Hydrology (CIMH) to develop the IBFS.  The project has been implemented in phases over a two-year period.  The phases include identifying the hazards, impacts, risks through stakeholder workshops; developing new standard operating procedures; adapting software tools to include the IBFS framework; training of stakeholders; testing and evaluation of system using case studies; and development of documentation for public outreach.  The system is expected to be operational in June 2020.  The presentation will provide an overview of the Barbados IBFS and lessons learned during the project.

How to cite: Kucera, P.: Implementation of an Impact-based Forecast System in Barbados, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20225, https://doi.org/10.5194/egusphere-egu2020-20225, 2020.

Set up in 2009, the UK Flood Forecasting Centre (FFC), is a successful partnership between the Environment Agency and the Met Office to provide national, operational, flood risk guidance. At the same time, we have a development programme to continuously improve flood forecasting. Operational for over a decade, the FFC has a strong portfolio and reputation amongst its users and customers. For example, the 2019 Responder Survey reported that 94% of those who have had contact with the FFC within the last 12 months are satisfied with the services provided.  

High impact, low probability events have been a feature of the first 10 years of the Flood Forecasting Centre. Probabilistic forecasting and risk-based approaches provide approaches to identify, forecast and warn for such events. Indeed, whilst these are currently successfully employed by various National Meteorological Hydrological Centres, there is also recognition (for example, World Meteorological Organisation) that effective forecasting and warning systems should be:

• ‘impact-based’;
• driven by ensembles or realistic scenarios through an ‘end-to-end’ system (rather than precipitation ranges being simplified);
• more objective, so using new tools such as ensemble ‘sub-setting’, pattern recognition and machine learning to extract most value.

The Environment Agency is implementing a new Delft-FEWS forecasting system this year, termed Incident Management Forecasting System (IMFS). This will introduce a step change in capability for probabilistic impact-based forecasting. Initially, rainfall and coastal scenarios (termed ‘best-estimate’ and ‘reasonable worst case’) will be used to drive end-to-end forecasting, which includes for example impact data bases for property, infrastructure and communities. This is very much a stepping stone in the technical (systems) and adaptive (people, culture) transformation to a fully probabilistic, end-to-end, impact-based, flood forecasting.

I will share some of our recent approaches to:

• objective, ensemble based, forecasting, including the Natural Hazards Partnership surface water hazard impact model (driven by the Met Office MOGREPS precipitation ensembles) which goes live this year;
• scenario generation and ensemble sub-setting to provide input to end-to-end, impact-based forecasting (IMFS);
• next steps in moving to a fully probabilistic, end-to-end, impact-based, flood forecasting and warning system

I will also highlight some of our current challenges that we would love to work with others to solve.

How to cite: Pilling, C.: Moving towards a fully probabilistic, end-to-end, impact-based, flood forecasting and warning system, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20183, https://doi.org/10.5194/egusphere-egu2020-20183, 2020.

Consistent hydrological status and outlook information across transboundary basins or regions of shared hydrological interest are not often available. Furthermore, whilst large-scale modelling capabilities are continually improving, there is an information and confidence gap between locally informed hydrological status information products and those developed globally.

HydroSOS is World Meteorological Organisation initiative that aims to increase global resilience to hydro-climatic risks through the production of hydrological status and outlooks assessments at different scales around the world. Currently in a pilot phase, HydroSOS is being developed through a collaboration between National Hydrometeorological Services, transboundary basin organisations, global modelling centres and the research community. The system will provide an appraisal of where current hydrological status is different from “normal”, as well as sub-seasonal to seasonal outlooks indicating whether this is likely to get better or worse over the coming weeks and months.

