Reservoir volume optimization and performance evaluation of rooftop catchment systems in arid regions: A case study of Birjand, Iran

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1 Water Science and Engineering 2017, 10(2): 125e133 HOSTED BY Available online at Water Science and Engineering journal homepage: Reservoir volume optimization and performance evaluation of rooftop catchment systems in arid regions: A case study of Birjand, Iran Zinat Komeh*, Hadi Memarian, Seyed Mohammad Tajbakhsh Faculty of Natural Resources and Environment, University of Birjand, Birjand 97175, Iran Received 9 August 2016; accepted 20 February 2017 Available online 1 June 2017 Abstract This study evaluated the performance of rooftop catchment systems in securing non-potable water supply in Birjand, located in an arid area in southeastern Iran. The rooftop catchment systems at seven study sites of different residential buildings were simulated for dry, normal, and wet water years, using 31-year rainfall records. The trial and error approach and mass diagram method were employed to optimize the volume of reservoirs in five different operation scenarios. Results showed that, during the dry water year from 2000 to 2001, for reservoirs with volumes of 200e20000 L, the proportion of days that could be secured for non-portable water supply was on average computed to be 16.4%e32.6% across all study sites. During the normal water year from 2009 to 2010 and the wet water year from 1995 to 1996, for reservoirs with volumes of 200e20000 L, the proportions were 20.8%e69.6% and 26.8%e80.3%, respectively. Therefore, a rooftop catchment system showed a high potential to meet a significant portion of non-potable water demand in the Birjand climatic region. Reservoir volume optimization using the mass diagram method produced results consistent with those obtained with the trial and error approach, except at sites #1, #2, and #5. At these sites, the trial and error approach performed better than the mass diagram method due to relatively high water consumption. It is concluded that the rooftop catchment system is applicable under the same climatic conditions as the study area, and it can be used as a drought mitigation strategy as well Hohai University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( creativecommons.org/licenses/by-nc-nd/4.0/). Keywords: Mass diagram analysis; Non-potable water demand; Reservoir volume optimization; Rooftop catchment; Rainwater harvesting 1. Introduction Water scarcity affects many places around the globe. About 1.2 billion people, or almost one-fifth of the world's population, live in areas of physical scarcity, and 500 million people are approaching this situation. Another 1.6 billion people or almost one-quarter of the world's population face economic water shortage, without sufficient infrastructure to take water from rivers and aquifers (Watkins et al., 2006). Urban development not only increases the frequency and magnitude of the peak flow but also reduces the base flow. Therefore, water management in urban areas should seek to prevent negative hydrological changes (Price, 2014). Rainwater catchment * Corresponding author. address: z.komeh@gmail.com (Zinat Komeh). Peer review under responsibility of Hohai University. systems are one of the management and operational methods of water harvesting that can affect the production of runoff and effectively improve the efficiency of rainwater use in different land uses (Oweis, 2001; Jacob, 2007; Sturm et al., 2009). Rainwater catchment systems are known as compatible systems in terms of water supply in arid and semi-arid areas and loss prevention in humid regions (Vohland and Boubacar, 2009; Tubeileh et al., 2009). Rainwater harvesting in upstream areas reduces further pollution and the cost of treatment (Teemusk and Mander, 2007). Rainwater harvesting for supplemental irrigation in dry seasons has been successfully used in many arid and semi-arid regions (Richardson et al., 2004; Qiang et al., 2006; Short and Lantzke, 2006; Arya and Yadav, 2006). The possibility of developing the use of rainwater harvesting systems in cities with water scarcity was investigated by Zhang et al. (2010). In another study by Villarreal and Dixon (2005), rainwater collection systems for / 2017 Hohai University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( creativecommons.org/licenses/by-nc-nd/4.0/).

