Effect of Climate Change on Reliability of Rainwater Harvesting Systems for Kabarole District, Uganda

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Article Effect of Climate Change on Reliability of Rainwater Harvesting Systems for Kabarole District, Uganda

Violet Kisakye 1,2,* ID , Mary Akurut 3 and Bart Van der Bruggen 2,4 1 School of Agriculture and Environmental Sciences, Mountains of the Moon University, Fort Portal P.O. Box 837, Uganda 2 Chemical Engineering Department, ProcESS division, KU Leuven, Box 2424, 3001 Heverlee, Leuven, Belgium; [email protected] 3 National Water and Sewerage Corporation, Kampala P.O. Box 7053, Uganda; [email protected] 4 Faculty of Engineering and the Built Environment, Tshwane University of Technology, Pretoria, Private 0183, South Africa * Correspondence: [email protected]; Tel.: +256-782-853-289

Received: 28 November 2017; Accepted: 10 January 2018; Published: 15 January 2018

Abstract: This paper assesses the effect of climate change on reliability of rainwater harvesting systems for Kabarole district, Uganda, as predicted by 6 best performing global circulation models (GCMs). A daily water balance model was used to simulate the performance of a rainwater harvesting system using historical daily rainfall data for 20 years. The GCMs used to generate daily rainfall projections for 2025–2055 and 2060–2090 periods included; ACCESS1-0, BCC-CSM-1-M, CNRM-CM5, HADGEM2-CC, HADGEM2-ES and MIROC5. Analysis was based on the Ugandan weather seasons which included March, April, May (MAM) and September, October, November (SON) rain seasons in addition to December, January, February (DJF) and June, July, August (JJA) dry seasons. While an increase in reliability is predicted for the SON season, the worst-case scenario is projected during the MAM season with a reliability reduction of over 40% for the 2055–2090 period. This corresponds to a 27% reduction in water security for the same period. The DJF season is also expected to experience reduced water security by 1–8% for 2025–2055 and 2060–2090 with a 0.5 m3 tank size. Therefore, some form of extra harvesting surface and increased tank size will be required to maintain 80% systems reliability considering climate change.

Keywords: domestic rainwater harvesting; climate change; reliability; Kabarole; Uganda

1. Introduction Competing global demands for water such as irrigated agriculture, climate change and population growth result in an increasing pressure on existing water resources to satisfy a demand that is approaching the limit of supply [1]. Global climate change estimates vary widely but most concur that some areas will have increased precipitation at the expense of other areas [2]. It is now widely accepted that climate change will, among others, lead to an increase in the frequency and intensity of climatic extremes such as droughts and ﬂoods, some of the very elements that deﬁne climate variability [3]. Historically, climate extremes such as strong El Niño events were easily predictable. However, with a warmer and drier climate predicted for Southern Africa, an increased frequency and intensity of El Niño events is expected [3]. In a review of climate dynamics and variability across East Africa, Nicholson reports that rainfall across East Africa has strong links to the El-Nino Southern Oscillation (ENSO) phenomena with above average rainfall recorded during ENSO years [4]. Shongwe et al. [5] also reports that for East Africa, an increase in mean precipitation rates and intensity of high rainfall events and less severe droughts is expected. Generally, although predictions vary, rainfall is generally projected to increase over the African continent, the exceptions being southern Africa and parts of the

Water 2018, 10, 71; doi:10.3390/w10010071 www.mdpi.com/journal/water Water 2018, 10, 71 2 of 34

Horn of Africa, where rainfall is projected to decline by 2050 by about 10%. Analyses from General Circulation Models (GCMs) indicate an upward trend in rainfall under global warming over much of Burundi, Kenya, Rwanda, southern Somalia and Uganda (as cited in [6]). Although the lack of adequate data has hampered research into climate change effects on a local scale, two major studies have been conducted in Uganda. Hisali, Birungi and Buyinza [7] used a United Kingdom Regional Climate Model (RCM) PRECIS (Providing REgional Climates for Impacts Studies) in simulating rainfall and temperature over Uganda. The model projected:

• An increase in mean rainfall by 0.2 mm per day in the future 2071–2100, during the March, April and May season compared to the climatological period (1961–1990) for almost the entire country. • A reduction in rainfall of about 0.4 mm per day in June, July and August and 0.7 mm per day in September, October and November [7].

Through the Ministry of Water and Environment climate change unit, a more recent study based on the newly released climate change scenarios [8] was conducted. Two realistic greenhouse gas emission scenarios were considered under the regional scale Climate Change study: a moderate concentration pathway (RCP 4.5), and a more extreme concentration pathway (RCP 8.5) [9]. The report concluded that:

• For both scenarios, projected annual rainfall totals are expected to differ little from what is presently experienced, with projected changes within a range of less than plus or minus 10% from present rainfall [9]. • On a seasonal scale however, an increase in seasonal rainfall for the December, January and February season (up to 100% from present), which is indicative of a longer wet season that extends from September towards February is expected for both scenarios. • In addition, the months from March to August might expect slightly less rainfall which also signiﬁes a drier dry season [9].

Overall, both studies agree that Uganda will have longer wet seasons and a drier dry season. Therefore, seasonal rainfall changes will be more important than annual rainfall changes and hence planning for any water infrastructure should account for this. Depending on estimated local impacts and prevailing economic conditions, different regions in the world are responding differently to such predictions. For most developing countries, rainwater harvesting is listed as one of the specific adaptation strategies that the water sector needs to undertake to cope with future climate change. It is often seen as a viable option especially for rural communities where centralized systems are lacking [10,11]. Rainwater harvesting for the provision of potable, non-potable and agricultural use, has been widespread throughout the world for over 4000 years [12]. In Africa, the earliest documentation dates back to the Roman times in the western Mediterranean coastal desert in Egypt [13]. Pacey and Cullis [14] adequately document the history of rainwater harvesting and popular methodologies up to 1985. Despite this, it is only recently that rainwater harvesting has been acknowledged as an important water source. The last 20 years have thus seen an increase in roof catchment systems in Africa and South-Asia [15], with Kenya and Thailand taking the lead in technological innovations [13]. It was only in 2005 that a rainwater harvesting strategy was developed for Uganda [15]. For rainwater harvesting to be feasible, current and future rainfall projections need to be considered in the systems’ design. A few studies have tried to incorporate climate change into rain water harvesting systems’ design and performance [16–19]. For instance, in South Africa, Kahinda et al. [18] developed a methodology that mainstreams climate change in domestic rain water harvesting projects. The methodology was then applied to different climatic zones within South Africa and the general trend was a 5–20% increase in water security for a tank size of 0.5 m3 in the dry sub humid, humid and semi-arid regions. Although the study uses 6 GCMs to evaluate climate change effects on water security, variations in changes in seasonal predictions were not addressed. Seasonal specific design recommendations are thus not recorded. In Australia, Rahman et al. [17] evaluated Water 2018, 10, 71 3 of 34 the impacts of climate change on rainwater harvesting. The study evaluated seasonal changes and concluded that climate change will have a negative effect on rainwater savings, reliability and the impacts will be more severe in the dry season than the wet season. The study however relied on a single GCM for the predicted rainfall but it is evident that different GCMs can yield conflicting predictions for the same location further complicating the planning process [18]. This is especially true for sub Saharan Africa where climate change modeling is still relatively new, which makes reliance on studies done elsewhere very unfeasible. A few studies that have incorporated different GCMs in water harvesting systems design assessments are mainly in developed nations like Australia [19] but since the effects are often location specific, predictions are highly variable. This is especially critical for developing nations where vulnerability is often high. Therefore, it is crucial to consider the variations in rainfall predictions using the best performing GCMs when designing rainwater harvesting systems for developing nations like Uganda. This can help in designing systems that account for uncertainties related to climate change predictions in such regions. The overall objective of this paper is therefore to assess the effect of climate change on reliability and water security of rainwater harvesting systems, using Kabarole district as a case study. Specifically, the paper generates rainfall projections for the near future (2025–2055) and long-term future (2060–2090) using 6 best performing GCMs; the paper then evaluates seasonal climate change effects on rain water harvesting reliability and water security for the same periods. Changes in design combinations to ensure a maintenance of 80% reliability are also analyzed and implications on rural households presented. Based on previous climate change assessments for Uganda, projected annual rainfall changes are not as significant as projected seasonal rainfall changes [9,20]. For instance, while total annual precipitation is expected to increase by less than 10% for the RCP4.5 scenario [9], seasonal rainfall is expected to significantly differ from the present (up to 100%) especially for December, January and February season [20]. Hence, using annual rainfall changes in designing rainwater harvesting systems may lead to either over-or underestimation of systems’ design. Identification of the most affected season thus informs the overall systems design since this would account for the worst-case scenario. Therefore, this analysis was done on a seasonal scale representing the 2 rainy seasons of March, April May (MAM) and September, October, November (SON) and 2 dry seasons of June, July, August (JJA) and December, January, February (DJF).

2. Materials and Methods

2.1. Location of Study Area To accomplish the above objectives, Kabarole district was used as a case study. As shown in Figure1, Kabarole District is in western Uganda and is bordered by Kamwenge District in the north, Bundibugyo District in the northwest, Kasese in the west and Kyenjojo in the east. Fort Portal is the main town of Kabarole district and is over 300 km from Kampala, Uganda’s capital city. It has a total area of 8319 sq. km and is estimated to have a population density of 112 persons per square kilometer [21]. The district receives some of the highest rainfall in Uganda and short dry spells. Despite this, households still depend on unsafe water sources and walk long distances during dry spells. Rainwater harvesting is hence seen as a feasible option.

2.2. Rainfall Pattern The district experiences a bimodal rainfall pattern and is characterized as a humid tropical environment. The first rain season occurs during March, April and May (MAM) and the second rain season occurs during September, October and November (SON). The first dry season is from June, July and August (JJA) while the second one occurs from December, January and February (DJF). April, October and November are normally the wettest months of the year while February and July are normally the driest months of the year. Annual rainfall ranges from less than 1000 to 2000 mm and is greatly influenced by altitude. Figure2 is based on historical data from Kyembogo station described in Water 2018, 10, 71 4 of 34

WaterdetailWater 20182018 in, Section, 1010,, 7171 2.4. Even though JJA and DJF months are traditionally known as dry seasons, 20–30%44 ofof 3333 of the dry season consisted of rain days during the period of 1992–2012. Figure2 also shows that alsoalthoughalso showsshows August thatthat althoughalthough is classiﬁed AugustAugust as a dryisis classifiedclassified month, it asas signiﬁes aa drydry month,month, the start itit signifies ofsignifies the second thethe startstart rain ofof season thethe secondsecond and hence rainrain seasonhasseason higher andand rainfall hencehence hashas than higherhigher the other rainfallrainfall dry thanthan season thethe months. otherother drydry seasonseason months.months.

FigureFigure 1. 1. LocationLocation of of Kabarole Kabarole district district on on theththee Ugandan Ugandan mapmap (Source:(Source: Google Google maps).maps).

FigureFigure 2. 2. DailyDaily observed observed rainfall rainfall for for Kyembogo Kyembogo station station (1992–2012).(1992–2012).

2.3.2.3. System’s System’s Reliability Reliability EstimationEstimation ForFor thisthis study,study,study, reliability reliabilityreliability refers refersrefers to toto the thethe number numbernumber of ofof days daysdays when whenwhen household householdhousehold demand demanddemand is fully isis fullyfully met metmet by the byby therainwaterthe rainwaterrainwater harvesting harvestingharvesting system systemsystem given givengiven a specific aa specificspecific tank and tatanknk roof andand size. roofroof The size.size. adopted TheThe adoptedadopted estimation estimationestimation approach approachapproach closely closelyfollowsclosely follows methodologiesfollows methodologiesmethodologies widely used widelywidely in daily usedused simulations inin dailydaily simulationssimulations of water harvesting ofof waterwatersystems harvestingharvesting [12, 17 systemssystems,22–24]. [12,17,22–24].[12,17,22–24]. DifferentDifferent reliabilityreliability estimationestimation modelsmodels werewere builtbuilt forfor differentdifferent modelmodel predictionspredictions andand seasons.seasons. InIn thisthis estimation,estimation, reliancereliance onon otherother wawaterter sourcessources waswas notnot incorporatedincorporated inin thethe model.model. FigureFigure 33 showsshows aa summarysummary ofof stepssteps takentaken inin thethe estiestimationmation ofof reliabilityreliability ofof aa rainwaterrainwater harvestingharvesting system.system.

Water 2018, 10, 71 5 of 34

Different reliability estimation models were built for different model predictions and seasons. In this estimation, reliance on other water sources was not incorporated in the model. Figure3 shows a summary of steps taken in the estimation of reliability of a rainwater harvesting system. To estimate tank reliability under current and future climate change, a water balance model was built in Ms. Excel 2016. Different parameters including daily rainfall, varying roof size, daily losses from catchment area, daily household demand and tank spillage were considered. 3 Water Tank2017, 9 capacity, x FOR PEER was REVIEW varied from 0.5, 1, 2, 5, 10, 15 and 20 m . This represented a range of storage6 of 37 capacities in the study area. Just as described in Mehrabadi et al. [24] the initial spillage, and stored 175 volume were set to zero.

Inputs 1. Daily rainfall (m) (P) Calculation of potential runoff volume 2. Projected (2040 & 2075) henceforth referred to as the Inflow (I) daily rainfall for 6 GCMs 3. Varying roof sizes (m2) (A) 4. Runoff Coefficient of 0.8 (Q)

Release (Rt) calculation Considering daily household demand (D)

Inputs

Spillage calculation (SP) 1. Various tank sizes

Calculation of daily stored volume (Vt) and systems’ Reliability (R)

176 Figure 3: Summary of steps taken in estimation of reliability of rainwater harvesting systems with climate change Figure 3. Summary of steps taken in estimation of reliability of rainwater harvesting systems with 177 and varyingclimate design change combinations and varying design combinations.

