# Generalized Storage–Yield–Reliability Relationships for Analysing Shopping Centre Rainwater Harvesting Systems

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

^{2}in 2010 [23]), the potential contribution of shopping centre RWH systems to water security could be significant. RWH is being promoted as a green building technology [24] and unpublished information reveals increased installation of RWH systems for buildings and other structures with large roofs. Since no guidelines for the hydrologic analysis of shopping centre RWH systems in South Africa were found in the literature, this study set out to formulate them. The aim was to obtain generalized guidelines that would be applicable for RWH feasibility analysis and preliminary design. This would help to forestall wrong investment decisions and inappropriate sizing of RWH systems.

## 2. Materials and Methods

#### 2.1. Selection and Acquisition of Data

^{2}), Community centres (12,000–30,000 m

^{2}), Large community/small regional centres (30,000–50,000 m

^{2}), Regional centres (50,000–100,000 m

^{2}) or Super regional centres (>100,000 m

^{2}). For the development of the model, it is decided to select one shopping centre from each category from four South African provinces (Kwa Zulu Natal, Gauteng, Limpopo and Western Cape). Selecting five shopping centre categories in four regions would provide 20 shopping centres but, since no super regional centre is located in Limpopo, a total of 19 shopping centres are therefore selected. For model verification, two regional and two small regional centres located in four other provinces (North West (NW), Eastern Cape (EC), Free State (FS) and Mpumalanga (MP) provinces) are used. All the selected shopping centres are located in cities.

^{3}/m

^{2}of floor area/year for South Africa. A study on 40 shopping centres of varying sizes in Western Australia [36] obtained highest demands of 2.828, 3.141, 1.347, and 1.383 m

^{3}/m

^{2}/per year for neighbourhood centres, large community/small regional centres, regional centres, and super regional centres, respectively. Since these demands were based on detailed field measurements and the demands in Western Australian malls are not likely to be substantially different from those in South Africa, they were adopted for analysis in this study. For community centres, the average overall demand of 2.18 m

^{3}/m

^{2}/per year obtained by Saunders [36] was adopted. It was assumed that the RWH systems would supply non-potable water use (cooling towers for air conditioning, toilets, urinals and cleaning) which was assumed as 45% of the total demand as found by Saunders [36]. Daily demand on weekends was assumed to be twice that on weekdays and the monthly demand for December was assumed to be twice the demand in the other months of the year.

#### 2.2. Simulation Analysis

_{e}(t) is the volume of water released to meet the demand in period t, D(t) is the demand in period t, S(t) is the volume of water in storage at the start of period t, C is the live storage capacity of the tank, η is the water collection efficiency, R(t) is the rainfall intensity in period t and A is the vertical projection of the effective roof area.

#### 2.3. Formulating Generalized Storage–Yield–Reliability Relationships

**S**and the other two dimensionless ratios was then carried out by regression analysis. This was done for reliabilities of 85%, 90%, 95%, 98% and 99%. To incorporate reliability into the regression equations, additional regression analysis seeking to define the parameters of the regression equations as functions of reliability was carried out.

_{P-r}_{SD}is the ratio of average supply to average demand, η is the efficiency of rainwater collection into storage, A is the vertical projection of the roof area, $\overline{P}$ is the average daily rainfall, $\overline{{D}_{t}}$ is the average daily demand, r is the reliability of supply, S

_{P-r}is proportion of the year fully supplied, N

_{r}is the expected number of days that the demand is fully met in a year at reliability r, R

_{TD-r}is the ratio of storage capacity at reliability r to the volume of annual demand, and C

_{r}is the storage capacity that is optimal at reliability r.

_{P-r}) increased.

_{LR}in Figure 6) and S

_{P-r}was then carried out, as this would extend the generalization to the non-optimal space below the Pareto front. Slopes S

_{LR}were therefore obtained for four ranges of reliability: 85–90%, 85–95%, 85–98% and 85–99% for the simulation runs of all 19 shopping centres using least squares fitting. Regression analysis between S

_{LR}and S

_{P}-r was used to search for a generalized relationship.

