Probabilistic estimation of the storage capacity of a rainwater harvesting system considering climate change

Resources, Conservation and Recycling 65 (2012) 136–144

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Probabilistic estimation of the storage capacity of a rainwater harvesting system considering climate change Seok-goo Youn a , Eun-Sung Chung a,∗ , Won Gu Kang b , Jang Hyun Sung c a b c

Department of Civil Engineering, Seoul National University of Science and Technology, Gongneung-gil 138, Nowon-gu, Seoul, Republic of Korea GS Engineering & Construction Corporation, Yeokjeon Tower 537, Namdaemun-ro 5-ga, Joong-gu, Seoul, Republic of Korea National Institute of Meteorological Research, 61 Yeoeuidaebang-ro 16-gil, Dongjak-gu, Seoul 156-720, Republic of Korea

a r t i c l e

i n f o

Article history: Received 16 January 2011 Received in revised form 4 May 2012 Accepted 12 May 2012 Keywords: Rainwater harvesting systems Climate change Probabilistic distribution Storage capacity

a b s t r a c t Although a rainwater harvesting system (RWHS) is an effective water supply alternative, its efficiency is often heavily influenced by the temporal distributions of precipitation and water demand. Furthermore, because recent precipitation patterns have changed due to climate change and will likely continue to do so, RWHS designs must take future precipitation forecasts into account. This study aimed to develop a methodology for establishing the probabilistic relationships between the storage capacity and deficit rate of an RWHS when considering climate change. A four-story building at a university was selected as a case study. The A2 scenario of the CGCM3 (Canadian Global Coupled Model 3) was considered and downscaled to the study area using the SDSM (Statistical DownScaling model), and the fitted probabilistic distributions were selected and modeled according to the results of goodness-of-fit tests. As a result, a set of curves describing the relationships between storage capacity and deficit rate was derived. From these curves, we determined that the studied RWHS’s storage capacity could be reduced due to increased annual mean precipitation when the impact of climate change is considered. However, climate change consideration may not be important to determine the storage capacity of RWHS when the places showing enough rainfall all year around are planned. This result can be helpful for RWHS engineers and decision makers. © 2012 Elsevier B.V. All rights reserved.

1. Introduction With increasing population and changing climate effects, water supply systems in many countries are under stress (Lee et al., 2008; Imteaz et al., 2011). These water-stressed conditions are characterized by low, erratic and poorly distributed rainfall, diminished baseflow, high evaporation and excessive runoff and soil losses. These climate change effects are likely to have significant impacts on the hydrologic cycle, affecting water resource systems worldwide and having different effects in different regions (Arnell, 1999; IPCC, 2001; Lee and Chung, 2007). In other words, climate change has become an environmental threat that places additional pressure on hydrological systems and water resources that are already stressed (Kahinda et al., 2010). The impacts of climate change are already visible, as temperature and rainfall variability have increased and intensified worldwide over the last three decades (Hewitson and Crane, 2006; Chung et al., 2011). Thus, recent studies have indicated our extreme vulnerability to the impacts of climate change and recommended that appropriate measures be taken by

∗ Corresponding author. Tel.: +82 8 970 9017; fax: +82 2 948 0043. E-mail address: [email protected] (E.-S. Chung). 0921-3449/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.resconrec.2012.05.005

the water sector to cope with future impacts (Huang et al., 1998; Payne et al., 2004; Vano et al., 2010; Jun et al., 2011; Yang et al., 2012). Rainwater is the most directly accessible water source. Rainwater storage is typically needed to regulate the nonuniformly distributed spatial and temporal characteristics of rainfall. Thus, scientific planning and management are needed to use rainwater efficiently. In the past, rainwater management systems typically involved linear management, concentrating on streams and wastewater treatment plants. Currently, however, rainwater is used as a supplementary water source for household and office uses, such as toilet flushing, lawn watering, ecological pools, and cooling for air conditioning (Handia et al., 2003). Rainwater harvesting (RWH) is listed as one of the specific adaptation strategies that the water sector should implement to cope with future climate change (Pandey et al., 2003; Mukheibir, 2008; Aladenola and Adeboye, 2010; Rozos et al., 2010; Boelee et al., 2012), as it has the potential to enhance ecosystem and livelihood resilience (Muller, 2007). Until recently, the importance of RWH as a buffer against climate-linked extreme weather events has been overlooked in water planning because countries have relied almost exclusively on the conventional sources of rivers and groundwater supplies (Kahinda et al., 2010). RWH systems (RWHSs) including