2 126 Zinat Komeh et al. / Water Science and Engineering 2017, 10(2): 125e133 storing water in Dansen, Sweden were analyzed. They created a computational model to determine the potential quantification of rainwater supply based on analysis of the optimal volume of a reservoir. Abu-Zreig et al. (2013) evaluated the potential of rainwater harvesting from rooftops in Jordan. In their study, it was estimated that about 14.7 million m 3 of rainwater could be extracted from rooftops, which would increase the water budget of the whole country by 6%. Abdulla and Al-Shareef (2009) and Assayed et al. (2013) described the role of rooftop catchment systems in saving water in a large part of Jordan, as well. The effectiveness of the system for securing water in arid areas with low rainfall and irregular distribution of precipitation was also clarified by Yuan et al. (2003). The application efficiency of a rooftop catchment system in desert areas of Palestine with an average rainfall of 90 mm was confirmed by Tabatabai Yazdi et al. (2006). The use of rooftop catchment systems was also recommended for securing an emergency water supply in dry regions of Australia (Stanton, 2005). A rooftop catchment system can be divided into two main parts: (1) an insulation surface that collects rainwater and (2) cisterns for rainwater storage on rainy days. The excess rainwater can be stored for use in dry seasons. In each rooftop catchment system, the cost of a water storage tank is the highest, so the determination of the optimal volume of reservoir is vital for structural stability and maximum capture of rainfall at a minimum cost (Kahinda et al., 2007; Tam et al., 2010). The reservoir size depends on runoff production potential and the demand for rainwater (Ghisi et al., 2007). Su et al. (2009) established a relationship between the volume of a reservoir and water supply for residents of Taipei. Several other studies have been conducted on hydrological analysis of the performance of rooftop catchment systems for rainwater harvesting in residential areas. For instance, Jones and Hunt (2010) presented a computational model to evaluate the performance of rainwater harvesting from rooftops in the Southern United States. Rahman et al. (2010) showed that larger rooftops are more efficient in terms of water saving and financial benefits. Reservoir payback period analysis showed that the total cost of reservoir construction can be amortized in 15e21 years, depending on the reservoir size and weather conditions. Imteaz et al. (2011) conducted a study to design rainwater collection tanks in Melbourne, Australia and to develop a comprehensive model for their performance analysis. Palla et al. (2011) studied the optimal performance of a rainwater harvesting system. They proposed a suitable model for assessment of the impacts of inflow, outflow, and storage volume alterations on system performance. In a survey by Campisano and Modica (2012), the optimal size of a reservoir was estimated based on daily rainfall, recorded at rainfall stations near the study region. Hashim et al. (2013) proposed a new way of designing a reservoir that could be implemented on a large scale, reducing the pressure on water resources. Singh et al. (2013) explored a methodology based on one of the most popular and versatile hydrological models, the soil conservation service-curve number (SCS-CN) model, and its variants. Results showed that the model has an inherent ability to incorporate the major factors of runoff production in rooftop/urban areas, i.e., the surface characteristics, initial abstraction, and antecedent dry weather period (ADWP) of catchments. They concluded that the SCS-CN model would be a better tool for runoff quantification than other methods only using empirical runoff coefficients. Birjand, in southeastern Iran, is currently experiencing water shortage problems due to extensive development, drought conditions, and unmanaged agricultural activities in surrounding basins, creating a need to find alternative and potential sources of potable and non-potable water. Rooftop catchment systems can be an efficient source of water, especially in arid and semi-arid areas. This study aimed at evaluating the performance of rooftop catchment systems in securing a non-potable water supply in Birjand. The optimal volume of reservoirs was investigated based on comparison of the results from the mass diagram method and trial and error approach. 2. Materials and methods 2.1. Study area Birjand is the capital of Southern Khorasan Province, centered at a latitude of N and a longitude of E (Fig. 1), with an area of 42.7 km 2 and an elevation of 1491 m above sea level. The weather in the area is categorized as a desert climate based on Gossen's classification approach (Memarian et al., 2016), with the xerothermic index of 321, and it normally has cold winters and hot dry summers. The average annual precipitation is mm, according to the daily rainfall data from 1970 to 2010, recorded at the Birjand Synoptic Station. According to the rainfall time series at the Birjand Synoptic Station, 87% of the annual precipitation occurs from November to March (Fig. 2) Methodology Hydrological simulation of rooftop catchment systems was conducted for seven residential buildings (Fig. 1) with different Fig. 1. Geographic locations of study sites.

