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A global performance assessment of rainwater harvesting under climate change
Resources, Conservation & Recycling 132 (2018) 62–70
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Full length article
A global performance assessment of rainwater harvesting under climate change
T
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Sardorbek Musayeva, , Elizabeth Burgessb, Jonathan Mellora a b
Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT, 06269, USA Dewberry Engineering, Inc, Parsippany, NJ, USA
A R T I C L E I N F O
A B S T R A C T
Keywords: Rainwater harvesting Drinking water General Circulation Models (GCM) Rainwater harvesting reliability Domestic water supply Future precipitation Water security Climates LARS – WG
Given that water security is likely to worsen with climate change, rainwater harvesting is one solution that may improve drinking water access directly at home. Although rainwater harvesting devices may be able to reduce drinking water insecurity, this has never been tested systematically across a variety of climates for different climate change scenarios using consistent assumptions. Therefore, the goal of this paper is to assess the ability of rainwater harvesting devices to improve domestic water security in each of the major climate zones under different climate change scenarios and to make design recommendations to achieve levels of reliability for each climatological region. The coupled model incorporates a stochastic weather generator (LARS-WG) to simulate synthetic daily rainfall using historic weather data from 94 sites chosen to represent all Koppen – Griegel climate classification zones. Simulations are run up to year 2099 for three different climate change scenarios using 15 downscaled General Circulation Models. Combinations of different roof area and tank sizes are studied to assess rainwater harvesting system reliability. Results indicate that climate change will have little impact on rainwater harvesting and that rainwater harvesting can reduce domestic water insecurity even in arid regions. Results of this study can be used by implementing agencies to prioritize regions where rainwater harvesting can be effective and to help communities design systems to meet given levels of reliability.
1. Introduction Globally, more than 2.1 billion people lack access to safe, readily available water at home, whereas a further 2.3 billion do not have improved sanitation (WHO and UNICEF, 2017). Even those that have access to improved water supplies often need to travel several kilometers every day in search of water sources (Mellor et al., 2012), which can seriously impact health (Pickering and Davis, 2012). Moreover, even improved water supplies frequently contain microbial contamination (Bain et al., 2014). Water security, particularly in low income regions, will be progressively threatened as the climate changes (Field et al., 2014). Consistent with observed warming, almost all glaciers in the tropical Andes have been shrinking rapidly (Rabatel et al., 2013). Similarly, Himalayan glaciers are also losing mass, with serious implications for runoff contributions, especially in the drier westerly dominated headwaters (Bolch et al., 2012). A global analysis of streamflow showed decreasing trends in low and mid – latitudes consistent with recent drying and warming in West Africa, southern Europe, southern and eastern Asia, eastern Australia, western North America, and
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northern South America (Dai, 2013). Global mean precipitation is likely to increase, but with substantial regional variations, including some decreases. Precipitation trends are likely to decrease in subtropical latitudes, particularly in the Mediterranean, Mexico and central America, and parts of Australia, and to increase elsewhere, notably at high northern latitudes, in India and parts of central Asia (Field et al., 2014). Regions where drought is projected to become longer and more frequent include the Mediterranean, central Europe, central North America, and southern Africa (Field et al., 2014). Increased precipitation and temperature variability as well as extreme events related to it are predicted to affect the availability and quality of water globally (Mellor et al., 2016). A possible solution to improve water security is the use of rainwater harvesting (RWH) which allows water collection to occur directly at home. Such systems can supply between 12% and 100% of a household’s potable water according to the specific environmental and social conditions (Herrmann and Schmida, 1999; Sazakli et al. 2007; Ghisi et al. 2007; Zhang et al. 2009; Abdulla and Al-Shareef, 2009). The storage of rainwater harvesting was simulated in three cities of varying climatic conditions in Iran. In humid, Mediterranean and arid Iranian
Corresponding author. E-mail addresses: [email protected] (S. Musayev), [email protected] (E. Burgess), [email protected] (J. Mellor).
https://doi.org/10.1016/j.resconrec.2018.01.023 Received 30 August 2017; Received in revised form 19 January 2018; Accepted 19 January 2018 0921-3449/ © 2018 Elsevier B.V. All rights reserved.
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supplemental drinking and irrigation water throughout China since 2001 (Gould et al., 2014). Australia, being one of the driest inhabited continents with highly variable rainfall, has one of the highest prevalence of RWH systems. According to the results of a survey by the Australian Bureau of Statistics (ABS, 2010), 19.3% or slightly 1.5 million households had fitted rainwater tanks to their households as a source of water for domestic purposes (Eroksuz and Rahman, 2010). The implementation of RWH systems in Europe is varied. Germany leads in the promotion and widespread use of this technology for domestic non-potable purposes with one third of new buildings are equipped with this system (Schuetze, 2013). Application of systems varies in North and South America. More than 100,000 residents use RWH in the form of a simple rain barrel or large volume tanks including those used for drinking in the United States (Lye, 2002). Brazil launched the “One Million Cistern” RWH program in 2001 to supply about two million people who live in rural areas (De Moraes and Rocha, 2013). Although prior research has established the potential efficacy of RWH to improve water security, there is a need to investigate how effective the technology can be in a variety of climates using consistent assumptions as they are impacted by climate change. Moreover, key design factors including household water demand, roof area and
climates, the study found that it is possible to supply at least 75% of residential water demand 70%, 40% and 23% of the time respectively (Mehrabadi et al., 2013). Results from an arid region of Jordan show potential water savings from RWH from 0.3% to 19.7% (Abdulla and Al-Shareef, 2009). A tank of 5 m3 is suggested for Abeokuta, Nigeria to supply 51% of water during dry periods (Aladenola and Adeboye, 2010). These results suggest that RWH can help maintain water security for households in arid regions. Since household water consumption is low in developing countries, RWH can provide a large proportion of household water supplies as demonstrated by Handia et al. (2003) in Africa. Large survey use of GIS tools have also shown opportunities for RWH in selected countries in Africa such as Botswana, Ethiopia, Kenya, Malawi, Mozambique, Rwanda, Tanzania, Uganda, Zambia, and Zimbabwe (Mati et al., 2006). This has led to the spread of RWH across Africa, and the formation of Rainwater Harvesting Associations in a number of countries (Campisano et al., 2017). RWH plays an important role in many Asian countries (Campisano et al., 2017). The Thai government has also supported RWH because of its low implementation costs using jar tank systems with different tank sizes ranging from 0.1 to 3 m3. Harvested rainwater is used during the dry season – up to six months a year (Wirojanagud and Vanvarothorn, 1990). More than 5.5 million tanks have been built to supply
Fig. 1. Flowchart of rainwater harvesting model algorithm with model elements and daily iteration cycle.