## 3. Results

#### 3.1. Simulation Analysis

^{3}. At 99% reliability, the respective ranges are 6–161 days per year and 90–6000 m

^{3}. From a hydrological perspective, RWH could be a viable source of water for some but not all the shopping centres. This viability could however be constrained by the high cost and space required to install large storages.

#### 3.2. Generalized Storage–Yield–Reliability Relationships

_{SD}and S

_{P-r}best at the five reliabilities of 85%, 90%, 95%, 98% and 99%. Figure 7 shows the fits for four of these. Figure 8 shows the relationships between the parameters of the power law models and reliability. These models are themselves highly correlated power law models. Figure 9 and Figure 10 show the respective relationships between S

_{P-r}and R

_{TD-r}and the parameters of the power models with reliability. The correlations between S

_{P-r}and R

_{TD-r}were lower than those between R

_{SD}and S

_{P-r}but they were still considered satisfactory. The generalized models of the RWH system could therefore be summarized as:

_{P-r}is proportion of the year fully supplied at reliability r, R

_{SD}is the ratio of average supply to average demand, R

_{TD-r}is the ratio of storage capacity at reliability r to the volume of annual demand, a and c are coefficients, and b and d are indices of the regression models.

_{LR}(Figure 6) and S

_{P-r}needs to be found. Figure 12 shows the best fitting power law models between the S

_{LR}and S

_{p-r}while Figure 13 shows the relationships obtained between the parameters of the power law model and the reliability at the Pareto front. The generalized model for the slope is defined as:

_{LR}is the slope of the yield–reliability plot, S

_{P-r}is proportion of the year fully supplied at reliability r, e is a coefficient, and f an index of the regression model.

_{LR}) is now applied to obtain the yield–reliability relationship below the Pareto front, as illustrated on Figure 14. For a RWH system whose storage is optimal at reliability r, the yield (proportion of year supplied) for reliability r

_{t}is obtained as:

_{p-rt}is the proportion of full supply for reliability r

_{t,}r is the reliability at the Pareto front and S

_{LR}is the slope of the yield–reliability plot for the storage capacity that is optimal (located at the Pareto front) for reliability r.

#### 3.3. Verification of Generalized Model

^{3}to diversify the supply to demand (R

_{SD}) ratios to use in verification. With this change, the supply to demand ratios were 0.146, 0.302, 0.698 and 0.862, respectively, for Mimosa, Baywest, Matlosana and Riverside mall. Figure 15 and Figure 16 compare the simulated and modelled storage–reliability and the yield–reliability relationships for hydrologically optimum configurations of the four RWH systems. The storages obtained as optimal at 98% reliability by the generalized model were then used to verify the modelling of hydrologically non-optimal systems. Simulation was carried out using these storages and the resulting yield–reliability plots were compared with those from the generalized model. These yield–reliability plots are compared on Figure 17. Figure 15, Figure 16 and Figure 17 reveal satisfactory verification performance of the generalized model. Figure 15 also indicates that the generalizing could probably be used to smooth the large scatter of the simulated storage–reliability relationships.

## 4. Case Study: RWH System for Maponya Mall

^{2}and a roof area of 60,000 m

^{2}. The generalized model was used to determine the hydrologically optimum RWH storage to supply the non-potable demand at 95% reliability. The probable change in the performance of this system due to climate change was then assessed. Daily rainfall was sourced from Lynch [34] and gauging station 0475736 W located 1.90 km from the mall provided 107 years of daily rainfalls. Overall, 72.5% of the rainfall was observed, 23.9% was patched and 3.6% could not be patched and was classified as missing. The station has an MAP of 655 mm/year. This would increase to 753 mm/year assuming the projected 18% increase in rainfall for Johannesburg in the climatic “near-future” (2046–2065) [55]. The current average temperature of Johannesburg is 16 °C and is projected to increase by an average of 2.4 °C in the “near future” [55]. Unpublished analysis by the first author shows that HVAC (air conditioning) water demand in Johannesburg varies in direct proportion to the temperature (expressed in °C). The 2.4 °C rise in temperature would therefore increase the HVAC demand by 15%. Assuming that HVAC demand takes 64% of the non-potable demand, as found by Saunders [36], the total non-potable water demand currently estimated as 0.606 m

^{3}/m

^{2}/year would increase to 0.665 m

^{3}/m

^{2}/year in the climatic “near future”.