The HydroSOS programme consists of five main activity streams:

1. Increasing the interoperability of hydrological status and outlook products through Common Technical Specifications.
2. Increasing national capabilities to generate hydrological status and sub-seasonal to seasonal outlook products through Guidance on Methods and Tools.
3. Increasing the utility of large-scale hydrological status and outlook modelling through Co-design of Global Products, with international partners working from local to global scale.
4. Increasing shared production of transboundary hydrological status and outlook products through Regional Pilots, initially in South Asia and the Lake Victoria Basin.
5. Integration of hydrological status and outlook products for national, regional and global users through a Demonstration Portal.

This PICO contribution will present progress in the pilot project to date, including a hands-on demonstration of the web portal.

How to cite: Smith, K., Silva Vara, L. R., Dixon, H., Barlow, V., Jenkins, A., Berod, D., Kim, H., Wang, G., Wolock, D., Tuteja, N., Paudyal, G., Kanyike, T., Blyth, E., and Wood, A.: HydroSOS: a pilot global Hydrological Status and Outlook System integrating national to global scale hydrological services for increased resilience to hydro-climatic risks, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7554, https://doi.org/10.5194/egusphere-egu2020-7554, 2020.

In many countries, rivers are the primary supply of water. A number of uses are concerned (drinking water, irrigation, hydropower…) and they can be strongly affected by water shortages. Therefore, there is a need of early anticipation of low-flow periods to improve water management. This is strengthened by the perspective of having more severe summer low-flows in the context of climate change. Several French institutes (Irstea, BRGM, Météo-France, EDF and Lorraine University) have been collaborating to develop an operational tool for low-flow forecasting, called PREMHYCE. It is tested in real time since 2017, and implemented on 259 catchments in metropolitan France, in cooperation with operational services which provide streamflow observations and use low-flow forecasts from the tool. PREMHYCE includes five hydrological models which can be calibrated on gauged catchments and which assimilate flow observations. Low-flow forecasts can be issued up to 90 days ahead, based on several inputs scenarios: ECMWF 10-days ensemble forecasts, ensemble streamflow prediction (ESP) using historical climatic data as ensembles of future input scenarios, and a no precipitation scenario. Climatic data (precipitation, evapotranspiration and temperature) are provided by Météo-France with the daily gridded SAFRAN reanalysis on the 1959-2019 period, which includes a wide range of conditions. The tool provides text files and graphical representation of forecasted low-flows, and probability to be under low-flow thresholds provided by users. Outputs from the different hydrological models can be combined within a multi-model approach to improve robustness of the forecastsThe presentation will show the main characteristics of this operational tool, the probabilistic evaluation framework, results on the recent low-flow periods, and how feedbacks from end-users can help improving the tool.

How to cite: Nicolle, P., Besson, F., Bourgin, F., François, D., Le Lay, M., Perrin, C., Rousset, F., Thiéry, D., Tilmant, F., Magand, C., and Jacob, E.: PREMHYCE: An operational tool for low-flow forecasting, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19335, https://doi.org/10.5194/egusphere-egu2020-19335, 2020.

Addressing the user needs at the local and large scales remains an ongoing scientific and operational effort to the various hydro-climatic service providers. The evolution of hydro-climatic services has received high attention, particularly given the recent scientific and computational advancements that have led to skillful meteorological forecasts at time horizons from sub-seasonal (up to 6 weeks ahead) to seasonal (up to a year ahead). Sub-seasonal to seasonal (S2S) forecasts have great potential for user groups that are affected by climatic variations and that could manage such variations to their advantage through better predictions. Therefore the Swedish Meteorological and Hydrological Institute co-developed with users from the water-related sectors a demonstrator interface to communicate the ensemble of pan-European and global hydro-climatic indicators at the catchment scale.