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in-between these two, with steadily increasing emissions (Nakicenovich and Swart, 2000). The synthetic daily rainfall data was generated based on historic weather station temperature and rainfall data from the National Centers for Environmental Information, National Oceanic and Atmospheric Administration (NOAA) Climate Data Online information system (www.ncdc.noaa.gov/cdo-web/datatools/findstation). LARSWG requires at least 10 years of continuous daily temperature and precipitation data (Semenov and Barrow, 2002). The selected stations had between 10 and 107 consecutive years of data prior to the year 2000 and are shown in Supplementary Data (Table S1). The 94 climate land-based stations were located in 41 countries and are shown in Fig. 2. These stations were chosen to represent all of the four main climate zones using the climate typology of the Koppen – Grieger climate classification. The climate zones are based on both temperature and precipitation conditions: tropical, arid, temperate and cold. Three letters are used to describe each climate type. The first letter (A–E) defines the main climate typology based on the mean annual precipitation and the monthly mean temperatures of the warmest and coldest months. The second letter (s, w, f, m, W, S, T and F) is based on the total rainfall depth of the driest and wettest months. The third letter (a–d, h, and k) describes additional temperature conditions (Peel et al., 2007). The simulated daily rainfall was then used to model the capture and storage of water for a single family household using an algorithm written in MATLAB (Mathworks – Natick, Massachusetts). The algorithm produced the predicted average number of empty days (when the tank was empty) and are calculated based on the volume of water in the tank at a given time. RWH reliability is calculated based on the percent of time that users have sufficient water for their daily needs. The volume of the water at a given time is determined by the following equation:
storage tank sizing are important to consider for each climatological zone. Such research is needed to aid in the planning, design and implementation of RWH systems for a variety of climates. The goals of this paper are to (1) determine the impact that climate change will have on RHW reliability (2) assess the ability of RWH to improve water security in a variety of climates around the world (3) make design recommendations to achieve levels of reliability for each climatological region and countries with different income status. This will be done using a stochastic weather generator informed with historic weather data to simulate baseline and future climates using 15 downscaled General Circulation Models (GCMs), three Special Report on Emissions Scenarios (SRES) and three timeframes. Results of this study can be used by implementing agencies to prioritize regions where RWH can be most effective and to help communities design effective RWH structures. 2. Methods Many researchers have sought to use water demand and availability to determine the reliability of RWH systems (DeBusk and Hunt, 2014; Melville-Shreeve et al., 2016). Ghisi (2010) and Palla et al. (2011) used the method of empirical relationships while Cowden et al. (2008) and Basinger et al. (2010) used stochastic analysis to design the sizing of rainwater harvesting system tanks. However, researchers usually need to fix the demand using average daily or monthly values to run the simulations (Parker and Wilby, 2012). Others have used a daily time step interval (Fewkes and Butler, 2000, Campisano et al., 2013). This study employs the coupled modelling approach outlined in Fig. 1. The front end of the model is LARS-WG, is a stochastic weather generator which uses historic weather station data to produce synthetic daily weather for both baseline and future climates (Semenov and Barrow, 2002). The future weather data is produced using 15 general circulation model simulations, three emissions scenarios, and three time periods. The emissions scenarios include the A2, A1B, and B1 and the time periods are 2011–2030, 2045–2065, and 2080–2099. The A2 Separated World scenario is the worst case, highest emission scenario while the B1 Sustainable World scenario assumes the world takes more aggressive actions to reduce emissions. The A1B Rich World scenario is
Vi = A× P+ Vi−1 − DWPD A is the roof area. The daily precipitation P values are those produced by the LARS-WG generator. Vi−1 is the calculated volume from the previous day and Vi is the current volume. The drinking water usage per day (DWPD) is calculated based on an average water consumption per person from the studies of the water footprint accounts of national consumption (Mekonnen and Hoekstra, 2011). This accounts for
Fig. 2. Location of weather stations in different Koppen – Grieger climate zones. The A zone is tropical, B represents arid regions, C for temperate zones and D for cold climates. Sites were chosen to represent all of the climate zones. adapted from Peel et al. (2007).
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account losses due to spillage, leakage, catchment surface wetting and evaporation (Singh, 1992). Heterogeneity between climate zones, timeframes, GCMs and emission scenarios was assessed using ANOVA analyses in R studio (Integrated Development for R. RStudio, Inc., Boston, MA). Countries were classified into low income, lower middle, upper middle and high income countries based on their income status (World Bank Atlas method).
Table 1 Selected combination of tank size and roof area. Tank sizes, L
Roof area, m2
1000 2500 5000 10,000 20,000
50 75 100 150 250
3. Results As shown in Fig. 3, LARS-WG was used to simulate 50 years of annual precipitation at the 94 selected climate stations to determine mean precipitation variations between climates zones, GCMs, SRES emission scenarios and time periods. 26 stations were chosen to represent tropical climates (Af, Am, Aw), 23 for dry climates (BSh, BSk, BWh, BWk), 31 in temperate climates (Cfa, Cfb, Csa, Csb, Cwa, Cwb) and 14 in cold climates (Dfa, Dfb, Dsa, Dwa). The results indicate that there is considerable annual precipitation variability between climate zones (ANOVA, F value = 835.2, p-value < 0.001), but no significant differences between the 15 GCMs (ANOVA, F value = 0.701, p = 0.776). No significant change is found between the of A1B, A2 and B1 emission scenarios (F = 0.063, p = 0.939) or time periods (ANOVA, F = 0.067, p = 0.936). As seen in Fig. 4a, when aggregating all sites in each climate zone and combining all GCMs for the future climate, the percent reliability per year varied significantly between climate zones (ANOVA, F = 216.2, p < 0.001). As seen in Fig. 4b, there was no statistically significant difference between the GCMs (ANOVA, F = 0.262, p = 0.997). Fig. 4c indicates that increasingly high emissions scenarios did not lead to changes in reliability (ANOVA, F = 0.147, p = 0.863).
differences in socioeconomic status and consumption habits between countries. Household size was determined for each country by using Population Reference Bureau data (PRB, 2006) and is shown in Supplementary Data (Table S2). It was assumed that the average household size is a sum of the average fertility rate for each country plus two parents (see Table S2 in Supplementary Data). 25 different combination of tank sizes and roof areas were selected to determine optimal design criteria for each site (Table 1). The roof area ranges were calculated based on a Google Earth analysis in the represented countries and climate zones to approximate typical roof areas for single family houses. Roof areas ranged between 50 and 250 m2. The size of rainwater storage tanks ranged from 750 to 20,000 liters. This range was determined by referencing tanks sold by the JoJo Tank Company (Midrant, South Africa) which are available for sale through a wide distribution network across South Africa and Zimbabwe. These tanks sizes represent a reasonable range in tank sizes used for domestic consumption across a broad range of countries. Evaporation of water from the tank was not considered as the tank is closed (Khastagir and Jayasuriya, 2010). A rainfall capture coefficient value of 0.9 (Lancaster, 2006) was used to
Fig. 3. Combined annual simulated precipitation in different climate zones for 2011–2099 year in (a). Precipitation values for 15 General Circulation Models (GCMs) in (b). (c) shows different emission scenarios and (d) precipitation in early, middle and last period of century.