_{SD}) for the current climate is obtained as 0.741 using Equation (4). By using Equation (8), a proportion of full supply (S

_{P-95}) of 0.479 is obtained for a reliability of 95%. By Equation (5), the expected number of days of full supply (N

_{r}) is obtained as 175 days per year at 95% reliability. Using Equation (9), the ratio of storage capacity to annual demand (R

_{TD-95}) comes to 0.119 and the storage capacity (C

_{r}) is then obtained as 5062 m

^{3}by Equation (6).

_{SD}) is 0.797 (Equation (4)) and the new storage to annual demand ratio (R

_{DR-r}) is 0.109 (Equation (6)). Because the supply and demand have changed, the capacity (5062 m

^{3}) that was hydrologically optimal at 95% reliability is now optimal at some other reliability. This new reliability is obtained by setting R

_{SD}as 0.797 in Equation (8) and R

_{TD-r}as 0.109 in Equation (9), and then determining r simultaneously using both equations. This obtains a reliability (r) of 0.965 (96.5%). The proportion of year supplied S

_{P-96.5}comes to 0.500 (Equation (8)) obtaining an expected full supply of 183 days per year at 96.5% reliability (Equation (5)). At reliabilities exceeding 96.5%, the proportion of year supplied (S

_{P-r}) is obtained using Equation (8) and, at lower reliabilities, Equations (10) and (11) are used to obtain S

_{P-rt}. The yield–reliability relationships for the current and the climatic “near future” are shown on Figure 18.