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Fig. 1. Mass balance model for deficit rates (Su et al., 2009).

a storage component with a specific capacity should be installed to use rainwater in various ways. RWHSs have been successfully implemented as alternative water sources in some countries, such as Japan, Hong Kong, Singapore, South Korea, the United States and even Central Sudan (Thomas, 1998; Hatibu and Mahoo, 1999; Li et al., 2000; Song et al., 2003; Ibrahim, 2009). The efficiency of RWHSs is largely affected by the distribution patterns of precipitation and water demand (Seo et al., 2011). In the long term, climate change caused by the greenhouse effect and carbon dioxide emissions must also be considered to determine reasonable storage capacities for RWHSs because it will affect precipitation trends. The Intergovernmental Panel on Climate Change (IPCC, 2007) showed that global annual mean precipitation is likely to increase by 15% between 2007 and 2100. For the Seoul weather station near Guro, annual mean precipitation is predicted to increase between 2001 and 2100. The summer intensity would increase because the historic percentage of annual precipitation occurring in the summer (60.4%) would increase to 64.7% (Chung et al., 2011). Because the total rainfall during summer would increase and the rainfall in the remaining months would decrease, flood control during the wet period and water-security management during the dry period would become increasingly difficult (Im et al., 2011). For California’s Sierra Nevada Mountains, the temperature increase associated with climate change would change the timing of snowmelt, leading to earlier stream runoff. This shift will change the reservoirs’ ability to adequately serve their intended functions of flood control, water supply, hydropower generation, environmental services, and navigation and recreation (Vinuna et al., 2007). Several studies have investigated optimization methods (Zhang and Cai, 2003; Wei et al., 2005; Rozos et al., 2010), regression models (Lee et al., 2000; Hanson, 2010) and simulation methods (Jenkins and Lund, 2000; Srivastava, 2001; Liaw and Tsai, 2004) for RWHS designs by describing the relationship between storage and deficit

rates using classical probabilistic approaches. These results were mostly presented as deficit variations under fixed storage capacities (Rahman and Yusuf, 2000; Panigrahi et al., 2007) or average deficits that vary with storage capacity (Rahman and Yusuf, 2000; Qian et al., 2004). Although these studies present guidelines for RWHS designs that are useful for engineers and decision makers, there have been few studies on RWHS designs that consider the altered patterns of precipitation caused by climate change. This study aimed to construct a framework for developing a relationship between an RWHS’s storage capacity and deficit rate using fitted probability distributions for comparing storage capacities with and without climate change impacts. 2. Theoretical background 2.1. Simulation model for deficit rates with varying storage capacities In the mass balance equation for the storage capacity, precipitation is regarded as the inflow, and the release or spill is considered to be the outflow. The mass balance model suggested by Su et al. (2009) was modified to obtain the deficit rates with different storage capacities (Fig. 1). From this model, releases are determined with the SOP (Standard Operating Procedure) rule that the demand will be met in each operating period as long as storage is available. The releases are estimated as shown in the following equation:



Rt =

Dt

if It + St−1 ≥ Dt

It + St−1

if It + St−1 < Dt

(1)

where the subscript t is the day, Dt is the daily demand (m3 ), St−1 is the water quantity in storage at the end of the previous day (m3 ), and Rt and It are the daily release and the inflow, respectively, in m3 . The spill on day t can be determined from Eq. (2). Spill occurs

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Fig. 2. CDF for deficit rates under different storage capacities.

Fig. 3. Relationships between storage capacities and deficit rates.

in an amount equal to the difference between the residual storage and maximum design storage capacity if the residual water after release exceeds the maximum storage capacity; otherwise, there is no spill.

is more realistically applicable than the traditional presentation of a deficit-rate distribution with a given design storage.