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Fig. 4. Percent reliability of RWH in different climate zones is shown in (a). (b) shows no significant differences between 15 General Circulation Models. (c) and (d) also show no difference between emission scenarios and periods respectively.
that as the roof area increases the percent reliability also increases. In tropical climate all selected roof area and tank size show higher than 80% reliability (less than 73 empty days per year). Dry climate zone exhibit less reliability. 80% reliability is impossible in dry regions unless the roof area is at least 150 m2. Similar levels of reliability are likely in temperate climates except for roof areas less than 50 m2. Colder regions need large roof areas (> 150 m2) to reach 80% reliability. In all cases, reliability increases dramatically between 1000 L and 5000 L (Table 3). Fig. 6 shows the geographic locations of the sites along with the percent reliability. For small roof areas and tank sizes, it is clear that RWH is insufficient for the drier regions, while larger roof areas and tanks sizes make RWH more reliable even in arid regions. Fig. 7. shows reliability of RWH in countries with different income status. Low income countries show higher percent reliability due to lower demand despite generally larger household sizes (Table 4). Reliability is more strongly dependent on roof area and tank size for higher income regions due to their very high demand.
Table 2 Mean values of percent reliability in each climate in different periods. Climates
Af Am Aw BSh BSk BWh BWk Cfa Cfb Csa Csb Cwa Cwb Dfa Dfb Dsa Dwa Average
Reliability, % 2011–2030
2046–2065
2080–2099
84.2 87.9 86.0 84.2 77.4 77.9 74.2 82.3 80.3 82.7 80.9 84.9 83.3 80.7 82.1 75.8 79.1 81.4
84.1 87.8 86.0 84.2 77.2 77.7 74.2 82.0 80.1 82.3 80.8 85.0 83.2 81.0 82.0 75.7 79.3 81.3
84.0 87.6 85.9 84.2 77.0 77.6 74.2 81.8 79.7 81.9 80.8 85.0 83.2 81.2 82.1 75.6 79.4 81.3
4. Discussion The results of this study indicate that climate change will have little impact on the ability of RWH systems at the household level (Fig. 4). Our results also highlight the fact that RWH can be an effective tool to improve water security 80% of the time even in arid regions with a large enough tank and roof size. However, seasonal rainfall variations can limit the amount of water available during dry periods as was found by others (Imteaz et al., 2011). RWH in tropical and temperate climates can maintain sufficient was supplies 80% of the time in most cases. However, it is difficult to
However, there was a small decrease in the percent reliability over the course of the century, but this decrease did not reach statistical significance (ANOVA, F = 0.456, p = 0.634). The average percent reliability are predicted to decrease over the century from 81.4% to 81.3% (Fig. 4d and Table 2). Fig. 5 describes the percent reliability in different climate zones based on various roof area and tank sizes. From all figures, it is seen 66
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Fig. 5. Percent reliability for different roof area and tank sizes in different climate zones.
America and western Amazonia (Marengo et al., 2009) and predicted increase in droughts in East and Southern Africa (Field et al., 2014). Indeed, Wallace et al. (2015) investigated the potential effects of future climate change on RWH systems in the Federated States of Micronesia. They found that systems would need larger storage tanks to achieve similar levels of reliability in the future. Other researchers have likewise found that RWH systems can operate efficiently under climate change (Campisano et al., 2013). Our finding is likely related to the fact that LARS-WG integrates climate change induced precipitation changes by modifying monthly precipitation totals. It does not change the frequency of wet and dry spells. LARS-WG works by computing parameters for probability distributions based on observed data. Synthetic weather time series are then generated by randomly picking values from these distributions. These distributions can be perturbed based on global climate models. LARS-WG uses a semi-empirical distribution to generate probability distributions for wet and dry periods and daily precipitation. Changes in dry and wet series are not possible because it would require daily output from global climate models which is not available (Semenov and Stratonovitch, 2010). It is clear that prolonged droughts, particularly in dry regions can make RWH less effective. Future work should specifically address this limitation. Other limitations of our approach include the fact that we assumed a uniform 10% loss rate. Leaky faucets, inefficient roofs or evaporative losses could further decrease the amount of precipitation that is stored and actually consumed. For example, thatched roofs frequently have runoff coefficients of 0.2 and are inappropriate for RWH. Finally, the
maintain a 100% reliability even in tropical climates. These results can be used by stakeholders when considering the appropriateness of RWH as a climate adaptation tool for a given region. As expected, the percent reliability of the system increased in all climates with increasing roof area and tank size (Fig. 5). However, the increase was non-linear with decreasing benefits per volume increase seen when tank sizes were greater than 5000 L. Similar threshold effects were found in prior studies. In Kerman, Iran a region with an arid climate, the effect of increasing tank capacity for the same roof area was not significant when increased from 5000 to 15,000 L (Mehrabadi et al., 2013). Likewise, Kahinda et al. (2010) found that increasing tank sizes from 5000 to 10,000 L did not substantially increase water security in arid and humid zones. This finding can help households keep tank construction costs to a minimum. The high level of RWH efficacy we found was likewise found by prior, regional studies. Small systems with a 50 m2 roof and a 500–1,000 L storage system are proving effective for urban users in central India (Cain, 2014). Imteaz et al. (2011) showed that, it is possible to achieve approximately 100% reliability with a roof size of 150–300 m2 and a tank size of 5000–10,000 L for a household of two occupants in Melbourne, Australia. Overall, it is clear the RWH can provide reliable access to basic levels of water much of the year in many climates. The finding that climate change will have little impact on RWH reliability is somewhat surprising in light of, for example, the predicted increased intensity of extreme precipitation events over most of South 67
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widespread implementation of RHW can have implications for runoff in certain watersheds. Researchers assess the impacts of land use changes on hydrological regime in upper Ewaso Ng’iro river basin in Kenya that are bound to have positive socio-economic impacts geared towards improving livelihoods, but could lead to negative impacts downstream. On the other hand, rainwater storage systems may lead to positive impacts by reducing water abstractions for irrigation during dry periods (Ngigi et al., 2007). Our study focused on water quantity rather than water quality. National or local regulations will make RWH impractical or not economically feasible in certain regions. Rainwater is usually free from physical and chemical contaminants such as pesticides, lead, and arsenic, color and suspended materials and it is low in salt and hardness (Abdulla and Shareef, 2009). However, water from RWH can be impacted by air pollution, dust from the soil, leaves from trees, insects, chemical deposits and bird droppings. These impacts can depend on roof typologies as well (GhaffarianHoseini et al., 2016). Metallic roofs are preferred over asbestos, plastic and tile roofs (Vasudevan and Pathak, 2000). While we have shown that RWH can be a technically a viable option, the capital investment costs can be high, but operation and maintenance costs are usually low (DTU, 2001). In Australia researchers have found that RWH is not financially viable without support from the government (Rahman et al., 2012). In the Mediteranian Farreny et al. (2011) found that RWH can be financially viable, but only if carried out at larger scales to ensure economies of scale. In South Africa resource poor households rely on government financial assistance for the capital cost of RWH storage tanks and related works (Kahinda et al., 2010). Greater consideration needs to be given to developing new, low-cost systems (Campisano et al., 2017). Despite these costs, RWH has a number of other benefits including reductions in energy consumption and greenhouse gas emissions, decentralization of water supplies which can improve resiliency and reducing collection times and associated costs in low income regions. There is a need for improved modelling of these multiple benefits (Campisano et al., 2017).