## 5. Discussion and Conclusions

## Author Contributions

## Conflicts of Interest

## References

- United Nations Educational, Scientific and Cultural Organization (UNESCO). The United Nations World Water Development Report 3: Water in a Changing World; United Nations Educational, Scientific and Cultural Organization: Paris, France, 2009. [Google Scholar]
- United Nations Educational, Scientific and Cultural Organization (UNESCO). World Water Development Report Volume 4: Managing Water under Uncertainty and RISK; United Nations Educational, Scientific and Cultural Organization: Paris, France, 2012; Volume 1. [Google Scholar]
- Department of Water Affairs. National Water Resource Strategy: South Africa; Department of Water Affairs: Pretoria, South Africa, 2013; 201p.
- O’Brien, O. Domestic Water Demand for Consumers with Rainwater Harvesting Systems. Master’s Thesis, Department of Civil Engineering, Division of Water and Environmental Engineering, Stellenbosch University, Stellenbosch, South Africa, 2014. [Google Scholar]
- Tito, M.P. Modelling and Sustainable Management of Rainwater Harvesting in Urban Systems. Ph.D. Thesis, Universitat Autònoma de Barcelona (UAB), Barcelona, Spain, 2012. [Google Scholar]
- Rahman, A.; Keane, J.; Imteaz, M.A. Rainwater harvesting in Greater Sydney: Water savings, reliability and economic benefits. Resour. Conserv. Recycl.
**2012**, 61, 16–21. [Google Scholar] [CrossRef] - Ghisi, E.; Schondermark, P.N. Investment Feasibility Analysis of Rainwater Use in Residences. Water Resour. Manag.
**2013**, 27, 2555–2576. [Google Scholar] [CrossRef] - Farreny, R.; Gabarrell, X.; Rieradevall, J. Cost-efficiency of rainwater harvesting strategies in dense Mediterranean neighbourhoods. Resour. Conserv. Recycl.
**2011**, 55, 686–694. [Google Scholar] [CrossRef] - Campisano, A.; Butler, D.; Ward, S.; Burns, M.J.; Friedler, E.; DeBusk, K.; Fisher-Jeffes, L.N.; Ghisi, E.; Rahman, A.; Furumai, H.; et al. Urban rainwater harvesting systems: Research, implementation and future perspectives. Water Res.
**2017**, 115, 195–209. [Google Scholar] [CrossRef] [PubMed] - Gupta, A.S. Cost Recovery in Urban Water Services: Select Experiences in Indian Cities; Water and Sanitation Program, World Bank: Washington, DC, USA, March 2011. [Google Scholar]
- Farolfi, S.; Gallego-Ayala, J. Domestic water access and pricing in urban areas of Mozambique: Between equity and cost recovery for the provision of a vital resource. Int. J. Water Resour. Dev.
**2014**, 30, 728–744. [Google Scholar] [CrossRef] - Sample, D.J.; Liu, J. Optimizing rainwater harvesting systems for the dual purposes of water supply and runoff capture. J. Clean. Prod.
**2014**, 75, 174–194. [Google Scholar] [CrossRef] - Chiu, Y.R.; Tsai, Y.L.; Chiang, Y.C. Designing rainwater harvesting systems cost-effectively in a urban water-energy saving scheme by using a GIS-simulation based design system. Water
**2015**, 7, 6285–6300. [Google Scholar] [CrossRef] - Dobrowksy, P.H.; Mannel, D.; De Kwaadsteniet, M.; Prozesky, H.; Khan, W.; Cloete, T.E. Quality assessment and primary uses of harvested rainwater in Kleinmond, South Africa. Water SA
**2014**, 40, 401–406. [Google Scholar] [CrossRef] - Abbott, S.E.; Douwes, J.; Caughley, B.P. A survey of the microbiological quality of roof-collected rainwater of private dwellings in New Zealand. N. Z. J. Environ. Health
**2006**, 29, 6–16. [Google Scholar] - Evans, C.A.; Coombes, P.J.; Dunstan, R.H. Wind, rain and bacteria: The effect of weather on the microbial composition of roof-harvested rainwater. Water Res.
**2006**, 40, 37–44. [Google Scholar] [CrossRef] [PubMed] - Chang, M.; McBroom, M.W.; Scott Beasley, R. Roofing as a source of nonpoint water pollution. J. Environ. Manag.
