Water Demand Forecasting for the City of the Future against the Uncertainties and the Global Change Pressures: Case of Birmingham K.B. Khatri1 and K. Vairavamoorthy1 Researcher, School of Civil Engineering, College of Engineering and Physical Sciences, B15 2TT Edgbaston, Birmingham, UK, PH (+44) 121 414 5152; email:
[email protected] Professor, School of Civil Engineering, College of Engineering and Physical Sciences, B15 2TT Edgbaston, Birmingham, UK, PH (+44) 121 414 5147; email:
[email protected]
ABSTRACT In order to ensure the adequate and sustainable water management for the city of the future, the impact of the global change pressures and sources of uncertainties should be analyzed appropriately. This paper presents a model for forecasting the future water demand addressing the uncertainties associated to the climate change, population and economic growth. It uses the historic time series records of water consumption for forecasting the future water demand, and applies the Monte Carlo sampling, Latin hypercube sampling and bootstrap methods to describe the associated uncertainties. The model was applied in Birmingham, UK to analyse the water demand for year 2035. Results showed that future water demand in Birmingham will be governed by the socio-economic factors not by the climate change impact. There is a very high likely risk of not meeting the future water demand from the existing supply sources. Key words: Global change pressures, uncertainties, scenarios, forecasting, sampling
INTRODUCTION Forecasting of water demand is a crucial component in the successful operation of water supply system. Accurately forecasted water demand either in short-term, or medium-term, or long-term time horizons can be very useful for capacity planning, scheduling of maintenance, future financial planning and rate adjustment, and optimization of the operations of a water system In addition, adequately forecasted demand will be a basis for the strategically decision making on future water sources selection, upgrading of the available water sources and designing for the future water demand management options, so that water resources are not exhausted, and competing users have adequate access to those resources. Most of the previous studies on water demand forecasting are based on the three approaches: end-use forecasting, econometric forecasting, and time series forecasting. End use forecasting is an approach that bases the forecast of water demand on a forecast of uses for water, which requires tremendous amounts of data and - 1(Accepted for: EWRI/ASCE: 2009, Conference: Kansas, USA May 17-21, http://content.asce.org/conferences/ewri2009/)
assumptions. The econometric approach is based on statistically estimating historical relationships between different factors (independent variables) and water consumption (the dependent variable) assuming that those relationships will continue into the future. Time series approach forecasts water consumption directly, without having to forecast other factors on which water consumption depends (Zhou et al., 2000). Many climatic variables, such as rainfall, air temperature, sunshine duration, relative humidity, wind speed, historical water uses were considered for developing a linear regression to the artificial neural network model for forecasting the water demand studies. Linear regression models developed by Jain et al. (2001) uses weekly maximum air temperature, weekly rainfall amount, weekly past water demand, and the occurrence and non-occurrence of rainfall as dependent parameters. The linear regression models developed by Graeser (1958) have the number of previous days with maximum air temperature above 100°F, and the number of weeks from last occurrence of one inch of rainfall as dependent variables. Box and Jenkins models were developed by Maidment et al. (1985) to predict the daily municipal water use. In more recent studies, artificial neural network (ANN) model are becoming prominent for water demand forecast as the neural network was found to outperform the regression and time-series models in some studies (Bougadis et al., 2005; Ghiassi et al., 2008). Similarly, a Takagi Sugeno fuzzy method for predicting future monthly water consumption values from three antecedent water consumption amounts was developed by Altukaynak et al., (2005) for future demand forecasting. Some other studies have included economic, demographic, and weather factors in their water demand models. Billings & Agthe (1998) used the marginal price of water and personal income per capita in their model. Zhou et al (2000) modelled daily water consumption by splitting it into two components: base (weather-insensitive) and seasonal (weather-sensitive) uses for Melbourne water supply. Arbués et al (2003) have reviewed the main variables that can affect demand and highlighted the water price, income, or household composition as the crucial determinants of residential consumption. Moreover, there are many studies done on water demand forecasting and impacts analysis in urban scale, catchments scale & regional scale, for examples see (Arnell, 2004; Arnell & Delaney, 2006; Bradley, 2004; Ruth et al., 2007; Sophocleous, 2004; Vörösmarty et al., 2000). Most of those studies are focusing on the future demand forecasting without considering the uncertainties associated to the future change pressures. The future changes pressures; such as climate changes and variability, population growth and urbanisation, changes in public behaviours and socio-economic condition, ageing and deterioration of buried infrastructures, increased rate of leakage, technological development, increased demand for water to maintain in stream ecosystems, alter in water quality, and (Khatri & Vairavamoorthy, 2007) and their impacts are highly uncertain. Consequently, without dealing with these future change drivers and associated uncertainties, the predicted forecast will not be reliable and sufficient for the planning and management of water supply system. The objective of this paper is to explore the different sources of uncertainties in future water demand forecasting due to climate changes, population growth, and changes in