Table 3 Percent reliability data for combinations of roof area and tank sizes in different climates. Tank size Roof Area 1000 L 2500 L 5000 L 10,000 L 20,000 L Roof Area 1000 L 2500 L 5000 L 10,000 L 20,000 L Roof Area 1000 L 2500 L 5000 L 10,000 L 20,000 L Roof Area 1000 L 2500 L 5000 L 10,000 L 20,000 L Roof Area 1000 L 2500 L 5000 L 10,000 L 20,000 L
Tropical
Arid
Temperate
Cold
79.2 81.1 82.3 83.3 84.2
75.5 76.2 76.6 77.1 77.4
77.0 77.9 78.5 79.1 79.3
74.3 74.6 74.7 74.7 74.8
80.6 83.1 84.7 86.1 87.2
76.2 77.2 77.8 78.4 78.8
78.3 79.9 80.9 81.7 82.2
75.5 76.2 76.5 76.7 76.8
81.5 84.4 86.2 87.8 89.1
76.8 78.1 78.8 79.5 80.0
79.3 81.4 82.8 83.9 84.6
76.7 77.8 78.4 78.8 79.0
82.7 86.0 88.1 90.0 91.5
77.6 79.3 80.4 81.3 81.9
80.6 83.4 85.4 87.0 88.1
78.4 80.4 81.7 82.6 83.2
83.9 87.6 90.1 92.2 93.9
78.5 80.8 82.4 83.7 84.8
82.1 85.6 88.3 90.5 92.1
80.6 83.8 86.1 87.9 89.3
50 m2
75 m2
100 m2
150 m2
250 m2
incomplete historic weather data might have impacted the ability of LARS-WG to accurately simulate precipitation conditions. To mitigate this possibility, we only chose stations with at least 10 years of continuous data. Lastly, our analysis only considered the physical aspects of water security. The ultimate feasibility and spread of RWH will also depend of local water quality regulations and costs. Moreover,
Fig. 6. Percent reliability for all geographical locations for different combinations of roof area and tank sizes.
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Fig. 7. Feasibility of RWH in countries with different income status. Low income countries show higher reliability than other upper and high income countries. Table 4 Percent reliability data for combinations of roof area and tank sizes in countries with different income status.
5. Conclusion The most important conclusion of this research is that climate change appears to have little effect on the feasibility of RWH and can help households have access to water at their household for most of the year. RWH can be an effective tool to improve water security 80% of the time even in arid regions. The data from the different climate scenarios and time periods indicate that RWH can be an important technology regardless of greenhouse gas emission scenario. This is important for the planning of RWH systems into the future and proves it to be a sustainable technology well into the coming century. However, when interpreting these results, it is important to consider the fact that it is easier for households to increase their tank sizes than it is for them to increase their roof areas. It is therefore possible that RWH might not be as useful in arid regions where people have small roofs. Results also show that increasing tank sizes after certain amount of sizes did not substantially increase benefits in arid and humid zones. The calculated design curves can inform designers of RWH systems to determine optimal roof and tank sizes for their particular location in the face of climate change.
Low income countries Tank size 50m2 1000 L 83.6 2500 L 85.7 5000 L 87.3 10,000 L 89.4 20,000 L 90.8 Lower middle income countries Tank size 50m2 1000 L 78.2 2500 L 80.0 5000 L 81.2 10,000 L 82.1 20,000 L 82.8 Upper middle income countries Tank size 50m2 1000 L 74.8 2500 L 75.4 5000 L 75.7 10,000 L 75.9 20,000 L 75.9 High income countries Tank size 50m2 1000 L 75.3 2500 L 75.9 5000 L 76.2 10,000 L 76.3 20,000 L 76.4
75m2 84.7 87.1 88.8 90.9 92.3
100m2 85.4 88.0 89.7 91.8 93.2
150m2 86.2 89.0 90.9 92.9 94.3
250m2 86.9 89.9 92.0 93.9 95.3
75m2 79.8 82.2 83.7 85.0 86.0
100m2 80.9 83.7 85.4 86.7 87.8
150m2 82.2 85.5 87.5 89.0 90.3
250m2 83.5 87.3 89.6 91.5 92.9
75m2 75.7 76.8 77.5 77.9 78.2
100m2 76.5 78.0 78.9 79.6 80.1
150m2 77.6 79.7 81.1 82.3 83.1
250m2 79.0 81.8 83.9 85.7 87.1
75m2 76.7 78.0 78.7 79.1 79.3
100m2 77.9 79.8 80.9 81.6 82.1
150m2 79.5 82.3 84.2 85.6 86.6
250m2 81.3 85.2 88.0 90.3 92.0
Acknowledgements This research was supported by Department of Civil and Environmental Engineering, University of Connecticut. We also thank Tara Walsh for her assistance in this research. 69
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Appendix A. Supplementary data
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Full length article
A global performance assessment of rainwater harvesting under climate change
T
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Sardorbek Musayeva, , Elizabeth Burgessb, Jonathan Mellora a b
Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT, 06269, USA Dewberry Engineering, Inc, Parsippany, NJ, USA
A R T I C L E I N F O
A B S T R A C T
Keywords: Rainwater harvesting Drinking water General Circulation Models (GCM) Rainwater harvesting reliability Domestic water supply Future precipitation Water security Climates LARS – WG
Given that water security is likely to worsen with climate change, rainwater harvesting is one solution that may improve drinking water access directly at home. Although rainwater harvesting devices may be able to reduce drinking water insecurity, this has never been tested systematically across a variety of climates for different climate change scenarios using consistent assumptions. Therefore, the goal of this paper is to assess the ability of rainwater harvesting devices to improve domestic water security in each of the major climate zones under different climate change scenarios and to make design recommendations to achieve levels of reliability for each climatological region. The coupled model incorporates a stochastic weather generator (LARS-WG) to simulate synthetic daily rainfall using historic weather data from 94 sites chosen to represent all Koppen – Griegel climate classification zones. Simulations are run up to year 2099 for three different climate change scenarios using 15 downscaled General Circulation Models. Combinations of different roof area and tank sizes are studied to assess rainwater harvesting system reliability. Results indicate that climate change will have little impact on rainwater harvesting and that rainwater harvesting can reduce domestic water insecurity even in arid regions. Results of this study can be used by implementing agencies to prioritize regions where rainwater harvesting can be effective and to help communities design systems to meet given levels of reliability.