3 Zinat Komeh et al. / Water Science and Engineering 2017, 10(2): 125e Fig. 2. Average monthly rainfall during normal, dry, and wet water years from 1970 to 2010 recorded at Birjand Synoptic Station (* means the month of the next year). numbers of units and a variety of rooftop areas and population densities (Table 1). An investigation was conducted to collect necessary information for rooftop catchment simulation during the summer of After questionnaries were filled out, we were able to obtain the basic information about water consumption at different sites in different seasons (Table 1). To simulate the performance of a reservoir, it was necessary to identify the variables that have an impact on the volume of reservoir. The evaporation component was ignored due to the consideration of covered reservoirs in hydrological analysis. The volume of runoff I t in L at time step t, captured by a rooftop catchment system was estimated with Eq. (1): I t ¼ 4R t A where 4 is the runoff coefficient, R t is the daily rainfall in mm at time step t, and A is the rooftop area in m 2. The volume of water storage was estimated using Eq. (2): V t ¼ I t þ V t-1 Y t P t where V t and V t-1 are the volumes of collected water in L in the reservoir at time steps t and t 1, respectively; Y t is the volume of outflow in L for the non-potable water supply at time step t; and P t is the volume of overflow in L at time step t. The volume of overflow is calculated with Eq. (3) (Villarreal and Dixon, 2005; Ghisi et al., 2009; Hajani et al., 2013): It þ V P t ¼ t-1 Y t V max ð1þ ð2þ I t þ V t-1 Y t > V max 0 I t þ V t-1 Y V max ð3þ Table 1 Characteristics of study sites and daily water consumption in different seasons. Study site House type Number of residents Number of units Rooftop area (m 2 ) Daily water consumption (L) Wet season Dry season #1 Villa #2 Villa #3 Villa #4 Villa #5 Villa #6 Apartment #7 Apartment where V max is the maximum storage capacity of the reservoir in L. In consultation with local authorities, it was suggested that we apply and introduce some executable optimization approaches, which could be easily used by local experts. The mass diagram method and trial and error approach benefit from these properties. Furthermore, these techniques are executable through computer programs or can be optimized using some other mathematical algorithms like linear programming. Thus, the mass diagram method was utilized to calculate the optimal volume of a reservoir. With this method, the optimal volume of reservoir S in L is equal to the maximum vertical distance between the inflow mass curve (cumulative inflow) and outflow mass curve (cumulative demand) (Subramanya, 2008), as expressed in Eq. (4): X S¼ max VS X V d ð4þ where V d is the volume of water demand in L, and V S is the volume of water inflow in L. The mass diagram method requires determining the critical period in time series with a maximum difference between cumulative inflow (rainfall) and cumulative outflow (water demand). According to Frasier and Myer (1983), average annual precipitation is not a good measure for determining the potential amount of extractable water from rooftop catchment systems. Therefore, it is essential to analyze rooftop catchment systems based on wet and dry spells with specified return periods. In this study, daily rainfall statistics at the Birjand Synoptic Station during the period from 1970 to 2010 were employed for hydrological analysis. Hydrological analysis involved computations of parameters I t, V t, P t, and S. Based on the comparison of annual precipitation records with the average annual precipitation (i.e., mm) over the period from 1970 to 2010, the water years from 1995 to 1996, 2000 to 2001, and 2009 to 2010 were considered the wet (annual precipitation of mm), dry (annual precipitation of 62.5 mm), and normal (annual precipitation of mm) conditions, respectively, in hydrological analysis of rooftop catchment systems. As depicted in Fig. 3, the months of November, December, January, February, and March were considered wet seasons, and others were considered dry seasons in the simulation. As a general rule, in order to avoid setting the reservoir water level at zero on the first day of the study period, a warm- Fig. 3. Ombrothermic curves of Birjand Synoptic Station for period from 1970 to 2010 (* means the month of the next year).

