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Posted: July 18th, 2022
Reliability-based Design of Rainwater Harvesting Systems
An Investigation into the Reliability-based Design of Rainwater Harvesting Systems in the UK under Current and Future Climatic Conditions
Abstract
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This dissertation investigates the relationship between key rainwater harvesting (RWH) variables and the resulting system reliability these provide, before exploring how this relation might be affected by climate change. As such, graphs specific to various UK regions are produced, linking the local precipitation (rainfall) regime, domestic water demand, catchment area, storage capacity and system reliability for both the present-day and 2080 forecasted climate. For the purposes of demonstration, the study focuses on two UK regions: North Scotland (NS), and South West England and Wales (SWEaW).
The use of rainwater harvesting systems (RWHS) in the UK was chosen as a field of study since although numerous studies have proved their effectiveness in various geographical contexts, the UK has failed to embrace this practice on a wide scale. More importantly, current UK guidelines do not consider reliability as a design parameter and ignore the potential impact of climate change on system performance. Consequently, when sizing their storage tanks as per UK guidelines, residents wishing to install RWHS do not know what reliability the recommended storage size will provide today or in the future.
To produce the reliability graphs artificial monthly rainfall series produced by a stochastic (random but statistically describable by a probability distribution) rainfall generator were used to simulate RWHS behaviour and thereby calculate reliability. This process was repeated for many possible parameter combinations and results were plotted. To model future reliability, the artificial rainfall series were modified to reflect predicted changes in rainfall as a result of climate change. These modified precipitation series were then once again used in simulations to obtain revised reliability values. Thereafter, differences in various reliability definitions and rainfall series types were compared and analysed to determine which produced the most accurate and meaningful results. Finally, a case study was presented to demonstrate how the graphs produced could be used for reliability assessment or reliability based design.
Results showed that the use of artificially generated monthly rainfall series accurately simulated reliability on a monthly basis, but that the use of daily simulation intervals could further improve simulation accuracy. The wide range in predicted changes in rainfall meant that simulated future reliabilities were also marked by a high degree of uncertainty. However, based on central climate change estimates, reliability was expected to decrease in most cases. Nevertheless, the graphs proved that RWHS can be highly effective.
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These graphs could be used for two purposes: assessment or design. For the former, the graphs could be incorporated into the UK guidelines to allow designers to determine the reliability achieved by the recommended storage capacity. Their second purpose could be for designer to identify possible parameter combinations required to achieve a desired level of present-day or future reliability. In an attempt to try promote RWHS in the UK, it is hoped that these graphs will facilitate their design and emphasize their potential effectiveness.
Table of Contents
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3.2 Sizing of Rainwater Harvesting Storage Tanks
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3.4 Overview of Dissertation Structure
4.1 Significance of Domestic RWHS
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4.1.1 Growing Importance and Benefits
4.1.2 Previous Studies showing Impact
4.2 Studies on the Sizing of RWHS
4.2.1 Scope, Purpose and Output
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4.2.3 Assessment of Performance
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4.3 Stochastic rainfall generators
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4.5 Climate Change Impact on Precipitation
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5.1 Overview and Rationale of Approach
5.2 Defining the Hydrological Year
5.3 Fitting Probability Density Functions to Annual Rainfalls
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5.4 Generating Monthly Rainfall Series
5.5 Calculating Current Reliability
5.6 Incorporating Effects of Climate Change
6 Discussion and Analysis of Results
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6.1 Accuracy of Stochastic Rainfall Series
6.1.1 Accuracy of Annual Stochastic Rainfall Series
6.1.2 Accuracy of Monthly Stochastic Rainfall Series
6.2 Method of Fragments: Impact of Subset Size
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6.3.1 Comparison of Volumetric and Temporal Reliability
6.3.2 Comparison of Monthly and Daily Reliability
6.3.3 Comparison of Historic and Generated Reliability
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7.4 Corresponding Reliabilities and Discussion
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Figure 1: Dimensionless Design Curves (Fewkes, 1999)…………………………….
Figure 2: Storage Sizing Contour Map (Liaw & Tsai, 2004)…………………………..