178 HarvestedHarvested runoff runoff volume volume was estimated was estimated as; as:

179 ∗∗It = P ∗ A ∗ Q (1) (1)

3 where It is daily harvestable runoff volume (m ) on day t, P is the daily rainfall (m), A is the roof area 180 where It is daily harvestable runoff volume (m3) on day t, P is the daily rainfall (m), A is the roof area (m2) and Q is the dimensionless runoff coefficient which was considered as 0.8. Such a runoff coefficient 2 181 indicates(m ) and aQ loss is ofthe 20% dimensionless of the rainwater runoff that coefficient is discarded which for roof was cleaning considered and evaporation as 0.8. Such [25 a]. Thisrunoff is 182 withincoefficient the typicalindicates ranges a loss of of 0.8–0.95 20% of the for rainwater roof catchments that is useddiscarded in similar for roof studies cleaning [12,25 and–28 evaporation]. However, 183 Van[25]. der This Sterren is within et al. the [29 typical] found ranges out that of a0.8 runoff‐0.95 for coefficient roof catchments of 0.9 tended used to in over-estimate similar studies the [12, runoff 25– 184 28] . However ,Van der Sterren et al., [29] found out that a runoff coefficient of 0.9 tended to over‐

Water 2018, 10, 71 6 of 34 from roof areas, hence the adoption of a runoff coefﬁcient of 0.8 for this study. The roof area was varied in 5 increments from 20 to 200 m2. Daily release, which is the volume of water used by the household, was estimated using Equations (2) and (3): Rt = Dt; i f It + Vt−1 ≥ Dt (2)

Rt = It + Vt−1; i f It + Vt−1 < Dt (3) 3 3 where Rt is daily tank outﬂow on day t (m ), Dt is the daily household demand on day t (m ) and Vt−1 3 is the tank storage on the day before (m ). Tank spillage on day t (SPt) was estimated based on the equation below. SPt = It + Vt−1 − Dt − Vmax; i f It + Vt−1 − Dt > Vmax (4)

SPt = 0; i f It + Vt−1 − Dt ≤ Vmax (5) where Vmax is the design storage capacity while tank storage at the end of each days was estimated from the equations below: Vt = Vmax; i f SPt > 0 (6)

Vt = It + Vt−1 − Rt; i f SPt = 0 (7)

System’s reliability (Rt) was estimated using the equation below: n R = × 100 (8) t N where n is the number of days when household demand is fully met while N is the total number of days in the simulation. This was adopted from similar studies in the literature [17,22,23]. The optimum design reliability was considered as 80%. In similar studies, reliability varied from 80% to 100% [19,27,30] depending on prevailing local conditions and the purpose of rainwater harvesting systems. However, designing for higher reliability rates requires larger catchment areas and/or larger storage tank capacity [19] and may result in more expensive systems [31], which could be a hindrance especially for rural households. On the other hand, designing for lower reliabilities can result in low water availability despite having sufﬁcient rainfall. Therefore, for this study, 80% design reliability was taken as a feasible tradeoff. Water security was estimated from the following equation: e W = × 100 (9) E where e is the number of days a tank of speciﬁed capacity will be completely empty while E is the number of days in the simulation. This was adopted from Rahman et al. [17]. Different models where built for each climate change scenario and projection period and results compared in terms of systems reliability with varying tank and roof sizes.

2.4. Observed Rainfall Data The historical daily rainfall data for 20 years (1992 to 2012) recorded at the Kyembogo station were used for this study. The station is located 5 km away from Fort Portal. The station number is 89,300,180 with an elevation of 1500 m. This station was chosen as the driver station, because it provided the longest uninterrupted array of data compared to other stations in the region. The driver station concept was adopted from [18]. Although this approach helps preserve the statistical properties of point rainfall, it oversimpliﬁes daily areal rainfall in large areas [18]. Water 2018, 10, 71 7 of 34

2.5. Climate Change Rainfall Projections For this study, the simulations were carried out with six General Circulation Models for a moderate concentration pathway (RCP 4.5) as shown in Table1. The daily projections were done up to 2025–2055 and 2060–2090. The models in Table1 were selected because they ranked as the best performing GCMs in simulating annual, seasonal and monthly precipitation over Lake Victoria, which is very close to Kabarole district, the study area [20]. The study concludes that for future projections of precipitation, high resolution GCMs namely CNRM-CM5, MIROC5, HadGEM2-CC, HadGEM2-ES and BCC-CSM1.1m gave the best performance in modeling past absolute precipitation over Lake Victoria hence they are favored to give more accurate estimates of seasonal variations [20]. Statistical downscaling was based on at least 20 years of historical rainfall data from Kyembogo weather station (described in 2.4). The 20 years historical data were statistically transformed using the perturbation tool (described in 2.6) to incorporate the 30-year rainfall characteristics that reﬂect changes in climate.

Table 1. Characteristics of the 6 General circulation models (GCMs) considered in this study.

Model Center Country Model Latitude Longitude Resolution Atmosphere and Ocean Research Institute (The University of Tokyo), National Institute for Japan MIROC5 1.40 1.40 High Environmental Studies, and Japan Agency for Marine-Earth Science and Technology Beijing Climate Center, China China BCC-CSM1.1 2.81 2.79 Low Meteorological Administration Commonwealth Scientific and Industrial Research Australia ACCESS1.0 1.87 1.25 Medium Organization/Bureau of Meteorology (CSIRO-BOM) Centre National de Recherches Meteorologiques/ Centre Europeen de Recherche et Formation Avancees France CNRM-CM5 1.41 1.40 High en Calcul Scientifique (CNRM/CERFACS) HadGEM2-CC 1.87 1.25 Medium Met Office Hadley Centre UK-Exeter HadGEM2-ES 1.75 1.25 Medium Source: Akurut et al., [20].

GCM simulations cannot be used directly as input to study projected future changes on catchments since GCM data are available at coarse spatial resolution only [32]. It is, therefore, important to downscale the GCM outputs such as rainfall and temperature to remove the bias before using them to study catchment responses. One such method is the statistical downscaling approach that has been applied by several researchers to study the impact of climate change on stream flows. The perturbation tool provides a means to achieve this statistical downscaling [33]. For this study, the statistical method was preferred over the dynamical approach because it allows the consideration of a large ensemble of available GCMs. Such ensemble approach is important as different GCMs may provide high differences in results; the GCM uncertainty hence has to be addressed [33–36]. Onyutha et al. [32] evaluated the performance of different statistical downscaling techniques, which included the change factor (Delta), simplified and advanced quantile-perturbation-based approaches in the Lake Victoria Basin. The study concluded that due to the higher accuracy of the downscaling techniques in capturing monthly rainfall totals, the difference between the projected changes of seasonal or annual rainfall totals from different downscaling techniques was not significant [32]. Therefore, for this study, the simplified quantile perturbation method was applied since the method captures the rainfall extremes and totals relatively well [32].

2.6. Simpliﬁed Quantile Perturbation Method The quantile perturbation method (QPM) was used in projecting future precipitation, since the climatic changes often also involve changes in temporal variability due to climate change. Willems and Vrac [36] demonstrated that a higher accuracy can be obtained in the future climate scenarios by Water 2018, 10, 71 8 of 34 transferring bias using quantile perturbation factors since changes in more extreme rain storms often differ from changes in less intense rainfall [35]. In this method, only rainfall intensities are perturbed as opposed to their frequency of occurrence [34]. Therefore, the Quantile Perturbation Method 2A, as discussed by Van Uytven and Willems [33], was applied. This method changes the wet day frequency and applies quantile relative changes when the total rainfall amount is above a certain threshold, e.g., at least 1 mm/day. For values less than the threshold wet day precipitation, an absolute change rather than relative change is proposed. This eludes the possibility of having very high perturbation factors and consequently unrealistic future perturbed precipitation. The computations closely followed the methodology described in detail by Onyutha et al. [32].

2.7. Household Water Demand Due to the long distances between households and common water sources (more than 1000 m), average water consumption in Kabarole district is only 10.6 L per capita [37]. According to Howard and Bartram [38], when a water source is more than 1000 m away or households take 30 min or more to collect water, consumption rarely exceeds 5 L per capita. For this study, it was assumed that since rainwater harvesting systems are located at the household, average water consumption would increase to 20 L per capita which is the minimum WHO recommendation [38]. This ensures cooking, drinking and basic hygiene like hand washing and bathing. An average household size of ﬁve people was considered based on the Ugandan national average household size [39].

2.8. Current State of Rainwater Harvesting To assess the current state of rainwater harvesting, a household survey randomly targeting 300 households was conducted between May and June 2015. The survey was aimed at identifying the most common rainwater harvesting technologies, household perceptions and adoption rates in the study area. This information was used to supplement secondary literature data.

3. Results

3.1. Rainwater Harvesting Practices and Technologies The results indicate that most households (73%) in fact practice rainwater harvesting. This accounts for both formal and informal rainwater harvesting systems. Formal systems include guttering, downpipe, covered storage and tap and informal systems are characterized with open containers under roof or gutter systems for the collection of rainwater [40]. Figure4a illustrates an example of formal rainwater harvesting systems in the district. Although formal systems are more ideal and provide the safest water for domestic use compared to the informal systems, only 23% of the households practice formal rainwater harvesting (refer to Figure5). This could be attributed to the high initial investment costs required especially for the large storage volume. Baguma and Loiskandl [41] concluded that subsidy provision is statistically signiﬁcant in determining adoption of formal rain water technologies in rural Uganda. The investment costs for informal systems is low because storage facilities range from sauce pans, jerry cans, fabricated containers, drums and other locally made containers which are often already available in the home or are cheap to purchase (refer to Figure4b). According to WHO and UNICEF [42], rainwater harvesting is considered a safe water source. Unfortunately, a distinction between different harvesting technologies is not mentioned. The quality of the rainwater can vary depending on the atmospheric pollution, harvesting method and storage [43]. While the quality of collected rainwater may vary, on the whole, harvested quality is found to be equal to that of the regular treated water supplied through the public mains [43]. However, the quantity and quality of rainwater from informal systems may not meet the WHO deﬁnition of what is considered a safe water source. Despite these limitations both formal and informal rainwater harvesting systems were found to be very common water sources within the district. The survey also shows that most households had Water 2018, 10, 71 8 of 33 rainwater harvesting systems are located at the household, average water consumption would increase to 20 L per capita which is the minimum WHO recommendation [38]. This ensures cooking, drinking and basic hygiene like hand washing and bathing. An average household size of five people was considered based on the Ugandan national average household size [39].

3. Results

3.1. Rainwater Harvesting Practices and Technologies The results indicate that most households (73%) in fact practice rainwater harvesting. This accounts for both formal and informal rainwater harvesting systems. Formal systems include guttering, downpipe, covered storage and tap and informal systems are characterized with open containers under roof or gutter systems for the collection of rainwater [40]. Figure 4a illustrates an example of formal rainwater harvesting systems in the district. Although formal systems are more ideal and provide the safest water for domestic use compared to the informal systems, only 23% of the households practice formal rainwater harvesting (refer to Figure 5). This could be attributed to the high initial investment costs required especially for the large storage volume. Baguma and Loiskandl [41] concluded that subsidy provision is statistically significant in determining adoption of formal rain water technologies in rural Uganda. The investment costs for informal systems is low because storage facilities range from sauce pans, jerry cans, fabricated containers, drums and other locally made containers which are often already available in the home or are cheap to purchase (refer to Figure 4b). According to WHO and UNICEF [42], rainwater harvesting is considered a safe water source. Unfortunately, a distinction between different harvesting technologies is not mentioned. The quality of the rainwater can vary depending on the atmospheric pollution, harvesting method and storage [43]. While the quality of collected rainwater may vary, on the whole, harvested quality is found to be equal to that of the regular treated water supplied through the public mains [43]. However, the quantity and quality of rainwater from informal systems may not meet the WHO

Waterdefinition2018, 10 of, 71 what is considered a safe water source. Despite these limitations both formal9 and of 34 informal rainwater harvesting systems were found to be very common water sources within the district. The survey also shows that most households had low storage volumes (less or equal to 2 m3) 3 2 andlow storagesmaller volumesroof sizes (less (average or equal of 30 to m 22 m) which) and could smaller be roof a main sizes hindrance (average oftowards 30 m )attaining which could higher be reliabilities.a main hindrance towards attaining higher reliabilities.

(a) (b)

Figure 4. IllustratesIllustrates the the difference difference between between formal formal and an informald informal rainwater rainwater harvesting harvesting systems. systems. (a) Formal (a) Water Formalharvesting2018, 10 ,harvesting 71 systems; systems; (b) Informal (b) Informal harvesting harvesting systems. systems. 9 of 33

23%

77%

informal systems Formal systems

Figure 5. Proportion of households practicingpracticing both technologies.

3.2. Current and Projected Daily Rainfall Despite major modelling advances, different global climate models still only predict generalgeneral directions andand sometimes sometimes give give mixed mixed signals signals [18]. [18]. This This is evident is evident from Figuresfrom Figures6 and7 where 6 and MIROC5 7 where isMIROC5 predicting is predicting contrasting contrasting deviations deviations when compared when compared to other to models. other models. From Figure From6 Figure, most 6, of most the modelsof the models except except MIROC5 MIROC5 seem to seem show to that show daily that rainfall daily willrainfall not deviatewill not much deviate from much what from is currently what is observedcurrently forobserved the 2025–2055 for the 2025–2055 period. For period. example, For exam duringple, the during first rainthe first seasons rain ofseasons MAM, of all MAM, models all exceptmodels MIROC5, except MIROC5, predict nopredict significant no significant changes ch inanges daily rainfallin daily while rainfall MIROC5 while MIROC5 predicts apredicts decrease a ofdecrease 11% to of 32% 11to from 32% the from observed the observed data. This data. corresponds This corresponds to a 2.7–3.7 to a mm2.7–3.7 reduction mm reduction in daily in rainfall daily fromrainfall 4.6 from to 6.6 4.6 mm to observed 6.6 mm scenario.observed Duringscenario. the During dry season the ofdry JJA season however, of JJA MIROC5 however, predicts MIROC5 that dailypredicts rainfall that daily will increase rainfall bywill 42% increase (2.7 mm) by 42% during (2.7 themm) month during of the June month and 48% of June (3.7 and mm) 48% in July.(3.7 mm) This isin contraryJuly. This to is findingscontrary fromto findings Taylor from et al. Taylor [44] who et al. predicted [44] who thatpredicted the month that the of Junemonth and of June July willand experienceJuly will experience a reduction a reduction in rainfall in ranging rainfall from ranging 27–48% from in 27–48% Kabarole indistrict. Kabarole This district. can be This attributed can be toattributed the different to the models different used models in the used simulations. in the simulations. Despite aDespite predicted a predicted increase increase in rainfall in inrainfall DJF byin TaylorDJF by etTaylor al. [44 et], al. all [44], models all models show thatshow there that willthere be will no be significant no significant changes changes in rainfall in rainfall during during this season.this season. Between Between October October to December, to December, HADGEM2-CC HADGEM2-CC and HADGEM2-ESand HADGEM2-ES predict predict a decrease a decrease in daily in rainfalldaily rainfall by 1% by (0.1 1% mm)–16% (0.1 mm)–16% (1.3 mm) (1.3 whilemm) while CNRM-CM5 CNRM-CM5 predicts predicts an increase an increase of about of about 21% (1.421% mm) (1.4 inmm) August. in August. ACCESS1-0 ACCESS1-0 and BCC-CSM1-1-M and BCC-CSM1-1-M do not do show not show significant significant deviations deviations from observedfrom observed daily rainfalldaily rainfall during during this period. this period.

2025-2055 8 7 6 5 4 3 2 1

Average daily rainfall (mm) daily Average 0 Jan Feb March April May June July Aug Sept Oct Nov Dec Months

Observed ACCESS1-0 BCC-CSM1-1-M CNRM-CM5

HADGEM2-CC HADGEM2-ES MIROC5

Figure 6. Climate change prediction of daily average rainfall for 2025–2055.

As shown in Figure 7, for 2060–2090 projections, greater deviations from observed rainfall are predicted by MIROC5, HADGEM2-CC and HADGEM2-ES models. For instance, during the MAM

Water 2018, 10, 71 9 of 33

23%

77%

informal systems Formal systems

Figure 5. Proportion of households practicing both technologies.

