Journal of Modern Science and Technology Vol. 3. No. 1. March 2015 Issue. Pp. 220 230 Impact of Climate Change on Reliability of Rainwater Harvesting System: A case Study in Mongla, Bangladesh Md. Maksimul Islam 1 *, Sadia Afrin 2, Asef Mohammad Redwan 3 and Md. Mujibur Rahman 4 As Bangladesh has tropical monsoon with large seasonal cycle in rainfall, Rainwater Harvesting (RWH) system and its utilization could be a good and environmentally sound solution for providing safe drinking water. Although rainwater harvesting has already been promoted in different coastal areas in Bangladesh to a limited scale for drinking purpose, improper storage reservoirs prohibit people getting full benefit from it. Moreover, temporal variability of precipitation, which is the main governing factor in the design of a storage tank in Rainwater Harvesting Systems (RWHS), is expected to be altered in future under the effects of climate change. This can introduce an element of uncertainty in the design of storage reservoirs if this variability is not taken into consideration. The objective of this paper is to report on reliability problems of existing rainwater harvesting system in coastal rural areas of Mongla, Bangladesh. This study aims at developing a simulation model as tank sizing tool incorporating all the design variables and also climate change variability and assessing reliability in terms of failure time units and demand satisfaction level with the help of this model.this simulation model is developed based on mass balance equation in which historical as well as predicted future climate data are used. Historical precipitation data are collected from Bangladesh Meteorological Department (BMD) whereas predicted future data are from a climate model named PRECIS. The physical data input includes roof area, run off coefficient and reservoir size whereas social status data includes water demand and household size. The study has shown that, for a water demand of 5 lpcd, volumetric reliability lies in the range of 35-37% in case of individual household based rainwater harvesting system for historical rainfall pattern of the study area whereas the range becomes 48-50% for predicted future precipitation scenario depending on existing roof areas and corresponding storage tank sizes. The volumetric reliability of community rainwater harvesting systems for historical data (26%) is almost half of the value obtained from future precipitation scenario (56%) for a water demand of 5 lpcd. Reliability becomes lower for higher water demand as expected. Time reliability of both individual and community rainwater harvesting system is considerably lower compared to volumetric reliability for both observed and predicted precipitation data. It implies that although existing systems manage to deliver a portion of water demand, the number of months in which demand is fully met is very small. Finally, tank sizing curves for the study area have been developed by which one could obtain the reliable size of the water storage tank for given data on household size, per capita water demand and roof area. The research findings presented in this paper could be applied in areas with similar socio-economic status and climatic condition. Field of Research: Environmental Engineering 1. Introduction Safe drinking water is considered indispensable for advancement of any society. But 1* Md. Maksimul Islam, Department of Civil Engineering, Bangladesh University of Engineering & Technology (BUET), Dhaka, Bangladesh, E-mail: maksimulislam075@gmail.com 2 Sadia Afrin, Department of Civil Engineering, Bangladesh University of Engineering & Technology (BUET), Dhaka, Bangladesh, E-mail: sadiaafrinbuet@gmail.com 3 Asef Mohammad Redwan, ITN, Bangladesh University of Engineering & Technology (BUET), Dhaka, Bangladesh, E-mail: asef_hims@yahoo.com 4 Dr. Md. Mujibur Rahman, Professor, Dept. of Civil Engineering, Bangladesh University of Engineering & Technology (BUET), Dhaka, Bangladesh; E-mail: mmujib4@gmail.com 220
increasing inadequacy of safe drinking water resources has made it necessary to think about the possible solution to cope with the crisis. Indiscriminate pollution of surface water sources (rivers), unhygienic activities polluting pond water in rural areas, high salinity in surface and groundwater in coastal belt, absence of good groundwater aquifers in hilly areas, difficulties in tube well construction in stony layers, arsenic contamination of groundwater, depletion of groundwater table due to overexploitation are the main constraints of developing a reliable water supply system in Bangladesh. Especially in the coastal areas, scarcity of potable water is very acute (Islam et al, 2011; and Kamruzzaman and Ahmed, 2006) as suitable aquifers at shallow depths