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Figure 3: Simplistic Approach to sizing RWHS (Gould 1999)………………………….
Figure 4: UK weather regions (Legg, 2017)………………………………………
Figure 5: UK precipitation change predictions (Defra, 2009)…………………………..
Figure 6: Methodology flowchart……………………………………………..
Figure 7: Bar chart of correlation values between consecutive monthly rainfall values for NS……
Figure 9: Annual precipitation histogram and fitted Normal probability density function for NS…..
Figure 9: Mass balance algorithm flowchart………………………………………
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Figure 11: Percentage change in monthly precipitation for 2080 dry, median and wet projections in SWEaW
Figure 12: Mean monthly precipitation values for current and future stochastic rainfall series in SWEaW
Figure 13: Empirical and theoretical cumulative probability functions for annual rainfall in SWEaW.
Figure 14: Sample and theoretical quantiles of annual rainfall in SWEaW…………………
Figure 15: Boxplot of generated and historical monthly precipitation for SWEaW……………
Figure 16: Scatter plot of monthly rainfall for historical and generated rainfall series (using 5% of sets of fragments) in NS.
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Figure 17: Scatter plot of monthly rainfall for historical and generated rainfall series (using 100% of sets of fragments) in NS.
Figure 18: Violin plot of monthly rainfall for historical and generated rainfall series (using 5% of sets of fragments) in NS.
Figure 19: Violin plot of monthly rainfall for historical and generated rainfall series (using 100% of sets of fragments) in NS.
Figure 20: Volumetric and temporal reliabilities using generated monthly rainfall series for SWEaW
Figure 21: Daily and monthly reliabilities using historic rainfall series for SWEaW……………
Figure 22: Generated and historic monthly reliabilities for SWEaW……………………..
Figure 23: Enlargement of Figure 4.3.3-1……………………………………….
Figure 24: Current and future dry-scenario reliabilities for SWEaW……………………..
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Figure 25: Current and future wet-scenario reliabilities for SWEaW……………………..
Figure 26: Current and future median-scenario reliabilities for SWEaW…………………..
Figure 27: Current and future median-scenario reliabilities for NS………………………
Figure 26: Annual average rainfall depths across the UK (BS 8515)…………………….
Figure 29: Simplified approach storage sizing graph for 4-person household (BS 8515)……….
Figure 30: Enlargement of graph depicting current and future monthly reliabilities for SWEaW….
Figure 31: Enlargement of graph depicting current monthly reliabilities for SWEaW………….
Table 1: Percentage changes in winter and summer rainfall for dry, median and wet scenario in SWEaW and NS
Table 2: Distribution goodness-of-fit tests to model annual rainfall in SWEaW……………..
Table 3: Recommended storage capacities as per intermediate approach (BS 8515)…………
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3.1 RWHS and their History
Rainwater harvesting (RWH) is a process comprising the collection, distribution and use of rainwater (Basinger, et al., 2010). The collection can occur via any catchment area, such as a field on a large scale, or a roof on a domestic scale. Due to the non-uniform distribution of rainfall over time, tanks are usually installed so that rainwater may be stored and used when needed (Su, et al., 2009). The nature of the storage system also depends on its scale: dams are on the larger end of the spectrum, storing water for entire communities, whereas domestic storage tanks lie on the smaller end of the spectrum, storing water for only a few individuals or families. The use of the water varies across agricultural, industrial or residential sectors (Hanson & Vogel, 2014). On a domestic scale, rainwater harvesting systems (RWHS) may be used as a primary water source, but in developed countries they are mostly employed as a supplementary water supply for non-potable purposes such as toilet flushing, clothes washing or outdoor watering (Environment Agency, 2010).
An ancient practice, RWH has been employed for thousands of years by many civilisations, such as the ancient Greeks, Romans and Etruscans, for a variety of purposes (Boers & Asher, 1982). Its first application dates to 2000BC when it was used for agricultural irrigation in Jordan (Hanson & Vogel, 2014). During the Middle Ages, RWH was used in Venice as a means of providing domestic water supply (Basinger, et al., 2010). Hanson and Vogel (2014) argue that its popularity as a supplementary water source in urban and suburban areas has increased over the past decades, both in developing and developed countries. Unfortunately, when compared to other developed countries, the UK has failed to embrace and promote RWHS on a large scale (Berndtsson, 2004). Fewkes (2012) suggests this issue may become especially significant given the increasing stress on the UK’s centralised water supply infrastructures, especially in highly populated regions where water is already deemed to be scarce.