1. Introduction Globally, more than 2.1 billion people lack access to safe, readily available water at home, whereas a further 2.3 billion do not have improved sanitation (WHO and UNICEF, 2017). Even those that have access to improved water supplies often need to travel several kilometers every day in search of water sources (Mellor et al., 2012), which can seriously impact health (Pickering and Davis, 2012). Moreover, even improved water supplies frequently contain microbial contamination (Bain et al., 2014). Water security, particularly in low income regions, will be progressively threatened as the climate changes (Field et al., 2014). Consistent with observed warming, almost all glaciers in the tropical Andes have been shrinking rapidly (Rabatel et al., 2013). Similarly, Himalayan glaciers are also losing mass, with serious implications for runoff contributions, especially in the drier westerly dominated headwaters (Bolch et al., 2012). A global analysis of streamflow showed decreasing trends in low and mid – latitudes consistent with recent drying and warming in West Africa, southern Europe, southern and eastern Asia, eastern Australia, western North America, and
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northern South America (Dai, 2013). Global mean precipitation is likely to increase, but with substantial regional variations, including some decreases. Precipitation trends are likely to decrease in subtropical latitudes, particularly in the Mediterranean, Mexico and central America, and parts of Australia, and to increase elsewhere, notably at high northern latitudes, in India and parts of central Asia (Field et al., 2014). Regions where drought is projected to become longer and more frequent include the Mediterranean, central Europe, central North America, and southern Africa (Field et al., 2014). Increased precipitation and temperature variability as well as extreme events related to it are predicted to affect the availability and quality of water globally (Mellor et al., 2016). A possible solution to improve water security is the use of rainwater harvesting (RWH) which allows water collection to occur directly at home. Such systems can supply between 12% and 100% of a household’s potable water according to the specific environmental and social conditions (Herrmann and Schmida, 1999; Sazakli et al. 2007; Ghisi et al. 2007; Zhang et al. 2009; Abdulla and Al-Shareef, 2009). The storage of rainwater harvesting was simulated in three cities of varying climatic conditions in Iran. In humid, Mediterranean and arid Iranian
Corresponding author. E-mail addresses: [email protected] (S. Musayev), [email protected] (E. Burgess), [email protected] (J. Mellor).
https://doi.org/10.1016/j.resconrec.2018.01.023 Received 30 August 2017; Received in revised form 19 January 2018; Accepted 19 January 2018 0921-3449/ © 2018 Elsevier B.V. All rights reserved.
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supplemental drinking and irrigation water throughout China since 2001 (Gould et al., 2014). Australia, being one of the driest inhabited continents with highly variable rainfall, has one of the highest prevalence of RWH systems. According to the results of a survey by the Australian Bureau of Statistics (ABS, 2010), 19.3% or slightly 1.5 million households had fitted rainwater tanks to their households as a source of water for domestic purposes (Eroksuz and Rahman, 2010). The implementation of RWH systems in Europe is varied. Germany leads in the promotion and widespread use of this technology for domestic non-potable purposes with one third of new buildings are equipped with this system (Schuetze, 2013). Application of systems varies in North and South America. More than 100,000 residents use RWH in the form of a simple rain barrel or large volume tanks including those used for drinking in the United States (Lye, 2002). Brazil launched the “One Million Cistern” RWH program in 2001 to supply about two million people who live in rural areas (De Moraes and Rocha, 2013). Although prior research has established the potential efficacy of RWH to improve water security, there is a need to investigate how effective the technology can be in a variety of climates using consistent assumptions as they are impacted by climate change. Moreover, key design factors including household water demand, roof area and
climates, the study found that it is possible to supply at least 75% of residential water demand 70%, 40% and 23% of the time respectively (Mehrabadi et al., 2013). Results from an arid region of Jordan show potential water savings from RWH from 0.3% to 19.7% (Abdulla and Al-Shareef, 2009). A tank of 5 m3 is suggested for Abeokuta, Nigeria to supply 51% of water during dry periods (Aladenola and Adeboye, 2010). These results suggest that RWH can help maintain water security for households in arid regions. Since household water consumption is low in developing countries, RWH can provide a large proportion of household water supplies as demonstrated by Handia et al. (2003) in Africa. Large survey use of GIS tools have also shown opportunities for RWH in selected countries in Africa such as Botswana, Ethiopia, Kenya, Malawi, Mozambique, Rwanda, Tanzania, Uganda, Zambia, and Zimbabwe (Mati et al., 2006). This has led to the spread of RWH across Africa, and the formation of Rainwater Harvesting Associations in a number of countries (Campisano et al., 2017). RWH plays an important role in many Asian countries (Campisano et al., 2017). The Thai government has also supported RWH because of its low implementation costs using jar tank systems with different tank sizes ranging from 0.1 to 3 m3. Harvested rainwater is used during the dry season – up to six months a year (Wirojanagud and Vanvarothorn, 1990). More than 5.5 million tanks have been built to supply
Fig. 1. Flowchart of rainwater harvesting model algorithm with model elements and daily iteration cycle.
