Sustainable Flood Retention Basins Research Articles

New methodology for identifying sustainable freshwater resources for the production of green hydrogen

ABSTRACT Managing sustainable and digital transformations in the water industry is a challenge. The identification of freshwater resources for green hydrogen production is necessary for the energy transition away from fossil fuels such as oil and gas. This can be achieved through the sustainable flood retention basin (SFRB) concept for identifying optimal locations for the retention of surface water. This unbiased concept helps in conflict resolution between stakeholders with water rights. Moreover, it serves as a simple tool to produce a digital water supply atlas based on dimensionless parameters with the short names’ population density, location advantage, precipitation, groundwater level, water rights, SFRB, excess running water and proximity to networks. While the first five parameters can be determined during a desk study, the last three parameters require site visits for verification purposes. Germany was used as a representative case study country to demonstrate the proposed methodology to obtain a water atlas based on mapped weighted values. The Kaiserstuhl in Germany served as a worked calculation example indicating well above average potential for water storage using existing retention basins. The water atlas can be used to supplement a (green) hydrogen atlas, supporting decision-makers in building hydrogen infrastructure.

Predicting Multiple Functions of Sustainable Flood Retention Basins under Uncertainty via Multi-Instance Multi-Label Learning

The ambiguity of diverse functions of sustainable flood retention basins (SFRBs) may lead to conflict and risk in water resources planning and management. How can someone provide an intuitive yet efficient strategy to uncover and distinguish the multiple potential functions of SFRBs under uncertainty? In this study, by exploiting both input and output uncertainties of SFRBs, the authors developed a new data-driven framework to automatically predict the multiple functions of SFRBs by using multi-instance multi-label (MIML) learning. A total of 372 sustainable flood retention basins, characterized by 40 variables associated with confidence levels, were surveyed in Scotland, UK. A Gaussian model with Monte Carlo sampling was used to capture the variability of variables (i.e., input uncertainty), and the MIML-support vector machine (SVM) algorithm was subsequently applied to predict the potential functions of SFRBs that have not yet been assessed, allowing for one basin belonging to different types (i.e., output uncertainty). Experiments demonstrated that the proposed approach enables effective automatic prediction of the potential functions of SFRBs (e.g., accuracy >93%). The findings suggest that the functional uncertainty of SFRBs under investigation can be better assessed in a more comprehensive and cost-effective way, and the proposed data-driven approach provides a promising method of doing so for water resources management.

Predicting dam failure risk for sustainable flood retention basins: A generic case study for the wider Greater Manchester area

This study aims to provide a rapid screening tool for assessment of sustainable flood retention basins (SFRBs) to predict corresponding dam failure risks. A rapid expert-based assessment method for dam failure of SFRB supported by an artificial neural network (ANN) model has been presented. Flood storage was assessed for 110 SFRB and the corresponding Dam Failure Risk was evaluated for all dams across the wider Greater Manchester study area. The results show that Dam Failure Risk can be estimated by using the variables Dam Height, Dam Length, Maximum Flood Water Volume, Flood Water Surface Area, Mean Annual Rainfall (based on Met Office data), Altitude, Catchment Size, Urban Catchment Proportion, Forest Catchment Proportion and Managed Maximum Flood Water Volume. A cross-validation R2 value of 0.70 for the ANN model signifies that the tool is likely to predict variables well for new data sets. Traditionally, dams are considered safe because they have been built according to high technical standards. However, many dams that were constructed decades ago do not meet the current state-of-the-art dam design guidelines. Spatial distribution maps show that dam failure risks of SFRB located near cities are higher than those situated in rural locations. The proposed tool could be used as an early warning system in times of heavy rainfall.

Multi-label classification models for sustainable flood retention basins

It is becoming good practice to prepare risk assessments of river basins and coastal areas on a global scale. The novel sustainable flood retention basin (SFRB) concept provides a rapid classification technique for impoundments, which have a pre-defined or potential role in flood defense. However, most SFRB do often perform multiple functions simultaneously and thus are associated with multiple SFRB types. Nevertheless, previous SFRB classification systems assign each SFRB to a specific type relying on its main function. To handle the problem, this study aims to comprehensively assess the multiple functions of SFRB with the help of multi-label classification. The popular multi-label classifiers multi-label support vector machine (MLSVM), multi-label K-nearest neighbor (MLKNN) and back-propagation for multi-label learning (BP-MLL) were applied to predict the types of SFRB based on two data sets (one from Scotland and one from Baden). Findings indicate that multi-label classification schemes provide deeper insights into all potential functions of SFRB and help planners and engineers to make better use of them.

