Rainfall Estimation For Water Tank System Selection In Five Different Areas

Selection of Rainfall Stations

A total of five different rainfall stations including Sydney (Observatory Hill), Seven Hills, Richmond-UWS Hawkesbury, Lucas Heights (ANSTO) and Badgerys Creek AWS have been featured based on the future scenarios. The objective is to provide the most effective and reliable water saving technique and tank system so that the optimum quantity of water can be saved and it can be used at the time of need.

The other aspects including the demand for water and roof area combination have also been taken into consideration while selecting the appropriate and feasible tank system (Amos & Rahman, 2016). The selection of the final tank model has taken into account all these estimations so that the most appropriate model that is reliable and suits the external environment can be introduced in order to save the rainwater. The data files that have been referred would basically help to identify the best combination of the tank system with respect to tank size, reliability and overall water saving capacity (Amos, Rahman & Gathenya, 2017).

All the locations of the rainfall station have been selected based on the projected amount of precipitation that is expected in the regions. Since the amount of rainfall that is expected to be received in the place would be one of the most influencing factors of the tank selection process, the same element has been taken into account while carrying out the study.

The Sydney (Observatory Hill) rainfall station is one of the areas of the research study. As per the available data, for the period 2012, the area experiences an average of 101 mm of rainfall. This figure indicates that the requirements of the tank must be based on the rainwater that can be actually conserved in order to be used in the future. The reliability, water saving capacity and tank size has been taken into consideration so that the most suitable tank model can be selected for the water storage purpose (Aye, et al., 2014).

As per the estimation for the period 2020-39, the area would experience a total of 753.05 mm rainfall during the period and it would have an average of 62.75 mm of rainfall. As per the estimation for the period 2040-79, the area would experience rainfall of 767.52 mm during the year with an average of 63.96 mm of rainfall (Campisano, et al., 2017).

Seven Hills rainfall station is the second area of the research study. The data that has been captured shows the rainfall details for the year 1973. As per the available data, for the period 1973, the area experiences an average of 75.99 mm of rainfall. As per the estimation for the period 2020-39, the area would experience a total of 726.05 mm rainfall during the period and it would have an average of 60.05 mm of rainfall. Similarly, for the period 2040-79, it has been estimated that the area would experience rainfall of 768.19 mm during the year with an average of 64 mm of rainfall (Castonguay, et al., 2016).

Projected Rainfall Data

The Richmond is the third area of the research study. In 1933, the area experiences an average of 66.68 mm of rainfall. As per the estimation for the period 2020-39 the area would experience a total of 1186.35 mm rainfall during the period and it would have an average of 98.86 mm of rainfall. Similarly, for the period 2040-79, it has been estimated that the area would experience rainfall of 768.19 mm during the year with an average of 64 mm of rainfall.

Lucas Heights is the fourth area of the research study. The data for the year 1986 indicate that it had experienced 85.65 mm of rainfall on an average in the year. As per the estimation for the period 2020-39, the area would experience a total of 753.05 mm rainfall during the period and it would have an average of 62.75 mm of rainfall. Similarly, for the period 2040-79, it has been estimated that the area would experience rainfall of 768.19 mm during the year with an average of 64 mm of rainfall (Christian Amos, Rahman & Mwangi Gathenya, 2016).

Badgerys Creek is the final area of the research study. The data of rainfall has been captured for the year 2003. It indicates that the average amount of rainfall that the area has experienced is 48.73 mm. According to the estimations, for the period 2020-39 the area would experience a total of 684.92 mm rainfall during the period and it would have an average of 57.07 mm of rainfall. For the period 2040-79, it has been estimated that the area would experience rainfall of 695.54 mm during the year with an average of 57.96 mm of rainfall (Coombes, 2015).