**2004**, 73, 307–315. [Google Scholar] [CrossRef] [PubMed] - Evison, L.; Sunna, N. Microbial regrowth in household water storage tanks. J. Am. Water Work Assoc.
**2001**, 93, 85–94. [Google Scholar] - Van der Sterren, M.; Rahman, A.; Dennis, G.R. Quality and Quantity Monitoring of Five Rainwater Tanks in Western Sydney, Australia. J. Environ. Eng.
**2013**, 139, 332–340. [Google Scholar] [CrossRef] - Sazakli, E.; Alexopoulos, A.; Leotsinidis, M. Rainwater harvesting, quality assessment and utilization in Kefalonia Island, Greece. Water Res.
**2007**, 41, 2039–2047. [Google Scholar] [CrossRef] [PubMed] - Mwenge Kahinda, J.; Taigbenu, A.E. Rainwater harvesting in South Africa: Challenges and opportunities. Phys. Chem. Earth
**2011**, 36, 968–976. [Google Scholar] [CrossRef] - Ndiritu, J.G.; McCarthy, S.; Tshirangwana, N. Probabilistic assessment of the rainwater harvesting potential of schools in South Africa. Proc. Int. Assoc. Hydrol. Sci.
**2014**, 364, 435–440. [Google Scholar] [CrossRef] - Prinsloo, D.A. Classification and Hierarchy of Retail Facilities in South Africa; Urban Studies: Johannesburg, South Africa, 2010; 76p. [Google Scholar]
- Green Building Council of South Africa. Green Star South Africa: Office V1.1 Technical Manual; Green Building Council of South Africa: Cape Town, South Africa, 2014. [Google Scholar]
- Fonseca, C.R.; Hidalgo, V.; Díaz-Delgado, C.; Vilchis-Francés, A.Y.; Gallego, I. Design of optimal tank size for rainwater harvesting systems through use of a web application and geo-referenced rainfall patterns. J. Clean. Prod.
**2017**, 145, 323–335. [Google Scholar] [CrossRef] - Hanson, L.S.; Vogel, R.M. Generalized storage-reliability-yield relationships for rainwater harvesting systems. Environ. Res. Lett.
**2014**, 9. [Google Scholar] [CrossRef] - Campisano, A.; Modica, C. Optimal sizing of storage tanks for domestic rainwater harvesting in Sicily. Resour. Conserv. Recycl.
**2012**, 63, 9–16. [Google Scholar] [CrossRef] - Liaw, C.H.; Chiang, Y.C. Dimensionless analysis for designing domestic rainwater harvesting systems at the regional level in Northern Taiwan. Water
**2014**, 6, 3913–3933. [Google Scholar] [CrossRef] - Imteaz, M.A.; Ahsan, A.; Shanableh, A. Reliability analysis of rainwater tanks using daily water balance model: Variations within a large city. Resour. Conserv. Recycl.
**2013**, 77, 37–43. [Google Scholar] [CrossRef] - Liaw, C.; Tsai, Y. Optimum Storage Volume of Rooftop Rain Water Harvesting Systems for Domestic Use. J. Am. Water Resour. Assoc.
**2004**, 901–912. [Google Scholar] [CrossRef] - Notaro, V.; Liuzzo, L.; Freni, G. Reliability Analysis of Rainwater Harvesting Systems in Southern Italy. Procedia Eng.
**2016**, 162, 373–380. [Google Scholar] [CrossRef] - Imteaz, M.A.; Adeboye, O.B.; Rayburg, S.; Shanableh, A. Rainwater harvesting potential for southwest Nigeria using daily water balance model. Resour. Conserv. Recycl.
**2012**, 62, 51–55. [Google Scholar] [CrossRef] - Prinsloo, D.A. Benchmarking the South African Shopping Centre Industry International and Local Trends; South African Council of Shopping Centres, Shopping Centre Directory: Gauteng, South Africa, 2013. [Google Scholar]
- Lynch, S. The Development of a Raster Database of Annual, Monthly and Daily Rainfall for Southern Africa; WRC Report No. 1156/0/1; Water Research Commission: Pretoria, Southern Africa, 2003. [Google Scholar]
- Council for Science and Industiral Research. Guidelines for Human Settlement Plannning and Design; Council for Science and Industiral Research: New Delhi, Delhi, 2005; Volume 2. [Google Scholar]