are rarely available and surface water especially the river water is highly saline and turbid. Rainfall in the coastal areas is much higher and roof catchments are suitable (Karim, 2010), thus rainwater harvesting has a good potential to supply drinking and cooking water in the coastal areas of Bangladesh. Rainwater harvesting system has already been introduced in different coastal areas of Bangladesh especially in Mongla to a limited scale for drinking purpose. But storage reservoirs devoid of proper design prohibit people getting full benefit from it. Design of rainwater harvesting systems requires careful consideration of storage capacity. Inappropriate sizing of a storage tank or reservoir can negatively affect a homeowner in many ways. Overestimation of the storage requirement can result in an oversized and needlessly expensive storage system. Undersized storage tanks, on the other hand, may not fulfill the homeowner s water use needs. Proper design is required to make the rain water harvesting technique a success. Moreover, Rainwater Harvesting System depends on precipitation; therefore both rainfall pattern and intensity are important variables in rain water harvesting system design. Rainfall pattern in Bangladesh exhibits strong spatial and temporal variation. The average yearly rainfall in Bangladesh varies from 2200 to 2800 mm, 75% of which occurs between May and September (Ahmed and Rahman, 2000). An increase of 4.26% was observed in the percent difference between the total annual precipitation (average of 34 meteorological station-data) of the past 20 years (1953-1972) and the recent 20 years on record (1985-2004) which represents that the annual rainfall follows an increasing trend (Rajib et al, 2011).The precipitation might continue to increase in all the months in future years. Percentage of precipitation increment is expected to be quite higher for dry and pre-monsoon months compared to the monsoon season. Also, the large scatters in the projected precipitation quantities of July and in most other monsoon months are expected, indicating that there will be years with more or less rainfall with significant fluctuations from average conditions (Rajib et al, 2011). According to the third assessment report of Intergovernmental Panel on Climate Change (IPCC, 2001), developing countries are expected to suffer the most from the negative impacts of climate change. The IPCC Special Report on the Regional Impacts of Climate Change (IPCC, 2007) indicates that there would be drastic changes in the rainfall patterns in the warmer climate and Bangladesh may experience 5-6% increase of rainfall by 2030. Due to the variability of rainfall under a climate change scenario, the rainfall harvesting units designed according to the present rainfall records may face large uncertainties in providing adequate storage quantities. 221
A rainwater tank can be considered as a storage reservoir that receives stochastic inflows (effective runoff) over time and is sized to satisfy the demand on the system (Fewkes, 2006). Tank size is the one parameter controlled by the designer who therefore requires some technique with which to determine the size that will provide the optimum level of service. Numerous methods are available for determining the size of the storage tank in rainwater harvesting system. These can be broadly categorized as: a) Demand side approach, b) Graphical (mass curve, Ac-Vc Method etc.) and c) Simulation or behavioural methods. Graphical methods are used for rapid assessment and are designated as preliminary design techniques. Its main limitation is that it is not possible to compute a storage size for a given reliability of supply. The demand side approach is a very simple method for estimating storage requirements for RWH units which assumes there is sufficient rainfall and catchment area to meet demand throughout the year. Likewise graphical methods, reliability based storage tanks could not be determined by this method. Moreover, true fluctuations in rainfall are not incorporated in the design. In behavioural analysis the changes in storage content of a finite reservoir (one that can overflow and empty) are computed using the water balance equation. The principles of mass balance equation can be illustrated mathematically as, 0 V t = (V t 1 + Q t D t ) V s Where, Vt, is the volume of water in the tank at present, Vt-1 is the volume of water in the tank remained from previous time step, Qt is the rainwater captured at present, Dt is the total consumption and Vs is the volume of the tank. The water in storage at the end of a prescribed time interval is therefore equal to the volume of water remaining in the storage from the previous interval plus any inflow and less any demand during the time