Primary Objective

The main objective of conducting the study is to identify the most suitable tank model based on the roof area, water storage capacity and tank size. The estimated amount of rainfall that is supposed to happen for the period 2020-39 and 2040-79 have been made so that the tank can be introduced to save maximum environmental water that can be used in the future

A total of 5 different areas or stations have been considered in the research study for the purpose of introduction of the tank system. The monthly rainfall data has been gathered from the Bureau of meteorology and the estimated future data for the periods 2020-39 and 2040-79 have been collected from World Bank so that the originality of the study can be maintained throughout the process. The CMIP3 model has been used to collect the futuristic data since the model basically shows a higher tendency towards the decline in the rainfall that occurs in the eastern and central Australia as compared to the CMIP5 model (Elgert, Austin & Picchione, 2016).

Tank Model Design based on Rainfall Estimation

The rainfall projections that have been included in the study form a vital segment of the entire process since it helps to understand the rainfall projection that can influence the effectiveness of the selected tank model. The table below represents the five stations that have been selected for the particular task. The latitude and longitude of the areas have been presented so that the amount of rainfall that has been predicted can be understood based on their geographic location.

Rainfall stations	Latitude	Longitude	Site Number	Direction
Sydney (Observatory Hill)	33.85° S	151.20° E	066062	Central
Seven Hills	33.77° S	150.94° E	067026	North-West
Richmond-UWS Hawkesbury	33.61° S	150.75° E	067021	North-West
Lucas Heights (ANSTO)	34.05° S	150.98° E	066078	South-West
Badgerys Creek AWS	33.90° S	150.73° E	067108	West

The following table presents the data relating to the amount of precipitation that is likely to be experienced in the five rainfall stations in the periods 2020-39 and 2040-79. The projected data that has been collected from World Bank indicates that during both the periods the Sydney (Observatory Hill) is likely to experience 753 mm and 768 mm of rainfall. The Seven Hills area would be experiencing the similar rainfall trend during the period. It would have 726 mm and 768 mm of rainfall during the specified periods. The same trend is not followed in case of Richmond-UWS Hawkesbury rainfall station since in the period 2020-39 the area will receive 1186 mm of rainfall whereas during 2040-79 the area would receive 768 mm of rainfall. The Lucas Heights (ANSTO) region would be receiving 753 mm of rainfall and 768 mm of rainfall during 2020-39 and 2040-79 respectively (Floyd, et al., 2014).

It has been estimated that the final rainfall station that has been considered for the study – Badgerys Creek AWS would receive the least amount of rainfall. During 2020-39 it would receive 685 mm of rainfall and during the period 2040-79 it would receive 696 mm of rainfall. The tabular representation shows the rainfall trend in the five selected rainfall stations (Gao, Kim & Lee, 2014).

Rainfall stations	Future years	Jan	Feb	Mar	Apr	May	Jun	Jul	Aug	Sep	Oct	Nov	Dec	Total rainfall in each station p.a. (mm)
Sydney (Observatory Hill)	2020-39	58	59	85	71	64	63	57	53	44	60	71	68	753
	2040-79	72	79	89	55	51	68	62	62	52	51	55	72	768
Seven Hills	2020-39	58	59	58	71	64	63	57	53	44	60	71	68	726
	2040-79	72	79	89	55	51	68	62	62	52	51	55	73	768
Richmond-UWS Hawkesbury	2020-39	58	492	85	71	64	63	57	53	44	60	71	68	1186
	2040-79	72	79	89	55	51	68	62	62	52	51	55	73	768
Lucas Heights (ANSTO)	2020-39	58	59	85	71	64	63	57	53	44	60	71	68	753
	2040-79	72	79	89	55	51	68	62	62	52	51	55	73	768
Badgerys Creek AWS	2020-39	0	58	59	85	71	64	63	57	53	44	60	71	685
	2040-79	0	72	79	89	55	51	68	62	62	52	51	55	696

 Based on the available rainfall estimations in the five rainfall stations, a number of tank models have been designed so that the best and most suitable model can be introduced in order to collect the maximum quantity of the rainfall water in the respective locations (Hajani & Rahman, 2013).