- Saunders, A. Shopping Centre Water Efficiency Report; HFM Asset Management: Perth, Australia, 2012. [Google Scholar]
- Santos, C.; Taveira-Pinto, F. Analysis of different criteria to size rainwater storage tanks using detailed methods. Resour. Conserv. Recycl.
**2013**, 71, 1–6. [Google Scholar] [CrossRef] - Ndiritu, J.; Odiyo, J.O.; Makungo, R.; Ntuli, C.; Mwaka, B. Yield-reliability analysis for rural domestic water supply from combined rainwater harvesting and run-of-river abstraction. Hydrol. Sci. J.
**2011**, 56, 238–248. [Google Scholar] [CrossRef] - Ward, S.; Memon, F.A.; Butler, D. Rainwater harvesting: Model-based design evaluation. Water Sci. Technol.
**2010**, 61, 85–96. [Google Scholar] [CrossRef] [PubMed] - Su, M.D.; Lin, C.H.; Chang, L.F.; Kang, J.L.; Lin, M.C. A probabilistic approach to rainwater harvesting systems design and evaluation. Resour. Conserv. Recycl.
**2009**, 53, 393–399. [Google Scholar] [CrossRef] - Yaziz, M.I.; Gunting, H.; Sapari, N.; Ghazali, A.W. Variations in rainwater quality from roof catchments. Water Res.
**1989**, 23, 761–765. [Google Scholar] [CrossRef] - Silva Vieira, A.; Weeber, M.; Ghisi, E. Self-cleaning filtration: A novel concept for rainwater harvesting systems. Resour. Conserv. Recycl.
**2013**, 78, 67–73. [Google Scholar] [CrossRef] - Fewkes, A. Modelling the performance of rainwater collection systems: Towards a generalised approach. Urban Water.
**2000**, 1, 323–333. [Google Scholar] [CrossRef] - Berwanger, H.; Ghisi, E. Investment feasibility analysis of rainwater harvesting in the city of Itapiranga, Brazil. Int. J. Sustain. Hum. Dev.
**2014**, 2, 104–114. [Google Scholar] - Melville-Shreeve, P.; Ward, S.; Butler, D. Rainwater harvesting typologies for UK houses: A multi criteria analysis of system configurations. Water
**2016**, 8. [Google Scholar] [CrossRef] [Green Version] - Mashau, F. Rainwater Harvesting for Multiple Uses in Siloam Village of Limpopo Province, South Africa; Unpublished Honours Research Dissertation; University of Venda: Thohoyandou, South Africa, 2006. [Google Scholar]
- Ghisi, E.; Tavares, D.D.F.; Rocha, V.L. Rainwater harvesting in petrol stations in Brasília: Potential for potable water savings and investment feasibility analysis. Resour. Conserv. Recycl.
**2009**, 54, 79–85. [Google Scholar] [CrossRef] - Basson, M.S.; Allen, R.B.; Pegram, G.G.S.; Van Rooyen, J. Probabilistic Management of Water Resource and Hydropower Systems; Water Resources Publications: Littleton, CO, USA, 1994. [Google Scholar]
- Basson, M.S.; van Rooyen, J.A. Practical Application of Probabilistic Approaches to the Management of Water Resource Systems. J. Hydrol.
**2001**, 241, 53–61. [Google Scholar] [CrossRef] - Ndiritu, J.; Odiyo, J.; Makungo, R.; Mwaka, B.; Mthethwa, N.; Ntuli, C.; Andanje, A. Development of probabilistic operating rules for Hluhluwe Dam, South Africa. Phys. Chem. Earth
**2017**, 100, 343–352. [Google Scholar] [CrossRef] - Weibull, W. A statistical theory of strength of materials. Ing. Vetensk. Akad. Handl.
**1939**, 151, 1–45. [Google Scholar] - Cunnane, C. Unbiased plotting positions—A review. J. Hydrol.
**1978**, 37, 205–222. [Google Scholar] [CrossRef] - Makkonen, L. Plotting positions in extreme value analysis. J. Appl. Meteorol. Climatol.
**2006**, 45, 334–340. [Google Scholar] [CrossRef] - Imteaz, M.A.; Shanableh, A.; Rahman, A.; Ahsan, A. Optimisation of rainwater tank design from large roofs: A case study in Melbourne, Australia. Resour. Conserv. Recycl.
**2011**, 55, 1022–1029. [Google Scholar] [CrossRef] - City of Johannesburg. Climate Change Adaptation Plan; City of Johannesburg: Johannesburg, South Africa, 2009. [Google Scholar]