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in-between these two, with steadily increasing emissions (Nakicenovich and Swart, 2000). The synthetic daily rainfall data was generated based on historic weather station temperature and rainfall data from the National Centers for Environmental Information, National Oceanic and Atmospheric Administration (NOAA) Climate Data Online information system (www.ncdc.noaa.gov/cdo-web/datatools/findstation). LARSWG requires at least 10 years of continuous daily temperature and precipitation data (Semenov and Barrow, 2002). The selected stations had between 10 and 107 consecutive years of data prior to the year 2000 and are shown in Supplementary Data (Table S1). The 94 climate land-based stations were located in 41 countries and are shown in Fig. 2. These stations were chosen to represent all of the four main climate zones using the climate typology of the Koppen – Grieger climate classification. The climate zones are based on both temperature and precipitation conditions: tropical, arid, temperate and cold. Three letters are used to describe each climate type. The first letter (A–E) defines the main climate typology based on the mean annual precipitation and the monthly mean temperatures of the warmest and coldest months. The second letter (s, w, f, m, W, S, T and F) is based on the total rainfall depth of the driest and wettest months. The third letter (a–d, h, and k) describes additional temperature conditions (Peel et al., 2007). The simulated daily rainfall was then used to model the capture and storage of water for a single family household using an algorithm written in MATLAB (Mathworks – Natick, Massachusetts). The algorithm produced the predicted average number of empty days (when the tank was empty) and are calculated based on the volume of water in the tank at a given time. RWH reliability is calculated based on the percent of time that users have sufficient water for their daily needs. The volume of the water at a given time is determined by the following equation:
storage tank sizing are important to consider for each climatological zone. Such research is needed to aid in the planning, design and implementation of RWH systems for a variety of climates. The goals of this paper are to (1) determine the impact that climate change will have on RHW reliability (2) assess the ability of RWH to improve water security in a variety of climates around the world (3) make design recommendations to achieve levels of reliability for each climatological region and countries with different income status. This will be done using a stochastic weather generator informed with historic weather data to simulate baseline and future climates using 15 downscaled General Circulation Models (GCMs), three Special Report on Emissions Scenarios (SRES) and three timeframes. Results of this study can be used by implementing agencies to prioritize regions where RWH can be most effective and to help communities design effective RWH structures. 2. Methods Many researchers have sought to use water demand and availability to determine the reliability of RWH systems (DeBusk and Hunt, 2014; Melville-Shreeve et al., 2016). Ghisi (2010) and Palla et al. (2011) used the method of empirical relationships while Cowden et al. (2008) and Basinger et al. (2010) used stochastic analysis to design the sizing of rainwater harvesting system tanks. However, researchers usually need to fix the demand using average daily or monthly values to run the simulations (Parker and Wilby, 2012). Others have used a daily time step interval (Fewkes and Butler, 2000, Campisano et al., 2013). This study employs the coupled modelling approach outlined in Fig. 1. The front end of the model is LARS-WG, is a stochastic weather generator which uses historic weather station data to produce synthetic daily weather for both baseline and future climates (Semenov and Barrow, 2002). The future weather data is produced using 15 general circulation model simulations, three emissions scenarios, and three time periods. The emissions scenarios include the A2, A1B, and B1 and the time periods are 2011–2030, 2045–2065, and 2080–2099. The A2 Separated World scenario is the worst case, highest emission scenario while the B1 Sustainable World scenario assumes the world takes more aggressive actions to reduce emissions. The A1B Rich World scenario is
Vi = A× P+ Vi−1 − DWPD A is the roof area. The daily precipitation P values are those produced by the LARS-WG generator. Vi−1 is the calculated volume from the previous day and Vi is the current volume. The drinking water usage per day (DWPD) is calculated based on an average water consumption per person from the studies of the water footprint accounts of national consumption (Mekonnen and Hoekstra, 2011). This accounts for
Fig. 2. Location of weather stations in different Koppen – Grieger climate zones. The A zone is tropical, B represents arid regions, C for temperate zones and D for cold climates. Sites were chosen to represent all of the climate zones. adapted from Peel et al. (2007).
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account losses due to spillage, leakage, catchment surface wetting and evaporation (Singh, 1992). Heterogeneity between climate zones, timeframes, GCMs and emission scenarios was assessed using ANOVA analyses in R studio (Integrated Development for R. RStudio, Inc., Boston, MA). Countries were classified into low income, lower middle, upper middle and high income countries based on their income status (World Bank Atlas method).
Table 1 Selected combination of tank size and roof area. Tank sizes, L
Roof area, m2
1000 2500 5000 10,000 20,000
50 75 100 150 250
3. Results As shown in Fig. 3, LARS-WG was used to simulate 50 years of annual precipitation at the 94 selected climate stations to determine mean precipitation variations between climates zones, GCMs, SRES emission scenarios and time periods. 26 stations were chosen to represent tropical climates (Af, Am, Aw), 23 for dry climates (BSh, BSk, BWh, BWk), 31 in temperate climates (Cfa, Cfb, Csa, Csb, Cwa, Cwb) and 14 in cold climates (Dfa, Dfb, Dsa, Dwa). The results indicate that there is considerable annual precipitation variability between climate zones (ANOVA, F value = 835.2, p-value < 0.001), but no significant differences between the 15 GCMs (ANOVA, F value = 0.701, p = 0.776). No significant change is found between the of A1B, A2 and B1 emission scenarios (F = 0.063, p = 0.939) or time periods (ANOVA, F = 0.067, p = 0.936). As seen in Fig. 4a, when aggregating all sites in each climate zone and combining all GCMs for the future climate, the percent reliability per year varied significantly between climate zones (ANOVA, F = 216.2, p < 0.001). As seen in Fig. 4b, there was no statistically significant difference between the GCMs (ANOVA, F = 0.262, p = 0.997). Fig. 4c indicates that increasingly high emissions scenarios did not lead to changes in reliability (ANOVA, F = 0.147, p = 0.863).
differences in socioeconomic status and consumption habits between countries. Household size was determined for each country by using Population Reference Bureau data (PRB, 2006) and is shown in Supplementary Data (Table S2). It was assumed that the average household size is a sum of the average fertility rate for each country plus two parents (see Table S2 in Supplementary Data). 25 different combination of tank sizes and roof areas were selected to determine optimal design criteria for each site (Table 1). The roof area ranges were calculated based on a Google Earth analysis in the represented countries and climate zones to approximate typical roof areas for single family houses. Roof areas ranged between 50 and 250 m2. The size of rainwater storage tanks ranged from 750 to 20,000 liters. This range was determined by referencing tanks sold by the JoJo Tank Company (Midrant, South Africa) which are available for sale through a wide distribution network across South Africa and Zimbabwe. These tanks sizes represent a reasonable range in tank sizes used for domestic consumption across a broad range of countries. Evaporation of water from the tank was not considered as the tank is closed (Khastagir and Jayasuriya, 2010). A rainfall capture coefficient value of 0.9 (Lancaster, 2006) was used to
Fig. 3. Combined annual simulated precipitation in different climate zones for 2011–2099 year in (a). Precipitation values for 15 General Circulation Models (GCMs) in (b). (c) shows different emission scenarios and (d) precipitation in early, middle and last period of century.
