Rainwater harvesting systems are often used as both an alternative water source and a stormwater management tool. Many studies have focused on the water-saving potential of these systems, but research into aspects that impact stormwater retention—such as demand patterns and climate change—is lacking. This paper investigates the short-term impact of demand on both water supply and stormwater management and examines future and potential performance over a longer time scale using climate change projections. To achieve this, data was collected from domestic rainwater harvesting systems in Broadhempston, UK, and used to create a yield-after-spillage model. The validation process showed that using constant demand as opposed to monitored data had little impact on accuracy. With regards to stormwater management, it was found that monitored households did not use all the non-potable available water, and that increasing their demand for this was the most effective way of increasing retention capacity based on the modelling study completed. Installing passive or active runoff control did not markedly improve performance. Passive systems reduced the outflow to greenfield runoff for the longest time, whereas active systems increased the outflow to a level substantially above roof runoff in the 30 largest events.

Water demand measurements have historically been conducted manually, from meter readings less than once per month. Leading water service providers have begun to deploy smart meters to collect high-resolution data. A low-cost flush counter was developed and connected to a real-time monitoring platform for 119 ultra-low flush toilets in 7 buildings on a university campus to explore how building users influence water demand. Toilet use followed a typical weekly pattern in which weekday use was 92% ± 4 higher than weekend use. Toilet demand was higher during term time and showed a strong, positive relationship with the number of building occupants. Mixed-use buildings tended to have greater variation in toilet use between term time and holidays than office-use buildings. The findings suggest that the flush sensor methodology is a reliable method for further consideration. Supplementary data from the study's datasets will enable practitioners to use captured data for (i) forecast models to inform water resource plans; (ii) alarm systems to automate maintenance scheduling; (iii) dynamic cleaning schedules; (iv) monitoring of building usage rates; (v) design of smart rainwater harvesting to meet demand from real-time data; and (vi) exploring dynamic water pricing models, to incentivise optimal on-site water storage strategies.

This study investigates the capability of Gaussian process regression (GPR) models in the probabilistic forecasting of water flow and depth in a combined sewer system. Traditionally, deterministic methods have been implemented in sewer flow forecasting and anomaly detection, two crucial techniques for a good wastewater network and treatment plant management. However, with the uncertain nature of the factors impacting on sewer flow and depth, a probabilistic approach which takes uncertainties into account is preferred. This research introduces a novel use of GPR in sewer systems for real-time control and forecasting. To this end, a composite kernel is designed to capture flow and depth patterns in dry- and wet-weather periods by considering the underlying physical characteristics of the system. The multi-input, single-output GPR model is evaluated using root mean square error (RMSE), coverage, and differential entropy. The model demonstrates high predictive accuracy for both treatment plant inflow and manhole water levels across various training durations, with coverage values ranging from 87.5% to 99.4%. Finally, the model is used for anomaly detection by identifying deviations from expected ranges, enabling the estimation of surcharge and overflow probabilities under various conditions.

Water demand management often focuses on quantifying the benefits of water efficiency rather than the potential impact of reduced flows on the sewer network. This study assessed the impact of a high-density deployment of ultra-low flush toilets (ULFT). A pre-installation washroom survey was carried out in July 2018. Water demand and sewer network condition were assessed ahead of the installation of 119 ULFTs and a real-time monitoring system across seven buildings on the University of Exeter campus. ULFTs were flushed 257,925 times in 177 days saving an estimated 2287 m³ per annum (compared to traditional 6 litre WCs). The annual cost saving of this reduction is approximately £12,580/annum, assuming a volumetric cost of £5.50/m³ of water. Mean discharge to the sewer network reduced by 6 m³/day. In the six-month period, 95 maintenance issues were reported, equating to 1 in 2700 flushes (0.037%). However, the frequency of incidents decreased after an initial commissioning period. There is no evidence, from blockage reports or photographs of manhole flow conditions, that the risk of blockage in the sewer network increased as a result of the ULFT installation programme.

This study investigates the capability of Gaussian process regression (GPR) models in probabilistic forecasting of water flow and depth in a combined sewer system. Traditionally, deterministic methods have been implemented in sewer flow forecasting and anomaly detection which are two crucial techniques for a good wastewater network and treatment plant management. However, with the uncertain nature of the factors impacting on sewer flow and depth, a probabilistic approach which takes uncertainties into account is preferred. Using hydraulic simulation data generated in this study, a composite kernel is designed to take the flow and depth patterns based on the flow characteristics, and the hyperparameters are optimised through maximisation of the log-likelihood function. The GPR model is a multi-input, single-output model taking time and precipitation as inputs. Prediction results show reliable model outputs and are evaluated by three metrics: root mean square error, coverage and differential entropy. The model effectively predicts treatment plant inflow and manhole water levels with different training periods. Finally, the model is used for anomaly detection by identifying deviations from expected ranges, enabling the estimation of surcharge and overflow probabilities under various conditions.