period. Provided, that is, the computed volume in the store does not exceed the capacity of the store. Behavioural models therefore simulate the operation of a reservoir with respect to time by routing simulated mass flows through an algorithm which describes the operation of the reservoir (Fewkes, 2006). The advantages of behavioural models are that they are relatively simple to develop, easily understood and mimic the behaviour of the physical system. Figure 1 shows a diagrammatic sketch of the storage tank water fluxes typically modelled as part of a behavioural analysis. Jenkins et al (1978) has identified two fundamental algorithms to describe the operation of a rainwater tank in simulation models: 1. Yield after spillage (YAS) algorithm, and 2. Yield before spillage (YBS) algorithm. 222
A number of researchers have investigated the YAS/YBS operating algorithms for the sizing of rainwater tanks, including (Jenkins et al, 1978; Fewkes and Butler, 2000; Liaw and Tsai, 2004; and Mitchell, 2007 amongst others. Fewkes and Butler (2000) recommend the use of YAS for design purposes because it gives a conservative estimate of system performance. In an Australian study, Mitchell (2007) recommend the use of YAS because the results of the investigation showed that it provided more accurate predictions of yield than did YBS. That s why, YAS algorithm was used in this study to assess the reliability of both individual and community rainwater harvesting systems of the study area. Yield after spillage (YAS) algorithm In the YAS algorithm the order of operations occurring in time interval t is given as: determine yield, runoff into tank (inflow), overflow, extract yield. The YAS operating rules are given in equations 1 and 2. Yt = min { Dt Vt 1 Vt 1 + Qt Yt Vt = min { Vs Yt Eqn. 1 Eqn.2 Where, Yt = yield (withdrawal) from the tank in time t (m³) and other terms are as previously defined. In the YAS algorithm, the yield is determined by comparing the demand in time interval t with the volume in the tank at time interval t-1 (the end of the previous time interval). The yield is assigned to the smaller of the two values. The runoff into the tank (inflow) in the current time interval t is then added to the volume of rainwater in the tank from time interval t-1. If the capacity of the tank is exceeded then any surplus exits via the overflow, and then finally the yield is extracted. 2. Material and Methods 2.1 Study Area As potable water is scarce mainly in coastal areas of Bangladesh (Kamruzzaman and Ahmed, 2006; and Islam et al, 2011), this study aims at assessing reliability and developiong tank sizing curves for Mongla, a coastal upazilla under Bagerhat district. Tube wells here get contaminated with high saline water. Pond water is available and comparatively less saline but turbid, colored and contaminated by pathogenic microorganisms. Moreover, during cyclone or flood disaster, sea water enters into the ponds that are used for Pond Sand Filters (PSF) and damage the whole systems 2.2 Data Collection The model development required data as inputs or assumptions as the boundary conditions. These data are water demand, roof area, rainfall data, and runoff coefficient. 223
A field survey was conducted in the study area to collect data on water demand, roof area and characteristics, household size etc. Based on the survey data the water demands considered were 5, 10, 15 and 20 L/ capita/ day. Roof areas of 80, 90, 100 and 120 sft were found during filed data collection for the individual household based RWH system, whereas 300, 330 and 360 sft were found for the community system. Average household size in the study area was 5. Runoff coefficient of 80% is used due to assumptions that rainwater is lost due to first flush water and leakages in the systems. Yusuf (1999) found insignificant difference among the end results for variation of this parameter in the range of 0.75 0.85. The secondary data were rainfall data. Two data sets were used in the analysishistorical observed rainfall data and future predicted data. The historical observed precipitation data of the study area was collected from Bangladesh Meteorological Department (BMD). It was in daily time step ranged from 1950-2010. The future precipitation scenario was predicted by PRECIS, a regional climate model (RCM) system developed by the Hadley Centre of United Kingdom, for different areas of Bangladesh in a previous study (Rajib et al, 2011). PRECIS was basically adapted for generating projections of some specific climatic parameters for Bangladesh, side by side with GCM projections. This data was monthly data predicted upto 2100. These two data sets were used in monthly format in the analysis to compare the reliability of the existing systems obtained from historical and predicted precipitation data. The high storage function (>0.125) of the community rainwater haresting systems of the study area justifies the use of monthly data in simulation model (Fewkes and Butler, 2000). An average monthly rainfall distribution with statistical parameters (range, maximum, minimum, mean, median, 1 st quartile, 3 rd quartile) for both the historical observed and future predicted data for the study area is shown in Figure 2. Figure shows same trend in precipitation for both set of data. The figure also indicates that precipitation might increase in all the months in future in the study area and this increment appear to be more in dry months compared to wet months. 