Rainwater Harvesting in Social Housing: An Analysis Across Twelve Cities in Brazil ^†

Abstract

Rainwater harvesting (RWH) has emerged as a promising technique to improve water security amid the escalating effects of climate change. However, a comprehensive evaluation of various rainwater harvesting solutions is needed to promote sustainable practices in the building sector. This study aims to evaluate the water saving potential in multi- and single-family social housing buildings in twelve cities in Brazil. Computer simulations were performed for 60 scenarios, comprising five social housing reference models and using rainfall data from twelve representative cities of Brazil’s bioclimatic zones. The results show that single-family houses presented a higher potential for potable water savings (20 to 22%) than multi-family housing models (2 to 3%), mainly due to their higher roof-area-to-resident ratio. Single-family buildings exhibit more significant variability in absolute savings (standard deviation), while multi-family buildings are more sensitive to variability relative to their means (higher CVs). Furthermore, due to uneven rainfall distribution and storage limitations, water savings potential does not correlate linearly with total annual rainfall. Normalised results reveal that buildings with a lower population density achieve higher water savings per area and inhabitant. This study demonstrated that building and climate characteristics influence rainwater harvesting, offering valuable insights for promoting sustainable water management practices in social housing.

1. Introduction

Climate change has rapidly altered water management around the world. Its effects on precipitation patterns and extreme weather events (floods, droughts, heat waves) show high spatial and temporal variations. This situation has led to significant variations in the hydrological cycle, thus affecting water use around the world [1]. In its Sixth Assessment Report (AR6), the Intergovernmental Panel on Climate Change (IPCC) emphasised the growing impacts of extreme weather events on water availability around the world, especially for low-income households [2]. Wilde and Coley [3] revealed the deep and complex relationship between climate change and buildings. The authors state that the long lifespan of buildings implies the need to provide a design adapted to future climates whilst providing lower environmental impact throughout their life cycle.

In this scenario, rainwater harvesting (RWH) is a thriving technique that can improve buildings and cities’ resilience to climate change and its consequences on water management. Musayev et al. [4] evaluated RWH in future climate scenarios in various climate zones and proved its effectiveness in improving water security 80% of the time, even in arid zones. Several studies also proved the effectiveness of RWH in improving water availability in Brazil. Ghisi [5] verified that applying RWH in the residential sector can significantly raise predicted water availability in endangered regions in Brazil to acceptable levels. Borgert and Ghisi [6] used the Netuno version 4 computer program to evaluate rainwater harvesting scenarios in single- and multi-family dwellings in Florianópolis, Brazil. The authors found that the system was technically and economically feasible in most cases. Additionally, RWH has been recognised as an essential tool to reduce the overload on cities’ drainage systems and mitigate the impacts of extreme weather events in cities, such as flash floods [7].

However, a single RWH solution cannot be suitable for all locations and building types, especially in countries with considerable variations in climates and cultures, such as Brazil [8]. Therefore, the influence of building characteristics and location variations must be considered to properly assess the benefits and drawbacks of RWH systems. This study aims to evaluate the water saving potential in multi- and single-family social housing buildings in twelve representative cities in Brazil.

2. Method

Computer simulations allow the quick and precise analysis of RWH, considering local rainfall data and building characteristics. The goal was to evaluate the water saving potential of five social housing models in twelve Brazilian representative cities, i.e., 60 scenarios. The following sections provide details on the simulation procedure.

2.1. Study Objects

Five social housing reference models developed by LabEEE/UFSC were chosen for this study. The buildings represent typical layouts found in Brazilian social housing programmes. The main characteristics of the buildings are summarised in

Table 1. Social housing reference models assessed.

Building Type *	Total Floor-Plan Area (m2)	Dwelling Units	Dwelling Unit Area (m2)	Number of Floors	Number of Residents	Total Roof Area (m2)	Roof Area per Resident (m2/Resident)
MFL	1611.47	32	39.67	4	128	421.52	3.29
			41.56
MFH	755.22	16	42.67	4	64	158.70	2.48
MSG	210.51	4	43.28	2	16	132.53	8.28
UTG	85.24	2	42.62	1	8	115.43	14.43
UTI	40.48	1	40.48	1	4	60.19	15.05

* MFL stands for multi-family rectangular-shape housing; MFH stands for multi-family H-shape housing; MSG stands for multi-family double-storey semi-detached housing; UTG stands for single-family semi-detached house; and UTI stands for single-family single-storey house.

