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The increasing number and severity of floods, driven by phenomena such as urbanization, deforestation, subsidence and climate change, creates a growing need for accurate and timely flood maps. This research focussed on creating flood maps using user generated content from Twitter. Twitter data has added value over traditional methods such as remote sensing and hydraulic models, since the data is available almost instantly, in contrast to remote sensing and requires less detail than hydraulic models. Deterministic flood maps created using these data showed good performance (F(2)=0.69) for a case study in York (UK). For York the main source of uncertainty in the probabilistic flood maps was found to be the error of the locations derived from the Twitter data. Errors in the elevation data and parameters of the applied algorithm contributed less to flood extent uncertainty. Although the generated probabilistic maps tended to overestimate the actual probability of flooding, they gave a reasonable representation of flood extent uncertainty in the area. This study illustrates that inherently uncertain data from social media can be used to derive information about flooding. [ABSTRACT FROM AUTHOR]

Previous research has found that users fail to comprehend flood hazard warning messages as well as the possible impacts of the forecast events. A proposed way to improve understanding and uptake is the implementation of impact‐based warning services. However, even though extensive qualitative research has been done on the provision of these impact‐based warnings (IBWs) for different types of hazards and extreme events, there is still little scientific evidence that the additional information on impacts in flood warnings positively affects decisions made by the general public. The research reported here explores whether including messages of potential impacts increased the general public's likelihood of making a protective decision when provided with a flood warning. Nine hypothetical flood scenarios were presented to participants who were then asked to score their likelihood of making a specific protective decision. Participants were either presented IBWs or hazard flood warnings (HWs). Results show that IBWs indeed led to higher likelihoods of participants making a protective decision than HWs. Interestingly, we found that key factors identified in previous studies as influential elements in the decision‐making process had little impact on their decision. These factors include gender and previous experience with flood warnings. [ABSTRACT FROM AUTHOR]

The availability of a skillful seasonal streamflow forecast may positively affect rice production on the island of Java, Indonesia. For that purpose, three forecasting approaches have been tested for skill. ESP and ESP-ENSO are climatology based forecasting approaches, where the latter is further conditioned on an ENSO index. The ECMWF-S4 system provides dynamic precipitation forecasts. The resulting forecasts were tested for skill. In all cases, skill is relatively modest. ESP-ENSO forecast skill trumps that of ESP, which, in turn is higher than that of the ECMWF-S4 based forecasts. Some recommendations are made for further understanding forecast skill, and for approaches that may lead to skill improvements. [ABSTRACT FROM AUTHOR]

Deltares operates two real-time forecasting systems with global coverage: the Global Flood Forecasting and Information System (GLOFFIS) and the Global Storm Surge Information System (GLOSSIS). GLOFFIS produces fluvial flood forecasts with global coverage. The system ingests multiple weather forecasting products including ensemble NWPs. These are used to force multiple hydrological models of various types and spatial resolutions. Data assimilation is applied for selected basins where observations are available in near real time. GLOSSIS continuously provides 10 day water level and storm-surge forecasts using the Global Tide and Surge Model (GTSM). The GTSM is a numerical model based on Delft3D Flexible Mesh (FM), that computes tides and storm surges around the world efficiently and accurately through the use of an unstructured mesh. In GTSM version 3 the coastal resolution is up to about 1km, which is sufficient to capture the local coastal variability in many parts around the world. Both systems are based on the Delft-FEWS forecast production system. Some results are disseminated via www.globalfloodforecast.com. In the presentation, some system characteristics as well as the rationale for forecasting at global scale will be highlighted. Werner, M., Schellekens, J., Gijsbers, P., van Dijk, M., van den Akker, O., and Heynert, K.: The Delft-FEWS flow forecasting system, Environ. Modell. Softw., 40, 65–77, doi:10.1016/j.envsoft.2012.07.010, 2013 [ABSTRACT FROM AUTHOR]