Development and Application of an Urban Flood Forecasting and Warning Process to Reduce Urban Flood Damage: A Case Study of Dorim River Basin, Seoul

Early and accurate flood forecasting and warning for urban flood risk areas is an essential factor to reduce flood damage. This paper presents the urban flood forecasting and warning process to reduce damage in the main flood risk area of South Korea. This process is developed based on the rainfall-runoff model and deep learning model. A model-driven method was devised to construct the accurate physical model with combined inland-river and flood control facilities, such as pump stations and underground storages. To calibrate the rainfall-runoff model, data of gauging stations and pump stations of an urban stream in August 2020 were used, and the model result was presented as an R2 value of 0.63~0.79. Accurate flood warning criteria of the urban stream were analyzed according to the various rainfall scenarios from the model-driven method. As flood forecasting and warning in the urban stream, deep learning models, vanilla ANN, Long Short-Term Memory (LSTM), Stack-LSTM, and Bidirectional LSTM were constructed. Deep learning models using 10-min hydrological time-series data from gauging stations were trained to warn of expected flood risks based on the water level in the urban stream. A forecasting and warning method that applied the bidirectional LSTM showed an R2 value of 0.9 for the water level forecast with 30 min lead time, indicating the possibility of effective flood forecasting and warning. This case study aims to contribute to the reduction of casualties and flood damage in urban streams and accurate flood warnings in typical urban flood risk areas of South Korea. The developed urban flood forecasting and warning process can be applied effectively as a non-structural measure to mitigate urban flood damage and can be extended considering watershed characteristics.

Communicating Heat-Health Information to the Public: Assessing Municipal Government Extreme Heat Event Website Content

Extreme heat events pose a threat to human health. Forecasting and warning strategies have been developed to mitigate heat-health hazards. Yet, studies have found that the public lacks knowledge about their heat-health risks and preventive actions to take to reduce risks. Local governmental websites are an important means to communicate preparedness to the public. The purpose of this study is to examine information provided to the public on municipal government webpages of the 10 most populous U.S. cities. A two-level document and content analyses were conducted. A direct content analysis was conducted using federal government websites and documents to create the Extreme Heat Event Public Response Rubric. The Rubric contains two broad categories of populations and actions that are further specified. The Rubric was then used to examine local government extreme heat event websites for the 10 most populous cities in the U.S. The examination of the local government sites found that information included on the websites failed to identify the breadth of populations at greater risk for adverse heat-health outcomes and omitted some recommended actions designed to prevent adverse heat-health events. Local governments often communicated concrete and simple content to the public but more complex information was not included on their websites.

Disaster Flood Scenario: Case Study of the Uh River at Lekárovce (Slovakia)

The occurrence of extreme floods in several river basins of the countries of Central and Eastern Europe over the last thirty years has drawn the attention of the public (as well as the competent authorities) to the problems of flood protection. Although the development and operational use of non-structural measures (such as flood forecasting and warning systems), represents one of the effective flood protection measures, the structural means (flood protection, levees, flood control reservoirs) are of great importance, too. Especially in the upper parts of the river basin, where the time between the detection of the causes of the flood (heavy rainfall) and its consequence (flood) is short and does not affect the effective protective activity (e.g. evacuation). Over the last 30 years, flood protections have been built along the Uh River (Slovakia, Ukraine) to protect the environment from floods. These dams adversely affected the storage capacity of water in the basin. This resulted in flood flows increase on the lower sections of the Uh River in Slovakia. These facts need to be demonstrated by the need to evaluate the proposed design values for those sections. The study presents an analysis of the long-term flood regime of the river Uh in the section Uzhhorod (Ukraine) - Lekárovce (Slovakia). The first part analyses the trend changes in the time series of maximum annual discharge Qmax in the stations Lekárovce and Uzhhorod on the basis of the observed Qmax data in these profiles (period 1931-2019). These Qmax series were subsequently used to estimate the maximum T-year discharge at the Lekárovce station for the changed conditions of the Uzhhorod - Lekárovce section. Using these derived data and the observed form of the summer flood hydrograph from July 1980, a 100-year flood scenario was developed for the Uh River in Lekárovce. The achieved results indicate a further increase in flood risk in Lekárovce.

A User-Oriented Local Coastal Flooding Early Warning System Using Metamodelling Techniques

Given recent scientific advances, coastal flooding events can be properly modelled. Nevertheless, such models are computationally expensive (requiring many hours), which prevents their use for forecasting and warning. In addition, there is a gap between the model outputs and information actually needed by decision makers. The present work aims to develop and test a method capable of forecasting coastal flood information adapted to users’ needs. The method must be robust and fast and must integrate the complexity of coastal flood processes. The explored solution relies on metamodels, i.e., mathematical functions that precisely and efficiently (within minutes) estimate the results that would provide the numerical model. While the principle of relying on metamodel solutions is not new, the originality of the present work is to tackle and validate the entire process from the identification of user needs to the establishment and validation of the rapid forecast and early warning system (FEWS) while relying on numerical modelling, metamodelling, the development of indicators, and information technologies. The development and validation are performed at the study site of Gâvres (France). This site is subject to wave overtopping, so the numerical phase-resolving SWASH model is used to build the learning dataset required for the metamodel setup. Gaussian process- and random forest classifier-based metamodels are used and post-processed to estimate 14 indicators of interest for FEWS users. These metamodelling and post-processing schemes are implemented in an FEWS prototype, which is employed by local users and exhibits good warning skills during the validation period. Based on this experience, we provide recommendations for the improvement and/or application of this methodology and individual steps to other sites.