The rainfall data were obtained from Australian Bureau of Meteorology was used as input in the Microsoft Excel spreadsheet model. The precipitation for all the months of the respective years has been taken into consideration to arrive at the annual rainfall figure (Hajani & Rahman, 2014). The projected rainfall figures have been collected from a valid source (World Bank) so that the most accurate and authentic data can be used for the purpose of introducing the tank system. Based on this collected data the above table and its figures have been ascertained. The other areas of the data management process have been highlighted in the below section which helps to ascertain the most suitable Tan model for the respective five locations.

CMIP3 vs CMIP5 Global Climate Models

As stated in the above section, the CMIP3 climate model has been adopted in order to estimate the projected rainfall in the five stations for a number of reasons. The Coupled Model Intercomparison Project is introduced by World Climate Research Programme (WCRP) every five or seven years to design the global climate projections. It is necessary to understand the difference between the CMIP3 and CMIP5 global climate models so that the most appropriate model can be used for the purpose of estimating climatic features (HU, TAKARA & ZHANG, 2013).

CMIP3 1. Coupled Model Intercomparison Project (CMIP3) refers to a set of climate model experiment that is the largest and most inclusive global coupled climate model. 2. The CMIP3 model was introduced prior to CMIP5 but its projection model makes it a highly preferred model as compared to its counterpart. 3. The existing CMIP3 climate projections are highly popular since these models take into account the various factors that have an impact on the environment and climate.

CMIP5 1. Coupled Model Intercomparison Project (CMIP5) model basically acts as the new opportunity that enhances the understanding of the climate science. 2. The CMIP5 is a newer version of CMIP3 but this element does not make it a better and more reliable model that can be used to project various aspects of climate. 3. The CMIP5 global climate model can be surely used as an additional model that can support and strengthen the projection outcome of CMIP3 but it cannot be used as a replacement of the currently existing CMIP3 projection system.

Due to the reliability and accuracy of the CMIP3 model, the same has been used to predict the rainfall for the periods 2020-39 and 2040-79.

A number of tank models have been designed that have taken into account various internal and external aspects including water saving capacity, roof size, demand, reliability, projected rainfall. The key features of the systems have been highlighted so that ultimately the most practical and simple yet effective tank model can be introduced for the purpose of water storage in the rainfall stations (Imteaz, Moniruzzaman & Karim, 2017).

This research study has basically considered the tanks that range between 2.5 KL to 10 KL. The four different tank sizes that have been selected include 2.5 KL, 5 KL, 7.5 KL and 10 KL sizes which are the most common sizes of tanks that are used in Australia. The demand that has been taken into account comes between 100 KL and 500 KL (Kandasamy, Kus & Vigneswaran, 2016). The various influencing factors such as the evaporation of water, overflow or loss of water and leakage have been taken into account while determining the demand of the rainwater. Thus the average demand of rainwater comes to 100 KL in each rainfall station. These factors have been included in the data collection process since they have a crucial bearing on the tank selection process (Kim, et al., 2016). The apt roof area of the tanks is between 100 m² to 500 m² so that the optimum area of the tank could be utilized for the purpose of storage of clean rainwater. In order to ascertain the best possible tank selection output, the available data including the internal features of the tank system have been considered for the five rainfall stations so that the annual water saving model can be strengthened and a reliable model can be introduced based on the requirements of the environmental water (Ladson, 2014).

The annual water saving model of Sydney (Observatory Hill) rainfall station for the different tank sizes have been presented in the tabular form below:

	Demand		RA=200
Tank size		2500	5000	7500	10000
2020-39	200	64	70	71	71
	400	89	106	115	120
2040-79	200	64	69	71	71
	400	89	107	107	120
Current	200	62	67	71	71
	400	55.1	63.3	105	109

Seven Hills rainfall station

The annual water saving model of Seven Hills rainfall station the different tank sizes have been presented in the tabular form below:

	Demand		RA=200
Tank size		2500	5000	7500	10000
2020-39	200	64	69	70	70
	400	90	108	114	117
2040-79	200	64	69	71	71
	400	92	110	119	121
Current	200	63	69	71	71
	400	84	99	106	108

Richmond-UWS Hawkesbury rainfall station

The annual water saving model of Richmond-UWS Hawkesbury rainfall station the different tank sizes have been presented in the tabular form below:

	Demand		RA=200
Tank size		2500	5000	7500	10000
2020-39	200	60	68	71	71
	400	76	101	112	117
2040-79	200	62	69	71	71
	400	79	104	115	119
Current	200	51	60	67	70
	400	67	83	93	98

The annual water saving model of Lucas Heights (ANSTO) rainfall station the different tank sizes have been presented in the tabular form below:

	Demand		RA=200
Tank size		2500	5000	7500	10000
2020-39	200	54	66	71	71
	400	77	96	107	115
2040-79	200	54	66	70	71
	400	77	98	112	120
Current	200	57	65	68	70
	400	70	83	92	99

The annual water saving model of Badgerys Creek AWS rainfall station rainfall station the different tank sizes have been presented in the tabular form below:

	Demand		RA=200
Tank size		2500	5000	7500	10000
2020-39	200	57	68	72	72
	400	73	96	112	118
2040-79	200	57	69	71	72
	400	72	95	110	120
Current	200	52	62	65	67
	400	66	82	88	93

The annual water savings model corresponding to the respective roof areas and water demand for all the five rainfall stations has been obtained as the particular output of the model. The water saving capacity of all the tank sizes has been reflected for the stations so that the most feasible tank size can be selected based on the projected rainfall that will be received in the specific locations(Matos, et al., 2015).

The term “reliability” can be defined as the percentage of the days on which the rainwater tank system is adequate to meet the specific water demand in the particular climatic setting. It is an effective element that helps to determine the optimum size of rainwater tank in the region where the rainwater tank could act as a major supplier of water.

Most feasible tank models for rainfall stations

After taking into account the various aspects such as the tank models have been determined. The most suitable combination including the particular tank size, roof area, and water demand form a vital part of the study to arrive at the most effective and suitable tank model (Moglia, et al., 2016).

Sydney (observatory Hill) rainfall station Tank model

The tank design that has been selected for Sydney (observatory Hill) rainfall station would be the 5KL model that would be having the roof area of 200 m². This particular area has been selected so that it could save the most feasible quantity of rainwater that could be effectively used for various purposes. One of the main reasons for the selection of the model is the high reliability as compared to the other tank designs (Moniruzzaman & Imteaz, 2017). It scores the maximum in terms of reliability aspect, thus during the rainy period, the maximum quantity of the water could be saved in the station. Even though the average demand is of rainwater is 100 KL, the model has been planned so that between 200 KL and 400 KL of rainfall can be saved for productive purposes.

For the Seven Hills rainfall station, the 5KL model with the roof area of 200 m² would be the most suitable model to save rainwater. Even though the higher capacity tanks could store a higher quantity of water, based on the projected rainfall in the rainfall station, this tank design would be the most optimum model (Oke & Oyebola, 2014). The reliability of the tank is also towards a favorable side which indicates that the collection of water by this tank system would be effective for the area. The graph represents the water saving capacity of the proposed tank.

In the Richmond-UWS Hawkesbury rainfall station, as per the rainfall estimation for the period 2020-39, the station would receive about 1186 mm of rainfall. Based on this external influence and the reliability factor, the 7.5 KL tank size with the roof area of 200 m² would be the most effective tank system (Panahian, Ghosh & Ding, 2017). The graph that has been presented above highlights that large quantity of rainwater could be saved by introducing this tank system in the particular station. This model would suit the rainfall pattern as well as the growing demands of water.

In the Lucas Heights (ANSTO) rainfall station the most effective and suitable tank model that could be introduced is the 5KL model with the roof area of 200 m². The highlighting feature of the specific model is the reliability factor (Partridge & Gan, 2017). Based on the rainfall projections, this tank system would help to save the maximum quantity of rainwater that would be received in the station. The 2.5 KL tank model would be the most ineffective model due to its poor reliability.