3.2. Current and Projected Daily Rainfall Despite major modelling advances, different global climate models still only predict general directions and sometimes give mixed signals [18]. This is evident from Figures 6 and 7 where MIROC5 is predicting contrasting deviations when compared to other models. From Figure 6, most of the models except MIROC5 seem to show that daily rainfall will not deviate much from what is currently observed for the 2025–2055 period. For example, during the first rain seasons of MAM, all models except MIROC5, predict no significant changes in daily rainfall while MIROC5 predicts a decrease of 11to 32% from the observed data. This corresponds to a 2.7–3.7 mm reduction in daily rainfall from 4.6 to 6.6 mm observed scenario. During the dry season of JJA however, MIROC5 predicts that daily rainfall will increase by 42% (2.7 mm) during the month of June and 48% (3.7 mm) in July. This is contrary to findings from Taylor et al. [44] who predicted that the month of June and July will experience a reduction in rainfall ranging from 27–48% in Kabarole district. This can be attributed to the different models used in the simulations. Despite a predicted increase in rainfall in DJF by Taylor et al. [44], all models show that there will be no significant changes in rainfall during this season. Between October to December, HADGEM2-CC and HADGEM2-ES predict a decrease in daily rainfall by 1% (0.1 mm)–16% (1.3 mm) while CNRM-CM5 predicts an increase of about 21% (1.4 mm)Water in 2018August., 10, 71 ACCESS1-0 and BCC-CSM1-1-M do not show significant deviations from observed10 of 34 daily rainfall during this period.

2025-2055 8 7

Water 20186 , 10, 71 10 of 33 5 rainy season,4 MIROC predicts a 25–57% decrease in daily rainfall which corresponds to a 1.8 to 4.8 mm reduction in daily rainfall. The model predicts the greatest increase in daily rainfall of 65–70% 3 (7.7–9.1 mm) between June and July and an increase of 18% (1.2 mm) in August. No significant deviations2 are observed for the other months. Both HADGEM2-CC and HADGEM2-ES predict a reduction1 in observed rainfall ranging from 17% (0.6 mm)–42% (3.1 mm) for the months of February

to June. rainfall (mm) daily Average 0 During August to November, both models predict an increase in daily rainfall of 28–57% which correspondsJan Feb to a March2.1–13.3 April mm increase May June in daily July rainfall. Aug This Sept in line Oct with Nov findings Dec from Rautenbach et al. [9] whose study shows that thereMonths will be more rainfall recorded during this season across Uganda for 2076–2095 period. No significant deviations are observed for the other months. ACCESS1-0 andObserved BCC-CSM1-1-M doACCESS1-0 not show significantBCC-CSM1-1-M deviations from observedCNRM-CM5 daily rainfall during the period of 2060–2090. It should be noted however that future simulations for instance after HADGEM2-CC HADGEM2-ES MIROC5 2040 have higher uncertainties than simulations of up to 2040. This can be attributed to the uncertainty in emissions of greenhouse gases—hence radiative forcing increases exponentially Figure 6. Climate change prediction of daily average rainfall for 2025–2055. especially after Figure2060 [20]. 6. Climate change prediction of daily average rainfall for 2025–2055. As shown in Figure 7, for 2060–2090 projections, greater deviations from observed rainfall are 2060-2090 predicted by MIROC5, HADGEM2-CC and HADGEM2-ES models. For instance, during the MAM 20

18 16 14 12 10 8 6 4 2 Average daily rainfall (mm) daily Average 0 Jan Feb March April May June July Aug Sept Oct Nov Dec Months Observed ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 HADGEM2-CC HADGEM2-ES MIROC5

Figure 7. Climate change prediction of daily average rainfall for 2060–2090. Figure 7. Climate change prediction of daily average rainfall for 2060–2090.

3.3. Systems’ Reliability under Climate Change As shown in Figure7, for 2060–2090 projections, greater deviations from observed rainfall are predictedAs explained by MIROC5, in Section HADGEM2-CC 2.3, system andreliability HADGEM2-ES is an indicator models. of how For often instance, full demand during is the met MAM by rainya rainwater season, harvesting MIROC predicts system awith 25–57% a specific decrease roof inand daily tank rainfall size. Due which to the corresponds changes in toseasonal a 1.8 to 4.8rainfall, mm reduction this is predicted in daily rainfall.to change, The and model the effect predicts may the vary greatest according increase to the in dailydesign rainfall specifications. of 65–70% (7.7–9.1Therefore, mm )this between section Junepresents and predicted July and changes an increase in seasonal of 18% systems (1.2 mm) reliability in August. using Noselected signiﬁcant tank 3 deviationsvolumes as are indicators. observed Due for to the an otherabundance months. of smaller Both HADGEM2-CCtank volumes (up and to 5 HADGEM2-ES m ) in the study predict area, areliability reduction inchanges observed for rainfallsmaller rangingtank volumes from 17% are (0.6more mm)–42% emphasized (3.1 mm)in this for section the months (projections of February for tohigher June. tank During capacities August are to presented November, in the both supplem modelsentary predict material, an increase Section in 1). daily The changes rainfall in of design 28–57% specifications required to maintain 80% reliability are also presented with special focus on changes which corresponds to a 2.1–13.3 mm increase in daily rainfall. This in line with ﬁndings from deemed more significant compared to current observations (design combinations for different Rautenbach et al. [9] whose study shows that there will be more rainfall recorded during this season reliabilities are presented in the supplementary material, Section 2).

3.3.1. Climate Change Effect on Reliability for DJF Season (2025–2055)

A tank volume of 2 m3 is used as an example to show the effect of climate change on reliability. It was specifically chosen because it is a typical example of tank sizes in Kabarole district. Figure 8 shows that when the predictions for observed scenario are compared with the model predictions, most of the models forecast a reduction in reliability during the DJF season up to the year 2055. Other

Water 2018, 10, 71 11 of 34 across Uganda for 2076–2095 period. No signiﬁcant deviations are observed for the other months. ACCESS1-0 and BCC-CSM1-1-M do not show signiﬁcant deviations from observed daily rainfall during the period of 2060–2090. It should be noted however that future simulations for instance after 2040 have higher uncertainties than simulations of up to 2040. This can be attributed to the uncertainty in emissions of greenhouse gases—hence radiative forcing increases exponentially especially after 2060 [20].

3.3. Systems’ Reliability under Climate Change As explained in Section 2.3, system reliability is an indicator of how often full demand is met by a rainwater harvesting system with a specific roof and tank size. Due to the changes in seasonal rainfall, this is predicted to change, and the effect may vary according to the design specifications. Therefore, this section presents predicted changes in seasonal systems reliability using selected tank volumes as indicators. Due to an abundance of smaller tank volumes (up to 5 m3) in the study area, reliability changes for smaller tank volumes are more emphasized in this section (projections for higher tank capacities are presented in the supplementary material, Section 1). The changes in design specifications required to maintain 80% reliability are also presented with special focus on changes deemed more significant compared to current observations (design combinations for different reliabilities are presented in the supplementary material, Section 2).

3.3.1. Climate Change Effect on Reliability for DJF Season (2025–2055) A tank volume of 2 m3 is used as an example to show the effect of climate change on reliability. It was speciﬁcally chosen because it is a typical example of tank sizes in Kabarole district. Figure8 shows that when the predictions for observed scenario are compared with the model predictions, Water 2018, 10, 71 11 of 33 most of the models forecast a reduction in reliability during the DJF season up to the year 2055. Other models like ACCESS1-0 predict a slight increase. The greatest reduction in reliability of over 13% is 2 shown by the HADGEM2-CC and HADGEM2-ES modelsmodels byby roofroof sizessizes rangingranging fromfrom 75–10075–100 mm2..

6 4 2 0 -2 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 -4 -6 -8 -10 Change in reliability (%) -12 -14 Roof area (m2)

ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 HADGEM2-CC HADGEM2-ES MIROC5

3 Figure 8. Change in reliability for DJF 2025–2055 period for 2 mm3.

When the tank volume is increased to 5 m3, changes in reliability are not as significant for larger When the tank volume is increased to 5 m3, changes in reliability are not as signiﬁcant for larger roof sizes compared to smaller roof areas (refer to Figure 9). Figure 9 shows that roof sizes below 100 roof sizes compared to smaller roof areas (refer to Figure9). Figure9 shows that roof sizes below 100 m 2 m2 with a tank volume of 5 m3 will have a reduction in reliability by a maximum of 13%, as predicted with a tank volume of 5 m3 will have a reduction in reliability by a maximum of 13%, as predicted by by HADGEM2-CC. Increasing the tank to 10 m3 makes households with roof areas greater or equal HADGEM2-CC. Increasing the tank to 10 m3 makes households with roof areas greater or equal to to 50 m2, less affected by changes in reliability but any increase in tank volume beyond 10 m3, shows no effect on reliability. In all cases roof sizes less than 50 m2 are predicted to be greatly affected by changes in reliability during this period. HADGEM2-ES and ACCESS1-0 represents the worst and best-case scenarios for this period hence they act as the reliability envelope for different roof and tank sizes.

20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 -4

-9 Change in relaibility (%)

-14 Roof area (m2) ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 HADGEM2-CC HADGEM2-ES MIROC5

Figure 9. Change in reliability for DJF 2025–2055 period for 5 m3.

3.3.2. Design Requirements to Achieve 80% Reliability for DJF Season (2025–2055)

Figures 10 and 11 indicate that for smaller tank sizes (0.5 and 1 m3), 80% reliability is not possible during the DJF season for all models. There is an exponential increase of systems’ reliability from 20 to about 50 m2 after which the increase becomes gradual from 50 m2 onwards. For instance, large roof

Water 2018, 10, 71 11 of 33 models like ACCESS1-0 predict a slight increase. The greatest reduction in reliability of over 13% is shown by the HADGEM2-CC and HADGEM2-ES models by roof sizes ranging from 75–100 m2.

6 4 2 0 -2 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 -4 -6 -8 -10 Change in reliability (%) -12 -14 Roof area (m2)

ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 HADGEM2-CC HADGEM2-ES MIROC5

Figure 8. Change in reliability for DJF 2025–2055 period for 2 m3.

When the tank volume is increased to 5 m3, changes in reliability are not as significant for larger roof sizes compared to smaller roof areas (refer to Figure 9). Figure 9 shows that roof sizes below 100 2 3 Waterm with2018 a, 10 tank, 71 volume of 5 m will have a reduction in reliability by a maximum of 13%, as predicted12 of 34 by HADGEM2-CC. Increasing the tank to 10 m3 makes households with roof areas greater or equal to 50 m2, less affected by changes in reliability but any increase in tank volume beyond 10 m3, shows 2 3 50no meffect, less on affected reliability. by changes In all cases in reliability roof sizes but less any than increase 50 m2 in are tank predicted volume to beyond be greatly 10 m affected, shows by no 2 effectchanges on reliability.in reliability In allduring cases this roof period. sizes less HADGEM2-ES than 50 m are and predicted ACCESS1-0 to be greatly represents affected the byworst changes and inbest-case reliability scenarios during for this this period. period HADGEM2-ES hence they act as and the ACCESS1-0 reliability envelope represents for the different worst androof best-caseand tank scenariossizes. for this period hence they act as the reliability envelope for different roof and tank sizes.

20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 -4

-9 Change in relaibility (%)

-14 Roof area (m2) ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 HADGEM2-CC HADGEM2-ES MIROC5

3 Figure 9. Change in reliability for DJF 2025–2055 period for 5 mm3.

3.3.2. Design Requirements to Achieve 80% Reliability for DJF Season (2025–2055) 3.3.2. Design Requirements to Achieve 80% Reliability for DJF Season (2025–2055) Figures 10 and 11 indicate that for smaller tank sizes (0.5 and 1 m3), 80% reliability is not possible Figures 10 and 11 indicate that for smaller tank sizes (0.5 and 1 m3), 80% reliability is not possible during the DJF season for all models. There is an exponential increase of systems’ reliability from 20 during the DJF season for all models. There is an exponential increase of systems’ reliability from 20 to toWater about 2018 50, 10 m, 712 after which the increase becomes gradual from 50 m2 onwards. For instance, large12 roofof 33 about 50 m2 after which the increase becomes gradual from 50 m2 onwards. For instance, large roof 2 3 sizes (over(over 200200 mm2) would only yield about 50% reliabilityreliability for 0.50.5 mm3 and close to 70% reliability for 3 3 3 11 m3 tanktank size. size. Increasing Increasing tank tank size size from from 0.5 0.5 m m3 to to1 m 13 monlyonly increases increases reliability reliability by 10–14% by 10–14% for all for roof all 2 roofsizes. sizes. In fact, In fact, reliability reliability increase increase peaks peaks at at60–80 60–80 m2 m roofroof size size after after which which it it begins begins to to decline. This patternpattern isis replicatedreplicated byby allall models. Therefore, this period, expansion of storage capacity will be more beneﬁcialbeneficial thanthan increasingincreasing roofroof size.size.

Observed ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 HADGEM2-CC HADGEM2-ES MIROC5 70 60 50 40 30

Reliability (%) 20 10 0 20 30 40 50 60 70 80 90 100110120130140150160170180190200 Roof Size (m2)

3 Figure 10.10. Reliability projections forfor DJFDJF seasonseason forfor 0.50.5 mm3 tank size for 2025–2055.

Observed ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 HADGEM2-CC HADGEM2-ES MIROC5 80 70 60 50 40 30 Reliability (%) 20 10 0 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 Roof Size (m2)

Figure 11. Reliability projections for DJF season for 1 m3 tank size for 2025–2055.

When the tank size is increased to 2 m3 ,as shown in Figure 12, 80% reliability is achieved between 100 to 110 m2 roof size for the observed scenario and by ACCESS1-0, BCC-CSM1-1-M, MIROC5 and CNRM-CM5 models. However, HADGEM2-CC predicts a roof size requirement of 160 m2 and 180 m2 for HADGEM2-ES as indicated in Figure 10. This is an increase of 60–80 m2 from what is currently needed to maintain 80% reliability. Figure 13 shows that increasing the storage capacity to 5 m3 reduces the roof size requirement for the attainment of 80% reliability to 50–60 m2 for the observed and ACCESS1-0, BCC-CSM1-1-M, MIROC5 and CNRM-CM5 models. 80% reliability is achieved at 70 and 85 m2 for HADGEM2-CC and HADGEM2-ES respectively. This is a 10–20 m2 increase in roof size from observed requirement. Notably, reliability projections for all models and observed scenario converge when roof sizes are increased beyond 80 m2 with tank volumes of 5 m3

Water 2018, 10, 71 12 of 33 sizes (over 200 m2) would only yield about 50% reliability for 0.5 m3 and close to 70% reliability for 1 m3 tank size. Increasing tank size from 0.5 m3 to 1 m3 only increases reliability by 10–14% for all roof sizes. In fact, reliability increase peaks at 60–80 m2 roof size after which it begins to decline. This pattern is replicated by all models. Therefore, this period, expansion of storage capacity will be more beneficial than increasing roof size.

Observed ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 HADGEM2-CC HADGEM2-ES MIROC5 70 60 50 40 30

Reliability (%) 20 10 0 20 30 40 50 60 70 80 90 100110120130140150160170180190200 Roof Size (m2)

Water 2018, 10, 71 13 of 34 Figure 10. Reliability projections for DJF season for 0.5 m3 tank size for 2025–2055.