, and the number of residents was calculated, considering two people per bedroom.

2.2. Simulation Parameters

Netuno [9] version 4 was used to simulate the rainwater harvesting system for all scenarios. The Netuno version 4 computer program performs the daily water balance according to input variables to obtain the potential for potable water savings. Such a procedure is conducted through a behavioural simulation model with pre-defined variables. The outputs include the water savings potential (percentage of the water consumption supplied by rainwater) and the rainwater tank capacity.

Twelve cities were chosen to represent all Brazilian bioclimatic zones. The rainfall data of the twelve cities were obtained from the INMET Meteorological Database (BDMEP), a Brazilian government database that provides daily historical rainfall data from meteorological stations in Brazil. To obtain a representative timeline, stations with over ten years of data were selected [10]. Daily rainfall data series for each city were collected and used as input for the computer simulations. Figure 1 shows each city’s location, data, bioclimatic zone [11] and average annual rainfall.

The average annual rainfall was obtained through the daily rainfall data series for each city. One sees differences among the rainfall patterns in Brazil, which corroborate the variability in the country’s rainfall characteristics. The roof area of each model was assessed using BIM software Revit (version 24.1.11.26, Autodesk, San Francisco, California, United States, 2024), considering the reference models in [12]. Five water demand scenarios were considered: 110, 130, 150, 170 and 190 l/resident/day. These figures represent the usual per capita water demand in Brazilian houses of various standards. The total number of residents was considered for each building type, as indicated in Table 1.

Rainwater demand is the percentage of the total water demand of the building that can be replaced with rainwater, i.e., the percentage of total water demand used for non-potable purposes. This percentage is calculated considering all the non-potable uses within the building for which potable water can be replaced with rainwater. These uses include toilet flushing, garden watering, and outdoor cleaning in houses. Table 2 shows the usual domestic demand for rainwater in the literature for typical Brazilian buildings. The results show that rainwater demand in residential buildings in Brazil ranges from around 30 to 60%. Teston et al. [8] reviewed the existing research on rainwater demand and observed that it ranges from 27.5 to 60.0% in most cases. Therefore, we considered three scenarios for rainwater demand, i.e., 35%, 45% and 55% of the total water demand.

A runoff coefficient is used to consider the loss of rainwater due to absorption, evaporation and initial water disposal on different catchment surfaces. All house models contain 10% slope roofs and have ceramic or fibre cement tiles as the roofing material. Typical runoff coefficients for roofs generally range from 0.70 to 0.90 [13,14], and for fibre cement tiles, they range from 0.80 to 0.90 [15,16,17]. Therefore, the runoff coefficient was set at 0.80, representing a 20% reduction in rainwater.

Table 2. Non-potable water demand for typical buildings in Brazil reported in the literature.

Reference	Non-Potable Demand (%)	Building Type	Total Residents	Location	End-Use Determination
Fugi et al. [18]	59.3	Single-family house	4	Blumenau	Questionnaire
Peters [19]	49.0	Single-family house	5	Florianópolis	Water meter
Ghisi and Ferreira [20]	36.6	Multi-storey-building flats	3	Florianópolis	Questionnaire
	33.8		2
Freitas and Ghisi [21]	42.2	Single-family house	4	Imbituba	Questionnaire
Maykot and Ghisi [22]	33.1	Multi-storey-building flats	159	Florianópolis	Questionnaire
Teston et al. [8]	33.0	Single/multi-family buildings	-	-	Literature review

Table 2. Non-potable water demand for typical buildings in Brazil reported in the literature.

Reference	Non-Potable Demand (%)	Building Type	Total Residents	Location	End-Use Determination
Fugi et al. [18]	59.3	Single-family house	4	Blumenau	Questionnaire
Peters [19]	49.0	Single-family house	5	Florianópolis	Water meter
Ghisi and Ferreira [20]	36.6	Multi-storey-building flats	3	Florianópolis	Questionnaire
	33.8		2
Freitas and Ghisi [21]	42.2	Single-family house	4	Imbituba	Questionnaire
Maykot and Ghisi [22]	33.1	Multi-storey-building flats	159	Florianópolis	Questionnaire
Teston et al. [8]	33.0	Single/multi-family buildings	-	-	Literature review

Lastly, Netuno version 4 requires the determination of the lower and upper rainwater tank capacities. For both rainwater tanks, the option of the automatic calculation of volume within Netuno software was considered. For the upper tank, a volume equal to the average daily non-potable water consumption was considered, and water was pumped from the lower tank whenever the upper tank volume fell lower than 10% of the total volume. A simulation procedure with capacities ranging from 0 to 50,000 litres was used for the lower tank, with a final volume obtained via a 1%/m³ threshold. More details on the simulation procedure may be obtained in the Netuno manual [23].