In the Badgerys Creek AWS rainfall station, the most feasible tank model that could be designed and implemented is the 5K tank size with the roof area of 200 m². The model’s strong reliability automatically strengthens its efficiency to collect an optimum quantity of rainwater (Rahman, Keane & Haddad, 2013). The graph that has been presented above shows that as the size of the tank increases, the quantity of water that is saved also gets strengthened. The reliability factor is an important determinant that has been taken into consideration in the tank design model along with the rainfall predictions for the future periods (Zhang & Hu, 2014).

The various graphs that have been represented in this section highlight the most feasible tank models that should be introduced in the different rainfall stations so that the maximum amount of rainwater could be stored that could be effectively used. The most effective and feasible combinations have been considered while selecting the model so that the implementation process can be a success (Rashid, Bhuiyan & Jayasuriya, 2016).

The key aspects that have been considered in the research process include the annual water saving capacity and the overall reliability of the rainwater storage tank. The ultimate purpose of the introduction of the new tank model in the particular stations is to enhance the water saving objective so that the same can be used at different times. Since it is the primary reason for the tank, the same has been given most significance in the study. The proper storage of environmental water is not a luxury but a mere necessity in the current unpredictable environmental setting (Sharmeen, Rahman & Kuruppu, 2013). The tank designs that have been proposed here focus on the area so that the optimum quantity of natural water can be stored and this energy can be utilized at the future time by the users.

The other component that supports the water capacity model of the tank is the reliability aspect of the tanks. As defined in the previous section, the reliability and durability of the new tank system would be an influencing element that would enhance the overall design of the tank model (Singh, Maheshwari & Malano, 2014).

While selecting the most practical and effective tank designs the reliability factor has been given vital importance throughout the process. This is a crucial element since the lack of reliability could adversely affect the primary objective and even pose threats for the people that reside close to the tank location. Any loopholes or fault in the designing of the tank system could lead to severe accidents.

Thus various elements have been included in the study while determining the new tank model that is to be introduced. Along with the demand, the roof area, rainfall prediction, water saving capacity, tank size and reliability aspect has been taken into consideration so that the new design can strengthen the water saving model in the respective rainfall stations in Australia (Snook, et al., 2015). The factors have been thoroughly integrated with the objective of the study so that the most suitable rainwater saving tank models can be introduced and the environment can be benefitted in the process (Van der Sterren, Rahman & Dennis, 2013).

The process that has been followed takes into account the major factors that influence the water storing capacity of the tanking model. But still, a few gaps exist in this process. The same has been highlighted in the section so that in future these elements must be considered while conducting a similar research study.

While generating the projected data of rainfall for the periods 2020-39 and 2040-79 only the Coupled Model Intercomparison Project (CMIP3) model was used to collect the data. In order to strengthen the projection the CMIP5 model could also have been adopted and a comparison of both the data set could have been carried out. This would have acted as a control measure that would help to have a thorough control over available projected rainfall data (Sountharajah, et al., 2017).

While using the actual rainfall data, the details were only collected for a single year for all the rainfall stations. The actual rainfall data could have been gathered for at least five years so that the projections could have been well supported and could be cross-verified with the actual rainfall trend (Tjandraatmadja, et al., 2015).

A number of factors such as construction model of the tank, the particular location of tank development, financial aspects and labor have not been considered in the process. These aspects also have a crucial impact on the effectiveness and success of the new rainwater storage tank.

Conclusion 

The study that has been conducted basically covers a large number of external and internal factors that need to be taken into account while introducing the best tank system in different rainfall stations that are situated in Australia. The projected rainfall estimations have been taken into account so that the influence of the climate can be considered while deciding the most suitable tank model. Similarly, the most significant internal factors have been taken into account in the process such as the water saving capacity, demand of water, tank size, roof area and reliability aspects. The most effective and suitable tank models for the five stations have been highlighted. The key factors play a significant role in the tank design model.