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public behaviour, and assess the risks. We apply the computing tool to describe, propagate and forecast the domestic demand considering these change variables, and apply in the real case of Birmingham, UK. It also attempts to assess the main governing drivers’ for the future water demand (i.e. climatic or socioeconomic) in Birmingham city. The paper is organized to address each of these topics in six sections. The second section presents the main sources of uncertainties associated to these change pressures. Third section presents the methodology applied for describing the uncertainties during the demand forecasting process. The fourth section introduces the case study area, and fifth section will present the current and potential future climatic and social condition of the Birmingham city followed by analysis. Finally, a brief discussion and conclusions of the study will be presented in the sixth section. SOURCES OF UNCERTAINTIES The sources of uncertainty refer to the causes that give rise to uncertainty in the forecasted state of the system that is modelled (Krzysztofowciz, 2001; Mannina et al., 2006; McIntyre et al., 2003; Paté-Cornell, 1996). A complete assessment of impacts caused by the future change pressures in water demand forecasting is being impossible either due to the complexities, or incomplete knowledge, or unknowable knowledge leading to a range of uncertainties. It may be either data uncertainty, or model uncertainty, or parameter uncertainty, or natural and operational uncertainty, or all. Here, we are exploring the sources of parameters uncertainties subjected to the future water demand forecasting. Climate Change Most of the climate change studies including IPCC (2007) had already concluded the greater impacts of climate change on water resources. Climate variability and change affect the availability of demand and quality of water, and runoff or temperature extremes. Different sources of uncertainties stemming from the climate change; such as future temperature, precipitation, sunshine duration, wind speed, relative humidity, evaporation rate, transpiration rate, soil moisture content will have significant impact on future water consumption. The extreme events (drought or frequent flood) will have also considerable impact on future demand. Therefore, how these climatic parameters changes in future will govern to water demand forecasting. However, prediction of future climate change and impacts contains the series of uncertainties, which cascades from scenarios to impacts studies. Such as future scenarios of how the future (complex non-linear dynamic system) will be; emission generated, natural process, and variability; different global climatic models for the prediction and their capacity; downscaling and error; uncertainties associated to hydrological modelling; and finally uncertainties associated to the impact model. These associated uncertainties are due to the incomplete knowledge or unknowable knowledge to be articulate and quantified (Berliner, 2003; Carter et al., 1999; New & Hulme, 2000). Planning for the future with less or no regret strategy against climate change impact, will be possible only if associated uncertainties are well described.
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Population Growth Urbanisation and population growth follow a very complex process and affected by a range of economic, political, social, cultural, and environmental factors. Traditional population forecasting techniques are based on the assumption that three components (fertility, mortality and migration) of population growth will be smooth and follow a monotonic path for future trends. However, demographic situation is neither known perfectly in the present, nor future trends in births, deaths, and net migrants are predictable. Future population growth can be influenced by affecting birth, death, or migration rates due to social, economic, political, technological, and scientific developments. We do not know whether new diseases, or drug-resistant strains of old diseases, will raise mortality in the future. We do not know whether migration toward industrial countries will accelerate or abate (Lee & Tuljapurkar, 1994; O'Neill et al., 2001). The two approaches commonly used for characterizing uncertainty in demographic forecasting are scenario and probabilistic. Scenario uses to describe its projections in consistent story in which fertility, mortality, and migration assumptions are embedded to provide a comprehensive picture of what the future might be. Building alternative scenarios to assess and communicate the uncertainty associated with their projections, is common in national forecasts. An alternative to scenarios as a means of communicating uncertainty is probability distributions. It explicitly accounts for uncertainty in projected trends of fertility, mortality, and migration; and derives the resulting probability distributions for projected population size and age structure. Further details on these areas can be found elsewhere (Keilman et al., 2002; Lee & Tuljapurkar, 1994; National Research Council (NRC), 2000; Wilson & Bell, 2004). Socio-economic Changes Many previous studies have revealed that there is a strong correlation between water consumption and socio-economic changes. Examples are changing life style, changing housing type and household size, acceptability, and market penetration of water efficient appliances alternative sources of water, economic development, employment opportunities, education level, and water pricing (Bradley, 2004; Clarke et al., 1997; Schneider & Whitlatch, 1991). Forecasting and describing these parameters are subjected to uncertainties. It has been recognised that economic prosperity, type of housing, occupancy, climate and technical developments in water-using devices influences the micro-components of water uses (Herrington, 1995). Micro-components of water demand typically uses water for the devices; such as toilets, showers, bath, washing machines, dishwashers, internal and external taps are considered and assessed separately. Future domestic water consumption will be decided by how these micro-component changes, which will be based on change in life styles, emerging new technology, ecological awareness and economic reason (Hensher et al., 2005). DESCRIPTION OF UNCERTAINTY In this study, we purpose a risk based approach for analysing the uncertainties while forecasting the future water demand. We are applying, therefore scenario approach, - 4(Accepted for: EWRI/ASCE: 2009, Conference: Kansas, USA May 17-21, http://content.asce.org/conferences/ewri2009/)