**Figure 5.**Illustration of the hydrologic optimality of storage, yield and reliability using the Capricon Square RWH system.

**Figure 8.**Relationships between supply level and supply-to-demand ratio model parameters with reliability.

**Figure 16.**Generalized model and simulation yield–reliability relationships for hydrologically optimal systems.

**Figure 17.**Generalized model and simulation yield–reliability relationships for hydrologically non-optimal systems.

**Figure 18.**Maponya mall RWH system yield–reliability relationships for current and the climatic “near future” condition.

Region | Mall | Retail Area (m^{2}) | Roof Area (m^{2}) | Rainfall Station No. | Distance from Mall (km) |
---|---|---|---|---|---|

Gauteng | Sandton City | 128,000 | 83,472 | 0476093 W | 6.67 |

South Gate Mall | 89,700 | 45,349 | 0476044 W | 6.24 | |

Norwood Mall | 32,344 | 32,194 | 0476129 W | 0.43 | |

Braamfontein Centre | 21,309 | 3416 | 0475881 W | 0.97 | |

Grayston Centre | 5000 | 4198 | 0476093 W | 5.68 | |

Cape Town | Canal Walk | 141,000 | 26,082 | 0020896 W | 4.38 |

Tygervally Centre | 90,000 | 55,403 | 0021230 W | 4.74 | |

Willow Bridge | 40,051 | 23,390 | 0021230 W | 3.95 | |

Howard Centre | 15,000 | 14,052 | 0021055 w | 2.88 | |

Capricon Square | 5889 | 6374 | 0020839 W | 13.11 | |

Limpopo | Mall of the North | 75,000 | 35,199 | 0678023 W | 1.26 |

Savanah Mall | 37,000 | 17,880 | 0677834 W | 2.15 | |

Limpopo Mall | 27,766 | 7446 | 0677834 W | 1.81 | |

Cycad Shopping Centre | 12,000 | 5267 | 0677834 W | 0.94 | |

Kwa Zulu Natal | Gateway Mall | 180,000 | 123,498/73,313 * | 0241103 W | 0.97 |

Liberty Midlands Mall | 75,000 | 74,702/55,241 * | 0239605 P | 3.21 | |

Musgrave Centre | 39,886 | 20,058 | 0240738 W | 8.36 | |

Phoenix Plaza | 24,162 | 29,307/18,070 * | 0241042 W | 2.72 | |

Granada Square | 5818 | 2097 | 0241103 W | 2.15 | |

NW | Matlosana | 65,000 | 50,100/40,000 ^{!} | 0436495 W | 8.03 |

FS | Mimosa | 25,000 | 5297 | 0261368 W | 2.30 |

MP | Riverside | 49,529 | 45,000 | 0556088 W | 9.02 |

EC | Baywest | 90,000 | 45,351 | 0035209 W | 14.85 |

^{!}Reduced area to diversify supply to demand ratios in verification.

Rainfall Station No. | MAP (mm/Year) | Length of Data (Years) | Percentage of Observed of Data | Percentage of In-Filled Data | Percentage of Missing Data |
---|---|---|---|---|---|

0476093 W | 552 | 107 | 55.2 | 40.9 | 3.9 |

0476044 W | 727 | 107 | 75.7 | 20.4 | 3.9 |

0476129 W | 752 | 107 | 73.7 | 22.4 | 3.9 |

0475881 W | 788 | 107 | 84.4 | 11.7 | 3.9 |

0020896 W | 563 | 149 | 44.3 | 54.7 | 1.0 |

0021230 W | 586 | 150 | 47.8 | 52.1 | 0.1 |

0021055 W | 483 | 149 | 60.6 | 38.4 | 1.0 |

0020839 W | 1183 | 149 | 44.4 | 54.6 | 1.0 |

0678023 W | 464 | 96 | 83.1 | 16.9 | 0.0 |

0677834 W | 485 | 96 | 92.9 | 7.1 | 0.0 |

0241103 W | 1144 | 125 | 59.6 | 37.5 | 2.9 |

0239605 P | 925 | 107 | 67.9 | 31.2 | 0.9 |

0240738 W | 876 | 127 | 45.7 | 52.4 | 1.9 |

0241042 W | 1072 | 125 | 49.1 | 48.0 | 2.9 |

0436495 W | 588 | 82 | 97.9 | 2.1 | 0.0 |

0556088 W | 718 | 98 | 69.4 | 30.6 | 0.0 |

0261368 W | 550 | 97 | 91.9 | 8.1 | 0.0 |

0035209 W | 590 | 124 | 54.0 | 45.8 | 0.2 |

Shopping Centre | Reliability (%) | 85 | 90 | 95 | 98 | 99 |
---|---|---|---|---|---|---|