Application of spatial statistics as a screening tool for sustainable flood retention basin management

AbstractIn the context of climate change and the EU flood directive, this paper analyses and explores sustainable flood retention basins (SFRB) as adaptive structures contributing to water resources management and flood risk control. A dataset of 371 potential SFRB (including many operating reservoirs) characterized by 40 variables have been surveyed across central Scotland. However, the spatial properties of these SFRB, such as water storage (which relates to flooding depth) in different regions, are ambiguous. This paper uses geostatistics on the Scottish dataset. Spatial analysis showed that ordinary kriging, which is a spatial interpolation method, could be successfully applied to estimate numerical values for all key flood control variables everywhere in the study area. Moreover, the probability that certain threshold values relevant to flood control managers were exceeded can also be calculated by using disjunctive kriging. The findings provide an effective screening tool in assessing flood control using SFRB.

Feature selection methods for characterizing and classifying adaptive Sustainable Flood Retention Basins

The European Union’s Flood Directive 2007/60/EC requires member states to produce flood risk maps for all river basins and coastal areas at risk of flooding by 2013. As a result, flood risk assessments have become an urgent challenge requiring a range of rapid and effective tools and approaches. The Sustainable Flood Retention Basin (SFRB) concept has evolved to provide a rapid assessment technique for impoundments, which have a pre-defined or potential role in flood defense and diffuse pollution control. A previous version of the SFRB survey method developed by the co-author Scholz in 2006 recommends gathering of over 40 variables to characterize an SFRB. Collecting all these variables is relatively time-consuming and more importantly, these variables are often correlated with each other. Therefore, the objective is to explore the correlation among these variables and find the most important variables to represent an SFRB. Three feature selection techniques (Information Gain, Mutual Information and Relief) were applied on the SFRB data set to identify the importance of the variables in terms of classification accuracy. Four benchmark classifiers (Support Vector Machine, K-Nearest Neighbours, C4.5 Decision Tree and Naïve Bayes) were subsequently used to verify the effectiveness of the classification with the selected variables and automatically identify the optimal number of variables. Experimental results indicate that our proposed approach provides a simple, rapid and effective framework for variable selection and SFRB classification. Only nine important variables are sufficient to accurately classify SFRB. Finally, six typical cases were studied to verify the performance of the identified nine variables on different SFRB types. The findings provide a rapid scientific tool for SFRB assessment in practice. Moreover, the generic value of this tool allows also for its wide application in other areas.

Guidance on variables characterising water bodies including sustainable flood retention basins

Climate change is likely to increase the frequency of flood events, thus increasing the associated hazard. This may threaten some existing flood defences, which were designed and built prior to climate change being identified as a major issue; therefore, they may require modification to ensure their sustainability. The European Union recognises that member states may face significant challenges in implementing the Flood Directive and has responded with programs such as the Strategic Alliance for Water Management Actions, which aims to provide tools and guidance to aid the member states in implementation. Flooding is a complex spatial planning issue and therefore requires a range of tools and approaches to solve the problem. Solutions are likely to require sustainable (urban) drainage system (SUDS) solutions applied on a small scale combined with sustainable flood retention basins (SFRB) used on a large scale. This paper aims to provide guidance on 40 variables used to rapidly assess water bodies including SFRB in terms of their flood and diffuse pollution control purposes and potentials. The variables described should be the basis for the characterisation of SFRB, which are adaptive structural measures well integrated within the landscape promoting best management practice as part of a sustainable flood risk management strategy. The variables engineered, outlet arrangement, aquatic animal passage, land animal passage, basin and channel connectivity, seasonal influence, relative total pollution and flotsam cover are novel. The list includes sum parameter such as relative total pollution, which address as part of a simplified methodology several complex environmental processes.

Classification of different sustainable flood retention basin types

Using a revised version of a previously published expert classification system, a database of potential Sustainable Flood Retention Basins has been developed for Scotland. The research shows that the majority of small and former (often old) drinking water reservoirs are kept full and their spillways are continuously in operation. Utilising some of the available capacity to contribute to flood control could significantly reduce the costs of complying with the European Union Flood Directive. Furthermore, the application of a previously developed classification model for Baden in Germany for the Scottish data set showed a lower diversity for basins in Scotland due to less developed infrastructure. The classification system appears to be robust and has the potential, with minor modifications, to be applied across Europe. The principle value of this approach is a clear and unambiguous categorisation, based on standard variables, which can help to promote communication and understanding between stakeholders.