Observed ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 HADGEM2-CC HADGEM2-ES MIROC5 80 70 60 50 40 30 Reliability (%) 20 10 0 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 Roof Size (m2)

3 Figure 11. Reliability projections for DJFDJF seasonseason forfor 11 mm3 tank size for 2025–2055.

When the tank size is increased to 2 m3 ,as shown in Figure 12, 80% reliability is achieved When the tank size is increased to 2 m3 ,as shown in Figure 12, 80% reliability is achieved between between 100 to 110 m2 roof size for the observed scenario and by ACCESS1-0, BCC-CSM1-1-M, 100 to 110 m2 roof size for the observed scenario and by ACCESS1-0, BCC-CSM1-1-M, MIROC5 and MIROC5 and CNRM-CM5 models. However, HADGEM2-CC predicts a roof size requirement of 160 CNRM-CM5 models. However, HADGEM2-CC predicts a roof size requirement of 160 m2 and 180 m2 m2 and 180 m2 for HADGEM2-ES as indicated in Figure 10. This is an increase of 60–80 m2 from what for HADGEM2-ES as indicated in Figure 10. This is an increase of 60–80 m2 from what is currently is currently needed to maintain 80% reliability. Figure 13 shows that increasing the storage capacity needed to maintain 80% reliability. Figure 13 shows that increasing the storage capacity to 5 m3 to 5 m3 reduces the roof size requirement for the attainment of 80% reliability to 50–60 m2 for the reduces the roof size requirement for the attainment of 80% reliability to 50–60 m2 for the observed observed and ACCESS1-0, BCC-CSM1-1-M, MIROC5 and CNRM-CM5 models. 80% reliability is and ACCESS1-0, BCC-CSM1-1-M, MIROC5 and CNRM-CM5 models. 80% reliability is achieved at achieved at 70 and 85 m2 for HADGEM2-CC and HADGEM2-ES respectively. This is a 10–20 m2 70 and 85 m2 for HADGEM2-CC and HADGEM2-ES respectively. This is a 10–20 m2 increase in roof Waterincrease 2018 ,in 10 ,roof 71 size from observed requirement. Notably, reliability projections for all models13 ofand 33 size from observed requirement. Notably, reliability projections for all models2 and observed scenario3 observed scenario converge when roof sizes are increased2 beyond 80 m with tank3 volumes of 5 m convergeor more. Increasing when roof ta sizesnk size are increasedbeyond 5 beyondm3 does 80 not m resultwith tankin a significant volumes of increase 5 m or more.in reliability Increasing and 3 tankreduction size beyond in roof 5size. m does not result in a signiﬁcant increase in reliability and reduction in roof size.

Observed ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 HADGEM2-CC HADGEM2-ES MIROC5 100 90 80 70 60 50 40 Reliability (%) 30 20 10 0 20 30 40 50 60 70 80 90 100110120130140150160170180190200 Roof Size (m2)

3 Figure 12. Reliability projections for DJF season for 2 m3 tank size for 2025–2055.

Observed ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 HADGEM2-CC HADGEM2-ES MIROC5 100 90 80 70 60 50 40 Reliability (%) 30 20 10 0 20 30 40 50 60 70 80 90 100110120130140150160170180190200 Roof Size (m2)

Figure 13. Reliability projections for DJF season for 5 m3 tank size for 2025–2055.

3.3.3. Climate Change Effect on Reliability for DJF Season (2060–2090) There is a lot of variation in model predictions for the 2060–2090 period. While ACCESS1-0, HADGEM2-ES and CNRM-CM5 predict an increase in reliability, MIROC5, HADGEM2-CC and BCC-CSM1-1-M predict a reduction in reliability. As earlier noted, the effect of climate change tends to be less significant as roof size and tank size increases. For instance, when the tank size is increased from 2 m3 to 5 m3, the reduction in reliability becomes less significant, as shown in Figures 14 and 15 respectively. Therefore, more models predict an increase in reliability during this period when the tank and roof size are increased. MIROC5 and HADGEM2-ES represent the worst and best-case scenarios respectively and hence show the reliability envelope for water harvesting systems during this period. Figures 14 and 15 emphasize the high uncertainty in reliability when roof sizes are less

Water 2018, 10, 71 13 of 33 or more. Increasing tank size beyond 5 m3 does not result in a significant increase in reliability and reduction in roof size.

Observed ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 HADGEM2-CC HADGEM2-ES MIROC5 100 90 80 70 60 50 40 Reliability (%) 30 20 10 0 20 30 40 50 60 70 80 90 100110120130140150160170180190200 Roof Size (m2)

Water 2018, 10, 71 14 of 34 Figure 12. Reliability projections for DJF season for 2 m3 tank size for 2025–2055.

Observed ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 HADGEM2-CC HADGEM2-ES MIROC5 100 90 80 70 60 50 40 Reliability (%) 30 20 10 0 20 30 40 50 60 70 80 90 100110120130140150160170180190200 Roof Size (m2)

3 Figure 13. Reliability projections for DJF season forfor 55 mm3 tank size for 2025–2055.

3.3.3. Climate Change Effect on Reliability for DJF Season (2060–2090) 3.3.3. Climate Change Effect on Reliability for DJF Season (2060–2090) There is a lot of variation in model predictions for the 2060–2090 period. While ACCESS1-0, There is a lot of variation in model predictions for the 2060–2090 period. While ACCESS1-0, HADGEM2-ES and CNRM-CM5 predict an increase in reliability, MIROC5, HADGEM2-CC and HADGEM2-ES and CNRM-CM5 predict an increase in reliability, MIROC5, HADGEM2-CC and BCC-CSM1-1-M predict a reduction in reliability. As earlier noted, the effect of climate change tends BCC-CSM1-1-M predict a reduction in reliability. As earlier noted, the effect of climate change tends to to be less significant as roof size and tank size increases. For instance, when the tank size is increased be less signiﬁcant as roof size and tank size increases. For instance, when the tank size is increased from 2 m3 to 5 m3, the reduction in reliability becomes less significant, as shown in Figures 14 and 15 from 2 m3 to 5 m3, the reduction in reliability becomes less signiﬁcant, as shown in Figures 14 and 15 respectively. Therefore, more models predict an increase in reliability during this period when the respectively. Therefore, more models predict an increase in reliability during this period when the tank tank and roof size are increased. MIROC5 and HADGEM2-ES represent the worst and best-case and roof size are increased. MIROC5 and HADGEM2-ES represent the worst and best-case scenarios Waterscenarios 2018, 10respectively, 71 and hence show the reliability envelope for water harvesting systems during14 of 33 respectively and hence show the reliability envelope for water harvesting systems during this period. this period. Figures 14 and 15 emphasize the high uncertainty in reliability when roof sizes are less Figuresthan 100 14 compared and 15 emphasize to larger roof the areas. high uncertaintyThe uncertainty in reliability is even greater when roof when sizes storage are lessvolumes than 100are comparedlow. to larger roof areas. The uncertainty is even greater when storage volumes are low.

5 4 3 2 1 0 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 -1

Change in reliability (%) -2 -3 -4 Roof area (m2) ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 HADGEM2-CC HADGEM2-ES MIROC5

3 Figure 14. Change in reliability for DJF 2060–2090 period for 2 m3.

0 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 Change in reliability (%) -2

-4 Roof sizes (m2)

ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 HADGEM2-CC HADGEM2-ES MIROC5

Figure 15. Change in reliability for DJF 2060–2090 period for 5 m3.

3.3.4. Design Requirements to Achieve 80% Reliability for DJF Season (2060–2090)

For the 2060–2090 period, models such as ACCESS1-0, BCC-CSM1-1-M and CNRM-CM5 predict no significant deviations from the observed rainfall. However, MIROC5, HADGEM2-CC and HADGEM2-ES predict an increase in rainfall in December by over 12% and a decrease of over 24% in February. This has implications on the design of rainwater harvesting systems if an 80% reliability is to be met. Table 2 shows design combinations for 80% reliability during DJF dry season. For storage facilities smaller than 2 m3, it will be impossible to achieve this even with larger roof sizes for all models. Storage capacities of about 2 m3 will require larger roof sizes of between 100 to 110 m2 to attain 80% systems reliability. This, however, is reduced by half when storage facilities are increased to 5 m3 but any further increase in tank size does not significantly decrease the roof area required to maintain 80% reliability. Table 2 also shows that climate change will not have significant effects on changes in design requirements to maintain 80% reliability during this time. More information on

Water 2018, 10, 71 14 of 33 than 100 compared to larger roof areas. The uncertainty is even greater when storage volumes are low.

5 4 3 2 1 0 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 -1

Change in reliability (%) -2 -3 -4 Roof area (m2) ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 HADGEM2-CC HADGEM2-ES MIROC5

Water 2018, 10, 71 15 of 34 Figure 14. Change in reliability for DJF 2060–2090 period for 2 m3.

0 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 Change in reliability (%) -2

-4 Roof sizes (m2)

ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 HADGEM2-CC HADGEM2-ES MIROC5

3. FigureFigure 15.15. Change in reliability forfor DJFDJF 2060–20902060–2090 periodperiod forfor 55 mm3.

3.3.4. Design Requirements to Achieve 80% Reliability for DJF Season (2060–2090) 3.3.4. Design Requirements to Achieve 80% Reliability for DJF Season (2060–2090) For the 2060–2090 period, models such as ACCESS1-0, BCC-CSM1-1-M and CNRM-CM5 predict no significantsignificant deviationsdeviations fromfrom thethe observedobserved rainfall.rainfall. However, MIROC5, HADGEM2-CC and HADGEM2-ES predictpredict anan increaseincrease inin rainfall rainfall in in December December by by over over 12% 12% and and a decreasea decrease of of over over 24% 24% in February.in February. This This has has implications implications on on the the design design of of rainwater rainwater harvesting harvesting systems systems if anif an 80% 80% reliability reliability is tois to be be met. met. Table Table2 shows 2 shows design design combinations combinations for fo 80%r 80% reliability reliability during during DJF DJF dry dry season. season. For For storage storage facilities smaller than 2 m3,, it it will will be be impossible to to achieve achieve this this even with larger roof sizes for all models. StorageStorage capacitiescapacities of of about about 2 m2 3mwill3 will require require larger larger roof roof sizes sizes of between of between 100 to 100 110 to m 1102 to attainm2 to 80%attain systems 80% systems reliability. reliability. This, however, This, however, is reduced is reduced by half by when half storage when storage facilities facilities are increased are increased to 5 m3 butto 5 anym3 but further any increasefurther increase in tank sizein tank does size not does significantly not significantly decrease decrease the roof areathe roof required area torequired maintain to 80%maintain reliability. 80% reliability. Table2 also Table shows 2 also that shows climate that change climate will change not have will significantnot have significant effects on effects changes on inchanges design in requirements design requirements to maintain to maintain 80% reliability 80% re duringliability thisduring time. this More time. information More information on design on combinations required for different reliabilities for this period are presented in the supplementary material Section 2.1.

Table 2. Shows roof sizes for 80% reliability for DJF season for 2060–2090.

Tank Size (m3) Climate Change Models 0.5 1 2 5 10 15 20 Observed - - 105–110 55–60 45–50 40–45 40–45 ACCESS1-0 - - 100–105 55 40–45 40–45 35–40 BCC-CSM1-1-M - - 105–110 55–60 40–45 40–45 35–40 CNRM-CM5 - - 110–115 55–60 45–50 40–45 40–45 HADGEM2-CC - - 115–120 50–55 40–45 40–45 35–40 HADGEM2-ES - - 105–110 45–50 40–45 35–40 35–40 MIROC5 - - 115–110 60–65 45–50 40–45 40–45

3.3.5. Climate Change Effect on Reliability for JJA Season (2025–2055) During the JJA season for the 2025–2055 period, most models predict an increase in reliability, with MIROC5 predicting the greatest increase of over 25% for both 2 and 5 m3 tank size (refer to Figures 16 and 17). ACESS1-0 however consistently predicts a decline in reliability of less than 5% during this period. From Figures 16 and 17, the best and worst-case scenarios are predicted by MIROC5 and Water 2018, 10, 71 15 of 33 Water 2018, 10, 71 15 of 33 design combinations required for different reliabilities for this period are presented in the design combinations required for different reliabilities for this period are presented in the supplementary material Section 2.1. supplementary material Section 2.1. Table 2. Shows roof sizes for 80% reliability for DJF season for 2060–2090. Table 2. Shows roof sizes for 80% reliability for DJF season for 2060–2090. Tank Size (m3) Climate Change Models Tank Size (m3) Climate Change Models 0.5 1 2 5 10 15 20 Observed 0.5- 1- 105–110 2 55–60 5 45–50 10 40–45 15 40–45 20 ACCESS1-0Observed - - 100–105105–110 55–60 55 40–45 45–50 40–45 35–4040–45 BCC-CSM1-1-MACCESS1-0 - - 105–110100–105 55–60 55 40–45 40–45 35–40 BCC-CSM1-1-MCNRM-CM5 - - 105–110110–115 55–60 40–4545–50 40–45 35–4040–45 HADGEM2-CCCNRM-CM5 - - 115–120110–115 50–5555–60 40–4545–50 40–45 35–4040–45 HADGEM2-CCHADGEM2-ES - - 115–120105–110 50–5545–50 40–45 40–4535–40 35–40 HADGEM2-ESMIROC5 - - 105–110115–110 45–5060–65 40–4545–50 35–4040–45 35–4040–45 MIROC5 - - 115–110 60–65 45–50 40–45 40–45 3.3.5. Climate Change Effect on Reliability for JJA Season (2025–2055) 3.3.5. Climate Change Effect on Reliability for JJA Season (2025–2055) During the JJA season for the 2025–2055 period, most models predict an increase in reliability, with During the JJA season for the 2025–2055 period, most models predict an increase in reliability, with MIROC5 predicting the greatest increase of over 25% for both 2 and 5 m3 tank size (refer to Figures 16 WaterMIROC52018, 10predicting, 71 the greatest increase of over 25% for both 2 and 5 m3 tank size (refer to Figures16 of 16 34 and 17). ACESS1-0 however consistently predicts a decline in reliability of less than 5% during this and 17). ACESS1-0 however consistently predicts a decline in reliability of less than 5% during this period. From Figures 16 and 17, the best and worst-case scenarios are predicted by MIROC5 and period. From Figures 16 and 17, the best and worst-case scenarios are predicted by MIROC5 and ACCESS1-0, respectively, hence representing the reliabilityreliability envelope for waterwater harvestingharvesting systemssystems ACCESS1-0, respectively, hence representing the reliability envelope for water harvesting systems during this season. As earlier mentioned, smaller r roofoof sizes are more affectedaffected byby thesethese uncertaintiesuncertainties during this season. As earlier mentioned, smaller roof sizes are more affected by these uncertainties although in this case case they they also also have have the the highest highest increase increase in in reliability reliability comp comparedared to to larger larger systems. systems. although in this case they also have the highest increase in reliability compared to larger systems.