4 128 Zinat Komeh et al. / Water Science and Engineering 2017, 10(2): 125e133 up year should be simulated at all study sites. However, due to the limited amount of storage in this climatic region, all storage is consumed by the end of the warm-up year. Therefore, at the start of the following year, the water level in the reservoir was set at zero. At all study sites, rooftop material was made of water insulation and asphalt. With regard to the type and material of rooftops, the runoff coefficient was determined to be 0.85 (Herrmann and Hasse, 1997). The amount of water output required for non-potable water demand of residents was estimated based on the obtained information (through questionnaires) about the average water consumption of residents in yard cleaning, car washing, and green space irrigation. The domestic non-potable water demand considered in this study did not include water consumption in showers, flushes, and dish washing. Based on the information extracted from questionnaires, the coefficient of domestic non-potable water consumption, which is the percentage of the domestic non-potable water consumption accounting for the total water consumption, was determined to be 0.2 and 0.14 for dry and wet seasons, respectively, for villas. However, this coefficient for the studied apartments was estimated at 0.07 for both dry and wet seasons. With regard to the rooftop area and number of residents, the trial and error approach was used to determine the appropriate capacity of reservoirs. The reservoir capacities of 200, 500, 1000, 2000, 3500, 5000, 6500, 8000, 10000, 15000, 20000, and L (only for study site #7) were chosen for simulation. Two key factors were considered in the selection of the volume of reservoir. The first factor is the space needed for placing reservoirs in the yard. For this factor, reservoirs with a volume greater than L cannot be placed in the yard of villas. However, for an apartment complex there is more space that accommodates reservoirs with a volume up to L. The second factor is the cost of reservoir purchases and construction. Field surveys and economic analysis indicate that building a reservoir with a volume greater than L will cost about 5000 dollars. This cost could be economically justifiable for humid areas or a country other than Iran. However, it should be considered that the study area has a dry climate with a small volume of harvestable rainwater. Furthermore, in Iran, water is subsidized and its price is very low (about 0.3 dollars per cubic meter), in comparison with other countries. Therefore, based on the calculations (Memarian et al., 2015), a rainwater harvesting system with expensive and bulky reservoirs and a service lift of more than 30 years does not make economic sense. However, large reservoirs would be more economically justifiable for the apartment complex, as compared with villas. Generally, it is considered that reservoirs with a volume less than 200 L cannot meet the need of residents for rainwater harvesting and, consequently, are not economically justifiable in this area. Five operation scenarios were defined to determine the optimal volume of reservoirs for securing water demand, especially in dry seasons. The base scenario S1 was defined based on the basic information about water consumption in wet seasons obtained from questionnaires, as shown in Table 1. The operation scenarios S2, S3, S4, and S5 were defined, respectively, by reducing 10%, 20%, 30%, and 50% of water consumption in wet seasons. To define these operation scenarios, rainfall records in wet seasons during the period from 1970 to 2010 were investigated. Statistical records indicate a decrease of rainfall in wet seasons over a range from 10% to 50% in this period. By assuming the adaptation of water consumption to rainfall reduction in wet seasons, the operation scenarios were defined as a drought mitigation strategy. In fact, by reducing the water consumption in wet seasons, we can supply the water needed in dry seasons as much as possible. 3. Results and discussion Results from hydrological simulation with the methodology described above show that, during the dry water year from 2000 to 2001 (referred to as the dry year hereafter), the volume of rainwater harvested from rooftop catchment systems was minimal, such that a reservoir with a volume of 1000 L (at study sites #1, #2, #4, #5, and #6) could store the maximum amount of extracted water (Fig. 4(a)). However, during the wet Fig. 4. Volumes of water storage in reservoirs with different sizes for non-portable water supply at different study sites during dry, wet, and normal years.

5 Zinat Komeh et al. / Water Science and Engineering 2017, 10(2): 125e water year from 1995 to 1996 (referred to as the wet year hereafter), the extractable amount of water was higher. With the optimization of the reservoir volume, more days during dry seasons are secured in terms of non-potable water demand of residents (Ghisi et al., 2007). With regard to high precipitation during the wet water year, using larger reservoirs leads to maximum rainwater harvesting. When the volume of a reservoir increased to L, 75% of non-potable water demand was satisfied (Fig. 4(b)). During the normal water year from 2009 to 2010 (referred to as the normal year hereafter), using a reservoir with a volume of L, 60% of non-potable water demand was satisfied (Fig. 4(c)). As presented in Fig. 4, during the dry, wet, and normal years, the amounts of rainwater storage in reservoirs with volumes of 200e20000 L for non-potable water supply were 2.8e16.0 m 3, 4.8e49.4 m 3, and 3.5e45.6 m 3, respectively. As confirmed by Rahman et al. (2010), the amount of rainwater harvested from small