SPt =

It + St−1 − Dt − Smax

if It + St−1 − Dt > Smax

0

if It + St−1 − Dt ≤ Smax

(2)

St , the water quantity in storage at the end of day t, can be determined using the following equation:



St =

Smax

if SPt > 0

St−1 + It − Rt

if SPt ≤ 0

(3)

where SPt is the daily spill (m3 ) and Smax is the design storage capacity (m3 ). The demand may not always be fulfilled by this SOP rule. Deficits may occur whenever the release, Rt , is smaller than the demand, Dt . The deficit at day t, Deft , can be determined by the difference between Dt and Rt . The annual deficit rate (DR) can be defined as the ratio of the total deficit volume and total demand, as shown in Eq. (4). DR =

  Def (D − Rt )  t = t Dt

Dt

2.3. GCM A GCM (General Circulation Model) is a numerical model, first developed by Manabe and Wetherald (1975), representing physical processes in the atmosphere, ocean, cryosphere, and land surface based on the Navier–Stokes equations. It is among the most advanced tools currently available for simulating the response of the global climate system to increasing greenhouse gas concentrations. The Atmospheric and Oceanic GCMs (AGCM and OGCM, respectively) are important components of the GCM, along with the sea-ice and land-surface components. Although simpler models have also been used to provide globally or regionally averaged climate response estimates, only GCMs, possibly in conjunction with nested regional models, have the potential to provide the geographically and physically consistent estimates of regional climate change required for impact analysis. A GCM depicts climate using a three-dimensional grid over the globe, typically having a horizontal resolution of between 250 and 600 km, with 10–20 vertical layers in the atmosphere and up to 30 layers in oceans. Thus, the model resolution is quite coarse relative to the scale of exposure units in most impact assessments.

(4)

2.2. Probabilistic model for deficit rates with different storage capacities The deficit rate (DR) data should be transformed into the CDF (cumulative density function) of a fitted probability distribution to calculate the exceedance probability, EP(= 1 − p), under a specific storage capacity. As shown in Fig. 2, however, describing the deficit rate as an exceedance probability may make it difficult for people such as decision makers to interpret. In addition, this method gives an exceedance probability under a fixed specific storage capacity. To address these issues, the quantiles of the fitted probability distribution should be obtained. When this methodology is applied to a different storage capacity, a specific exceedance probability, EPi , can be established to describe the relationship between the design storage capacity and the corresponding deficit rate as well as the return period (Tr) for each deficit rate (Fig. 3). These relationships can be used by engineers during the design process. At a chosen exceedance probability of failure, the engineer can decide the storage capacity from the curve under a preset deficit rate. This more comprehensive perspective of the design of the storage–deficit rate

2.4. SDSM Unfortunately, GCMs are restricted in their usefulness for local impact studies by their coarse spatial resolution (typically of the order 50,000 km2 ) and an inability to resolve important sub-gridscale features such as clouds and topography (Wilby et al., 2002). As a consequence, two groups of techniques have emerged as a means of relating regional-scale atmospheric predictor variables to local-scale weather: the ‘statistical downscaling method’ and the ‘dynamic downscaling method’. Statistical downscaling methodologies have several practical advantages over dynamical downscaling approaches. In situations where a low-cost, rapid assessment of highly localized climate change impacts is required, statistical downscaling represents the more promising option. SDSM (Statistical DownScaling Model) is a software package and an accompanying statistical downscaling methodology that enables the construction of climate change scenarios for individual sites at daily time scales, using grid-resolution GCM outputs. SDSM is a Windows-based decision support tool for regionaland local-scale climate change impact assessments. Full technical details, including model validation and usage, are described by Wilby et al. (2002). SDSM is best categorized as a hybrid of the

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Fig. 4. Methodology of this study.

stochastic weather generator and regression-based downscaling methods (Wilby and Wigley, 1997). The stochastic element is used to inflate the variance of the downscaled output to better agree with the observed daily data and to generate ensembles of climate time series that differ in their individual time evolution, interannual means and variance. The SDSM software reduces the task of statistically downscaling daily weather series into seven discrete steps (Wilby et al., 2002): (1) (2) (3) (4) (5) (6) (7)

Quality control and data transformation. Screening of predictor variables. Model calibration. Weather generation (using observed predictors). Statistical analyses. Graphing model output. Scenario generation (using climate model predictors).

