The Vietnamese Mekong Delta (VMD) has an important role in terms of food security and socio-economic development of the region; however, it is one of many areas of the world vulnerable to floods resulting from sea level rise (SLR) due to climate change. Therefore, management of flooding is a priority at national and regional levels in Vietnam. The Long Xuyen Quadrangle is the most important region in the VMD in terms of agriculture and economy. In the present work, flood hazard, vulnerability and risk were assessed and mapped to identify the priority areas in the Long Xuyen Quadrangle for flood mitigation. A hydrodynamic model was used to simulate the flood event of 2000 when a flood of 20-year return period occurred and caused loss of human lives and extensive damage. The calibrated model was then used to simulate a possible flood event in 2050 due to SLR. The resulting flood depth of the simulation was used to prepare inundation maps and to analyse flood hazard in this region, as well. The flood vulnerability of the region was assessed using the coastal areas flood vulnerability index (FVI) method. The FVI was determined by district, and flood vulnerability maps were developed based on these data. The results indicate that the major part of the study area (35.4%) can be classified as being at high risk. It was also found that 32.7% of the area is under medium risk and only about 18.4% is under very low and low risk; 10.2% of the total area is not subjected to flood risk. We show that district level flood vulnerability maps are potentially useful for decision makers and the public in planning better measures for adaptation and mitigation of the negative impacts of flooding.

This paper explores the use of flow length and travel time as a pre-processing step for incorporating spatial precipitation information into Artificial Neural Network (ANN) models used for river flow forecasting. Spatially distributed precipitation is commonly required when modelling large basins, and it is usually incorporated in distributed physically-based hydrological modelling approaches. However, these modelling approaches are recognised to be quite complex and expensive, especially due to the data collection of multiple inputs and parameters, which vary in space and time. On the other hand, ANN models for flow forecasting are frequently developed only with precipitation and discharge as inputs, usually without taking into consideration the spatial variability of precipitation. Full inclusion of spatially distributed inputs into ANN models still leads to a complex computational process that may not give acceptable results. Therefore, here we present an analysis of the flow length and travel time as a basis for pre-processing remotely sensed (satellite) rainfall data. This pre-processed rainfall is used together with local stream flow measurements of previous days as input to ANN models. The case study for this modelling approach is the Ganges river basin. A comparative analysis of multiple ANN models with different hydrological pre-processing is presented. The ANN showed its ability to forecast discharges 3-days ahead with an acceptable accuracy. Within this forecast horizon, the influence of the pre-processed rainfall is marginal, because of dominant influence of strongly auto-correlated discharge inputs. For forecast horizons of 7 to 10 days, the influence of the pre-processed rainfall is noticeable, although the overall model performance deteriorates. The incorporation of remote sensing data of spatially distributed precipitation information as pre-processing step showed to be a promising alternative for the setting-up of ANN models for river flow forecasting.

This paper presents an approach to enhance the role of local stakeholders in dealing with urban floods. The concept is based on the DIANE-CM project (Decentralised Integrated Analysis and Enhancement of Awareness through Collaborative Modelling and Management of Flood Risk) of the 2nd ERANET CRUE funding initiative. The main objective of the project was to develop and test an advanced methodology for enhancing the resilience of local communities to flooding. Through collaborative modelling, a social learning process was initiated that enhances the social capacity of the stakeholders due to the interaction process. The other aim of the project was to better understand how data from hazard and vulnerability analyses and improved maps, as well as from the near real-time flood prediction, can be used to initiate a public dialogue (i.e. collaborative mapping and planning activities) in order to carry out more informed and shared decision-making processes and to enhance flood risk awareness. The concept of collaborative modelling was applied in two case studies: (1) the Cranbrook catchment in the UK, with focus on pluvial flooding; and (2) the Alster catchment in Germany, with focus on fluvial flooding. As a result of the interactive and social learning process, supported by sociotechnical instruments, an understanding of flood risk was developed amongst the stakeholders and alternatives for flood risk management for the respective case study area were jointly developed and ranked as a basis for further planning and management.

Contamination emergency in water distribution systems is a complex situation where optimal operation becomes important for public health. In case of emergency corrective operational actions for flushing the pollutant out of the network are needed, which have to be fast and accurate. Under such a stressful situation, trial-and-error simulation experiments with the hydrodynamic and water quality models cannot be applied since significant number of model evaluations may be required to identify the optimal solution. This paper presents a methodology for finding sets of operational interventions in a supply network for flushing a contaminant by minimizing the impact on the population. The situation is treated as both single- and multiobjective optimization problem, which is solved by using evolutionary optimization approaches, in combination with the EPANET solver engine. The methodology is tested on a simple imaginary network configuration, as well as on a real case study for the city of Villavicencio in Colombia. The results prove the usefulness of the approach for advising the operators and decision makers.

The remediation of groundwater contamination by pumping and injection is generally a long-term and costly strategy. Aquifer cleanup time is a highly nonlinear and nonconvex function of pumping rates. The cleanup objective often involves minimizing or constraining the cleanup time or cleanup cost. Linear programming and nonlinear optimization cannot guarantee the global solution. In this study, four global optimization (GO) algorithms, including a popular genetic algorithm, are used to minimize both cleanup time and cleanup cost taking pumping rates and/or well locations as decision variables. Groundwater flow and particle-tracking models (MODFLOW and MODPATH) and a GO tool (GLOBE) are used. Real and hypothetical contaminated aquifers are considered for application. The results are satisfactory and show that GO techniques can be widely applied in groundwater remediation strategy and planning. The comparison of the performance of algorithms did not reveal a clear winner. The results also show that in the pa...