3.2 Sizing of Rainwater Harvesting Storage Tanks
The reliability of a RWHS is determined by the relationship between the inflow of water into the storage tank (dependent on the local precipitation regime and catchment area), the outflow of water from the storage tank (dependent on the demand), and the storage capacity, which limits the quantity of water that may be stored at any point in time (Basinger, et al., 2010). Thus, as Hanson and Vogel (2014) argue, “the most important and difficult design decision is how much storage capacity to build”, since this is usually the only parameter controlled by the designer, and often the most expensive part of the RWHS (Lee, et al., 2000). Furthermore, Imtaez et al. (2011) argue that the lack of information on optimal storage size, and on effectiveness of RWHS, are the key factors hindering the widespread use of RWHS.
Current UK guidelines for the design of RWHS are outlined in the 2009 British Standard 8515: Rainwater harvesting systems – Code of practice (BS). The BS proposes three methods for sizing tanks, of which only two are thoroughly explained. However, these two methods consider neither the seasonal variation of rainfall nor the reliability that certain storage sizes will provide. Thus, a house owner wishing to install a RWHS will not know with what reliability a given storage tank will meet his domestic non-potable water demand.
The BS also ignores the impact of climate change. Studies agree that the UK precipitation regime will develop greater variance: summers will become drier and winters will become wetter (Defra, 2009). Since the BSfails to address reliability in the first place the omission of this phenomenon is less evident. However, for a reliability-based storage sizing approach it seems intuitive that the change in one of the key parameters – the precipitation regime – should be considered.
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3.3 Objectives and Scope
The first aim will be to produce regional graphs, relating the local precipitation regime, catchment area, daily demand, storage capacity and reliability. Two UK regions with differing rainfall regimes – Northern Scotland (NS) and South West England and Wales (SWEaW) – will be selected for the purposes of demonstration. Precipitation data for SWEaW and NS start in 1873 and 1931 respectively. To choose representative UK parameter values the scope of the study will be defined as follows. First, the study will be limited to the use of RWH for non-potable domestic purposes. As such, only demand for toilet flushing and clothes washing will be considered. Second, it will be assumed that rainfall will be collected via the roof area. Lastly, storage sizes similar to those available on the UK market will be considered.
The second objective will be to explore how the reliability of RWHS might change as a result of climate change. This will involve using the 2009 UK Climate Change Projections (Defra, 2009)to assess how rainfall might evolve under dry, median and wet future scenarios.
Finally, a case study will be presented. Two UK cities will be chosen, RWH storage sizes will be determined using BS guidelines, and the reliability graphs produced will be used to determine the reliability of the RWHS under present-day and future climate conditions. Furthermore, it will be demonstrated how the graphs may be used to identify parameter combinations to achieve a desired level or reliability.
3.4 Overview of Dissertation Structure
The dissertation is structured in the following manner. A literature review will first be undertaken, which will discuss the significance of RWHS, storage sizing methods, artificial rainfall generation and the impact of climate change on UK rainfall. Thereafter the methodology, which was used to generate synthetic rainfall, simulate RWHS behaviour, create the reliability plots, and modify these to account for climate change, will be presented. These results will then be analysed and discussed. The case study, mentioned above, will then ensue. Finally, the conclusion, recommendations for further study and final remarks will be shared.
4.1 Significance of Domestic RWHS
4.1.1 Growing Importance and Benefits
The increased global water demand (Fewkes, 2012) and effects of climate change have put water supply infrastructures around the world under stress (Imtaez, et al., 2011). This is especially evident in the many cities that have experienced large urban growth throughout the 20th century (Imtaez, et al., 2011).
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To face the problem of increasing water scarcity, Postel (1992) argues that the most direct source of fresh water – rain – should not be ignored. RWHS can provide a sustainable alternative water supply (Basinger, et al., 2010), especially in cities, where the large impervious roof areas make them a viable option (Liaw & Tsai, 2004).