Here we present these operational hydro-climatic services for the long time horizons, and focus on the setup, the implementation and the challenges. The provided hydro-climatic forecasts are based on the bias-adjusted meteorological forecasts from ECMWF (i.e. daily precipitation and daily mean, maximum and minimum temperature) and the pan-European E-HYPE and global WW-HYPE hydrological models (http://hypeweb.smhi.se/). The forecasts are updated frequently when the newly initialised forecasts become available. Hydro-climatic information for variables such as river flow, water discharge, actual and potential evapotranspiration, soil water content, precipitation and temperature is presented as maps and graphs, for both climatology and forecast period. The service provides also the option to download the forecast information (catchment scale) including also the metadata and forecast skill information. The map shows the anomaly for each catchment and lead time using as reference either the catchment’s normal conditions (based on terciles) or extremes (10^th and 90^th percentiles) for the period of interest. To overcome misinterpretation of the forecasted information, we set as default the option to the user to mask the catchments in which forecasts have no skill (based on re-forecast analysis); meaning that climatology is more predictive than ECMWF forecasts. The graphs display the median and different percentiles of the ensemble of forecasts, and the high and low thresholds of the normal and extreme conditions for the period of interest.

Keywords

Seasonal hydro-meteorological forecasting, Copernicus C3S, global climate services

How to cite: Bosshard, T., Arheimer, B., Crochemore, L., Gyllensvärd, F., Pechlivanidis, I., and Photiadou, C.: Continental and global hydro-climatic forecasting services to address user needs for the water-related sectors, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16035, https://doi.org/10.5194/egusphere-egu2020-16035, 2020.

The warm and dry years of the recent past have once again impressively shown that bark beetle outbreaks are among the most dangerous natural hazards that occur in forests of Central Europe and North America.

The European spruce bark beetle (Ips typographus L.) in particular is one of the most important pests in Central European forests. Induced by the ongoing climate change, it seems to be quite likely that the growing conditions of Norway spruce (Picea abies L.) will deteriorate considerably due to predicted rising temperatures and increasing frequency and intensity of droughts and further extreme weather events. In contrast, the spruce bark beetle is favored by the same trends. As a result, it tends to mass outbreaks and can thereby also infest healthy spruces, causing forests to die off over large areas. Since management resources and warning tools needed for a just-in-time detection of infested trees will remain limited, efficient operational systems are highly desired to enhance and to facilitate bark beetle risk management.

For this reason, we developed the prototype of an operational early warning system to assess the current risk of potentially endangered spruce stands to bark beetle infestations at a high temporal (daily) and spatial (≤ 250-m-grid) resolution.

The system considers the following input layers:

(a) a quasi-static base-risk layer that is calculated from stand and site characteristics;

(b) an annually updated layer determining the bark beetle population density; and

daily-updated layers for increased host tree susceptibility by (c.1) drought stress or (c.2) storm damage and (c.3) the swarming activity of the bark beetle.

From these inputs a daily overall infestation risk plus a 7-day-forcast is calculated and made available online to forest owners and managers in the form of a risk map providing different risk levels (e.g., low – medium – high).

As one of the main driving factors, the (c.1) drought stress induced disposition of spruce forests to bark beetle infestation is assessed by applying a grid-based soil water balance model at daily resolution. The plausibility of the model is checked via representative soil hydrological measuring areas in the three German project areas Black Forest National Park, Saxon Switzerland National Park, and Hunsrück-Hochwald National Park. At the same time, suitable water scarcity indicators are identified and defined for these threshold values, below or above which an increased susceptibility of spruce trees to bark beetle attack is to be expected. Hence, in connection with daily updated weather forecasts, the water-related disposition of spruce stands to bark beetle infestation can be predicted with reasonable accuracy.

The developed early warning system or implemented sub-systems have the flexibility to be adapted to other bark beetles or further forest pests and can be applied at local, regional and national scales. Furthermore, its functionality can be extended by integrating novel modern approaches, e.g. machine-learning methods or remote sensing technologies.

How to cite: Hallas, T., Puhlmann, H., Wehberg, J., and Conrad, O.: Development of an operational early warning system to enhance bark beetle risk management – Application of soil water balance models to assess the drought-stress induced disposition of spruce forests to bark beetle infestations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18323, https://doi.org/10.5194/egusphere-egu2020-18323, 2020.

