Green infrastructure (GI) has been regarded as an effective intervention for urban runoff reduction. Despite the growing interest in GI, the technical knowledge that is needed to demonstrate their advantages, cost, and performance in reducing runoff and pollutants is still under research. The present paper describes a framework that aims to obtain the optimal configuration of GI (i.e., the optimal number of units distributed within the catchment) for urban runoff reduction. The research includes an assessment of the performance of GI measures dealing with pollution load, peak runoff, and flood volume reduction. The methodological framework developed includes: (1) data input, (2) GI selection and placement, (3) hydraulic and water quality modelling, and (4) assessing optimal GI measures. The framework was applied in a highly urbanized catchment in Cali, Colombia. The results suggest that if the type of GI measure and its number of units are taken into account within the optimisation process, it is possible to achieve optimal solutions to reduce the proposed reduction objectives with a lower investment cost. In addition, the results also indicate a pollution load, peak runoff, and flood volume reduction for different return periods of at least 33%, 28%, and 60%, respectively. This approach could assist water managers and their stakeholders to assess the trade-offs between different GI.

This paper presents the performance quantification of different green-grey infrastructures, including rainfall-runoff and infiltration processes, on the overland flow and its connection with a sewer system. The present study suggests three main components to form the structure of the proposed model-based assessment. The first two components provide the optimal number of green infrastructure (GI) practices allocated in an urban catchment and optimal grey infrastructures, such as pipe and storage tank sizing. The third component evaluates selected combined green-grey infrastructures based on rainfall-runoff and infiltration computation in a 2D model domain. This framework was applied in an urban catchment in Dhaka City (Bangladesh) where different green-grey infrastructures were evaluated in relation to flood damage and investment costs. These practices implemented separately have an impact on the reduction of damage and investment costs. However, their combination has been shown to be the best action to follow. Finally, it was proved that including rainfall-runoff and infiltration processes, along with the representation of GI within a 2D model domain, enhances the analysis of the optimal combination of infrastructures, which in turn allows the drainage system to be assessed holistically.

Rainfall-runoff transformation on urban catchments involves physical processes governing runoff production in urban areas (e.g., interception, evaporation, depression, infiltration). Some previous 1D/2D coupled models do not include these processes. Adequate representation of rainfall–runoff–infiltration within a dual drainage model is still needed for practical applications. In this paper we propose a new modelling setup which includes the rainfall–runoff–infiltration process on overland flow and its interaction with a sewer network. We first investigated the performance of an outflow hydrograph generator in a 2D model domain. The effect of infiltration losses on the overland flow was evaluated through an infiltration algorithm added in a so-called Surf-2D model. Then, the surface flow from a surcharge sewer was also investigated by coupling the Surf-2D model with the SWMM 5.1 (Storm Water Management Model). An evaluation of two approaches for representing urban floods was carried out based on two 1D/2D model interactions. Two test cases were implemented to validate the model. In general, similar results in terms of peak discharge, water depths and infiltration losses against other 1D/2D models were observed. The results from two 1D/2D model interactions show significant differences in terms of flood extent, maximum flood depths and inundation volume.

Resumen The availability of rainfall information with high spatial resolution is of fundamental importance in many applications in the field of water resources. Commonly, the rainfall data in developing countries are obtained by rain gauge stations. However, many studies show that traditional measures based on rain gauge stations may not reflect the spatial variation of rainfall effectively. Although satellite data have been widely used around the world, when applied to local regions the spatial resolution of these products is too coarse. In this paper, an approach to identify a downscaling method through geostatistical regionalization to improve water resources models with short spatial and temporal scales and with limited rainfall data is presented. Three different models were applied: Cokriging, Inverse Distance Weight (IDW) and Kriging. Statistical parameters such as mean absolute error (MAE) and root mean square error (RMSE) were computed. A cross-validation process showed a better fit for most of the stations using the Cokriging method. The regionalization results were quite comparable with the rain gauge stations data. Although the model outcomes did not improve remarkably, the contribution of this approach may have the potential to provide useful rainfall data at spatial scales shorter than the present resolution.