random sampling techniques (Monte Carlo simulation and Latin Hypercube sampling), and bootstrapping techniques for describing the uncertainties. Domestic water demand will be forecasted based on the micro-components of water demand analysis and total population in the future. Impacts of climatic variables will be assessed using the regression models developed from historic time series records. Monte Carlo Simulation (MCS) will be applied for prediction of total future population with uncertainty. MCS is a well established and tested method of overall assessment of the uncertainty relies on the central limit theorem. MCS technique generates an estimate of the overall uncertainty in the predictions due to all the uncertainties in the input parameters (Macdonald & Strachan, 2001). Time series population data will be used for trend analysis, moving average technique for the smoothing in forecasting, and lognormal distribution for the future probabilistic simulation of total population using MC simulation (see section 5). Population forecasted with a large numbers of random simulations and predicted population in 95% confidence interval will describe the uncertainty ranges in total future population. Latin Hypercube Sampling (LHS) technique will be used to analyse the micro components of water demand and to get the distribution for per capita water consumption. Latin Hypercube Sampling (LHS) is a stratified sampling procedure, and converges more quickly than other stratified sampling procedures. It produces the more precise results than MCS results for same numbers of model runs in case of linear and monotonic problems (Ferson et al., 2004; McKay, 1992). Uniform distribution will be developed for per capita water consumption considering the occupant numbers and ACRON (see section 5), and get the present water demand distribution. Secondly, micro-component of water demand will be classified in 8 – groups with uniform probability distribution. Future per capita water use will be obtained after LHS sampling. The ranges of micro-components water demand values were selected based on UK study results. Bootstrap technique will be applied to describe the uncertainties in future average temperature and precipitation. The bootstrap is the simplest technique for simulating the probability distribution of any statistic, without making any assumptions or estimating any parameters. Bootstrapping amounts to re-sampling a record, with replacement, to generate ( B ) bootstrap samples, from which one can simulate B estimates of a given statistic, leading to an empirical probability distribution of the statistic. In order to resample the records assuring the temporal and spatial covariance structure of the original time series, moving block bootstrap techniques is applied. In a moving-blocks bootstrap, one chooses a block length λ ≈ n / k , where n is the record length and k is the number of blocks to resample. The idea is to choose a large enough block length λ , so that observations more than λ time units apart will be nearly independent. Details on bootstrap and moving block bootstrap can be found in (Efron & R. J. Tibshirani, 1993; Hidalgo, 2003; Ku¨nsch, 1989; Vogel, 1996). Similarly, scenario approach will be considered for the climate change impacts analysis. We considered the different emission scenarios and predicted climatic variables. Wide spectrum of possible climate change scenarios, prediction and their
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impact in water demand will cover the associated uncertainties. We have used the predicted results obtained after downscaling from climate impact study UKCIP02. CASE STUDY: BIRMINGHAM Birmingham is the second most populated British city after London. It is situated in 52° 29′ 1″ N, 1° 54′ 23″ W, having total area 267.77 km2; average elevation of 150 to 350 m; and population 1113600 (city council, 2008 estimate). The City of Birmingham forms part of the larger West Midlands conurbation and includes several neighbouring towns and cities; such as Solihull, Wolver Hampton and the towns of the Black Country. Although Birmingham's industrial importance has declined, it has developed into a national commercial centre, being named as the third best place in the UK to locate a business, and the 21st best in Europe and fourth most visited city by foreign visitors in the UK. Severn Trent Water Limited (STWL) and South Staffordshire Water (SSW) Company are responsible for supplying drinking water, managing waste water and environment within the Birmingham. STWL provides water from the Birmingham water resource zone (WRZ) - one of the six water resource zone (Figure 1). The Birmingham WRZ includes most of the city of Birmingham, which is principally supplied from the Elan Valley reservoirs in mid Wales, via a 70 mile long aqueduct. The estimated total population within this zone was 1125183 in 2007. There is no raw water abstracted from within this resource zone. The deployable output of this zone is approximately 329.6 Mega liters per day (ML/d), and there is approximately 314.9 ML/d of water available for use. The service area has an average occupancy rate of 2.2 per household, and per capita water consumption of 146.98 litres (STWL, 2008).