Dengue fever is now present in over 150 countries world-wide, affecting 390 million people per year. In Vietnam the number of cases has increased by 100% since 2000, and 2019 exhibited exceptional high numbers of reported dengue fever cases. Transmission of this mosquito-borne disease is dependent on a variety of climate and socio-economic factors. Among those water availability plays a crucial role in creating or destroying suitable mosquito breeding grounds.

At present mitigating actions are taken based on reported dengue fever cases and local knowledge, leading to a reactive rather than proactive approach of disease control. By combining Earth Observation and vector-borne disease modelling expertise we have developed D-MOSS (Dengue Model Forecasting Satellite based System). The D-MOSS system is funded by the UK Space Agency’s International Partnership Programme and aims to predict the likelihood of future dengue epidemics for Vietnam on a province scale with a lead time of up to six months.

D-MOSS integrates multiple stressors such as water availability, land-cover, precipitation and temperature with data of past dengue fever incidents.  This information is used to develop statistical models of disease incidence, that can then be used to forecast dengue outbreaks based on seasonal weather and hydrological forecasts.  It is the first fully integrated dengue fever forecasting system incorporating Earth Observation data and seasonal climate forecasts to routinely issue warnings. 

D-MOSS takes the form of a web-based platform.  The system’s architecture is based on open and non-proprietary software, where possible, and on flexible deployment into platforms including cloud-based virtual storage and application processing. Working closely with public health authorities in Vietnam enabled us to develop a system tailored to the local needs and decision making procedures.

How to cite: Hofmann, B., Tsarouchi, G., Colon, F., Ainscoe, E., Ferrario, I., Harpham, Q., James, S., Lumbroso, D., Malde, S., Moschini, F., and Robertson, C.: D-MOSS: Dengue Fever Forecasting for Vietnam – Assessment of an Operational System., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9541, https://doi.org/10.5194/egusphere-egu2020-9541, 2020.

The target of this study is diversified development and uploading and sharing of prevention warning (or inquiry) information in real time, and geovisual display the information after analyzing the observations and predictions, making it easier for the government and the public to browse the latest various marine meteorological information. Terrible marine environment such as large tidal difference, swell, typhoon huge waves, and storm surge, etc. could easily cause severe maritime disasters of trapped by high tide, missing in the sea, ships to hit the reef, collide or even capsize, causing oil spills. In response to these disasters around Taiwan water, Taiwan maritime disaster prevention and environmental information service platform has been developed after nearly 3 years technological R&D under commissioned and supported by Central Weather Bureau (4-year project, 2017-2020). In order to confirm the rationality of these products, the verification were done through the past maritime disaster events.

As traditional, static maps have a limited exploratory capability, GIS and geovisualization allow for more interactive maps and display on a computer or smartphone, including the ability to explore different layers of the map, to zoom in or out, and to change the visual appearance of the map. Further, geographic information infrastructures need to be integrated with the database of observations and predictions to ensure that government agencies have timely access to real time geographic information so that decisions on sustainability and disaster resilience can be effectively done. Visualize each warning (or inquiry) information through a GIS system in this study, including coastal tideline forecast, tracking drifting objects, coastal swell warning, historical typhoon wave and storm surge query, marine meteorology information for vessel route, ship sailing safety warning and oil spill tracking etc. will increase the ability of implement early warning and prevention for various maritime disaster events, effectively reduce the losses caused by various disasters. For instance, coastal swell warning to improve the safety of coastal recreation through warning colours; the ship sailing safety warning can instantly provide the impact of future wave conditions on various types of vessels to improve the safety of navigation operations.

How to cite: Fan, Y.-M., Doong, D.-J., and Hsueh, P.-C.: Geovisualization prevention warning information service for maritime disaster, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1321, https://doi.org/10.5194/egusphere-egu2020-1321, 2020.