References

Amos, C.C. and Rahman, A., 2016. Rainwater harvesting potential in Sydney: Are we harvesting enough?. In 37th Hydrology & Water Resources Symposium 2016: Water, Infrastructure and the Environment (p. 422). Engineers Australia.

Amos, C.C., Rahman, A. and Gathenya, J.M., 2017. Economic Analysis of Rainwater Harvesting Systems Comparing Developing and Developed Countries: a Case Study of Australia and Kenya. Journal of Cleaner Production.

Aye, L., Nawarathna, B., George, B., Nair, S. and Malano, H., 2014. Greenhouse Gas Emissions of Decentralised Water Supply Strategies in Peri-urban Areas of Sydney. In The Security of Water, Food, Energy and Liveability of Cities (pp. 355-363). Springer Netherlands.

Campisano, A., Butler, D., Ward, S., Burns, M.J., Friedler, E., DeBusk, K., Fisher-Jeffes, L.N., Ghisi, E., Rahman, A., Furumai, H. and Han, M., 2017. Corrigendum to “Urban rainwater harvesting systems: Research, implementation and future perspectives”[Water Res. 115 (2017) 195–209]. Water Research, 121, p.386.

Castonguay, A.C., Urich, C., Iftekhar, M.S. and Deletic, A., 2016. Modelling urban transition: a case of rainwater harvesting.

Christian Amos, C., Rahman, A. and Mwangi Gathenya, J., 2016. Economic analysis and feasibility of rainwater harvesting systems in urban and peri-urban environments: A review of the global situation with a special focus on Australia and Kenya. Water, 8(4), p.149.

Coombes, P.J., 2015. Discussion on “Influence of roofing materials and lead flashing on rainwater tank contamination by metals” by MI Magyar, AR Ladson, C Daiper and VG Mitchell. Australian Journal of Water Resources, 19(1), pp.86-90.

Elgert, L., Austin, P. and Picchione, K., 2016. Improving water security through rainwater harvesting: a case from Guatemala and the potential for expanding coverage. International Journal of Water Resources Development, 32(5), pp.765-780.

Floyd, J., Iaquinto, B.L., Ison, R. and Collins, K., 2014. Managing complexity in Australian urban water governance: transitioning Sydney to a water sensitive city. Futures, 61, pp.1-12.

Gao, X., Kim, Y. and Lee, H.W., 2014. Life-cycle Cost Analysis of Using Rainwater Harvesting Systems in Hong Kong Residential Buildings. Journal of the Korean housing association, 25(3), pp.53-62.

Hajani, E. and Rahman, A., 2013. Effectiveness of rainwater harvesting system in peri-urban region: a case study for the greater Sydney. J Hydrol Environ Res, 1(1), pp.21-29.

Hajani, E. and Rahman, A., 2014. Reliability and cost analysis of a rainwater harvesting system in peri-urban regions of Greater Sydney, Australia. Water, 6(4), pp.945-960.

HU, M., TAKARA, K. and ZHANG, X., 2013. Analysis of Optimum Rainwater Tank Size in a Multi-building.

Imteaz, M.A., Moniruzzaman, M. and Karim, M.R., 2017. Rainwater tank analysis tools, climatic and spatial variability: a case study for Sydney. International Journal of Water, 11(3), pp.251-265.

Kandasamy, J., Kus, B. and Vigneswaran, S., 2016. Rainwater harvesting in New South Wales, Australia. Green Technologies for Sustainable Water Management.

Kim, H.W., Li, M.H., Kim, H. and Lee, H.K., 2016. Cost-benefit analysis and equitable cost allocation for a residential rainwater harvesting system in the city of Austin, Texas. International Journal of Water Resources Development, 32(5), pp.749-764.