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Fig. 4. Percent reliability of RWH in different climate zones is shown in (a). (b) shows no significant differences between 15 General Circulation Models. (c) and (d) also show no difference between emission scenarios and periods respectively.
that as the roof area increases the percent reliability also increases. In tropical climate all selected roof area and tank size show higher than 80% reliability (less than 73 empty days per year). Dry climate zone exhibit less reliability. 80% reliability is impossible in dry regions unless the roof area is at least 150 m2. Similar levels of reliability are likely in temperate climates except for roof areas less than 50 m2. Colder regions need large roof areas (> 150 m2) to reach 80% reliability. In all cases, reliability increases dramatically between 1000 L and 5000 L (Table 3). Fig. 6 shows the geographic locations of the sites along with the percent reliability. For small roof areas and tank sizes, it is clear that RWH is insufficient for the drier regions, while larger roof areas and tanks sizes make RWH more reliable even in arid regions. Fig. 7. shows reliability of RWH in countries with different income status. Low income countries show higher percent reliability due to lower demand despite generally larger household sizes (Table 4). Reliability is more strongly dependent on roof area and tank size for higher income regions due to their very high demand.
Table 2 Mean values of percent reliability in each climate in different periods. Climates
Af Am Aw BSh BSk BWh BWk Cfa Cfb Csa Csb Cwa Cwb Dfa Dfb Dsa Dwa Average
Reliability, % 2011–2030
2046–2065
2080–2099
84.2 87.9 86.0 84.2 77.4 77.9 74.2 82.3 80.3 82.7 80.9 84.9 83.3 80.7 82.1 75.8 79.1 81.4
84.1 87.8 86.0 84.2 77.2 77.7 74.2 82.0 80.1 82.3 80.8 85.0 83.2 81.0 82.0 75.7 79.3 81.3
84.0 87.6 85.9 84.2 77.0 77.6 74.2 81.8 79.7 81.9 80.8 85.0 83.2 81.2 82.1 75.6 79.4 81.3
4. Discussion The results of this study indicate that climate change will have little impact on the ability of RWH systems at the household level (Fig. 4). Our results also highlight the fact that RWH can be an effective tool to improve water security 80% of the time even in arid regions with a large enough tank and roof size. However, seasonal rainfall variations can limit the amount of water available during dry periods as was found by others (Imteaz et al., 2011). RWH in tropical and temperate climates can maintain sufficient was supplies 80% of the time in most cases. However, it is difficult to
However, there was a small decrease in the percent reliability over the course of the century, but this decrease did not reach statistical significance (ANOVA, F = 0.456, p = 0.634). The average percent reliability are predicted to decrease over the century from 81.4% to 81.3% (Fig. 4d and Table 2). Fig. 5 describes the percent reliability in different climate zones based on various roof area and tank sizes. From all figures, it is seen 66
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Fig. 5. Percent reliability for different roof area and tank sizes in different climate zones.
America and western Amazonia (Marengo et al., 2009) and predicted increase in droughts in East and Southern Africa (Field et al., 2014). Indeed, Wallace et al. (2015) investigated the potential effects of future climate change on RWH systems in the Federated States of Micronesia. They found that systems would need larger storage tanks to achieve similar levels of reliability in the future. Other researchers have likewise found that RWH systems can operate efficiently under climate change (Campisano et al., 2013). Our finding is likely related to the fact that LARS-WG integrates climate change induced precipitation changes by modifying monthly precipitation totals. It does not change the frequency of wet and dry spells. LARS-WG works by computing parameters for probability distributions based on observed data. Synthetic weather time series are then generated by randomly picking values from these distributions. These distributions can be perturbed based on global climate models. LARS-WG uses a semi-empirical distribution to generate probability distributions for wet and dry periods and daily precipitation. Changes in dry and wet series are not possible because it would require daily output from global climate models which is not available (Semenov and Stratonovitch, 2010). It is clear that prolonged droughts, particularly in dry regions can make RWH less effective. Future work should specifically address this limitation. Other limitations of our approach include the fact that we assumed a uniform 10% loss rate. Leaky faucets, inefficient roofs or evaporative losses could further decrease the amount of precipitation that is stored and actually consumed. For example, thatched roofs frequently have runoff coefficients of 0.2 and are inappropriate for RWH. Finally, the
maintain a 100% reliability even in tropical climates. These results can be used by stakeholders when considering the appropriateness of RWH as a climate adaptation tool for a given region. As expected, the percent reliability of the system increased in all climates with increasing roof area and tank size (Fig. 5). However, the increase was non-linear with decreasing benefits per volume increase seen when tank sizes were greater than 5000 L. Similar threshold effects were found in prior studies. In Kerman, Iran a region with an arid climate, the effect of increasing tank capacity for the same roof area was not significant when increased from 5000 to 15,000 L (Mehrabadi et al., 2013). Likewise, Kahinda et al. (2010) found that increasing tank sizes from 5000 to 10,000 L did not substantially increase water security in arid and humid zones. This finding can help households keep tank construction costs to a minimum. The high level of RWH efficacy we found was likewise found by prior, regional studies. Small systems with a 50 m2 roof and a 500–1,000 L storage system are proving effective for urban users in central India (Cain, 2014). Imteaz et al. (2011) showed that, it is possible to achieve approximately 100% reliability with a roof size of 150–300 m2 and a tank size of 5000–10,000 L for a household of two occupants in Melbourne, Australia. Overall, it is clear the RWH can provide reliable access to basic levels of water much of the year in many climates. The finding that climate change will have little impact on RWH reliability is somewhat surprising in light of, for example, the predicted increased intensity of extreme precipitation events over most of South 67
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widespread implementation of RHW can have implications for runoff in certain watersheds. Researchers assess the impacts of land use changes on hydrological regime in upper Ewaso Ng’iro river basin in Kenya that are bound to have positive socio-economic impacts geared towards improving livelihoods, but could lead to negative impacts downstream. On the other hand, rainwater storage systems may lead to positive impacts by reducing water abstractions for irrigation during dry periods (Ngigi et al., 2007). Our study focused on water quantity rather than water quality. National or local regulations will make RWH impractical or not economically feasible in certain regions. Rainwater is usually free from physical and chemical contaminants such as pesticides, lead, and arsenic, color and suspended materials and it is low in salt and hardness (Abdulla and Shareef, 2009). However, water from RWH can be impacted by air pollution, dust from the soil, leaves from trees, insects, chemical deposits and bird droppings. These impacts can depend on roof typologies as well (GhaffarianHoseini et al., 2016). Metallic roofs are preferred over asbestos, plastic and tile roofs (Vasudevan and Pathak, 2000). While we have shown that RWH can be a technically a viable option, the capital investment costs can be high, but operation and maintenance costs are usually low (DTU, 2001). In Australia researchers have found that RWH is not financially viable without support from the government (Rahman et al., 2012). In the Mediteranian Farreny et al. (2011) found that RWH can be financially viable, but only if carried out at larger scales to ensure economies of scale. In South Africa resource poor households rely on government financial assistance for the capital cost of RWH storage tanks and related works (Kahinda et al., 2010). Greater consideration needs to be given to developing new, low-cost systems (Campisano et al., 2017). Despite these costs, RWH has a number of other benefits including reductions in energy consumption and greenhouse gas emissions, decentralization of water supplies which can improve resiliency and reducing collection times and associated costs in low income regions. There is a need for improved modelling of these multiple benefits (Campisano et al., 2017).