2.3. Result Normalisation and Grouping

The results obtained for each simulation were used to understand the impacts of water savings on each building. However, these results must be compared with similar functional units, which can be evaluated using two key dimensions. First, the total water savings (m³/year) were calculated by multiplying the number of residents by the consumption per capita and the average water savings of each building type across the cities. Then, a normalisation procedure was conducted with the division of the annual water savings of each building (m³/year) per number of residents (m³/person·year) and the roof area (m³/m²·year). Then, a final weighted assessment was conducted with a parameter of m³/person·m²·year.

For the city analysis, the coefficient of variation (CV) of potable water savings potential was calculated for each building type across the twelve cities. The CV is a statistical measure that quantifies variability relative to the mean of a data set. It is determined by dividing the standard deviation by the mean and is expressed as a percentage. This parameter allows for a normalised comparison of variation, regardless of the magnitude of the data, highlighting the consistency or sensitivity of the rainwater harvesting potential across different rainfall conditions for each building type.

3. Results and Discussion

The results were evaluated to understand the differences between the buildings selected, the overall potable and non-potable water consumption in each case and the potable water savings potential.

3.1. Rainwater Harvesting Systems per Building

Table 3. Potable water savings potential and water consumption per building.

Building Type	Total Water Consumption (m3/Year)	Average Potable Water Potential (%)	Average Potable Water Consumption (m3/Year)	Average Non-Potable Water Consumption (m3/Year) ¹	Average Rainwater Consumption (m3/Year)	Average Normalised Values of Rainwater Consumption per Building
						Per Inhabitant (m3/Year/ inhab.)	Per Roof Area (m3/Year/ m2)	Per Area and Inhabitant (m3/Year/ inhab./m2)
MFL	7008 ± 1869	2.37	3854.40	3001.71	151.89	1.19	0.360	0.003
MFH	3504 ± 934	3.25	1927.20	1468.72	108.08	1.69	0.681	0.011
MSG	876 ± 234	13.11	481.80	283.07	111.13	6.95	0.839	0.052
UTG	438 ± 117	20.69	240.90	108.66	88.44	11.05	0.766	0.096
UTI	219 ± 58	22.68	120.45	50.05	48.50	12.12	0.806	0.201

¹ Potable water used for non-potable purposes.

shows each building type’s average water savings and consumption (m³/year) and normalised values per inhabitant and roof area. Although multi-family buildings (MFL and MFH) have a lower percentage of potable water savings, they achieve a higher total volume of potable water saved (ranging from 108 to 152 m³/year) due to their greater overall water consumption. For single-family buildings, the average total volume of water saved ranges from 49 m³/year (UTI) to 88 m³/year (UTG). However, rainwater consumption per inhabitant in single-family buildings (UTI and UTG) is approximately ten times higher than in multi-family buildings (MFL and MSH), mainly due to a higher roof-area-to-resident ratio. MSG has intermediate performance, with a 13.11% potable water savings potential. Therefore, one can observe the influence of rainwater availability (driven by roof-area-to-resident ratio) on the potable water savings potential.

The normalised values of rainwater consumption per roof area reveal that MSG has the optimal roof design for rainwater harvesting, with water savings up to 0.839 m³/year/m². On the other hand, single-family buildings have a similar volume of water saved per roof area (0.766 and 0.806 m³/year/m²) with a much higher volume of water saved per inhabitant.

However, roof area distribution is not the only factor that dictates RWH efficiency. The normalisation per area and inhabitant suggests that population density influences rainwater consumption. Although MFL has a higher roof-area-per-resident ratio than MFH, and both models have a similar average potential for potable water savings and rainwater consumption per inhabitant, the rainwater consumption per area and inhabitant in MFL is 3.6 times lower than in the MFH model. Such a difference could be explained by the MFL population being twice as large as that in the MFH model. The same effect is observed for single-family buildings—although the UTG and UTI share a similar roof-area-to-resident ratio and average rainwater consumption per inhabitant, the water savings per area and inhabitant in the UTI are more than twice as high as in the UTG model, which has double the population of the UTI.