2.3 Development of Simulation Model The simulation model (also known as behavioral model) was developed for the study based on mass balance equation, which was used to determine the effective tank size and also to investigate the performance of rain water harvesting systems. As mentioned earlier, YAS algorithm was used for depicting tank behaviour in the behavioural model. The model was implemented in MATLAB environment which is a high level technical computing language and interactive environment for algorithm development, data visualization, data analysis, and implimenting numerical methods. 3.4 Reliability analysis The reliability concept is important for the rainwater harvesting system as the performance of RWH system is generally described in terms of reliability (Karim et al, 2013; and Liaw and Tsai, 2004). Reliability is defined as the probability that a given size of rainwater harvesting system will be sufficient to supply the necessary amount of water. In this study, two types of reliability will be considered: 224
Figure 2: Average Monthly Distribution of Rainfall of the Study Area (A) Historical Observed Data (B) Future Predicted Data (a) (b) 1) Volumetric Reliability, Rv or Demand Satisfaction Level: It can be expressed as the total actual rain water supply over demand. Rv = actual supply/demand 2) Reliability in terms of failure time units or Time Reliability (Re): It can be expressed as the fraction of time that the demand is fully met. Re = = 1-Σ (time units when demand exceeds supply) / total time units Time reliability intends to find out the time units in the whole simulation period in which demand is fully met. One limitation of the time reliability indicator is that it can give seemingly poor results even for systems that meet a high percentage of demand. This limitation indicates that reliability in terms of failure time unit alone cannot reflect the true performance of the system. That s why volumetric reliability concept was introduced (Fewkes and Butler, 1999). In this study, both Re and Rv are considered to assess their relative effectiveness for the study area. 3. Results and Discussion 3.1 Reliability Assessment of Existing Systems The size of storage reservoir of individual household based rainwater harvesting systems was 1.0 m³-1.2 m³ while the size was 330-360 m³ in case of community systems. The community systems were designed for 15 single households i.e 75 people. It is seen from the simulation model that for a water demand of 5 lpcd, volumetric reliability lies in the range of 35-37% in case of individual household based rainwater harvesting system for historical rainfall pattern of the study area depending on existing roof areas and corresponding storage tank sizes. It implies that about 36% of total demand would be fulfilled by the existing system for the simulation period. Reliability (Rv) was found to be higher (48-50%) for predicted future precipitation scenario. As precipitation might increase in future months, this higher reliability is expected. Reliability becomes lower in case of fulfilling higher water demand. For a water demand of 10 lpcd, volumetric reliability reduces to 18% for historical rainfall 225
pattern of the study area whereas it is 24% for predicted future precipitation scenario. The volumetric reliability of community rainwater harvesting systems for historical data (26%) is almost half of the value obtained from future precipitation scenario (56%) for a water demand of 5 lpcd. Time reliability of individual rainwater harvesting system is found to be significantly lower compared to volumetric reliability and it is only 11-16% for water demand of 5 lpcd based on the observed precipitation data. It indicates that demand would be met fully in almost 14% month of the simulation period. Rt is considerably lower (23-25%) compared to volumetric reliability for future predicted data. Likewise Rv, Rt is higher for the predicted data than observed data for the individual household based RWH system. The same is true for community rainwater harvesting system also (3% and 17% for observed and predicted data respectively for 5 lpcd demand). 