30 30 25 25 20 20 15 15 10 10 5 5 0 0 -5 20 30 40 50 60 70 80 90 100110120130140150160170180190200 -5 20 30 40 50 60 70 80 90 100110120130140150160170180190200 Change in reliability (%) -10 Change in reliability (%) -10 Roof size (m2) Roof size (m2) ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 HADGEM2-CCACCESS1-0 HADGEM2-ESBCC-CSM1-1-M MIROC5CNRM-CM5 HADGEM2-CC HADGEM2-ES MIROC5

Figure 16. JJA Change in reliability for 2 m33 during 2025–2055. Figure 16. JJA Change in reliability forfor 22 mm3 during 2025–2055. 35 35 30 30 25 25 20 20 15 15 10 10 5 5 0 Change in reliability (%) 0 20 30 40 50 60 70 80 90 100110120130140150160170180190200 Change in reliability (%) -5 -5 20 30 40 50 60 70 80 90 100110120130140150160170180190200 -10 -10 Roof size (m2) Roof size (m2) ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 HADGEM2-CC HADGEM2-ES MIROC5 HADGEM2-CC HADGEM2-ES MIROC5

Figure 17. JJA Change in reliability for 5 m3 during 2025–2055. Figure 17. JJA Change in reliability for 5 m33 during 2025–2055. Figure 17. JJA Change in reliability for 5 m during 2025–2055.

3.3.6. Design Requirements to Achieve 80% Reliability for JJA Season (2025–2055) During this period, MIROC5 predicts that daily rainfall will increase by 42% (2.7 mm) for the month of June and 48% (3.7 mm) in July while other models show no significant deviation from observed rainfall during this season. This projected increase in rainfall in this dry season is reflected in the increased projected reliability for MIROC5 compared to other models. Table3 shows that even with lower tank sizes of 1 m3, 80% reliability can be achieved at roof sizes ranging from 125 to 130 m2. This is however not possible for both observed scenario and the other models. For other models 80% reliability is only achievable when tank size in increased to 2 m3 or greater. Therefore, according to MIROC5 predictions, climate change will have a positive impact in that 80% reliability of rainwater harvesting systems will be achieved even with low storage volumes which is not possible under current observation. This is unfortunately not confirmed by the other models. More information on design combinations required for different reliabilities are presented in the supplementary material Section 2.2. Water 2018, 10, 71 16 of 33

3.3.6. Design Requirements to Achieve 80% Reliability for JJA Season (2025–2055)

During this period, MIROC5 predicts that daily rainfall will increase by 42% (2.7 mm) for the month of June and 48% (3.7 mm) in July while other models show no significant deviation from observed rainfall during this season. This projected increase in rainfall in this dry season is reflected in the increased projected reliability for MIROC5 compared to other models. Table 3 shows that even with lower tank sizes of 1 m3, 80% reliability can be achieved at roof sizes ranging from 125 to 130 m2. This is however not possible for both observed scenario and the other models. For other models 80% reliability is only achievable when tank size in increased to 2 m3 or greater. Therefore, according to MIROC5 predictions, climate change will have a positive impact in that 80% reliability of rainwater harvesting systems will be achieved even with low storage volumes which is not possible under Water 2018current, 10, 71observation. This is unfortunately not confirmed by the other models. More information on17 of 34 design combinations required for different reliabilities are presented in the supplementary material Section 2.2. Table 3. Roof size requirement during JJA for 80% reliability (2025–2055). Table 3. Roof size requirement during JJA for 80% reliability (2025–2055). Tank Size (m3) Tank Size (m3) ClimateClimate Change Change Models Models 0.50.5 1 1 2 2 5 5 10 10 15 1520 20 ObservedObserved -- - - 170–175170–17580–85 80–85 75–80 75–80 75–80 75–80 75–80 75–80 ACCESS1-0ACCESS1-0 -- -- >200>200 85–9085–90 80–85 80–85 80–85 80–85 80–85 80–85 BCC-CSM1-1-M - - 175–180 80–85 75–80 75–80 75–80 CNRM-CM5BCC-CSM1-1-M -- - - 105–110175–18050–55 80–85 75–80 45–50 75–80 45–50 75–80 45–50 HADGEM2-CCCNRM-CM5 -- - - 135–140105–11060–65 50–55 45–50 60–65 45–50 60–65 45–50 60–65 HADGEM2-ESHADGEM2-CC -- - - 140–145135–14070–75 60–65 60–65 65–70 60–65 65–70 60–65 65–70 MIROC5HADGEM2-ES - 125–130- 55–60140–145 35–40 70–75 65–70 35–40 65–70 35–40 65–70 35–40 MIROC5 125–130 55–60 35–40 35–40 35–40 35–40 3.3.7. Climate Change Effect on Reliability for JJA Season (2060–2090) 3.3.7. Climate Change Effect on Reliability for JJA Season (2060–2090) Most models predict more increase in reliability by as much as 50% for the MIROC5 model and about 10%Most for models other models.predict more Figure increase 18 shows in reliability that forby as smaller much as roof 50% sizes, for the reliability MIROC5 model is projected and to about 10% for other models. Figure 18 shows that for smaller roof sizes, reliability is projected to increase by over 50%. The increase is even more when tank size is increased to 5 m3 as shown in increase by over 50%. The increase is even more when tank size is increased to 5 m3 as shown in Figure 19. The increase in reliability is greatest for roof sizes ranging from 20–70 m2. The best and Figure 19. The increase in reliability is greatest for roof sizes ranging from 20–70 m2. The best and worst-caseworst-case scenarios scenarios are are predicted predicted by by MIROC5 MIROC5 andand ACCESS1-0, respectively. respectively.

10 Change in reliability (%) 0 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 -10 Roof size (m2) ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 HADGEM2-CC HADGEM2-ES MIROC5

FigureFigure 18. 18.JJA JJA showing showing change changein in reliabilityreliability for for 2060–2090 2060–2090 period period for for2 m 23. m3. Water 2018, 10, 71 17 of 33

70 60 50 40 30 20 10 Change in reliability (%) 0 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 -10 Roof size (m2)

ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 HADGEM2-CC HADGEM2-ES MIROC5

FigureFigure 19. 19.JJA JJA showing showing change change in in reliabilityreliability for for 2060–2090 2060–2090 period period for for5 m 53. m3.

3.3.8. Design Requirements to Achieve 80% Reliability for JJA Season (2060–2090) As shown in Figures 20 and 21, reliability projections for ACCESS1-0, BCC-CSM1-1-M and CNRM-CM5, HADGEM2-CC and HADGEM2-ES only have marginal deviations from the observed scenario. Projections from MIROC5 however, are significantly higher than projections from other models and observed scenario. In fact, even with a very small tank size of 0.5 m3 for instance, MIROC5 predicts that over 75% of reliability can be achieved as roof size increases. The highest reliability for other models and observed scenario is about 60% with larger roof sizes (refer to Figure 20). It is thus not possible to attain 80% reliability when storage is at 0.5 m3 even with roof sizes as large as 200 m2. However, Figure 21 shows that when tank size is doubled to 1 m3 for instance, 80% reliability is achieved at roof sizes slightly higher than 200 m2 but predictions from MIROC5 indicate that with a roof size of between 45 and 50 m2, 80% reliability can be achieved with reliability increasing to over 90% when roof sizes are further increased. An increase in tank size to 2 m3, as shown in Figure 22, shows that 80% reliability is achieved between 80 and 130 m2 roof size for other models while roof sizes of 30 m2 are sufficient for MIROC5.

Observed ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 HADGEM2-CC HADGEM2-ES MIROC5 80 70 60 50 40 30 Reliability (%) 20 10 0 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 105 110 115 120 125 130 135 140 145 150 155 160 165 170 175 180 185 190 195 200 Roof Sizes (m2)

Figure 20. Reliability projections for JJA season for 0.5 m3 tank size for 2060–2090 period.

Water 2018, 10, 71 17 of 33

70 60 50 40 30 20 10 Change in reliability (%) 0 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 -10 Roof size (m2)

ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 HADGEM2-CC HADGEM2-ES MIROC5 Water 2018, 10, 71 18 of 34

Figure 19. JJA showing change in reliability for 2060–2090 period for 5 m3. 3.3.8. Design Requirements to Achieve 80% Reliability for JJA Season (2060–2090) 3.3.8. Design Requirements to Achieve 80% Reliability for JJA Season (2060–2090) As shown in Figures 20 and 21, reliability projections for ACCESS1-0, BCC-CSM1-1-M and CNRM-CM5,As shown HADGEM2-CC in Figures 20 and and HADGEM2-ES 21, reliability projections only have marginalfor ACCESS1-0, deviations BCC-CSM1-1-M from the observed and scenario.CNRM-CM5, Projections HADGEM2-CC from MIROC5 and HADGEM2-ES however, are only signiﬁcantly have marginal higher deviations than projections from the observed from other scenario. Projections from MIROC5 however, are significantly higher than projections from other models and observed scenario. In fact, even with a very small tank size of 0.5 m3 for instance, MIROC5 models and observed scenario. In fact, even with a very small tank size of 0.5 m3 for instance, MIROC5 predicts that over 75% of reliability can be achieved as roof size increases. The highest reliability predicts that over 75% of reliability can be achieved as roof size increases. The highest reliability for for other models and observed scenario is about 60% with larger roof sizes (refer to Figure 20). It is other models and observed scenario is about 60% with larger roof sizes (refer to Figure 20). It is thus thus not possible to attain 80% reliability when storage is at 0.5 m3 even with roof sizes as large as not possible to attain 80% reliability when storage is at 0.5 m3 even with roof sizes as large as 200 m2. 2 3 200However, m . However, Figure Figure 21 shows 21 shows that when that whentank size tank is size doubled is doubled to 1 m to3 1for m instance,for instance, 80% 80%reliability reliability is 2 is achievedachieved at roof sizes slightly slightly higher higher than than 200 200 m m2 butbut predictions predictions from from MIROC5 MIROC5 indicate indicate thatthat with with a 2 a roofroof size size ofof betweenbetween 4545 andand 50 m2,, 80% 80% reliability reliability can can be be achieved achieved with with reliability reliability increasing increasing to toover over 3 90%90% when when roof roof sizes sizes are are further further increased. increased. AnAn increase in in tank tank size size to to 2 2m m3, as, as shown shown in inFigure Figure 22, 22 , 2 showsshows that that 80% 80% reliability reliability is is achieved achieved betweenbetween 8080 and 130 m 2 roofroof size size for for other other models models while while roof roof sizessizes of of 30 30 m 2mare2 are sufﬁcient sufficient for for MIROC5. MIROC5.

3 Figure 20. Reliability projections for JJA season for 0.5 m3 tank size for 2060–2090 period. Water 2018, 10Figure, 71 20. Reliability projections for JJA season for 0.5 m tank size for 2060–2090 period. 18 of 33

Observed ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 HADGEM2-CC HADGEM2-ES MIROC5 100

40 Reliability (%) 20

0 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 105 110 115 120 125 130 135 140 145 150 155 160 165 170 175 180 185 190 195 200 Roof Size (m2)

3 FigureFigure 21. 21.Reliability Reliability projections projections for forJJA JJA seasonseason for 1 m 3 tanktank size size for for 2060–2090 2060–2090 period. period.

Observed ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 HADGEM2-CC HADGEM2-ES MIROC5 100 90 80 70 60 50 40

Reliability (%) 30 20 10 0 20 30 40 50 60 70 80 90 100110120130140150160170180190200 Roof Size (m2)

Figure 22. Reliability projections for JJA season for 2 m3 tank size for 2060–2090 period.

According to Figure 23, with a tank size of 5 m3, MIROC5 predicts that close to 100% reliability can be achieved at roof sizes as small as 30 m2 while other models and the observed scenario attain over 80% reliability at roof sizes ranging from 55–120 m2. Increasing the tank volume to 10 m3 does not show significant changes in reliabilities for all models. In addition, model predictions converge as tank and roof size increase. Comparing increase in reliability with increasing tank volume reveals that when tank volume is increased from 0.5 to 1 m3, reliability increases by 8–15% for all roof sizes and different models. The peak increase is obtained from roof sizes ranging from 50–100 m2 while for MIROC5, the peak reliability increase is obtained at only 20 m2. Increasing tank volume from 0.5 to 2 m3 increases reliability by 12–25% for all roof sizes and model but the highest increase is still obtained between 50 and 100 m2. When the volume is further increased to 5 m3, reliability increases by 14–35% with roof size of 50–100 m2 yielding the highest increase except for MIROC5 which peaks at 20 m2. Increasing the tank volume beyond 5 m3, only marginally increases reliability but roof size requirement is reduced to 45–85 m2 for all other models except MIROC5 which peaks at very small roof sizes.

Water 2018, 10, 71 18 of 33

Observed ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 HADGEM2-CC HADGEM2-ES MIROC5 100

40 Reliability (%) 20

0 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 105 110 115 120 125 130 135 140 145 150 155 160 165 170 175 180 185 190 195 200 Roof Size (m2)

Water 2018, 10, 71 19 of 34 Figure 21. Reliability projections for JJA season for 1 m3 tank size for 2060–2090 period.

Observed ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 HADGEM2-CC HADGEM2-ES MIROC5 100 90 80 70 60 50 40

Reliability (%) 30 20 10 0 20 30 40 50 60 70 80 90 100110120130140150160170180190200 Roof Size (m2)

3 Figure 22. Reliability projections for JJA season for 2 m3 tank size for 2060–2090 period.

According to Figure 23, with a tank size of 5 m3, MIROC5 predicts that close to 100% reliability According to Figure 23, with a tank size of 5 m3, MIROC5 predicts that close to 100% reliability can be achieved at roof sizes as small as 30 m2 while other models and the observed scenario attain can be achieved at roof sizes as small as 30 m2 while other models and the observed scenario attain over 80% reliability at roof sizes ranging from 55–120 m2. Increasing the tank volume to 10 m3 does over 80% reliability at roof sizes ranging from 55–120 m2. Increasing the tank volume to 10 m3 does not show significant changes in reliabilities for all models. In addition, model predictions converge not show signiﬁcant changes in reliabilities for all models. In addition, model predictions converge as as tank and roof size increase. tank and roof size increase. Water Comparing2018, 10, 71 increase in reliability with increasing tank volume reveals that when tank volume19 of 33is increased from 0.5 to 1 m3, reliability increases by 8–15% for all roof sizes and different models. The peak increase is obtainedObserved from roof ACCESS1-0sizes ranging fromBCC-CSM1-1-M 50–100 m2 whileCNRM-CM5 for MIROC5, the peak 2 3 reliability increase HADGEM2-CCis obtained at onlyHADGEM2-ES 20 m . IncreasingMIROC5 tank volume from 0.5 to 2 m increases reliability by 12–25% for all roof sizes and model but the highest increase is still obtained between 50 100 and 100 m2. When the volume is further increased to 5 m3, reliability increases by 14–35% with roof size of 50–100 m802 yielding the highest increase except for MIROC5 which peaks at 20 m2. Increasing the tank volume beyond 5 m3, only marginally increases reliability but roof size requirement is reduced to 45–8560 m2 for all other models except MIROC5 which peaks at very small roof sizes.