rooftops is partial. Moreover, in most cases, the amount of the maximum rainwater storage in large reservoirs is constant. This implies that, regarding the maximum potential amount of rainwater harvesting and daily non-potable water demand, using large reservoirs has little effect on water supply, except for the cases at site #7 during the wet and normal water years. Fig. 5(a) presents the volumes of water overflow from reservoirs, which are 3.7e0.2 m 3 on average from reservoirs with volumes of 200e2000 L during the dry year. Under the same conditions, the volume of water overflow from reservoirs with volumes greater than 2000 L is estimated to be zero. During the wet and normal years, the average volumes of water overflow from reservoirs with volumes of 200e20000 L are 22.0e2.2 m 3 and 15.0e0.7 m 3, respectively (Fig. 5(b) and (c)). Tables 2 through 4 show the proportion of days that can be secured for non-potable water supply during the dry, normal, and wet years. During the dry year, the proportion for reservoirs with volumes of 200e2000 L is 16.4%e32.2%, respectively, and the proportion for reservoirs with volumes of 2000e20000 L is 32.6% on average. During the normal and wet years, for reservoirs with volumes of 200e20000 L, 20.8%e69.6% and 26.8%e80.3% of days can be secured for non-potable water supply, respectively. These values are averages across all study sites. According to Tables 2 through 4, during the dry year, reservoirs with volumes of 500e3500 L can reach the highest amounts of water storage. However, due to dry conditions, reservoirs with a volume greater than 3500 L cannot receive and store a greater volume of water. During the wet year, the highest storage is achieved by reservoirs with volumes of 8000e30000 L. To determine the optimal volume of a reservoir, the mass diagram method was also employed. With this method, the optimal volume was determined based on the maximum difference between input and demand curves, which was influenced by factors such as the building area, number of residents, and volume of non-potable water consumption. Fig. 6 depicts the mass diagrams for different sites during the normal and wet years in the base scenario S1. Tables 5 and 6 present the optimal volumes of the reservoir of the study sites obtained with the mass diagram method during the normal and wet years in different scenarios (S1 through S5), and the comparisons of the optimal Fig. 5. Volumes of water overflow from reservoirs with different sizes for different study sites during dry, wet, and normal years. Table 2 Proportions of days that can be secured for non-potable water supply at different study sites during dry year. Reservoir Proportion at different sites (%) volume (L) #1 #2 #3 #4 #5 #6 #

6 130 Zinat Komeh et al. / Water Science and Engineering 2017, 10(2): 125e133 Table 3 Proportions of days that can be secured for non-potable water supply at different study sites during normal year. Reservoir Proportion at different sites (%) volume (L) #1 #2 #3 #4 #5 #6 # Table 4 Proportions of days that can be secured for non-potable water supply at different study sites during wet year. Reservoir Proportion at different sites (%) volume (L) #1 #2 #3 #4 #5 #6 # volume of the reservoir in the base scenario S1 with those in other scenarios (S2 through S5). As shown in Fig. 6, with a decrease in water consumption in the wet season, as defined in different scenarios, the slope of the demand curve decreases. With respect to a constant rate of input, the distance between the input and demand curves increases, except at sites #1, #2, and #5. As a result, in order to prevent overflow, the reservoir volume should be considerably increased. In these buildings, the demand curve is above the input curve on the whole. Therefore, with a reduction of water consumption in the wet season, the distance between the two curves decreases, and, accordingly, the reservoir volume is reduced. However, in the case of site #1, the trend of changes in the optimal volume of the reservoir in different scenarios during the wet year is contrary to that during the normal year (Tables 5 and 6). This occurs because the input curve is above the demand curve during the wet year. During the normal and wet years, with a reduction of water consumption in the wet season, site #5 shows the greatest reduction in the optimal volume of the reservoir in different scenarios, compared to that in the base scenario. During the normal year, with a reduction of water consumption in wet seasons, site #7 shows a significant increase in the optimal volume of the reservoir, i.e., 46.4% in S5, compared to that in the base scenario. This occurs because of the decrease in the slope of the demand curve, resulting in an increase in the distance between the input and demand curves. During the wet year, site #1 shows the same condition, in which the optimal volume of the reservoir is increased by 43.28% in S5, compared to that in the base scenario. As shown in Fig. 5 and Tables 2 through 4, based on the trial and error approach, the optimal volume of the reservoir was determined based on two criteria: the minimum volume of overflow obtained with Eq. (3) and maximum number of days that can be secured for non-potable water supply. Comparison of the optimal volumes obtained from the mass diagram method and trial and error approach shows an R 2 of 0.92 and 0.91 in normal and wet years, respectively. Thus, it can be seen that reservoir volume optimization using the mass diagram method leads to results consistent with those obtained from the trial and error approach, except at sites #1, Fig. 6. Mass diagrams for different study sites during normal and wet years in base scenario.