6 2
1
4
3 Served by STWL 5
Fig. 1. Severn Trent Water resource Zones and supply area for Birmingham
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DATA, LIMITATION AND ANALYSIS Water Demand We obtained water consumption data for this analysis from STWL from Jan 1996 to March 2005 (see Figure 2). We have only total consumption data, so we are unable to distinguish water use for residential, commercial, and industrial sectors. For the monthly analysis, we aggregated the data into monthly totals, normalizing each total for the length of the month so that all data assume a 30.416 day month. We don’t have sufficient data on total population served from year 1996, so adjustment for water consumption to a per capita basis was not possible. Total Water Demand in Birmingham (From 1996 -2005) 2400 Demand in ML/Month
2300 2200 2100 2000 1900 1800 1700 1600
Ja n 9 Ju 6 n 9 N 6 ov 9 A 6 pr 97 Se p 9 Fe 7 b 98 Ju l9 D 8 ec M 98 ay 9 O 9 ct 9 M 9 ar 0 A 0 ug 0 Ja 0 n 0 Ju 1 n 0 N 1 ov 0 A 1 pr S 02 ep -0 Fe 2 b0 Ju 3 l-0 D 3 ec M 03 ay -0 O 4 ct -0 M 4 ar -0 5
1500
Month & Year
Fig. 2. Total Water consumption per month in Birmingham served by STWL In the time series records, water demand is decreasing from 2111 ML/Month in January 1996 to 1837 ML/Month in January 1998. Although, we lack the data on water demand by sector, the reduction in demand might be due to decreasing numbers of industries. From 1998 to 2001, there is no considerable increase in water demand with average rate of 1943 ML/month. Average water consumption during the winter appears fairly consistent and summer time consumption figures are well above normal. Apparently, the peak demand is increasing in a warmest summer month of the year compared to normal year, such as year 1996. Historical and Future Climate Change Scenarios Historical time series records for the temperature and precipitation parameters for two rain gauge stations (Sutton Bonnington, Nottingham shire and Penkridge, Shropshire) was provided by STWL. The average value of the two stations was used for the case of Birmingham. Although water demand has direct affect of rainfall patterns, air temperature, sunshine duration, relative humidity and wind speed, we are limiting our work only on temperature and precipitation due to unavailability of data.