Fig. 5. Study area.

makers because they can be easily applied in the design of RWHS storage. Step 5 is to compare the relationship between storage capacity and deficit rate with and without the consideration of climate change. The future situation must be known to manage an RWHS efficiently. 4. Case study A four-story building at SNU, shown in Fig. 5, was selected as the study area. It consists of four classrooms, four laboratories, and ten faculty offices.

3. Methodology 4.1. Precipitation Fig. 4 shows a framework of the methodology for this study. Step 1 is to select the study area. Observed precipitation and water-demand data are required to calculate the exact storage capacity of an RWHS. It is difficult to obtain observed daily waterdemand data because water demand is generally not measured on a daily basis. We previously obtained monthly water demands from the buildings of Seoul National University (SNU) rather than daily data. Thus, several assumptions were made to model the deficit rate, such as a weighting factor corresponding to the daily demands. Step 2 is to gather the GCM data while taking the effects of climate change into account. The GCM data were taken from the IPCC database (http://www.ipcc-data.org). To consider the latest trend, CGCM (Canadian Global Coupled Model) 3, which was presented in the fourth report of the IPCC (2007), was applied in this study. Step 3 is to downscale the precipitation data. The GCM cannot generate hydrological data for features at a sub-grid scale (less than 50,000 km2 ). Because the statistical downscaling method is computationally undemanding and readily transferable, the SDSM was used to determine the future precipitation trends in the study area by taking the effects of climate change into account. Step 4 is to identify the relationship between storage capacity and deficit rate. The deficit rate is calculated by the simulation model (Fig. 1) under a specific storage capacity. A goodness-of-fit test is then performed to verify the fitted probability distribution describing the deficit rate. The quantiles of the fitted probability distribution showing the exceedance probability curves as return periods would be intuitive tools for engineers and decision

To obtain precipitation data in the study area, the Thiessen polygon should first be derived. As shown in Fig. 6, the weights of the Guro and Anyang gauges were 52.9% and 47.1%, respectively, in the SNU watershed containing the study building. The precipitation data taken on the rooftop of building “Bldg. 34” were calculated using 23 years (1986–2008) of precipitation-gauge data at Guro and Anyang, with an annual mean of 1135 mm.

Fig. 6. Thiessen polygon for study area.

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Observed A2

Precipitation (mm)

10

1

0.1

0.01 0

10

20

30

40

50 60 Probability (%) (a) Guro Gage

70

80

90

100

Observed A2

Precipitation (mm)

10

1

0.1

0.01 0

10

20

30

40

50 60 Exceedance (%) (b) Anyang Gage

70

80

90

100

Fig. 7. Duration curves of downscaled and observed precipitation data (verification). (a) Guro and (b) Anyang.

The GCM scenario was selected to consider climate change. Among the many available GCMs, CGCM3, which was developed by the Canadian Center for Climate Modeling, was used as the climate scenario in this study because it is the most recent model available. The spatial resolution of CGCM3 is roughly 3.75◦ lat./lon. The GCM output contains 29 daily predictors (describing atmospheric circulation, thickness, and moisture content at the surface at 850hPa and 500-hPa levels) for four regions covering the Republic of Korea for the period 1961–2100. In this study, predictors drawn from a grid box overlaying the Korean peninsula were employed for the A2 scenario of the IPCC Special Report on Emission Scenarios (SRES) (IPCC, 2001) because it was explicitly constructed to explore future developments in the global environment with special reference to the production of greenhouse and aerosol precursor emissions. Korean researchers to study the effects of climate change Korea have typically only used the A1B and A2 scenarios due to their realism (Bae et al., 2007; Ahn et al., 2009; Chung et al., 2011; Jun et al., 2011; Yang et al., 2012). The A2 climate-change scenario of CGCM3 was downscaled to the Guro and Anyang gauges using the SDSM. Precipitation data from 1986 to 2000 were used to generate precipitation data for

the future (2020–2042). To verify the SDSM output, it was fitted to precipitation data from 2001 to 2009, and the coefficient of determination (R2 ), was calculated according to Eq. (5).

