Operational medium range flood forecasting systems are increasingly moving towards the adoption of ensembles of numerical weather predictions (NWP), known as ensemble prediction systems (EPS), to drive their predictions. We review the scientific drivers of this shift towards such ‘ensemble flood forecasting’ and discuss several of the questions surrounding best practice in using EPS in flood forecasting systems. We also review the literature evidence of the ‘added value’ of flood forecasts based on EPS and point to remaining key challenges in using EPS successfully.

Ensemble flood forecasting: A review

This section is composed of three basic subareas: combined sewer overflows (CSOs), sanitary sewer overflows (SSOs), and stormwater discharges. Much of the literature cited came from documents covering noteworthy global conferences (Bathala, 1996; Engineering Foundation, 1996; Hallam et al., 1996; Maxwell et al., 1996; Sieker and Verworn [Eds.], 1996; Society of Environmental Toxicology and Chemistry, 1996; U.S. EPA 1996a; Water Environment Federation, 1996a,b,c). In addition, the U.S. Environmental Protection Agency (U.S. EPA) published guidance documents (U.S. EPA, 1996,c,d,e), which are discussed in more detail in the subsection Regulatory Policies and Financial Aspects.

https://www.jstor.org/stable/pdfplus/29763265.pdf

Urban wet-weather flows

Protecting and restoring Europe's waters: An analysis of the future development needs of the Water Framework Directive

Preliminary feasibility analysis of a hybrid pumped-hydro energy storage system using abandoned coal mine goafs

Underground space utilization of coalmines in China: A review of underground water reservoir construction

Though the Ecosystem Service (ESS) approach is considered promising for integrated ecosystem management, its operationalisation is hampered by the lack of agreed evaluation instruments. To demonstrate the suitability of a structured ESS evaluation, we conducted a case study estimating the impact of the restoration of the Emscher River and its tributaries on the provision, use and benefit of ESS. The Emscher restoration is a large-scale project with immense temporal and financial efforts. To assess the values generated by this restoration, we applied an ESS evaluation framework and quantified the regulation and maintenance ESS ‘self-purification capacity’, ‘maintaining nursery populations and habitats’ and ‘flood protection’ as well as cultural ESS such as aesthetic, recreational, educational and existence values. Final ESS were monetized using economic methods, e.g. ‘damage costs avoided’, ‘contingent valuation’ and ‘benefit transfer’. We estimated a market value/direct economic impact of 21,441,572 € per year as a result of the restoration. Furthermore, a non-market value for people who care about the local environment of 109,121,217 € per year was determined, representing the benefit with ‘non-use value’ from the Emscher restoration. Our case study demonstrated the successful application of the structured evaluation framework in practice. Its implications and limitations are discussed.

Large-scale river restoration pays off: A case study of ecosystem service valuation for the Emscher restoration generation project

Coalmines as Underground Pumped Storage Power Plants (UPP) - A Contribution to a Sustainable Energy Supply?

Abstract Because of climate change, the frequency, intensity and/or duration of extreme weather events such as floods, droughts, storms and extreme temperatures is increasing. These events are often related to loss of property, money and life, especially in poor and developing countries where there is no or poor disaster management due to social and financial difficulties or due to a lack of synergy between the mitigation actions taken. Negative impacts can be reduced and losses can be better handled with proper water management techniques. However, these should not be handled solely with traditional management. The actual problem cannot be over simplified as merely a question of coping with resources availability and demand. Therefore, the present paper aims to summarize advances in weather forecasting and reservoir operation in the Upper São Francisco River, strategic to Brazil because it provides water to the semi-arid region and energy for economically thriving Brazilian regions. Moreover, it discusses challenges, opportunities and improvements needed to implement these advances in the current national integrated water resources management. This is mainly focused on water-related disaster mitigation.

Climate change, water-related disasters, flood control and rainfall forecasting: a case study of the São Francisco River, Brazil

Storm water management in an urban catchment: effects of source control and real-time management of sewer systems on receiving water quality.

This study presents a deep-learning-based forecast model for spatial and temporal prediction of pluvial flooding. The developed model can produce the flooding situation for the upcoming time steps as a sequence of flooding maps. Thus, a dynamic overview of the forthcoming flooding situation is generated to support the decision of crisis management actors. The influence of different input data, data formats, and model setups on the prediction results was investigated. Data from multiple sources were considered as follows: precipitation information, spatial information, and an overflow forecast. In addition, models with different layers and network architectures such as convolutional layers, graph convolutional layers, or generative adversarial networks (GANs) were considered and evaluated. The data required to train and test the models were generated using a coupled hydrodynamic 1D/2D model. The model setup with the inclusion of all available input variables and an architecture with graph convolutional layers presented, in general, the best results in terms of root mean square error (RMSE) and critical success index (CSI). The prediction results of the final model showed a high agreement with the simulation results of the hydrodynamic model, with drastic reductions in computation time, making this model suitable for integration into an early warning system for pluvial flooding.

/pdf/a-spatiotemporal-deep-learning-approach-for-urban-pluvial-3k41wo2y.pdf

A Spatiotemporal Deep Learning Approach for Urban Pluvial Flood Forecasting with Multi-Source Data

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