40 Reliability (%) 20

0 20 30 40 50 60 70 80 90 100110120130140150160170180190200 Roof size (m2)

3 Figure 23. Reliability projections for JJAJJA seasonseason forfor 55 mm3 tank size for 2060–2090 period.

3.3.9. Climate Change Effect on Reliability for MAM Season (2025–2055) Comparing increase in reliability with increasing tank volume reveals that when tank volume is increasedFigures from24 and 0.5 25 to show 1 m3 ,that reliability the models increases conflict by in 8–15% their forprediction all roof but sizes most and notably, different MIROC5 models. Thepredicts peak a increase reduction is obtainedin reliability from by roof as sizesmuch ranging as 30%. from The 50–100effect is m more2 while significant for MIROC5, for roof the peaksizes reliabilityranging from increase 20–60 is m obtained2. BCC-CSM1-1-M, at only 20 however, m2. Increasing predicts tank the volumehighest fromincrease 0.5 in to reliability 2 m3 increases by as reliabilitymuch as 8%. by 12–25%The two for models all roof thus sizes represent and model the but worst the highestand best-case increase scenarios is still obtained and thus between form the 50 andwater 100 reliability m2. When envelope the volume for this is furtherseason. increased to 5 m3, reliability increases by 14–35% with roof size of 50–100 m2 yielding the highest increase except for MIROC5 which peaks at 20 m2. Increasing 15 10 5 0 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 -5 -10 -15 -20 Change in reliability (%) -25 -30 -35 Roof area (m2)

ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 HADGEM2-CC HADGEM2-ES MIROC5

Figure 24. Change in reliability for MAM 2025–2055 period for 2 m3 tank size.

3.3.10. Design Requirements to Achieve 80% Reliability for MAM Season (2025–2055)

Table 4 shows that for a small tank size of 0.5 m3 very large roof sizes ranging from 85 m2 to over 200 m2 are required to ensure 80% reliability. However, roof size requirement is almost halved when the tank capacity is doubled to 1 m3. Any increase of tank capacity beyond 2 m3 does not result in a reduction in roof size requirement to maintain 80% reliability. For MIROC5, the minimum roof size requirement to maintain 80% reliability is 35–40 m2 which is higher than requirements for other models. When reliability from different design combinations are compared, the highest increase in

Water 2018, 10, 71 19 of 33

Observed ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 HADGEM2-CC HADGEM2-ES MIROC5 100

40 Reliability (%) 20

0 Water 2018, 10, 71 20 30 40 50 60 70 80 90 100110120130140150160170180190200 20 of 34 Roof size (m2) the tank volume beyond 5 m3, only marginally increases reliability but roof size requirement is reduced to 45–85 m2Figurefor all 23. other Reliability models projections except MIROC5 for JJA season which for peaks 5 m3 tank at very size smallfor 2060–2090 roof sizes. period.

3.3.9. Climate Change Effect on Reliability for MAM Season (2025–2055) Figures 24 and 25 show that the models conﬂictconflict in theirtheir predictionprediction but most notably, MIROC5MIROC5 predicts a reductionreduction inin reliabilityreliability byby asas muchmuch asas 30%.30%. The effect is more signiﬁcantsignificant for roof sizes 2 ranging from 20–6020–60 mm2. BCC-CSM1-1-M, BCC-CSM1-1-M, however, however, predicts predicts the the highest increase in reliability by asas much asas 8%.8%. TheThe two two models models thus thus represent represent the the worst worst and and best-case best-case scenarios scenarios and thusand formthus theform water the reliabilitywater reliability envelope envelope for this for season. this season.

15 10 5 0 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 -5 -10 -15 -20 Change in reliability (%) -25 -30 -35 Roof area (m2)

Water 2018, 10, 71 20 of 33 ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 HADGEM2-CC HADGEM2-ES MIROC5 reliability (12–25%) is achieved when tank size is increased from 0.5 to 5 m3 and a roof size of 25–45 m2.

A further increase in tank size does not yield a significant increase in reliability. Other design 3 combinationsFigure for different 24. Change reliabilit in reliabilityy projections for MAM are shown 2025–2055 in the periodperiod supplementary forfor 22 mm3 tank material size. (Section 2.3).

3.3.10. Design Requirements to Achieve 80% Reliability for MAM Season (2025–2055) 15 Table 4 shows10 that for a small tank size of 0.5 m3 very large roof sizes ranging from 85 m2 to over 2 200 m are required5 to ensure 80% reliability. However, roof size requirement is almost halved when the tank capacity is doubled to 1 m3. Any increase of tank capacity beyond 2 m3 does not result in a 0 reduction in roof size20 requirement 30 40 50 60 to maintain 70 80 90 80% 100 reliability. 110 120 130 For 140 MIROC5, 150 160 170the 180minimum 190 200 roof size -5 requirement to maintain 80% reliability is 35–40 m2 which is higher than requirements for other models. When-10 reliability from different design combinations are compared, the highest increase in -15 -20 -25 Change in reliability (%) -30 -35 -40 Roof size (m2)

ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 HADGEM2-CC HADGEM2-ES MIROC5

3 FigureFigure 25. 25.Change Change in in reliability reliabilityfor for MAMMAM 2025–2055 period period for for 5 5 m m3 tanktank size. size.

Table 4. Roof sizes required to achieve 80% reliability during MAM season (2025–2055).

Tank Capacity (m3) Climate Change Models 0.5 1 2 5 10 15 20 Observed 100–105 45–50 30–35 25–30 20–25 20–25 20–25 ACCESS1-0 135–140 50–55 35–40 25–30 25–30 25–30 20–25 BCC-CSM1-1-M 85–90 40–45 25–30 20–25 20–25 20–25 20–25 CNRM-CM5 110–115 40–45 30–35 20–25 20–25 20–25 20–25 HADGEM2-CC 115–120 45–50 30–35 20–25 20–25 20–25 20–25 HADGEM2-ES 95–100 40–45 25–30 20–25 20–25 20–25 20–25 MIROC5 >200 90–95 60–65 40–45 35–40 35–40 35–40

3.3.11. Climate Change Effect on Reliability for MAM Season (2060–2090) Figures 26 and 27 show that during this period, reliability is expected to reduce by over 40% according to MIROC5, HADGEM2-CC and HADGEM2CC projections. The rest of the models have slight deviations from the observed scenario. The changes in reliability are less distinct when the roof area exceeds 80 m2 and tank volume is increased to 5 m3. Although most models agree on the reduction in reliability for this period, MIROC5 still represents the worst-case scenario.

3.3.12. Design Requirements to Achieve 80% Reliability for MAM Season (2060–2090) Figure 28 shows that just like the observed scenario, predictions from ACCESS1-0, BCC-CSM-1- M, and CNRM-CM5 show that with a small tank size of 0.5 m3, an 80% reliability can be achieved with roof sizes of about 100 m2 or greater. On the contrary, HADGEM2-CC, HADGEM2-ES and MIROC5 predict that during this period, it will be impossible to attain 80% reliability even with roof sizes as large as 200 m2. When the tank volume is increased to 1 m3, 80% reliability is achieved at roof sizes as small as 50 m2 for the observed scenario and ACCESS1-0, BCC-CSM-1-M, and CNRM-CM5 models. For HADGEM2-CC, HADGEM2-ES and MIROC5 models 80% reliability is achieved at roof

Water 2018, 10, 71 21 of 34

3.3.10. Design Requirements to Achieve 80% Reliability for MAM Season (2025–2055) Table4 shows that for a small tank size of 0.5 m 3 very large roof sizes ranging from 85 m2 to over 200 m2 are required to ensure 80% reliability. However, roof size requirement is almost halved when the tank capacity is doubled to 1 m3. Any increase of tank capacity beyond 2 m3 does not result in a reduction in roof size requirement to maintain 80% reliability. For MIROC5, the minimum roof size requirement to maintain 80% reliability is 35–40 m2 which is higher than requirements for other models. When reliability from different design combinations are compared, the highest increase in reliability (12–25%) is achieved when tank size is increased from 0.5 to 5 m3 and a roof size of 25–45 m2. A further increase in tank size does not yield a signiﬁcant increase in reliability. Other design combinations for different reliability projections are shown in the supplementary material (Section 2.3).

Table 4. Roof sizes required to achieve 80% reliability during MAM season (2025–2055).

3.3.11. Climate Change Effect on Reliability for MAM Season (2060–2090) Figures 26 and 27 show that during this period, reliability is expected to reduce by over 40% Water according2018, 10, 71 to MIROC5, HADGEM2-CC and HADGEM2CC projections. The rest of the models have21 of 33 slight deviations from the observed scenario. The changes in reliability are less distinct when the sizesroof 130 aream2 or exceeds larger. 80 The m2 modelsand tank tend volume to converge is increased as roof to 5 mand3. Although tank size most is increases. models agree This ondifference the is particularlyreduction ingreater reliability in roof for this sizes period, smaller MIROC5 than 60 still m represents2. the worst-case scenario.

0 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 -10

-20

-30

Change in reliability (%) -40

-50 Roof area (m2) ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 HADGEM2-CC HADGEM2-ES MIROC5

3 FigureFigure 26. 26.ChangeChange inin reliability reliability for for MAM MAM 2060–20902060–2090 period period for for 2 m 23 mtank tank size. size.

0 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 -10

-20

-30

-40 Change in reliability (%) -50

-60 Roof size (m2)

ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 HADGEM2-CC HADGEM2-ES MIROC5

Figure 27. Change in reliability for MAM 2060–2090 period for 5 m3 tank size.

As shown in Figures 29 and 30, when tank size increases to 2 and 5 m3, 80% reliability is achieved at very small roof sizes of 35 m2 or even smaller under observed scenario. ACCESS1-0, BCC-CSM-1- M, and CNRM-CM5 models closely follow the observed scenario and hence show the same reliability. For HADGEM2-CC, HADGEM2-ES and MIROC5 predictions, due to lower reliability predictions, 80% reliability can only be achieved at 80 and 60 m2 for tank sizes of 2 and 5 m3 respectively. Figures 29–31 also show that with larger roof sizes and increased tank capacity, model predictions converge. Increasing tank volume to 10 m3 or more (as shown in Figure 31) does not significantly decrease the roof size requirement for 80% reliability for all models. In fact, the graphs show that for smaller roof sizes (up to 70 m2), model predictions are widely different regardless of tank size. It is thus more beneficial to increase roof size than tank size during this period.

Water 2018, 10, 71 21 of 33 sizes 130 m2 or larger. The models tend to converge as roof and tank size is increases. This difference is particularly greater in roof sizes smaller than 60 m2.

0 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 -10

-20

-30

Change in reliability (%) -40

-50 Roof area (m2) ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 HADGEM2-CC HADGEM2-ES MIROC5

Water 2018, 10, 71 22 of 34 Figure 26. Change in reliability for MAM 2060–2090 period for 2 m3 tank size.

0 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 -10

-20

-30

-40 Change in reliability (%) -50

-60 Roof size (m2)

ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 HADGEM2-CC HADGEM2-ES MIROC5

3 Figure 27. Change in reliability for MAM 2060–2090 period forfor 55 mm3 tank size.

3 3.3.12.As Design shown Requirements in Figures 29 and to Achieve 30, when 80% tank Reliability size increases for MAM to 2 and Season 5 m (2060–2090), 80% reliability is achieved at very small roof sizes of 35 m2 or even smaller under observed scenario. ACCESS1-0, BCC-CSM-1- M, andFigure CNRM-CM5 28 shows models that just closely like the follow observed the observed scenario, scenario predictions and from hence ACCESS1-0, show the same BCC-CSM-1-M, reliability. 3 andFor HADGEM2-CC, CNRM-CM5 show HADGEM2-ES that with a small and tankMIROC5 size ofpredictions, 0.5 m , an 80%due to reliability lower reliability can be achieved predictions, with 2 roof80% sizesreliability of about can 100only m be orachieved greater. at On 80 theand contrary, 60 m2 for HADGEM2-CC, tank sizes of 2 and HADGEM2-ES 5 m3 respectively. and MIROC5 Figures predict29–31 also that show during that this with period, larger it roof will sizes be impossible and increased to attain tank 80%capacity, reliability model even predictions with roof converge. sizes as 2 3 largeIncreasing as 200 m . tank When volume the tank to volume10 m3 or is more increased (as shown to 1 m in ,Figure 80% reliability 31) does isnot achieved significantly at roof decrease sizes as 2 smallthe roof as size 50 m requirementfor the observed for 80% scenario reliability and for ACCESS1-0, all models. BCC-CSM-1-M, In fact, the graphs and show CNRM-CM5 that for models.smaller Forroof HADGEM2-CC,sizes (up to 70 m2 HADGEM2-ES), model predictions and MIROC5are widely models different 80% regardless reliability of istank achieved size. It atis thus roof more sizes 2 130beneficial m or to larger. increase The roof models size tendthan totank converge size during as roof this and period. tank size is increases. This difference is 2 particularlyWater 2018, 10, greater71 in roof sizes smaller than 60 m . 22 of 33

Observed ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 HADGEM2-CC HADGEM2-ES MIROC5

100 90 80 70 60 50 40 Reliability (%) 30 20 10 0 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 Roof Size (m2)

Figure 28. Reliability projections for MAM season for 0.5 m3 tank size for 2060–2090 period. Figure 28. Reliability projections for MAM season for 0.5 m3 tank size for 2060–2090 period.

Observed ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 HADGEM2-CC HADGEM2-ES MIROC5 100 90 80 70 60 50 40

Reliability (%) 30 20 10 0 20 30 40 50 60 70 80 90 100110120130140150160170180190200 Roof Size (m2)

Figure 29. Reliability projections for MAM season for 2 m3 tank size for 2060–2090 period.

3.3.13. Climate Change Effect on Reliability for SON Season (2025–2055) Figures 32 and 33 show that during the SON season, there are mixed predictions on the changes in reliability. While HADGEM2-CC, HADGEM2-ES and ACCESS1-0 predict a 1–5% decrease in reliability, BCC-CSM1-1M and CNRM-CM5 forecast an increase in reliability of 1–4%. The deviations are not significant with larger roof areas. As earlier reported, roof sizes of 20–40 m2 are most prone to changes in reliability than larger systems. BCC-CSM1-1M and HADGEM2-ES represent the best and worst-case scenarios for this season. Increasing the tank size beyond 5 m3 does not show any significant changes in reliability hence roof size is more important than tank size for this season.

Water 2018, 10, 71 22 of 33

Observed ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 HADGEM2-CC HADGEM2-ES MIROC5 100 90 80 70 60 50 40 Reliability (%) Water 2018, 1030, 71 23 of 34 20

As shown10 in Figures 29 and 30, when tank size increases to 2 and 5 m3, 80% reliability is achieved at very small0 roof sizes of 35 m2 or even smaller under observed scenario. ACCESS1-0, BCC-CSM-1-M, and CNRM-CM520 models 30 40 closely 50 60 follow 70 the80 observed 90 100 110 scenario 120 130 and 140 hence 150 show 160 170 the 180 same 190 reliability. 200 2 For HADGEM2-CC, HADGEM2-ES and MIROC5 predictions,Roof Size (m ) due to lower reliability predictions, 80% reliability can only be achieved at 80 and 60 m2 for tank sizes of 2 and 5 m3 respectively. Figures 29–31 also showFigure that 28. with Reliability larger roof projections sizes and forincreased MAM season tank for capacity, 0.5 m3 tank model size predictions for 2060–2090 converge. period.