n R2 =

i=1

(Mob − Qob,i )2 −

n

i=1

n i=1

(Qsm,i − Qob,i )2

(Mob − Qob,i )2

(5)

where Qob,i is the ith observed data point, Mob is the mean of the observed data, Qsm,i is the ith simulated data point, and n is the number of data points. The calculated R2 values for Guro and Anyang were 0.94 and 0.96, respectively. The verification results for precipitation are shown in Fig. 7; here, the flow-duration curves can be used to identify how well the downscaled daily precipitation data agree with the observed data. The daily rainfall distribution of Guro station is accurately simulated while the smaller amounts of precipitation (under 1.5 mm) of Anyang station are typically overestimated. Because the larger amounts of downscaled precipitation, closely followed the observed amounts, we utilized those results. From the Mann–Kendall trend test (2012–2042), we noted that the Guro and Anyang gauges had a strong tendency for

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(a) Case I: 50 year

(b) Case I:100 year Case I-a Case I-a* Case I-b Case I-b* Case I-c Case I-c*

0.4

Case I-a Case I-a* Case I-b Case I-b* Case I-c Case I-c*

0.6

DR (Deficit Rates)

DR (Deficit Rates)

0.6

0.2

0.0

0.4

0.2

0.0 0

5

10

15

20

0

Storage Capacity (m3)

10

15

20

(d) Case II:100 year Case II-a Case II-a* Case II-b Case II-b* Case II-c Case II-c*

0.4

Case II-a Case II-a* Case II-b Case II-b* Case II-c Case II-c*

0.6

DR (Deficit Rates)

0.6

DR (Deficit Rates)

5

Storage Capacity (m3)

(c) Case II: 50 year

0.2

0.4

0.2

0.0

0.0 0

5

10

15

0

20

5

10

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20

Storage Capacity (m3)

Storage Capacity (m3)

(e) Case III: 50 year

(f) Case III:100 year Case III-a Case III-a* Case III-b Case III-b* Case III-c Case III-c*

0.4

Case III-a Case III-a* Case III-b Case III-b* Case III-c Case III-c*

0.6

DR (Deficit Rates)

0.6

DR (Deficit Rates)

141

0.2

0.4

0.2

0.0

0.0 0

5

10

15

20

Storage Capacity (m3)

0

5

10

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20

Storage Capacity (m3)

Fig. 8. Storage–deficit relationships at specific return period.

increasing precipitation due to a high uc and small p-value, as shown in Table 1. This was the main change in the input variable for this simulation. 4.2. Water demand for toilet flushing The water use data of the study building were measured every month by the technical division of SNU for the last two years (2008–2009). However, as the data during the previous period Table 1 Results of Mann–Kendall test of the future scenarios. Element

Guro

Anyang station

Variance uc p-Value

2.1E+12 2.3 3.9E−15

2.1E+12 1.8 9.4E−15

(1986–2007) were not available, the mean values of 2008 and 2009 were used for this study. The daily demand and the demand for toilet flushing among the total water use of the study building were not clear because there were no measurements. Thus, we assumed scenarios of varying daily water demands for weekdays and weekends and for toilet flushing.