3.2 Tank Sizing Curves The tank sizing curves were developed for demand of 5, 10 & 20 lpcd considering the household size of 5 and available roof areas of the study area for both observed and predicted rainfall data. Both volumetric and time reliability were considered while developing these sizing curves (Figure 3 &4). The curves have nearly same pattern irrespective of particular demand and roof area. It is observed from the curves that, reliability increases with increase of storage tank size as expected except for higher demand and lower roof area; in such situation reliability remain nearly constant with increase in storage tank. This is because lower roof catchments fail to catch sufficient water that satisfies such high demand. Given data on household demand, household size and roof area, the storage tank could be estimated from the curves for any desired reliability. It should be noted that, while choosing a tank size, both time and volumetric reliability should be considered to ensure a reliable rainwater harvesting system. 4. Conclusion In this study, reliability of rainwater harvesting systems of a coastal water scarce area of Bangladesh was assessed by developing a simulation model considering historical observed rainfall data. The reliability values were then compared with the values obtained from future predicted data of the study area. The results show that reliability increases while considering predicted data. This increase was expected as precipitation 226
Figure 3: Tank Sizing Curves Based on Volumetric Reliability for Different Roof Areas and Water Demand (A, C, E Observed Data; B, D, F Future Predicted Data (a) (b) (c) (d) (e) (f) 227
Figure 4: Tank Sizing Curves Based On Time Reliability for Different Roof Areas and Water Demand (A, C, E Observed Data; B, D, F Future Predicted Data (a) (b) (c) (d) (e) (f) 228
might increase in all the months in future in the study area and this increment appear to be more in dry months compared to wet months. Finally model results were represented in curves so that they can be used by people as tank sizing curves who lack knowledge of hydrology and probability. These curves were developed considering both volumetric and time reliability so that a suitable storage tank could be chosen that would ensure a reliable RWH system. Finally it can be said that, for a reliable and sustainable rainwater harvesting system, this type of analysis should be conducted for a particular geographical area of Bangladesh before undertaking a rainwater harvesting project for domestic water supply. Reference Ahmed, M. F., and Rahman, M. M. (2000), Water supply and sanitation rural and low income urban communities, 4 th ed, ITN-Bangladesh Centre for Water Supply and Waste Management, BUET, Dhaka, ISBN 984-31-0936-8, Pp. 444. Fewkes, A. (2006), The technology, design and utility of rainwater catchment systems, In: Butler D, Memon FA (eds), Water demand management, London. Fewkes, A., and Butler, D. (2000), 'Simulating the performance of rainwater collection and reuse systems using behavioural models', Building Services Engineering Research and Technology, vol. 21, no. 99, Pp. 99-106. Fewkes, A., and Butler, D. (1999), 'The sizing of rainwater stores using behavioural models ', 9th International Rainwater Catchment Systems Conference, July, Petrolina, Brazil, Technology of Rainwater Catchment Systems Association, sec. 4, paper 4.11. Fewkes, A., and Warm, P. (2000), 'A Method of Modelling the Performance of Rainwater Collection Systems in the UK', Building Services Engineering Research and Technology, vol. 21, no. 4, Pp. 257-265. IPCC (2001), The Scientific Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change. 3 rd ed, Cambridge University Press, Cambridge, United Kingdom, Pp. 156-159. IPCC (2007), The Physical Science Basis, Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel for Climate Change, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, Pp. 996. Islam, M., Sakakibara, H., Karim, M. R., Sakine, M., and Mahmud, Z. H. (2011), 'Bacterial assessment of alternative water supply options in Coastal Areas of Bangladesh', Journal of Water and Health, vol. 9.2, pp. 415-428. Jenkins, D., Pearson, F., Moore, E., Sun, J. K., and Valentine, R. (1978), Feasibility of rainwater collection systems in California, USA: Californian Water Resources Centre, University of California. Kamruzzaman, A. K. M., and Ahmed, F. (2006), 'Study of performance of existing pond sand filters in different parts of Bangladesh', 32nd WEDC International Conference, Colombo, Sri Lanka, November, Loughborough, Leicestershire, UK, WEDC Loughborough, Pp. 377-380. 229
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