Table 3 Percent reliability data for combinations of roof area and tank sizes in different climates. Tank size Roof Area 1000 L 2500 L 5000 L 10,000 L 20,000 L Roof Area 1000 L 2500 L 5000 L 10,000 L 20,000 L Roof Area 1000 L 2500 L 5000 L 10,000 L 20,000 L Roof Area 1000 L 2500 L 5000 L 10,000 L 20,000 L Roof Area 1000 L 2500 L 5000 L 10,000 L 20,000 L
Tropical
Arid
Temperate
Cold
79.2 81.1 82.3 83.3 84.2
75.5 76.2 76.6 77.1 77.4
77.0 77.9 78.5 79.1 79.3
74.3 74.6 74.7 74.7 74.8
80.6 83.1 84.7 86.1 87.2
76.2 77.2 77.8 78.4 78.8
78.3 79.9 80.9 81.7 82.2
75.5 76.2 76.5 76.7 76.8
81.5 84.4 86.2 87.8 89.1
76.8 78.1 78.8 79.5 80.0
79.3 81.4 82.8 83.9 84.6
76.7 77.8 78.4 78.8 79.0
82.7 86.0 88.1 90.0 91.5
77.6 79.3 80.4 81.3 81.9
80.6 83.4 85.4 87.0 88.1
78.4 80.4 81.7 82.6 83.2
83.9 87.6 90.1 92.2 93.9
78.5 80.8 82.4 83.7 84.8
82.1 85.6 88.3 90.5 92.1
80.6 83.8 86.1 87.9 89.3
50 m2
75 m2
100 m2
150 m2
250 m2
incomplete historic weather data might have impacted the ability of LARS-WG to accurately simulate precipitation conditions. To mitigate this possibility, we only chose stations with at least 10 years of continuous data. Lastly, our analysis only considered the physical aspects of water security. The ultimate feasibility and spread of RWH will also depend of local water quality regulations and costs. Moreover,
Fig. 6. Percent reliability for all geographical locations for different combinations of roof area and tank sizes.
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Fig. 7. Feasibility of RWH in countries with different income status. Low income countries show higher reliability than other upper and high income countries. Table 4 Percent reliability data for combinations of roof area and tank sizes in countries with different income status.
5. Conclusion The most important conclusion of this research is that climate change appears to have little effect on the feasibility of RWH and can help households have access to water at their household for most of the year. RWH can be an effective tool to improve water security 80% of the time even in arid regions. The data from the different climate scenarios and time periods indicate that RWH can be an important technology regardless of greenhouse gas emission scenario. This is important for the planning of RWH systems into the future and proves it to be a sustainable technology well into the coming century. However, when interpreting these results, it is important to consider the fact that it is easier for households to increase their tank sizes than it is for them to increase their roof areas. It is therefore possible that RWH might not be as useful in arid regions where people have small roofs. Results also show that increasing tank sizes after certain amount of sizes did not substantially increase benefits in arid and humid zones. The calculated design curves can inform designers of RWH systems to determine optimal roof and tank sizes for their particular location in the face of climate change.
Low income countries Tank size 50m2 1000 L 83.6 2500 L 85.7 5000 L 87.3 10,000 L 89.4 20,000 L 90.8 Lower middle income countries Tank size 50m2 1000 L 78.2 2500 L 80.0 5000 L 81.2 10,000 L 82.1 20,000 L 82.8 Upper middle income countries Tank size 50m2 1000 L 74.8 2500 L 75.4 5000 L 75.7 10,000 L 75.9 20,000 L 75.9 High income countries Tank size 50m2 1000 L 75.3 2500 L 75.9 5000 L 76.2 10,000 L 76.3 20,000 L 76.4
75m2 84.7 87.1 88.8 90.9 92.3
100m2 85.4 88.0 89.7 91.8 93.2
150m2 86.2 89.0 90.9 92.9 94.3
250m2 86.9 89.9 92.0 93.9 95.3
75m2 79.8 82.2 83.7 85.0 86.0
100m2 80.9 83.7 85.4 86.7 87.8
150m2 82.2 85.5 87.5 89.0 90.3
250m2 83.5 87.3 89.6 91.5 92.9
75m2 75.7 76.8 77.5 77.9 78.2
100m2 76.5 78.0 78.9 79.6 80.1
150m2 77.6 79.7 81.1 82.3 83.1
250m2 79.0 81.8 83.9 85.7 87.1
75m2 76.7 78.0 78.7 79.1 79.3
100m2 77.9 79.8 80.9 81.6 82.1
150m2 79.5 82.3 84.2 85.6 86.6
250m2 81.3 85.2 88.0 90.3 92.0
Acknowledgements This research was supported by Department of Civil and Environmental Engineering, University of Connecticut. We also thank Tara Walsh for her assistance in this research. 69
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Appendix A. Supplementary data
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