Flood Stage Forecasting at the Gurye-Gyo Station in Sumjin River Using LSTM-Based Deep Learning Models

Instances of flood damage caused by extreme storm rainfall due to climate change and variability have been showing an increasing trend. Particularly, a flood forecasting and warning system has been recognized as an important nonstructural measure for flood damage reduction, including loss of life. Flood forecasting and warning have been performed by the forecasts of flood discharge and flood stage using the physically based rainfall-runoff models. However, recently, studies involving the application of a machine learning-based flood forecasting models, which addresses the limitations of extant physically based flood stage forecasting models, have been performed. We may require various case studies to determine more accurate methods. Therefore, this study performed the real-time forecasting of the river water level or stage at the Gurye station of the Sumjin river with lead times of 1, 3, and 6 h by applying a long short-term memory (LSTM)-based deep learning model. In addition, the applicability of the LSTM model was evaluated by comparing the results with those from widely used models based on support vector machine and multilayer perceptron. Consequently, we noted that the LSTM model exhibited a relatively better forecasting performance. Therefore, the applicability of the LSTM model should be extensively studied for flood forecasting applications.

Benchmarking an operational hydrological model for providing seasonal forecasts in Sweden

Probabilistic seasonal forecasts are important for many water-intensive activities requiring long-term planning. Among the different techniques used for seasonal forecasting, the ensemble streamflow prediction (ESP) approach has long been employed due to the singular dependence on past meteorological records. The Swedish Meteorological and Hydrological Institute is currently extending the use of long-range forecasts within its operational warning service, which requires a thorough analysis of the suitability and applicability of different methods with the national S-HYPE hydrological model. To this end, we aim to evaluate the skill of ESP forecasts over 39 493 catchments in Sweden, understand their spatio-temporal patterns, and explore the main hydrological processes driving forecast skill. We found that ESP forecasts are generally skilful for most of the country up to 3 months into the future but that large spatio-temporal variations exist. Forecasts are most skilful during the winter months in northern Sweden, except for the highly regulated hydropower-producing rivers. The relationships between forecast skill and 15 different hydrological signatures show that forecasts are most skilful for slow-reacting, baseflow-dominated catchments and least skilful for flashy catchments. Finally, we show that forecast skill patterns can be spatially clustered in seven unique regions with similar hydrological behaviour. Overall, these results contribute to identifying in which areas and seasons and how long into the future ESP hydrological forecasts provide an added value, not only for the national forecasting and warning service, but also, most importantly, for guiding decision-making in critical services such as hydropower management and risk reduction.

Toward operational flood forecasting and warning services across West Africa – recent experiences at national and regional scales

<p>Flooding is a rapidly growing concern in West Africa. In 2020 alone, several hundred people died and 100 000 were displaced by the floods that occurred across the region. The floods damaged houses and crops and washed away livestock, threatening the livelihoods of millions. Niamey, the capital of Niger, experienced a record flood with the highest ever recorded water levels in nearly 100 years. Flooding is also projected to increase with climate change. One component in addressing this challenge – and a concrete way to adapt to the changing climate – is to provide operational forecasting and warning services to enable pre-emptive stakeholder action and thereby minimize damages.</p><p>Since 2018, a pre-operational flood forecasting and warning service for West Africa has been co-designed, co-developed, co-adapted, and co-operated within the FANFAR project (https://fanfar.eu/, https://doi.org/10.5194/egusphere-egu2020-7660). This study presents results from two approaches employed to assess the accuracy and utility of the service.</p><p>Firstly, representatives from hydrological services, emergency management agencies, river basin organisations, and regional expert centres in 17 countries have contributed to develop and evaluate the service. Specifically, each participating organisation was asked to test the service during the 2019 and 2020 rainy seasons, to record the most critical flood events and the extent to which FANFAR captured the location, timing, magnitude and severity of the floods. The results indicate that both the use and accuracy of the service varies substantially (e.g. from 90% correct in some countries to not even used in others). This people-centred assessment approach also provided an important opportunity to learn about the many events that occur outside of hydrometric monitoring networks, and the way in which agencies communicate flood risk information to multiple audiences for appropriate decision-making.</p><p>Secondly, we evaluated FANFAR forecasts against conventional gauge observations at key locations (e.g. Niamey). The effect of different system configurations on forecast performance was assessed (e.g. the benefit of model calibration and assimilation of gauge observations). The results likewise indicate a performance spread, and sometimes ability to capture certain features of a flood but not all. For example, for the record flood in Niamey in 2020, FANFAR managed to forecast the timing and severity level at the onset of the flood, but not the extent or long duration of the flood.</p><p>We finish off by reflecting on some challenges and opportunities for operational, scalable and reliable 24/7 weather and climate services in West Africa, with potential applicability in the global South.</p>