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In order to correlate the temperature and precipitation affect on water demand, a linear regression model was developed from year 2001 as shown in Figure 3 (a) & (b). From the regression results, water demand is negatively correlated with precipitation 2 Y = 1.9934*1000000 − 0.0006* P with adjusted regression coefficient of R = 0.26, having standard error in repressors of 0.3369. Temperature is positively correlated Y = 1.9389*1000000 + 0.0021* T with adjusted coefficient of R2 = 0.19, standard error of 0.0533. Although there is lower correlation of variables to the prediction, we have applied here single linear regression technique to estimate the future implication in water demand from available information. a) Regression with Precipitation
b) Regression with Temperature 2100
2050
2050
W ater D em and (M M 3/m onth )
W a ter D em a nd (M M 3/m on th)
2100
2000
2000
1950
1950
1900
1900
1850 20
40
60
80
100
1850
120
Average Monthly Precipitation (Jan 2001 to March 2005)
2
4
6
8
10
12
14
16
18
Average Monthly Temperature (Jan 2001 to March 2005)
Fig.3 Regression results on water demand and average precipitation (a) and temperature (b) Future climate change scenarios for Birmingham are based on the scenarios developed and studied by the UK Climate Impacts Programme-2002 (UKCIP02). UKCIP02 uses the Hadley Global Climate Model (GCM) of the atmosphere including a dynamic ocean circulation model (see www.ukcip.org.uk). It provides climate change database for three time slices 2020s (predictions for 2011 to 2040), 2050s (predictions for 2041 to 2070), 2080s (predictions for 2071 to 2100) and for four core emissions scenarios (Low, Medium-Low, Medium-High, and High) at both 50Km and 5Km resolutions. In this case, we are using the temperature and precipitation parameters for four emissions scenarios for time 2020s at 50Km resolutions (Table 1 & 2). For the future water demand prediction, we are considering the two cases of climatic variables. Firstly, average monthly temperature and total change in precipitation for four scenarios (Table: 1&2) was analysed using moving block bootstrapping. We grouped data of the predicted values in three months (block length) time periods for the four scenarios. After bootstrapping by moving block for 1000 times, a seasonal average value with 95% confidence intervals was estimated (see Table 3). It embeds a wide range of possible intra - and inter-annual variation in climate. Average future demand will be predicted with respect to these mean values. There are uncertainties in
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future extreme events of drought; however the average values will provide a sufficient base for the future planning. Table 1. Monthly average temperature and changes in daily mean temperature simulated by HadRM3 with SRES A2 for time slice 2020 Scenarios Dec Jan Feb March April May Jun July Aug Base case Average Temp º C 4.55 4.85 5.89 8.21 11.41 14.34 16 15.8 13.4 Low 0.69 0.63 0.6 0.63 0.69 0.77 0.89 1.05 1.15
Sep
Oct
Nov
9.87 1.1
6.75 0.95
4.98 0.8
Medium to Low
0.77
1.17 1.28
1.23
1.06
0.89
Medium to High High
0.77 0.7 0.67 0.71 0.82 0.75 0.72 0.75
0.77 0.86 1 1.17 1.28 0.82 0.92 1.06 1.25 1.36
1.23 1.31
1.06 1.14
0.89 0.95
0.7 0.67 0.71
0.77 0.86
1
Table 2. Monthly average precipitation rate (mm/month) and changes in total precipitation rate (%) simulated by HadRM3 with SRES A2 for time slice 2020 Scenarios Base case Average Low Medium to Low Medium to High High
Dec
Jan
Feb March April
48.12 44.32 45.72 55.98 4.93 5.41 3.68 0.91 4.09 1.01 -2.5 -6.02
May
Jun
July
Sep
53.08 45.67 40 40.73 47.5 52.79 -2.25 -5.41 -8.34 -10.4 -9.98 -6.64 -9.29 -11.59 -11.1 -7.4 -2.4 2.25
4.09
1.01
-2.5 -6.02
-9.29 -11.59 -11.1
5.87
6.44
4.37
-2.67
1.08
Aug
-7.4
Oct
Nov
52.29 -2.16 5.49
48.17 2.02 6.03
-2.4
2.25
5.49
6.03
-6.43 -9.92 -12.38 -11.9
-7.9
-2.57
2.4
Table: 3 Seasonal average changes in temperature and total precipitation rate (%) after bootstrap sampling Parameters Lower 95%
Dec
Jan
Feb March April
May
Jun
July
Aug
Sep
Oct
Tempt ºC Precipitation % Mean Value
0.6 -2.5
0.63 -11.59
0.89 -12.38
0.8 -7.9
Tempt ºC Precipitation % Upper 95%
0.7073 2.2894
0.7743 -6.8608
1.1405 -8.1021
1.0528 2.2608
Tempt ºC Precipitation %
0.82 6.44
0.92 0.91
1.36 -2.4
1.31 6.03
Nov
Population Growth and Socio-economic Changes Population trends of Birmingham city was obtained from the Birmingham City council. However, STWL covers the partial area of Birmingham, and includes the additional neighbouring area, so population figures obtained from STWL is different than total city population. For the probabilistic population forecasting, demographic data of total population obtained from city council was used to find the best distribution of total population growth. The lognormal distribution was obtained as the best fit from total population and applied for the population forecasting in STWL
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service area. Future forecasted population was smoothened by moving average method with 5- year time steps. Sufficient MCS was run (1000 times) to get the future total population for STWL area with 95% confidence interval (see Figure 4).