Observed ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 HADGEM2-CC HADGEM2-ES MIROC5 100 90 80 70 60 50 40

Reliability (%) 30 20 10 0 20 30 40 50 60 70 80 90 100110120130140150160170180190200 Roof Size (m2)

Figure 29. Reliability projections for MAM season for 2 m33 tank size for 2060–2090 period. Water 2018, 10,Figure 71 29. Reliability projections for MAM season for 2 m tank size for 2060–2090 period. 23 of 33 3.3.13. Climate Change Effect on Reliability for SON Season (2025–2055) Observed ACCESS1-0 BCC-CSM1-1-M Figures 32 and 33 showCNRM-CM5 that during the SONHADGEM2-CC season, there are mixedHADGEM2-ES predictions on the changes in reliability. While HADGEM2-CC, HADGEM2-ES and ACCESS1-0 predict a 1–5% decrease in reliability, BCC-CSM1-1M100 and CNRM-CM5 forecast an increase in reliability of 1–4%. The deviations are not significant90 with larger roof areas. As earlier reported, roof sizes of 20–40 m2 are most prone to changes in reliability80 than larger systems. BCC-CSM1-1M and HADGEM2-ES represent the best and worst-case scenarios70 for this season. Increasing the tank size beyond 5 m3 does not show any significant changes60 in reliability hence roof size is more important than tank size for this season. 50 40

Reliability (%) 30 20 10 0 20 30 40 50 60 70 80 90 100110120130140150160170180190200

Roof Size (m2)

3 FigureFigure 30. 30. ReliabilityReliability projections projections for for MAM season forfor 55m m3 tank tank size size for for 2060–2090 2060–2090 period. period.

No climate change3 ACCESS1-0 BCC-CSM1-1-M Increasing tank volumeCNRM-CM5 to 10 m or more (asHADGEM2-CC shown in Figure 31)HADGEM2-ES does not signiﬁcantly decrease the roof size requirement100 for 80% reliability for all models. In fact, the graphs show that for smaller roof sizes (up to 70 m2), model predictions are widely different regardless of tank size. It is thus more beneﬁcial to increase80 roof size than tank size during this period.

40 Reliability (%) 20

0 20 30 40 50 60 70 80 90 100110120130140150160170180190200 Roof Size (m2)

Figure 31. Reliability projections for MAM season for 20 m3 tank size.

4 3 2 1 0 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 -1 -2 -3

Change in reliability (%) -4 -5 -6 Roof area (m2)

ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 HADGEM2-CC HADGEM2-ES MIROC5

Figure 32. Change in reliability for SON season for 2025–2055 period for 2 m3 tank size.

Water 2018, 10, 71 23 of 33

Observed ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 HADGEM2-CC HADGEM2-ES

100 90 80 70 Water 2018, 10, 71 60 23 of 33 50 40 Observed ACCESS1-0 BCC-CSM1-1-M Reliability (%) 30 CNRM-CM5 HADGEM2-CC HADGEM2-ES 20 10010 900 20 30 40 50 60 70 80 90 100110120130140150160170180190200 80 2 70 Roof Size (m ) 60 Water 2018, 10, 71 24 of 34 Figure 5030. Reliability projections for MAM season for 5 m3 tank size for 2060–2090 period. 40

Reliability (%) 30 No climate change ACCESS1-0 BCC-CSM1-1-M 20 CNRM-CM5 HADGEM2-CC HADGEM2-ES 10100 0 8020 30 40 50 60 70 80 90 100110120130140150160170180190200 Roof Size (m2) 60

Figure 30.40 Reliability projections for MAM season for 5 m3 tank size for 2060–2090 period. Reliability (%)

20 No climate change ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 HADGEM2-CC HADGEM2-ES 1000 20 30 40 50 60 70 80 90 100110120130140150160170180190200 80 Roof Size (m2)

60 FigureFigure 31. 31.ReliabilityReliability projections projections for MAMMAM season season for for 20 m203 tankm3 tank size. size. 40 3.3.13. Climate Change Effect on Reliability for SON Season (2025–2055) Reliability (%) Figures4 3220 and 33 show that during the SON season, there are mixed predictions on the changes in reliability. While3 HADGEM2-CC, HADGEM2-ES and ACCESS1-0 predict a 1–5% decrease in reliability, BCC-CSM1-1M0 and CNRM-CM5 forecast an increase in reliability of 1–4%. The deviations are not 20 30 40 50 60 70 80 90 100110120130140150160170180190200 signiﬁcant with2 larger roof areas. As earlier reported, roof sizes of 20–40 m2 are most prone to changes Roof Size (m2) in reliability1 than larger systems. BCC-CSM1-1M and HADGEM2-ES represent the best and worst-case scenarios for this season. Increasing the tank size beyond 5 m3 does not show any signiﬁcant changes 0 in reliability henceFigure roof 31. size Reliability is more importantprojections than for tankMAM size season for this for season. 20 m3 tank size. 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 -1 -2 4 -3

Change in reliability (%) 3 -4 2 -5 1 -6 0 Roof area (m2) 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 ACCESS1-0-1 BCC-CSM1-1-M CNRM-CM5 HADGEM2-CC HADGEM2-ES MIROC5 -2 3 Figure-3 32. Change in reliability for SON season for 2025–2055 period for 2 m tank size.

Change in reliability (%) -4 -5 -6 Roof area (m2)

ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 HADGEM2-CC HADGEM2-ES MIROC5

FigureFigure 32. Change 32. Change in reliability in reliability for for SON SON season seasonfor for 2025–2055 2025–2055 period period for 2for m3 2tank m3 size.tank size.

Water 2018, 10, 71 25 of 34 Water 2018, 10, 71 24 of 33

0 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 -2

-4 Change in reliability (%) -6

-8 Roof size (m2) ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 HADGEM2-CC HADGEM2-ES MIROC5

3 Figure 33. Change in reliability for SON seasonseason forfor 2025–20552025–2055 periodperiod forfor 55 mm3 tank size.

3.3.14. Design Requirements to Achieve 80% Reliability for SON Season (2025–2055) 3.3.14. Design Requirements to Achieve 80% Reliability for SON Season (2025–2055) During thisthis period, period, all all model model predictions predictions are notare signiﬁcantly not significantly different different from the from observed the observed scenario. Tablescenario.5 shows Table that 5 shows with small that with design sm combinationsall design combinations like 1 m 3 and like 50 1 mm23 androof 50 size, m2 it roof is possible size, it is to possible achieve reliabilitiesto achieve reliabilities of 80% and of more. 80% Comparingand more. Comparin reliabilityg from reliability different from design different combinations design combinations shows that whenshows tank that sizewhen is increasetank size from is increase 0.5–1 m from3, reliability 0.5–1 m3 increased, reliability by increased 8–11% for by all 8–11% models for and all models the greatest and increasethe greatest was increase obtained was for obtained roof sizes for less roof than sizes 30 less m 2than. A further 30 m2. A increase further ofincrease the tank of the volume tank volume to 2 m3 increasedto 2 m3 increased reliability reliability by 15–16%. by When15–16%. tank When volume tank is volume 5 m3, reliability is 5 m3, reliability increases by increases 15–25%. by Increasing 15–25%. theIncreasing volume the to 10volume m3 and to more10 m3yields and more an increase yields an in reliabilityincrease in by reliability 20–26%. by The 20–26%. greatest The increase greatest in reliabilityincrease in is recordedreliability at is roof recorded sizes of at 30 roof m2 orsizes less. of All 30 models m2 or haveless. almostAll models similar have predictions almost similar during thispredictions period andduring agree this that period with and small agree roof that sizes, with 80%small reliability roof sizes, is 80% achievable reliability even is achievable with relatively even smallerwith relatively tank volumes smaller of tank 0.5 tovolumes 2 m3. More of 0.5 design to 2 m combinations3. More design required combinations for different required reliabilities for different are presentedreliabilities in are the presented supplementary in the su materialpplementary (Section material 2.4). (Section 2.4).

Table 5. SON roof size required forfor 80%80% reliabilityreliability forfor varyingvarying tanktank sizessizes (2025–2055).(2025–2055).

Tank Capacity (m2) Climate Change Models Tank Capacity (m2) Climate Change Models 0.5 1 2 5 10 15 20 0.5 1 2 5 10 15 20 Observed 90–95 45–50 35–40 25–30 25–30 25–30 20–25 ObservedACCESS1-0 90–95100–105 45–50 50–55 35–40 35–40 25–30 25–30 25–30 20–25 25–30 20–25 20–25 ACCESS1-0 100–105 50–55 35–40 25–30 25–30 20–25 20–25 BCC-CSM1-1-MBCC-CSM1-1-M 90–9590–95 40–45 40–45 30–35 30–35 25–30 20–25 20–25 20–25 20–25 20–25 20–25 CNRM-CM5CNRM-CM5 90–9590–95 45–50 45–50 35–40 35–40 20–35 25–30 25–30 20–25 20–25 20–25 20–25 HADGEM2-CCHADGEM2-CC 85–9085–90 45–50 45–50 35–40 35–40 30–35 25–30 25–30 25–30 25–30 25–30 25–30 HADGEM2-ESHADGEM2-ES 85–9085–90 45–50 45–50 35–40 35–40 30–35 25–30 25–30 25–30 25–30 25–30 25–30 MIROC5 90–95 40–45 30–35 25–30 25–30 25–30 20–25 MIROC5 90–95 40–45 30–35 25–30 25–30 25–30 20–25

3.3.15. Climate Change Effect on Reliability for SON Season (2060–2090)(2060–2090) From FiguresFigures 34 34 and and 35 35,, most most of of the the models models predict predict an increasean increase in reliability in reliability by over by 40%over while 40% while small 3 deviationssmall deviations are recorded are recorded by other modelsby other for models tank size for of tank 2 to 5size m . HADGEM2-ESof 2 to 5 m3. HADGEM2-ES and HADGEM2-CC and 2 predictHADGEM2-CC the greatest predict increase the greatest in reliability. increase This in is reliability. however only This reflected is however with only roof reflected sizes under with 50 roof m especiallysizes under when 50 m the2 especially tank size when is increased. the tank size is increased.

Water 2018, 10, 71 26 of 34 Water 2018, 10, 71 25 of 33 Water 2018, 10, 71 25 of 33

50 50

40 40

30 30

20 20

10 10 Change in reliability (%) Change in reliability (%) 0 0 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 -10 -10 Roof size (m2) Roof size (m2) ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 HADGEM2-CC HADGEM2-ES MIROC5 ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 HADGEM2-CC HADGEM2-ES MIROC5

Figure 34. Change in reliability for SON season for 2060–2090 period for 2 3m3 tank. FigureFigure 34. 34. ChangeChange in in reliability reliability for SON seasonseasonfor for 2060–2090 2060–2090 period period for for 2 m2 mtank.3 tank.

50 50

40 40

30 30

20 20

10 Change in reliability (%) 10 Change in reliability (%)

0 0 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 -10 -10 Roof size (m2) Roof size (m2) ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 HADGEM2-CC HADGEM2-ES MIROC5 ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 HADGEM2-CC HADGEM2-ES MIROC5

3 Figure 35. Change in reliability for SON season for 2060–2090 period for 5 m3 tank. FigureFigure 35. 35. ChangeChange in in reliability reliability for SON seasonseasonfor for 2060–2090 2060–2090 period period for for 5 m5 3mtank. tank. 3.3.16. Design Requirements to Achieve 80% Reliability for SON Season (2060–2090) 3.3.16.3.3.16. Design Design Requirements Requirements to to Achieve Achieve 80% Reliability forfor SONSON Season Season (2060–2090) (2060–2090) Figures 36 and 37 show that with small storage volumes of 0.5 and 1 m3, 80% reliability or more Figures 36 and 37 show that with small storage volumes of 0.5 and 13 m3, 80% reliability or more is achievableFigures even36 and with 37 show roof thatsizes with as smallsmall storageas 50 m volumes2 for the of observed 0.5 and 1 m scenario., 80% reliability Other models or more isalso is achievableachievable even even with with roof roof sizes sizes as small as small as 50 as m 250for m the2 for observed the observed scenario. scenario. Other models Other also models predict also predict the same requirement. HADGEM2-ES and HADGEM2-CC however, show that 100% predictthe same the requirement.same requirement. HADGEM2-ES HADGEM2-ES and HADGEM2-CC and HADGEM2-CC however, show however, that 100% show reliability that 100% is reliability is possible even with roof sizes as small as 20 m2 or less. reliabilitypossible is even possible withroof even sizes with as roof small sizes as 20 as m small2 or less. as 20 m2 or less.

Water 2018, 10, 71 27 of 34 Water 2018, 10, 71 26 of 33 Water 2018, 10, 71 26 of 33

Observed ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 Observed ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 HADGEM2-CC HADGEM2-ES MIROC5 HADGEM2-CC HADGEM2-ES MIROC5 100 100 90 90 80 80 70 70 60 60 50 50 40 40 Reliability (%)

Reliability (%) 30 30 20 20 10 10 0 0 20 30 40 50 60 70 80 90 100110120130140150160170180190200 20 30 40 50 60 70 80 90 100110120130140150160170180190200 Roof Size (m2) Roof Size (m2)

Figure 36. Reliability projections for SON season for 0.5 m3 3 tank size for 2060–2090 period. FigureFigure 36. 36.ReliabilityReliability projections projections for for SON SON season forfor 0.5 0.5 m m3tank tank size size for for 2060–2090 2060–2090 period. period.

Observed ACCESS1-0 BCC-CSM1-1-M Observed ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 HADGEM2-CC HADGEM2-ES CNRM-CM5 HADGEM2-CC HADGEM2-ES MIROC5 MIROC5 100 100 90 90 80 80 70 70 60 60 50 50 40 40 Reliability (%) 30 Reliability (%) 30 20 20 10 10 0 0 20 30 40 50 60 70 80 90 100110120130140150160170180190200 20 30 40 50 60 70 80 90 100110120130140150160170180190200 Roof Size (m2) Roof Size (m2)

3 Figure 37. Reliability projections for SON season for 1 m3 tank size for 2060–2090 period. FigureFigure 37. 37.ReliabilityReliability projections projections for for SON SON season forfor 1 1 m m3 tank tank size size for for 2060–2090 2060–2090 period. period.

2 A further increase in tank size beyond 1 m2, is not necessary especially for HADGEM2-ES and A further increase in tank size beyond 1 m2, is not necessary especially for HADGEM2-ES and HADGEM2-CCA further reliability increase inpredictions tank size beyond since almost 1 m , is 100% not necessary reliability especially is achieved for HADGEM2-ES at tank sizes andranging HADGEM2-CCHADGEM2-CC reliability reliability predictions predictions since since almost almost 100%100% reliabilityreliability is is achieved achieved at tankat tank sizes sizes ranging ranging from 0.5 to 1 m3 as shown by Figures 38 and 39 Increasing tank volume beyond 1 m3 only increases fromfrom 0.5 to 0.5 1 to m 13 mas3 shownas shown by by Figures Figures 38 38 and and 3939 IncreasingIncreasing tank tank volume volume beyond beyond 1 m 13 onlym3 only increases increases reliability by 3–4% with no reduction in roof size requirement. As Figures 36–39 show, models reliabilityreliability by by3–4% 3–4% with with nono reductionreduction in roofin roof size requirement.size requirement. As Figures As 36 Figures–39 show, 36–39 models show, converge models converge when roof sizes are increased but greater differences are reflected when roof areas are small. convergewhen when roof sizes roof are sizes increased are increased but greater but differences greater di arefferences reﬂected are when reflected roof areas when are roof small. areas are small.