3.2. Rainwater Harvesting Systems per City

Figure 2 shows the average potable water savings potential per city. Following the order of their annual rainfall level, Canela has the highest potential (up to 47.0% for the UTI), and Vitória da Conquista has the lowest potential (0.14% for MFL). Recife, São Paulo, Porto Alegre, Palmas, Brasília, Goiânia, Curitiba and Rio de Janeiro have a similar average water saving potential (15.3% to 11.5%).

Figure 3 shows the coefficient of variation (CV) of the potable water savings potential for each building type across the twelve cities. Multi-family buildings (MFH and MFL) display the highest relative variability, suggesting that city-specific conditions more strongly influence their rainwater harvesting (RWH) potential. In contrast, single-family buildings (UTG and UTI) have lower CVs, indicating that the larger roof areas per resident may compensate for variations in rainfall, leading to more reliable savings overall. The MSG model, while having slightly higher (around 20% higher) CV values than the single-family buildings, demonstrates a similar pattern of variability, indicating comparable sensitivity to rainfall conditions.

In summary, while single-family buildings show more variability in absolute savings (standard deviation), multi-family buildings are more sensitive to variability relative to their means (higher CVs). The difference observed highlights how the specific design constraints of multi-family buildings (like smaller roof areas) impact their efficiency in different rainfall scenarios, even if the absolute savings appear more consistent across cities. Although cities with high rainfall, like Canela and Recife, tend to have lower CVs for most building types, there are significant variations across the cities. Therefore, the overall variation in water savings potential does not correlate linearly with total annual rainfall, likely due to uneven distribution—cities with concentrated rainfall events see reduced RWH efficiency, limited by rainwater storage capacities.

3.3. Rainwater Harvesting System—Building Analysis

The boxplots of each building’s potable water savings potential (%) across the twelve cities, shown in Figure 4, demonstrate the influence of building characteristics on water savings potential. Single-family houses (UTG and UTI) have a higher potential for potable water savings (around 20% to 22%). In comparison, the multi-family housing models (MFL and MFH) have a smaller average potable water savings potential (around 2 to 3%) due to their smaller roof-area-to-population ratio, which limits the potential of rainwater per person. The multi-family housing models’ boxplot interquartile ranges (box size) are higher, indicating that these building types have more significant variability and are more sensitive to different water consumption profiles and cities’ rainfall patterns.

4. Conclusions

This study showcases how rainwater harvesting systems are influenced by building and climate characteristics. Analysing social housing reference models in different bioclimatic zones in Brazil provides valuable information to the local authorities, encouraging decisions to implement sustainable technologies. The building analysis results indicate that rainwater harvesting systems are significantly more efficient in smaller housing units like the single-family semi-detached house (UTG) and the single-family single-storey house (UTI) than in larger multi-family housing. The normalisation of each building’s water savings per inhabitant and roof area shows the influence of the roof-area-to-resident ratio. Thus, water savings per area and inhabitant are highly influenced by population density. However, it is crucial to highlight that the differences between the cities’ results show the importance of understanding the different rainfall patterns, which highly influence RWH efficiency. Along with the positive environmental impact, the annual water savings could represent significant economic savings, showcasing RWH’s potential as an effective measure for social housing programmes.

This research can guide stakeholders in Brazil—including researchers, engineers and government authorities—in terms of the suitability of RWH systems based on building type and location rather than solely relying on their overall benefits for the decision-making process. Vaz et al. [24] found that local climate and design parameters, like those evaluated in this study, are key drivers of the cost-effectiveness of RWH systems. Therefore, stakeholders can apply this study’s methodology to assess the feasibility of RWH systems. Future research on RWH in Brazil’s residential sector should also consider the potential impacts of climate change on system performance and suitability, as addressed by the literature [3,4,25]. Finally, some limitations of this work include the use of deterministic simulations rather than statistical analysis within the water balance modelling, the number of cities assessed, the simulation using the building perspective, the lack of analysis using the city or water cycle scales, and the lack of other perspectives such as economical and social. These elements should be addressed in future research.