Rainwater harvesting for urban flood management - An integrated modelling framework.

It is well known that rainwater harvesting (RWH) can augment water supply and reduce stormwater pollutant discharges. Due to the lack of continuous 2D modelling of urban flood coverage and its associated damage, the ability of RWH to reduce urban flood risks has not been fully evaluated. Literature suggests that small distributed storage spaces using RWH tanks will reduce flood damage only during small to medium flooding events and therefore cumulative assessment of their benefits is needed. In this study we developed a new integrated modelling framework that implements a semi-continuous simulation approach to investigate flood prevention and water supply benefits of RWH tanks. The framework includes a continuous mass balance simulation model that considers antecedent rainfall conditions and water demand/usage of tanks and predicts the available storage prior to each storm event. To do so, this model couples a rainfall-runoff tank storage model with a detailed stochastic end-use water demand model. The available storage capacity of tanks is then used as a boundary condition for the novel rapid flood simulation model. This flood model was developed by coupling the U.S. EPA Storm Water Management Model (SWMM) to the Cellular-Automata Fast Flood Evaluation (CA-ffé) model to predict the inundation depth caused by surcharges over the capacity of the drainage network. The stage-depth damage curves method was used to calculate time series of flood damage, which are then directly used for flood risk and cost-benefit analysis. The model was tested through a case study in Melbourne, using a recorded rainfall time series of 85 years (after validating the flood model against 1D-2D MIKE-FLOOD). Results showed that extensive implementation of RWH tanks in the study area is economically feasible and can reduce expected annual damage in the catchment by up to approximately 30 percent. Availability of storage space and temporal distribution of rainfall within an event were important factors affecting tank performance for flood reduction.

Jamali B ，Bach PM ，Deletic A 《-》

Can smart rainwater harvesting schemes result in the improved performance of integrated urban water systems?

Behzadian K ，Kapelan Z ，Mousavi SJ ，Alani A ... -  《-》

Feasibility of rainwater harvesting for sustainable water management in urban areas of Egypt.

Gado TA ，El-Agha DE 《-》

Water Sensitive Urban Design (WSUD) Spatial Prioritisation through Global Sensitivity Analysis for Effective Urban Pluvial Flood Mitigation.

Water Sensitive Urban Design (WSUD) has attracted growing attention as a sustainable approach for mitigating pluvial flooding (also known as flash flooding), which is expected to increase in frequency and intensity under the impacts of climate change and urbanisation. However, spatial planning of WSUD is not an easy task, not only due to the complex urban environment, but also the fact that not all locations in the catchment are equally effective for flood mitigation. In this study, we developed a new WSUD spatial prioritisation framework that applies global sensitivity analysis (GSA) to identify priority subcatchments where WSUD implementation will be most effective for flood mitigation. For the first time, the complex impact of WSUD locations on catchment flood volume can be assessed, and the GSA in hydrological modelling is adopted for applications in WSUD spatial planning. The framework uses a spatial WSUD planning model, the Urban Biophysical Environments and Technologies Simulator (UrbanBEATS), to generate a grid-based spatial representation of catchment, and an urban drainage model, the U.S. EPA Storm Water Management Model (SWMM), to simulate catchment flooding. The effective imperviousness of all subcatchments was varied simultaneously in the GSA to mimic the effect of WSUD implementation and future developments. Priority subcatchments were identified based on their influence on catchment flooding computed through the GSA. The method was tested for an urbanised catchment in Sydney, Australia. We found that high priority subcatchments were clustering in the upstream and midstream of the main drainage network, with a few distributed close to the catchment outlets. Rainfall frequency, subcatchment characteristics, and pipe network configuration were found to be important factors determining the influence of changes in different subcatchments on catchment flooding. The effectiveness of the framework in identifying influential subcatchments was validated by comparing the effect of removing 6% of the Sydney catchment's effective impervious area under four WSUD spatial distribution scenarios. Our results showed that WSUD implementation in high priority subcatchments consistently achieved the largest flood volume reduction (3.5-31.3% for 1% AEP to 50% AEP storms), followed by medium priority subcatchments (3.1-21.3%) and catchment-wide implementation (2.9-22.1%) under most design storms. Overall, we have demonstrated that the proposed method can be useful for maximising WSUD flood mitigation potential through identifying and targeting the most effective locations.

Wu W ，Jamali B ，Zhang K ，Marshall L ，Deletic A ... -  《-》

BK-SWMM flood simulation framework is being proposed for urban storm flood modeling based on uncertainty parameter crowdsourcing data from a single functional region.

In recent years, urban flood disasters caused by sudden heavy rains have become increasingly severe, posing a serious threat to urban public infrastructure and the life and property safety of residents. Rapid simulation and prediction of urban rain-flood events can provide timely decision-making reference for urban flood control and disaster reduction. The complex and arduous calibration process of urban rain-flood models has been identified as a major obstacle affecting the efficiency and accuracy of simulation and prediction. This study proposes a multi-scale urban rain-flood model rapid construction method framework, BK-SWMM, focusing on urban rain-flood model parameters and based on the basic architecture of Storm Water Management Model (SWMM). The framework comprises two main components: 1) constructing a SWMM uncertainty parameter sample crowdsourcing dataset and coupling Bayesian Information Criterion (BIC) and K-means clustering machine learning algorithm to discover clustering patterns of SWMM model uncertainty parameters in urban functional areas; 2) coupling BIC and K-means with SWMM model to form BK-SWMM flood simulation framework. The applicability of the proposed framework is validated by modeling three different spatial scales in the study regions based on observed rainfall-runoff data. The research findings indicate that the distribution pattern of uncertainty parameters, such as depression storage, surface Manning coefficient, infiltration rate, and attenuation coefficient. The distribution patterns of these seven parameters in urban functional zones indicate that the values are highest in the Industrial and Commercial Areas (ICA), followed by Residential Areas (RA), and lowest in Public Areas (PA). All three spatial scales' REQ, NSEQ, and RD2 indices were superior to the SWMM and less than 10%, greater than 0.80, and greater than 0.85, respectively. However, when the study area's geographical scale expands, the simulation's accuracy will decline. Further research is required on the scale dependency of urban storm flood models.

Liu C ，Li W ，Zhao C ，Xie T ，Jian S ，Wu Q ，Xu Y ，Hu C ... -  《-》