4.3. Simulation cases As mentioned in Section 4.3, three water demand cases for weekdays and weekends, three water demand cases for toilet flushing and two cases with and without the consideration of climate change were simulated to estimate a suitable storage capacity for the RWHS in the study building. These 18 cases are denoted as follows:

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(a) Case I: 50 year

(b) Case I:100 year 0.4

Case I-a*Seoul Case I-a*Suwon Case I-b*Seoul Case I-b*Suwon Case I-c*Seoul Case I-c*Suwon

0.3

DR (Deficit Rates)

DR (Deficit Rates)

0.4

0.2

0.1

0.0

Case I-a*Seoul Case I-a*Suwon Case I-b*Seoul Case I-b*Suwon Case I-c*Seoul Case I-c*Suwon

0.3

0.2

0.1

0.0

0

5

10

15

20

0

Storage Capacity (m3)

15

20

(d) Case II:100 year

0.4

0.4

Case II-a*Seoul Case II-a*Suwon Case II-b*Seoul Case II-b*Suwon Case II-c*Seoul Case II-c*Suwon

0.3

DR (Deficit Rates)

DR (Deficit Rates)

10

Storage Capacity (m3)

(c) Case II: 50 year

0.2

0.1

0.0

Case II-a*Seoul Case II-a*Suwon Case II-b*Seoul Case II-b*Suwon Case II-c*Seoul Case II-c*Suwon

0.3

0.2

0.1

0.0

0

5

10

15

20

0

Storage Capacity (m3)

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Storage Capacity (m3)

(e) Case III: 50 year

(f) Case III:100 year

0.4

0.4 Case III-a*Seoul Case III-a*Suwon Case III-b*Seoul Case III-b*Suwon Case III-c*Seoul Case III-c*Suwon

0.3

DR (Deficit Rates)

DR (Deficit Rates)

5

0.2

0.1

0.0

Case III-a*Seoul Case III-a*Suwon Case III-b*Seoul Case III-b*Suwon Case III-c*Seoul Case III-c*Suwon

0.3

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20

Storage Capacity (m3)

0

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Storage Capacity (m3)

Fig. 9. Storage–deficit relationships of Seoul and Suwon considering climate change.

- Case I: The daily water demands for Monday–Friday and Saturday–Sunday are 70% and 30%, respectively, of the weekly water demand. - Case II: The daily water demands for Monday–Friday and Saturday–Sunday are 80% and 20%, respectively, of the weekly water demand. - Case III: The daily water demands for Monday–Friday and Saturday–Sunday are 90% and 10%, respectively, of the weekly water demand. - Case a: The water demand for toilet flushing is 25% of the total water demand. - Case b: The water demand for toilet flushing is 50% of the total water demand.

- Case c: The water demand for toilet flushing is 75% of the total water demand. - Case *: Climate change considered. 5. Results and discussion 5.1. Selection of fitted probability distributions Normal, Lognormal, Gumbel, Logistic, Gamma, Gen. Pareto, Weibull, Gen. Logistic, GEV, and Log-Pearson type III distributions were considered to fit the probability distribution under the different cases, and the Kolmogorov–Smirnov (K–S) and chisquared (2 ) tests were used to test for the goodness of fit. Table 2

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Table 2 Fitted probability distribution under different cases. Case

I-a, I-a* I-b, I-b* I-c, I-c* II-a, II-a* II-b, II-b* II-c, II-c* III-a, III-a* III-b, III-b* III-c, III-c*

Fitted probability distribution

Gen. Pareto Gumbel GEV Gen. Pareto Gen. Pareto GEV Gen. Pareto Gen. Pareto GEV

shows the fitted probability distributions under the different cases. The best-fitting probability distributions of cases a and c were the Gen. Pareto and GEV. distributions, respectively. In case b, Gumbel and Gen. Pareto distributions were provided the best fit. Most of the fitted probability distributions were concluded to be appropriate because the significance levels were within the limit (10–15%).

