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Rainwater harvesting (RWH) systems are effective in alleviating water supply shortages, while green roofs (GRs) can contribute to stormwater management, air quality improvement, thermal regulation of buildings, and biodiversity support. Despite their individual benefits, both systems are not frequently combined. This paper investigates the potential for integrating these systems through a hydrologic modeling and optimization approach, using a case study in Paris, France. The study utilized a Conceptual Interflow model (CI-model) coupled with a Water Balance (WB) model to describe the rainfall-runoff relationship of integrated green roof and rainwater harvesting (GR-RWH) systems. An NSGA-II optimization was then applied to the CI-WB model to determine the optimal tank sizing of GR-RWH systems for meeting different water demands. The results show that GR-RWH systems have water reliability (WR) values similar to those of traditional RWH systems without GR, albeit with larger tank volumes. For new buildings in Paris, a GR-RWH system with approximately 25 to 75% GR coverage meets rainwater utilization needs with low investment while also providing the added benefits of GRs. [ABSTRACT FROM AUTHOR]

Green Roofs (GRs) are increasing in popularity due to their ability to manage roof runoff while providing a number of additional ecosystem services. Improvement of hydrological models for the simulation of GRs will aid design of individual roofs as well as city scale planning that relies on the predicted impacts of widespread GR implementation. Machine learning (ML) has exploded in popularity in recent years, however there are no studies focusing on the use of ML in hydrological simulation of GRs. We focus on two types of ML-based model: long short-term memory (LSTM) and gated recurrent unit (GRU), in modelling GRs hydrological performance, with sequence input andsingle output (SISO), and synced sequence input and output (SSIO) architectures. Results of this paper indicate that both LSTM and GRU are useful tools for GR modelling. As the time window length (memory length, time step length of input data) increases, SISO appears to have a higher overall forecast accuracy. SSIO delivers the best overall performance, when the SSIO is close to, or even exceeds, the maximum window size. [ABSTRACT FROM AUTHOR]

China has begun a rapid move towards Sponge City implementation, which will include widespread installation of various Low Impact Development (LID) stormwater infrastructure. Ambitious goals have been set for all Sponge Cities, including a target capture rate of 80–85% of annual precipitation in Beijing. However, there has so far been limited investigation into whether these goals are obtainable at the catchment scale given the physical restrictions of the existing urban landscape. This paper demonstrates the use of long-term continuous hydrologic modelling to evaluate the potential of meeting rainfall capture goals over a long time period for a case study area in Beijing. Detailed land cover classification derived from satellite imagery aided in the parameterization of a SWMM (Stormwater Management Model) model representing a 133 km2 urban study area. The calibrated and validated model demonstrated satisfactory agreement between computed and observed runoff for a variety of precipitation events ranging from 10 to 207 mm. A synthetic high resolution rainfall time series was generated from historic daily observations then used as input to run the model continuously for a 35 year period. Feasible LID model scenarios based on varying degrees of implementation of rain gardens, permeable pavements and green roofs were compared in terms of their potential volume capture of annual rainfall. Modelling results demonstrated that the conversion of 30% of roof area to green roof, 10% of green areas to rain gardens and 35% of paved areas to permeable pavement could increase the baseline volume capture ratio from 59.9% to 82.2%, indicating that Sponge City goals can be met within the case study area with realistic levels of LID implementation. This study provides a decision-making basis for future planning in the study area. • Satellite imagery based hydrologic model shows good agreement with observed flows. • Continuous modelling approach suggested for evaluation of Volume Capture Ratio. • >80% of annual rainfall captured with moderate catchment wide implementation of LID. [ABSTRACT FROM AUTHOR]

• Unit area runoff decreases slightly with the increase of roof area but this effect is not statistically significant. • Plant richness and substrate type did not affect the runoff from the GRs. • Substrate depth was the only structural factor which demonstrated a statistically significant effect on the runoff per unit area. • No interactions between the four factors that affect the hydrological process of GRs were statistically significant. Green Roofs (GRs) are a type of Blue Green Infrastructure, growing in popularity due to their ability to provide numerous benefits. Designers and modelers of GRs typically make the assumption that GRs are homogeneous, which means that for different roof areas and the same structural factors such as substrate depth and composition, they have the same runoff per unit area. Based on this assumption, the measured data from small scale GRs in the laboratory can be applied to large GRs in the field. However, this assumption of homogeneity has not been sufficiently verified. To investigate this gap, an outdoor experiment was carried out from April 25 to December 1, 2021. Thirty-six GRs were constructed following an orthogonal experimental design with four primary structural factors investigated (i.e., substrate type (ST), substrate depth (SD), plant richness (PR), and roof area (RA)). Rain and runoff data were recorded and N -way ANOVA was used to evaluate the impact of the structural factors. Results showed that there were no statistically significant interactions between the structural factors that affected the GR runoff. The order of influence of the individual parameters, determined by the F value statistical metric was: SD > RA > ST > PR. However, substrate depth was the only structural factor which significantly affected the runoff per unit area of GR. Plant richness and substrate types showed no statistically significant effect on the GR runoff. The results of this study indicate that the implicit assumption: GRs are homogeneous, is acceptable. [ABSTRACT FROM AUTHOR]