Water 2018, 10, 71 28 of 34 Water 2018, 10, 71 27 of 33 Water 2018, 10, 71 27 of 33

Observed ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 Observed ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 HADGEM2-CC HADGEM2-ES MIROC5 HADGEM2-CC HADGEM2-ES MIROC5

100 100 90 90 80 80 70 70 60 60 50 50 40 40 Reliability (%) 30 Reliability (%) 30 20 20 10 10 0 0 20 30 40 50 60 70 80 90 100110120130140150160170180190200 20 30 40 50 60 70 80 90 100110120130140150160170180190200 Roof Size (m2) Roof Size (m2)

Figure 38. Reliability projections for SON season for 2 m33 tank size for 2060–2090 period. FigureFigure 38. 38. ReliabilityReliability projections projections for for SON season forfor2 2m m3tank tank size size for for 2060–2090 2060–2090 period. period.

Observed ACCESS1-0 BCC-CSM1-1-M Observed ACCESS1-0 BCC-CSM1-1-M CNRM-CM5 HADGEM2-CC HADGEM2-ES CNRM-CM5 HADGEM2-CC HADGEM2-ES MIROC5 MIROC5 100 100 90 90 80 80 70 70 60 60 50 50 40

Reliability (%) 40 Reliability (%) 30 30 20 20 10 10 0 0 20 30 40 50 60 70 80 90 100110120130140150160170180190200 20 30 40 50 60 70 80 90 100110120130140150160170180190200 Roof Size (m2) Roof Size (m2)

3 Figure 39. Reliability projections for SON season for 5 m3 tank size for the 2060–2090 period. FigureFigure 39. 39. ReliabilityReliability projections projections for for SON season forfor 55 mm3 tanktank size size for for the the 2060–2090 2060–2090 period. period. 3.4. Water Security 3.4.3.4. Water Water Security Security As explained in Section 2.3, water security is defined as the ratio of frequency of having an empty As explained in Section 2.3, water security is defined as the ratio of frequency of having an empty tank toAs the explained number inof Sectiondays in 2.3 the, water simulation. security This is deﬁned was also as the analyzed ratio of frequencyper season of and having is presented an empty in tanktank to tothe the number number of of days days in in the the simulation. simulation. This This was was also also analyzed analyzed per per season season and and is is presented presented in this section. Tables 6–9 present estimated water security as predicted by the different models for the thisin section. this section. Tables Tables 6–9 6present–9 present estimated estimated water water secu securityrity as aspredicted predicted by by the the different different models models for for the specified periods. The data presented are for a roof size of 30 m2 because this is representative of a specifiedthe speciﬁed periods. periods. The data The datapresented presented are arefor fora roof a roof size size of of30 30 m m2 2becausebecause this this is is representative representative ofof a typical roof area for rural households in Kabarole district. typicala typical roof roof area area for forrural rural hous householdseholds in inKabarole Kabarole district. district.

Table 6. Observed and projected water security (%) for MAM rain season. Table 6. Observed and projected water security (%) for MAM rain season. Tank Size (m3), 30 m2 Roof Size Scenario Tank Size (m3), 30 m2 Roof Size Scenario Period 0.5 1 2 5 10 15 20 Period 0.5 1 2 5 10 15 20 a 35 26 19 10 5 5 5 Observed a 35 26 19 10 5 5 5 Observed b 35 26 19 10 5 5 5 b 35 26 19 10 5 5 5

Water 2018, 10, 71 29 of 34

Table 6. Observed and projected water security (%) for MAM rain season.

Tank Size (m3), 30 m2 Roof Size Scenario Period 0.5 1 2 5 10 15 20 a 35 26 19 10 5 5 5 Observed b 35 26 19 10 5 5 5 a 39 29 22 13 8 8 8 ACCESS1.0 b 37 27 19 10 6 6 6 a 33 23 15 6 5 5 5 BCC-CSM1.1 b 36 27 19 8 5 5 5 a 35 25 17 8 5 5 5 CNRM-CM5 b 33 24 17 7 5 5 5 a 34 25 17 8 5 5 5 HADGEM2-CC b 62 59 57 55 51 49 46 a 33 23 16 8 5 5 5 HADGEM2-ES b 62 58 56 54 51 48 48 a 52 46 42 40 37 34 31 MIROC5 b 62 59 57 55 52 49 49 Notes: a 2025–2055; b 2060–2090.

As shown in Table6, the models provide mixed estimates for water security. For instance, for the 2025–2055 period, BCC-CSM1.1, HADGEM2-ES and HADGEM2-ES models predict that a tank of 0.5 m3 capacity would be less likely to be empty by only 1–2% when compared to observed data. ACCESS1.0 and MIROC5 however predict that such a tank will be more likely to be empty by 4% and 17%, respectively. For the 2060–2090 period, however, most of the models predict that a 0.5m3 tank will be more likely to be empty by 1–27%. HADGEM2-ES, HADGEM2-ES and MIROC5 show the greatest deviation (27%) from observed scenario. Table6 also shows that when tank volume is increase beyond 10 m3, there is no signiﬁcant increase in water security thus is it more beneﬁcial to increase roof area.

Table 7. Observed and projected water security for SON rain season.

Tank Size (m3) 30 m2 Roof Size Scenario Period 0.5 1 2 5 10 15 20 a 36 28 22 14 11 9 9 Observed b 36 28 22 14 11 9 9 a 37 28 22 14 9 7 7 ACCESS1.0 b 39 30 24 15 10 8 8 a 35 26 19 11 7 7 7 BCC-CSM1.1 b 31 21 15 7 4 4 4 a 36 27 21 13 8 7 7 CNRM-CM5 b 37 28 22 14 8 8 8 a 37 29 24 17 13 13 13 HADGEM2-CC b 4 2 <1 <1 <1 <1 <1 a 37 29 24 17 14 13 13 HADGEM2-ES b 5 2 <1 <1 <1 <1 <1 a 36 27 21 14 10 9 9 MIROC5 b 38 29 24 16 13 11 11 Notes: a 2025–2055; b 2060–2090.

From Table7, just like the MAM forecasts, the models provide mixed predictions for water security for the SON season. For instance, for the 2025–2055 period, ACCESS1.0, HADGEM2-ES and HADGEM2-ES models predict that a tank of 0.5 m3 capacity would be more likely to be empty by only Water 2018, 10, 71 30 of 34

1% when compared to observed data. On the other hand, predictions for BCC-CSM1.1 and MIROC5 for the same tank capacity, show that such a tank will be less likely to be empty by 1–2% when compared to observed scenario. For the 2060–2090 period, ACCESS1.0, MIROC5 and CNRM-CM5 show that such a tank will be more likely to be empty by 1–3% when compared to observed scenario. However, HADGEM2-ES, HADGEM2-CC and BCC-CSM1.1 show that a 0.5 m3 tank will be less likely to be empty by 31%, 32% and 1%, respectively. Despite these contradictions, however, water security does not signiﬁcantly increase when the tank size is increased beyond 5 m3. In fact, both HADGEM2-ES and HADGEM2-CC predict that a tank size as small as 1 m3 will be sufﬁcient for the 2060–2090 period.

Table 8. Observed and projected water security for JJA dry season.

Tank Size (m3) 30 m2 Roof Size Scenario Period 0.5 1 2 5 10 15 20 a 67 61 59 57 54 52 52 Observed b 67 61 59 57 54 52 52 a 70 64 61 59 56 55 55 ACCESS1.0 b 68 62 57 55 52 49 48 a 67 61 59 57 54 52 52 BCC-CSM1.1 b 61 54 50 46 44 41 39 a 59 52 48 46 43 40 39 CNRM-CM5 b 58 51 46 43 41 38 35 a 63 56 52 50 48 45 42 HADGEM2-CC b 64 56 50 45 42 40 37 a 64 57 54 52 49 46 44 HADGEM2-ES b 63 56 49 44 41 38 35 a 53 43 33 26 23 23 23 MIROC5 b 36 22 8 2 2 2 2 Notes: a 2025–2055; b 2060–2090.

Table 9. Observed and projected water security for DJF dry season.

Tank Size (m3) 30 m2 Roof Size Scenario Period 0.5 1 2 5 10 15 20 a 64 59 53 46 43 43 43 Observed b 64 59 53 46 43 43 43 a 63 57 50 41 39 36 36 ACCESS1.0 b 63 57 50 41 39 36 36 a 65 59 53 45 42 41 41 BCC-CSM1.1 b 64 59 53 45 42 41 41 a 65 69 54 48 45 45 45 CNRM-CM5 b 65 60 53 48 45 45 45 a 71 66 61 56 55 55 55 HADGEM2-CC b 64 66 61 56 55 55 55 a 72 66 61 61 56 56 56 HADGEM2-ES b 62 66 61 61 56 56 56 a 64 58 52 45 42 41 41 MIROC5 b 66 58 52 45 41 41 41 Notes: a 2025–2055; b 2060–2090.

From Table8, during the JJA dry season, all models predict an increase in water security except for ACCESS1.0 model, which predicts a reduction of water security by 1–3% for a 0.5 m3 tank for both periods. The rest of the models predict that a 0.5 m3 tank will be less empty by 3–31% for both periods. Water 2018, 10, 71 31 of 34

In addition, expanding the tank beyond 10 m3 does not significantly increase water security unless the roof size is increased as well. From Table9, for 2025–2055 period, most models except ACCESS1.0 predict that the likelihood of a 0.5 m3 tank being empty increases by 1–8% from the observed scenario. The ACCESS1.0 model, however does not deviate much from observed scenario. The models still show conflicting forecasts for the 2060–2090 period, but the decrease and increase are by only 1–2% from observed scenario. Like the JJA dry season, expanding the tank beyond 10 m3 does not significantly increase water security unless the roof size is increased too.

4. Discussion Due to the high costs required for construction of bigger houses, most households in rural areas in Uganda have roof sizes as small as 30 m2 or less. In addition, the high cost of purchasing or building bigger tank capacities makes it hard for such households to have tank sizes larger than 2 m3. Despite these limitations, the results show that smaller tank and roof sizes will be sufficient for 80% reliability during most of the rain seasons. Contrary to findings by Rahman et al. [17], reliability increases with changes in projected rainfall during the SON rain season for 2060–2090 period while no significant changes are observed for the 2025–2055 period. Unlike in Australia where larger tank sizes will be required, for Kabarole district, the increase in reliability will mean that even low volumes and roof areas will be sufficient to maintain 80% reliability. In fact, with small roof sizes of 30 m2 and tank volumes of 1 m3, more than 80% reliability will be achievable during the SON season for 2060–2090 period. Under current observation, this can only be achieved with a roof size of 50 m2. For MAM rain season (2025–2055), considering the worst-case scenario, a roof size of 60–65 m2 and tank volume of 5 m3 will be needed during this period which is double the current requirement. For the 2060–2090 MAM season, the worst-case scenario predicts a drastic decline in reliability of over 40% which is more evident with smaller roof sizes. The implication is that for households to maintain 80% reliability, it is imperative that roof size is increased to 60 m2 or more and tank size increased to 5 m3. This is almost double the current requirement of 35 m2 with a 5 m3 tank volume. During the dry season of DJF (2025–2055), current observation shows that unless roof sizes are higher than 200 m2, it is not possible to achieve 80% reliability with low storage volumes of 0.5 or 1 m3. Although there are some deviations in model predictions, generally, rural households will need a larger roof area and tank volume if 80% reliability is to be achieved. Considering the worst-case scenario, a roof size of 85 m2 and 5 m3 tank volume will be sufficient to maintain 80% reliability during this period. This is in line with findings by Rahman et al. [17] who found out that reliability will be negatively affected by climate change in Australia in the dry season. For the 2060–2090 period, although reliability is expected to increase, small tank sizes of 2 m3 or less will still not be sufficient to maintain 80% reliability even with larger roof sizes both under observed scenario and all model projections. When the worst-case scenario is considered, a 5 m3 tank volume and 60–70 m2 roof size will be sufficient to achieve 80% reliability. Projections for 2025–2055 JJA dry season show that for attainment of 80% reliability, a tank size of 5 m3 with corresponding roof size of 50–90 m2 will be sufficient under observed scenario. Other models do not predict any significant changes in reliability from observed scenario except for MIROC5 which predicts a much smaller roof size of 40 m2. For design purposes however, it is always advisable to account for the worst-case scenario which in this case is the current observation. For 2060–2090, MIROC predicts very high reliabilities compared to observed scenario which would necessitate small roof sizes and tank volumes for the attainment of 80% reliability. Under the observed scenario, a tank size of 5 m3 and roof size of 50–100 m2 will be appropriate to ensure 80% system’s reliability during this season. While this is still true for other model predictions, projections from MIROC5 show that roof areas as small as 30 m2 will be sufficient. It is worth noting that although JJA and DJF are considered dry seasons in Uganda, they still get 20–30% rain days compared to over 40% rain days in MAM and SON rain seasons. While the dry period length (consecutive days without rain) varies from 30–65 days in the dry season, the longest dry period length in the rain season is Water 2018, 10, 71 32 of 34

15 days. However, the seasonal rainfall totals of MAM and SON are double the seasonal rainfall totals of JJA and DJF. Londra et al. [12,45] demonstrated that tank size is strongly affected by the dry period length. This could explain the need for low tank volumes even in dry seasons for Kabarole district. The same pattern is observed for the different climate change model predictions. The biggest water security reduction is recorded during the MAM season which is a 27% decrease from observed scenario. This is predicted by MIROC5 in 2060–2090 and a decrease in water security of 17% for 2025–2055 period. DJF period is also expected to experience reduced water security by 1–8% for both periods and tank size of 0.5 m3. The other periods are projected to experience increased water security compared to observed scenario. It is thus imperative that households harness the increased water volumes in the seasons of SON and JJA to cater for the reductions in water security during the DJF and MAM seasons.

5. Conclusions This paper assessed the effect of climate change on reliability of rainwater harvesting systems for Kabarole district, Uganda using an ensemble of 6 best performing GCMs. Seasonal analysis was performed based on the two (2) rain seasons of March, April, May (MAM) and September, October, November (SON) and the two (2) dry seasons of June, July, August (JJA) and December, January, February (DJF). While MIROC5 predicts a decline in daily rainfall during the rain seasons and an increase in the dry seasons, other models do not predict signiﬁcant deviations of daily rainfall from current observation for the 2025–2055 period. However, greater deviations are predicted by all models for the 2060–2090 period. While an increase in reliability is predicted for the SON season, MAM season is projected to experience the greatest reduction in reliability for the 2055–2090 period. This corresponds to a reduction in water security of 27% from current observation. For the dry seasons, most models predict a slight decline in reliability and water security except MIRCO5 that predicts otherwise. Overall, the study recommends an increase in both roof and tank size from the current average of 30 m2 to 50 m2 and tank volume of 5 m3. However, the uncertainties in climate change projections exacerbated by the limited meteorological data are some of the limitations of this study.

Supplementary Materials: The following are available online at www.mdpi.com/2073-4441/10/1/71/s1. Acknowledgments: Special thanks go to the Flemish Interuniversity Council—Inter University Cooperation (VLIR-IUC) programme for funding this research and Mountains of the Moon University staff for the constant encouragement, advice and providing an excellent working environment. The Ministry of Water and Environment is appreciated for availing historical data used in the simulations. The Authors also thank the 3 anonymous reviewers for their comments that greatly improved the quality of the paper. Author Contributions: Violet Kisakye built the rainwater simulation model and wrote the paper, Mary Akurut generated the daily rainfall projections due to climate change and contributed to the writing of the paper while Bart Van der Bruggen reviewed and streamlined the paper. Conﬂicts of Interest: The authors declare no conﬂict of interest.

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