South Gate | Yield * | 142 | 132 | 122 | 101 | 72 |

Storage ** | 4750 | 4250 | 2750 | 1500 | 1500 | |

Braamfontein | Yield | 12 | 11 | 8 | 7 | 6 |

Storage | 130 | 160 | 90 | 130 | 90 | |

Grayston | Yield | 70 | 61 | 55 | 45 | 40 |

Storage | 240 | 165 | 240 | 150 | 105 | |

Norwood | Yield | 116 | 107 | 95 | 84 | 76 |

Storage | 1800 | 3200 | 2000 | 1600 | 1600 | |

Sandton | Yield | 120 | 112 | 104 | 69 | 68 |

Storage | 6800 | 6800 | 5600 | 2000 | 1600 | |

Capricon | Yield | 242 | 228 | 195 | 172 | 161 |

Storage | 3000 | 3750 | 1250 | 1000 | 750 | |

Howard | Yield | 91 | 84 | 77 | 69 | 63 |

Storage | 800 | 550 | 500 | 350 | 300 | |

Willow Bridge | Yield | 35 | 32 | 28 | 26 | 25 |

Storage | 1000 | 600 | 450 | 450 | 450 | |

Tyger Valley | Yield | 139 | 125 | 117 | 108 | 101 |

Storage | 5100 | 3300 | 3000 | 2400 | 2400 | |

Canal Walk | Yield | 21 | 19 | 16 | 12 | 8 |

Storage | 750 | 1125 | 750 | 450 | 300 | |

Mall of North | Yield | 65 | 58 | 54 | 44 | 30 |

Storage | 4000 | 3250 | 3000 | 2000 | 1250 | |

Savanah | Yield | 23 | 22 | 18 | 13 | 11 |

Storage | 525 | 825 | 825 | 525 | 525 | |

Limpopo | Yield | 16 | 15 | 11 | 10 | 8 |

Storage | 240 | 300 | 240 | 390 | 270 | |

Cycad | Yield | 23 | 22 | 18 | 13 | 12 |

Storage | 160 | 240 | 240 | 160 | 180 | |

Gateway-Reduced area | Yield | 140 | 131 | 108 | 101 | 85 |

Storage | 32,000 | 24,000 | 10,000 | 8000 | 6000 | |

Liberty-reduced area | Yield | 243 | 196 | 162 | 142 | 138 |

Storage | 15,300 | 9000 | 4500 | 2700 | 1800 | |

Musgrave | Yield | 41 | 37 | 30 | 27 | 18 |

Storage | 1100 | 1000 | 600 | 600 | 400 | |

Phoenix-reduced area | Yield | 153 | 143 | 121 | 104 | 90 |

Storage | 7500 | 8500 | 2500 | 1500 | 1500 | |

Granada | Yield | 46 | 39 | 33 | 27 | 26 |

Storage | 255 | 135 | 120 | 90 | 105 |

^{3}).

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Ndiritu, J.; Moodley, Y.; Guliwe, M.
Generalized Storage–Yield–Reliability Relationships for Analysing Shopping Centre Rainwater Harvesting Systems. *Water* **2017**, *9*, 771.
https://doi.org/10.3390/w9100771

**AMA Style**

Ndiritu J, Moodley Y, Guliwe M.
Generalized Storage–Yield–Reliability Relationships for Analysing Shopping Centre Rainwater Harvesting Systems. *Water*. 2017; 9(10):771.
https://doi.org/10.3390/w9100771

**Chicago/Turabian Style**

Ndiritu, John, Yashiren Moodley, and Mondli Guliwe.
2017. "Generalized Storage–Yield–Reliability Relationships for Analysing Shopping Centre Rainwater Harvesting Systems" *Water* 9, no. 10: 771.
https://doi.org/10.3390/w9100771