Fig. 4. Total population forecasted for Birmingham (served by STWL)
W ater Consum ptio n:L PCD
We consider the micro-component of water demand and geo-demographic classification to assess the future water demand. ACRON is a geo-demographic classification of population used in UK. It groups the entire UK population into 5 categories, 17 groups and 56 types using more than 400 variables (see www.caci.co.uk). The water consumption according to the ACRON number (1 to 5) and family size (1 to 5) obtained from STWL was used for the analysis. Water consumption patterns per capita, for different ACRON types for Birmingham is shown in Figure 5. Water Conumption LCPD (Based on ACRON & Ocupants Number) 250
ACORN:1
200
ACORN:2
150
ACORN:3
100
ACORN:4
50
ACORN:5
0 Ocup. 1
Ocup. 2
Ocup. 3
Ocup.4
Ocup.>5
Ocupants Number
Fig. 5. Per capita water consumption based on ACRON
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From the water consumption for ACRON (1 to 5), we assumed uniform distribution for different occupants’ numbers (ACRON: 1(120-167); ACRON: 2(85-200) ACRON: 3(110-178): ACRON: 4(104-160): ACRON: 5(115-226)). Using LHS, we sampled random samples (1000 simulations) and finally, we got a distribution, which gives the most likely value 146.50 LPCD, and 95% confidence intervals (see Figure 6(A)).
A) LPCD with Mean=146.50, STD =10.98, Sample =1000
200
150
B) LPCD with Mean=141.3714, STD=16.7148, Sample=1000 200
250 95% Upper Level: 164.45
180
Lower Level: 127.79
140
95% Upper Level: 169.77;
Lower Level: 114.36
160
120 100
100
80 60 40 20
0
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130 140
LPCD
150 160
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180
0 100
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140 150 LPCD
160
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Fig. 6. Water consumption based on ACRON for preset (A) and for the future (B) We divide the micro-components of water consumption in 8-groups (WC:(10-40%), Washing Machine(10-20%), Dish wash:(5-8%), Bath(10-20%), Shower: (4-8%), Sink Kitchen:(8-20%), Wash basin: (4-12%), and outside and losses(10-15%) as a uniform distribution of present LPCD, and future water per capita was forecasted in terms of distribution applying the LHS techniques (see Figure 6(b)). The range of microcomponents was considered based on the STWL survey results and Environmental Agency study in UK. Future Water Consumption We have population forecasted with confidence intervals, LPCD forecasted for the future (considering the micro-components of the water demand) which will give the future domestic demand. Regression models on temperature and precipitation showsthere will be 2.10% increase and 0.06 % reduction in total monthly demand, if temperature increases by 1ºC, and rainfall increases by 1mm/Month. We are forecasting the climatic affect for year 2035, considering the average value obtained in Table 3. We have no detailed data base on non - domestic demand, and leakage but we are using information available from STWL. From this analysis, water demand situation in Birmingham for year 2035 is shown in Figure 7.
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Water Demand For Year 2035
Water demand -ML/day
400 350 300 250 200 150 100 50 0
1
2
3
Total demand
318.0717
327.8076
338.0925
Total demand
Domestic
170.5629
178.7765
187.7676
Domestic
Leakage
90.0000
90.0000
90.0000
Leakage
Non domestic
50.7500
50.7500
50.7500
Non domestic
Climatic
6.7588
8.2811
9.5748
Climatic
Fig. 7. Future water demand for three scenarios (minimum, average and maximum)
DISCUSSION AND CONCLUSIONS This study is limited by numbers of constraints. First, the unavailability of sectorspecific data, social data (exact population coverage and GDP rate), water price data, leakage rate, and climatic data in smaller time scale lacks the better understanding of these variables and representation into the model including description and propagation of the uncertainties. As a reason, we limited our analysis only in domestic demand, which is most uncertain part for the future water demand. We could not be able to assess the water demand affect due to climate change. Second, the monthly time step does not allow us to see whether climate characteristics that occur within months (which most extreme events are likely to be) will threaten the city’s ability to maintain adequate reserves of drinking water. Regardless, this paper demonstrates and emphasises on uncertainties associated to the future and future change pressures. We cannot describe the future water demand deterministically as those change pressures are highly uncertain, and new study on demand forecasting should have significant emphasise on associated uncertainties description, which can be described either by scenarios approach, or probabilistically. Our efforts in this regards will be new insights for the future planning for the Birmingham or similar cities. From the analysis, it is clear that future water demand (at the monthly aggregate level) is largely driven by change in population and socio-economic changes rather than climate changes. From the analysis, we found average water demand for year 2035 318.07