In developed regions, RWHS are primarily used as a secondary water supply for non-potable domestic uses (Su, et al., 2009). Nevertheless, they can help reduce stresses on urban water infrastructures (UWIs), by reducing urban potable water demand and storm water discharge. Thus, they help increase resilience of UWIs to drought, population growth, and floods (Basinger, et al., 2010). Furthermore, if engineered correctly, RWHS can have cost benefits and reduce energy consumption, making them an integral part of “green building” design (Guo & Baetz, 2007).
In less-developed or developing countries, RWHS can be even more important as they are often used as a major / primary water supply (Su, et al., 2009), due to their ability to improve household water access without government infrastructure support or economic aid. Furthermore, RWHS may have numerous advantages over centralised water treatment systems, such as increased reliability, simplicity, and lower cost (Cowden, et al., 2008).
4.1.2 Previous Studies showing Impact
Previous studies, conducted in various regions around the world, have concluded that RWH could considerably reduce the water currently having to be supplied by centralised water infrastructures.
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Ghisi et al. (2007) estimated that in Brazil, a rapidly developing country, RWHS could reduce residential potable water demand by as much as 48% in the south east of the country, and 100% in the north. In the developed world, the impact of RWHS can be just as significant: studies have deemed that in Germany, the use of RWHS for toilet flushing could reduce domestic water demand by 30-60% (Hermann & Schmida, 2000).
In Jordan and in Australia – arid regions where the problem of water scarcity is even more pronounced – estimates have concluded that RWHS could reduce potable water demand by up to 20% (Abdulla & Al-Shareef, 2009) and 60% (Coombes, et al., 2000) respectively.
Zhang et al. (2009) demonstrated that in megacities like Beijing, the use of RWHS for water flushing could reduce potable water demand by 25%. Even in NYC, one of the world’s most densely populated cities, studies have estimated that RWHS could meet up to 40% of the toilet flushing water demand (Basinger, et al., 2010).
4.2 Studies on the Sizing of RWHS
4.2.1 Scope, Purpose and Output
Many studies have been undertaken on RWHS, each varying in scope, purpose and output.
Since a key parameter influencing the performance of RWHS is the local weather regime (discussed in section 4.2.2), all studies are confined to a geographical area. Some aim to focus on particular cities, such as Taipei (Su, et al., 2009), NYC (Basinger, et al., 2010), Melbourne (Khastagir & Jayasuriya, 2010) or Nottingham (Fewkes, 1999), whilst other try to generalise across entire countries or regions such as Taiwan (Liaw & Tsai, 2004), USA (Hanson & Vogel, 2014) or Sub-Saharan West Africa (Cowden, et al., 2008).
Most of these studies focus on the use of RWHS for domestic purposes, but some consider their use in agriculture. Lee et al. (2000), for instance, investigated the use of RWHS for tea cultivation. Nevertheless, the aim for most RWHS studies is to provide a method enabling domestic users to size their system for desired levels of reliability.
The output of RWHS investigations usually fall into three categories: curves, contour maps, or equations. The first category includes graphs that try to establish the relationship between the various parameters considered. One example would be the dimensionless curves produced by Fewkes (1999), shown in Figure 1, relating catchment area, demand, rainfall level, system efficiency and storage volume. The second category comprises maps with contours showing the required storage capacity to achieve a given reliability. Figure 2 is an example of a map with contours indicating the required storage capacity (in
m3) for a volumetric reliability of 90%, a roof area of
100m2and a daily dwelling water demand of
150l(Liaw & Tsai, 2004). Although such maps are easy to understand and quick to read, the infinite number of parameter combinations and many graphs that would therefore need to be published make them impractical. The third category constitutes empirical regression formulae and analytically derived equations defining the relationship between various parameters. Equations
(1) and (2) are examples of such formulae, the former being derived analytically (Guo & Baetz, 2007), and the latter being derived via linear regression methods (Hanson & Vogel, 2014).
| Re=AϕψAϕψ+ζGe-ζvff1-e-ζBAϕ-ψBG |
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