7 Zinat Komeh et al. / Water Science and Engineering 2017, 10(2): 125e Table 5 Optimal volumes of reservoir of study sites in different scenarios obtained with mass diagram method during normal year and comparison of optimal volume of reservoir in S1 with those in other scenarios. Study site Optimal volume of reservoir (L) Increasing rate of optimal volume compared to that in S1 (%) S1 S2 S3 S4 S5 S2 S3 S4 S5 # # # # # # # Table 6 Optimal volumes of reservoirs of study sites in different scenarios obtained with mass diagram method during wet year and comparison of optimal volume of reservoir in S1 with those in other scenarios. Study site Optimal volume of reservoir (L) Increasing rate of optimal volume compared to that in S1 (%) S1 S2 S3 S4 S5 S2 S3 S4 S5 # # # # # # # #2, and #5 (Table 7). These buildings, with rooftop areas of 151, 80, and 70 m 2, have six, seven, and eight residents, respectively. Their water consumption is higher relative to the catchment area. At these sites, the mass diagram method overestimates the optimal volume of the reservoir. Hence, the trial and error approach is preferable. Overall, results indicate the applicability of a rooftop catchment system to securing domestic non-potable water supply in Birjand, especially in dry conditions. Calculated results based on the standardized precipitation index (SPI) in Birjand over the period from 1970e2010 indicate that the water years of 1972e1973 and 2000e2001 demonstrated dry conditions, and the water year of 1984e1985 was categorized as severe drought. Meanwhile, the water years of 1973e1974, 1981e1982, 1985e1986, 1990e1991, 1995e1996, and 2008e2009 represented wet conditions. In 1998, Birjand Table 7 Comparison of optimal volumes of reservoir at study sites in S1 obtained with mass diagram method and trial and error approach in wet and normal years. Study site Optimal volume of reservoir in normal year (L) Trial and error approach Mass diagram method Optimal volume of reservoir in wet year (L) Trial and error approach Mass diagram method # # # # # # # entered a long period of drought that has remained up to the present (Memarian et al., 2016). Therefore, a rooftop catchment system can be used as a drought mitigation strategy in the study area. This is supported by several works in arid and semi-arid regions around the globe (Stanton, 2005; Abdulla and Al-Shareef, 2009; Assayed et al., 2013; Abu-Zreig et al., 2013). As shown in this study, the use of a rooftop catchment system can reduce the pressure on water resources and consequently decrease the cost of household water. For example, at sites #4 and #6, with total rooftop areas of 85 and 90 m 2, the total non-potable water demand during the normal and wet years can be secured using reservoirs with volumes of L and 8000 L, respectively. At site #7, using reservoirs with volumes of L and L, 68.8% and 80.3% of the total non-potable water demand can be secured, respectively, during the wet year. 4. Conclusions This study addressed the following issues: Reservoir volume optimization using the mass diagram method produced outcomes consistent with those acquired through the trial and error approach, except at sites #1, #2, and #5. At these sites, the trial and error approach was preferable to the mass diagram method. During the dry year, for reservoirs with volumes of 200e20000 L, the proportion of days that can be secured for non-portable water supply was on average determined to be 16.4%e32.6%. During normal and wet years, for reservoirs with volumes of 200e20000 L, the proportions are 20.8%e 69.6% and 26.8%e80.3%, respectively.

8 132 Zinat Komeh et al. / Water Science and Engineering 2017, 10(2): 125e133 Comparison of scenarios S2 through S5 with the base scenario S1 under different climatic conditions showed significant differences in the optimal volume of reservoir. For example, during the normal year, with a reduction of water consumption in wet seasons, site #7 showed a significant increase in the optimal volume of the reservoir in S5, with an increasing rate of 46.4% as compared with that in S1. This would secure the non-potable water supply by as much as 46.4% or more in the dry year. In conclusion, the rooftop catchment system is applicable under the same climatic conditions as the study area and can secure a considerable portion of non-potable water demand of residents. Therefore, with consideration of the existing water crisis in Birjand, rainwater harvesting based on rooftop catchment systems can be used as a drought mitigation strategy in this area. 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