Ladson, A.R., 2014. Reply to: Discussion on" Influence of roofing materials and lead flashing on rainwater tank contamination by metals" by Magyar MI, Ladson A. R., Diaper C., and Mitchell, VG 2014. Australian Journal of Water Resources Vol. 18, No. 1, pp 71-?83. Australian Journal of Water Resources, 18(1), pp.71-83.

Matos, C., Bentes, I., Santos, C., Imteaz, M. and Pereira, S., 2015. Economic analysis of a rainwater harvesting system in a commercial building. Water resources management, 29(11), pp.3971-3986.

Moglia, M., Gan, K., Delbridge, N., Sharma, A.K. and Tjandraatmadja, G., 2016. Investigation of pump and pump switch failures in rainwater harvesting systems. Journal of Hydrology, 538, pp.208-215.

Moniruzzaman, M. and Imteaz, M.A., 2017. Generalized equations, climatic and spatial variabilities of potential rainwater savings: A case study for Sydney. Resources, Conservation and Recycling, 125, pp.139-156.

Oke, M. and Oyebola, O., 2014. Assessment of rainwater harvesting potential and challenges in ijebu ode, southwestern part of nigeria for strategic advice. Analele Stiintifice ale Universitatii" Al. I. Cuza" din Iasi. Serie Noua. Geografie, 60(2).

Panahian, M., Ghosh, S. and Ding, G., 2017. Assessing potential for reduction in carbon emissions in a multi-unit of residential development in Sydney. Procedia Engineering, 180, pp.591-600.

Partridge, L. and Gan, E.S., 2017. The Latest Demonstrated Technical Innovations in Sydney CBD Tall Building: Case Study. Procedia Engineering, 180, pp.49-60.

Rahman, A., Keane, J. and Haddad, K., 2013. Rainwater harvesting as a means of water conservation: an Australian experience. Water Conservation: Practices, Challenges and Future Implications, pp.13-25.

Rashid, A.R.M., Bhuiyan, M.A. and Jayasuriya, N., 2016. Rainwater harvesting impacts on environment. In 37th Hydrology & Water Resources Symposium 2016: Water, Infrastructure and the Environment (p. 454). Engineers Australia.

Sharmeen, L., Rahman, A. and Kuruppu, U., 2013. Regionalisation of water savings from rainwater harvesting system in Greater Sydney. In Adapting to Change: the Multiple Roles of Modelling: Proceedings of the 20th International Congress on Modelling and Simulation (MODSIM2013), 1-6 December 2013, Adelaide, South Australia (pp. 2688-2694).

Singh, R., Maheshwari, B. and Malano, H., 2014. Securing water supply in Western Sydney: an analysis of water use, demand and availability in the South Creek Catchment. In The Security of Water, Food, Energy and Liveability of Cities (pp. 121-136). Springer Netherlands.

Snook, C., Rahman, A., van der Sterren, M., Haque, M.M. and Hajani, E., 2015. Developing design curves for rainwater harvesting in Greater Sydney. 9th International Water Sensitive Urban Design (WSUD 2015), p.495.

Sountharajah, D.P., Kus, B., Kandasamy, J. and Vigneswaran, S., 2017. Quantifying the Reduction in Water Demand due to Rainwater Tank Installations at Residential Properties in Sydney. Journal of Sustainable Development of Energy, Water and Environment Systems, 5(2), pp.202-218.

Tjandraatmadja, G., Retamal, M., Umapathi, S. and Hauber-Davidson, G., 2015. Understanding energy usage in rainwater tank systems through laboratory and household monitoring. Rainwater Tank Systems for Urban Water Supply, p.127.

Van der Sterren, M., Rahman, A. and Dennis, G.R., 2013. Quality and quantity monitoring of five rainwater tanks in Western Sydney, Australia. Journal of Environmental Engineering, 139(3), pp.332-340.

Zhang, X. and Hu, M., 2014. Effectiveness of rainwater harvesting in runoff volume reduction in a planned industrial park, China. Water resources management, 28(3), pp.671-682.