Classification of Flood Retention Basins: The Kaiserstuhl Case Study

The aim of this paper is to describe a conceptual classification model for Sustainable Flood Retention Basins (SFRB) used to control runoff in temperate climates. The author has defined a SFRB as an aesthetically pleasing (from the general publics' point of view) retention basin, predominantly used for flood protection and adhering to sustainable drainage and best management practices. The classification model concept was developed on the basis of an example database of 147 SFRB in Baden-Wurttemberg (Germany). The data were of high quality because the researchers had detailed local knowledge of the case study area. The model quality was improved on the basis of a more detailed case study of The Kaiserstuhl, a region (no clearly defined boundaries) with a very high density of flood retention basins (one SFRB per 2 km2; 51 sites in total). The model is based on an agglomerative cluster analysis and is intended to be used by landscape planners, environmental engineers, and environmental scientists to adequately classify the following different types of SFRB with the purpose of improving communication among stakeholders: Hydraulic Flood Retention Basin, Traditional Flood Retention Basin, Sustainable Flood Retention Wetland, Aesthetic Flood Retention Wetland, Integrated Flood Retention Wetland, and Natural Flood Retention Wetland. Despite the limited regional variation in flood retention basin characteristics within The Kaiserstuhl, the classification model example had an accuracy of 60 percent. A scoring template for the classification of future SFRB was devised on the basis of researcher experience, and this was optimized with a genetic algorithm.

Conceptual classification model for Sustainable Flood Retention Basins

The aim of this paper is to recommend a rapid conceptual classification model for Sustainable Flood Retention Basins (SFRB) used to control runoff in a temperate climate. An SFRB is an aesthetically pleasing retention basin predominantly used for flood protection adhering to sustainable drainage and best management practices. The classification model was developed on the basis of a database of 141 SFRB using the River Rhine catchment in Baden (part of Baden-Württemberg, Germany) as a case study. It is based on an agglomerative cluster analysis and is intended to be used by engineers and scientists to adequately classify the following different types of SFRB: Hydraulic Flood Retention Basin, Traditional Flood Retention Basin, Sustainable Flood Retention Wetland, Aesthetic Flood Retention Wetland, Integrated Flood Retention Wetland and Natural Flood Retention Wetland. The selection of classification variables was supported by a principal component analysis. The identification of SFRB in the data set was based on a Ward cluster analysis of 34 weighted classification variables. Scoring tables were defined to enable the assignment of the six SFRB definitions to retention basins in the data set. The efficiency of these tables was based on a scoring system which gave the conceptual model for the example case study sites an overall efficiency of approximately 60% (as opposed to 17% by chance). This conceptual classification model should be utilized to improve communication by providing definitions for SFRB types. The classification definitions are likely to be applicable for other regions with both temperate oceanic and temperate continental climates.

Expert system outline for the classification of sustainable flood retention basins (SFRBs)

The aim of this paper is to recommend a rapid classification system for sustainable flood retention basins (SFRBs) to control runoff in a temperate climate. The system should improve communication among engineers and scientists, and was developed with a general database (approximately 660 basins) and a very detailed database (56 basins), using the River Rhine catchment in the South of Baden (Germany) as a case study. The classification system can be used by civil engineers, environmental scientists and planners to determine the following types of SFRBs: hydraulic flood retention basin, traditional sustainable flood retention basin, sustainable flood retention wetland, aesthetic flood retention wetland, integrated flood retention wetland and natural flood retention wetland. The most important system variables were rainfall, dam height, flood water volume, elevation, dam length, flotsam, floodplain elevation, forest and animal passage. However, 34 variables were applied to determine the different types of SFRBs, which were predominantly used for hydraulic purposes.

Classification methodology for Sustainable Flood Retention Basins

This paper recommends a rapid classification methodology for Sustainable Flood Retention Basins to control runoff in a temperate climate. Particular emphasis is given towards vegetated retention basins in Baden (Germany). The most important groups of variables were of engineering, environmental and land use nature. The classification methodology can be used by urban and landscape planners to determine the type of Sustainable Flood Retention Basin based on a total sum of weights obtained for 34 qualitative or quantitative variables. The following six types of Sustainable Flood Retention Basins (SFRB's) were defined: Hydraulic Flood Retention Basin, Traditional Sustainable Flood Retention Basin, Sustainable Flood Retention Wetland, Aesthetic Flood Retention Wetland, Integrated Flood Retention Wetland and Natural Flood Retention Wetland. The methodology was developed with a general database (approximately 460 basins) and a very detailed database (24 basins). Most of the thoroughly researched SFRB of the latter database are Traditional Sustainable Flood Retention Basins. The most important variables for Baden were Elevation, Dam Existance, Flotsam, Forest, Dam Length and Vegetation. These variables were applied to determine the type of Sustainable Flood Retention Basin (predominantly used for hydraulic purposes) with an error of <8% in comparison to the application of all 34 variables.