5.2. Storage–deficit relationships at specific return periods For 50-years and 100-years return periods of the deficit rate, the storage capacity corresponding to each case was estimated by the quantiles of its fitted probability distribution. The storage–deficit relationships in the different cases at 50-years and 100-years return periods are shown in Fig. 8. Storage capacity was more sensitive to the daily water use pattern than to the ratio of water demand for toilet flushing to total water demand. Thus, when an engineer is designing a suitable storage capacity for a RWHS, the amount of water demand for toilet flushing should be determined prior to design. For clarity, the data of Seoul and Suwon stations showing the higher annual precipitations (about 1401 mm and 1312 mm, respectively) were also applied to this RWHS as shown in Fig. 9. We can find out the more distinct phenomena that the differences of the water demand for toilet flushing affect too little variations of deficit rates because the water demand is satisfied enough. On the other hand, daily water use pattern changed the deficit rates, largely as shown in both Figs. 8 and 9. At a given deficit rate, the storage capacity considering the impact of climate change was smaller than that without consideration because the annual mean precipitation is 1135 mm in the base cases and 1268 mm in the climate change scenarios. Thus, if RWHS storage for the study building is maintained for 50 years into the future, it is possible to efficiently design the RHWS, and the water-supply plan can be implemented by determining the ratio of the amount of tap water to rainwater used for toilet flushing by inspecting Fig. 8. This result is also shown in the simulations using Seoul and Suwon weather stations. The variation, however, is not too large since the future precipitations considering climate change scenarios are analyzed to be 1952 mm and 1697 mm, respectively, which are much higher than 1268 mm of the study area. That is, climate change consideration may not be important to determine the storage capacity of RWHS when the places showing enough rainfall all year around are planned. Clearly, with a longer return period for a given deficit rate, a larger RWHS storage capacity is required to ensure an adequate rainwater supply. Thus, relevant rules and regulations, the needs of the residents, and the budget for RWHS design and installation must all be taken into consideration when a storage capacity is estimated by both engineers and decision makers.

Goodness-of-fit test K–S

2

10% significance level passed 10% significance level passed 15% significance level passed 10% significance level passed 10% significance level passed 10% significance level passed 15% significance level passed 10% significance level passed 15% significance level passed

10% significance level passed 10% significance level passed 10% significance level passed 5% significance level passed 5% significance level passed 10% significance level passed 10% significance level passed 10% significance level passed 10% significance level passed

6. Conclusions Many studies, including those by the IPCC (2007) and Chung et al. (2011), have shown that the global mean precipitation and annual mean precipitation in Seoul, Korea, have been gradually increasing. Thus, RWHSs, which have been regarded as an important measure for climate change adaptation, should be designed with the consideration of climate change impacts to promote sustainable development. Here, we estimated the future precipitation data at the study building using a GCM model, and the SDSM and developed 18 plausible scenarios reflecting water demands for weekdays, weekends and toilet flushing, both with and without consideration for the effects of climate change. The fitted probability distributions of various scenarios were derived, and the relationships between storage capacity and deficit rate were determined. We found that the capacity of an RWHS required to satisfy the specific deficit rates at the SNU study building were lower when climate change was taken into account; i.e., if climate change occurs as predicted, the utility of the RWHS will increase. However, climate change consideration may not be important to determine the storage capacity of RWHS when the places showing enough rainfall all year around are planned. These results can be readily used as intuitive tools for engineers and decision makers in the design of RWHSs because they reflect the impacts of climate change. To obtain a more realistic storage capacity for an RWHS, it is most important to acquire specific daily water-demand data. Nevertheless, this study has two limitations: (1) compared to GCMs, regional climate models will estimate the optimal RWHS capacity more accurately because RWHSs are typically planned for a small area, and (2) various weather stations should be investigated for more generalized conclusions because some regions will need RWHSs with larger capacities under the impacts of climate change. Acknowledgements This study was financially supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2010-0010609). References Ahn SR, Park MJ, Park GA, Kim SJ. Assessing future climate change impact on hydrologic components of Gyeongancheon Watershed. Journal of Korea Water Resources Association 2009;42(1):33–50 (in Korean). Aladenola OO, Adeboye OB. Assessing the potential for rainwater harvesting. Water Resources Management 2010;24:2129–37. Arnell NW. Climate change and global water resources. Global Environmental Change 1999;9(S):S31–49. Bae DH, Jung IW, Chang H. Regional impacts of climate change on water resources in Korea by using a high resolution scenario. Climate Research 2007;35(3):213–26. Boelee E, Yohannes M, Poda J, McCartney M, Cecchi P, Kibret S, et al. Options for water storage and rainwater harvesting to improve health and resilience

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