- freely available
Water 2017, 9(5), 359; doi:10.3390/w9050359
- Sampling: representative samples of rooftops are obtained and extrapolated to the total area. This method is suitable for estimating roof areas for large areas;
- Multivariate sampling: correlations are drawn between additional variables (e.g., population) and roof area;
- Complete census: gives the most accurate results but involves the computation of the entire area of the rooftops in the area of interest by using statistical information like floor area, number of floors, and number of housing units;
- Digitization or image classification tools can be used from remotely sensed high-resolution images to compute the roof areas with a Geographical Information System (GIS).
- Determination and discrimination of rooftop areas and different roof types from high resolution satellite images;
- Setup and parameterization of an extended WEAP model with an implemented simple RRWH scheme for large scale planning;
- Implementation of future scenarios in WEAP and evaluation of their implications and potential for long-term management of the urban water supply.
2. Materials and Methods
2.1. Study Area and Data
2.2. Overview of the Methodology
2.3. Roof Area Estimation
2.4. The WEAP Model for Mombasa City
2.4.1. Conceptual Model Scheme
- Demand site: Even though six different demand sites have been shown in the model (Mombasa City, Malindi Town, Kilifi Town, Kwale Town, Mariakani Town, and Voi Town), the study is focused only on Mombasa City and the rest are used to provide a complete picture of the sharing of water resources in the Coastal region.
- Water sources:
- Current situation:
- The city receives water from Mzima Springs, Baricho boreholes, Marere Springs, Tiwi-Likoni boreholes, and individual dug-out wells.
- The rivers of Marere, Mwache, Sabaki, and Rare are some of the rivers that flow around Mombasa City. However, currently there is no abstraction from these rivers.
- Future (presented in the model):
- The head flow generated from the Mwache catchment feeds the Mwache River. The Mwache Reservoir is expected to supply water from 2020.
- The rooftop areas are implemented as five catchment nodes, corresponding to the roof areas for each of the four zones in Mombasa, namely North Mainland (NML), South Mainland (SML), West Mainland (WML), Island, and new buildings to be constructed in the future. The water, which is collected from the rooftops of these five catchments, is directed into one reservoir “RRWH”, which is modelled as a local reservoir.
- Operation of Mkurumudzi Dam in supplying water to Mombasa is expected to start from 2030 .
- Return flows are not considered in the WEAP model because the city mainly depends on onsite wastewater disposal methods such as pit latrines, cesspits, and septic tanks that do not allow any return flows to the rivers, and the sewer system of the city drains to the Indian Ocean. The two wastewater treatment plants, Kizingo and West Mainland, serve a very small population and also discharge directly to the Indian Ocean.
2.4.2. Catchment and RRWH Implementation
2.4.3. Baseline Scenario
2.4.4. Future Scenarios for Mombasa
3. Results and Discussion
3.1. Determination of Rooftop Area
3.2. WEAP Scenarios
Conflicts of Interest
- Abdulla, F.; Al-Shareef, A. Roof rainwater harvesting systems for household water supply in Jordan. Desalination 2009, 243, 195–207. [Google Scholar] [CrossRef]
- Siegert, K. Introduction to Water Harvesting: Some Basic Principles for Planning, Design and Monitoring; Water Reports FAO: Rome, Italy, 1994. [Google Scholar]
- Angrill, S.; Segura-Castillo, L.; Petit-Boix, A.; Rieradevall, J.; Gabarrell, X.; Josa, A. Environmental performance of rainwater harvesting strategies in Mediterranean buildings. Int. J. Life Cycle Assess. 2016. [Google Scholar] [CrossRef]
- Gould, J.; Nissen-Petersen, E. Rainwater Catchment Systems for Domestic Supply: Design, Construction and Implementation; Intermediate Technology Publications: London, UK, 1999. [Google Scholar]
- Domènech, L.; Saurí, D. A comparative appraisal of the use of rainwater harvesting in single and multi-family buildings of the Metropolitan Area of Barcelona (Spain): Social experience, drinking water savings and economic costs. J. Clean. Prod. 2011, 19, 598–608. [Google Scholar] [CrossRef]
- Handia, L.; Tembo, J.M.; Mwiindwa, C. Potential of rainwater harvesting in urban Zambia. Phys. Chem. Earth 2003, 28, 893–896. [Google Scholar] [CrossRef]
- Thomas, T.H.; Martinson, D.B. Roof Water Harvesting: A Handbook for Practitioners; IRC International Water and Sanitation Centre: Delft, The Netherlands, 2007. [Google Scholar]
- Kahinda, J.M.; Taigbenu, A.E.; Boroto, R.J. Domestic rainwater harvesting as an adaptation measure to climate change in South Africa. Phys. Chem. Earth 2010, 35, 742–751. [Google Scholar] [CrossRef]
- Taffere, G.R.; Beyene, A.; Vuai, S.A.H.; Gasana, J.; Seleshi, Y. Reliability analysis of roof rainwater harvesting systems in a semi-arid region of sub-Saharan Africa: Case study of Mekelle, Ethiopia. Hydrol. Sci. J. 2016, 61, 1135–1140. [Google Scholar] [CrossRef]
- Lee, J.Y.; Bak, G.; Han, M. Quality of roof-harvested rainwater—Comparison of different roofing materials. Environ. Pollut. 2012, 162, 422–429. [Google Scholar] [CrossRef] [PubMed]
- Farreny, R.; Morales-Pinzo, T.; Guisasola, A.; Taya, C.; Rieradevall, J.; Gabarrell, X. Roof selection for rainwater harvesting: Quantity and quality assessments in Spain. Water Res. 2011, 45, 3245–3254. [Google Scholar] [CrossRef] [PubMed]
- Gikas, G.D.; Tsihrintzis, V.A. Assessment of water quality of first-flush roof runoff and harvested rainwater. J. Hydrol. 2012, 466–467, 115–126. [Google Scholar] [CrossRef]
- UNEP; CEHI. A Handbook on Rainwater Harvesting in the Caribbean; The United Nations Environment Programme (UNEP): Washington, DC, USA; The Caribbean Environmental Health Institute (CEHI): Castries, Saint Lucia, 2009. [Google Scholar]
- Ward, S.; Memon, F.A.; Butler, D. Performance of a large building rainwater harvesting system. Water Res. 2012, 46, 5127–5134. [Google Scholar] [CrossRef] [PubMed]
- Mehrabadi, M.H.R.; Saghafian, B.; Haghighi Fashi, F. Assessment of residential rainwater harvesting efficiency for meeting non-potable water demands in three climate conditions. Resour. Conserv. Recycl. 2013, 73, 86–93. [Google Scholar] [CrossRef]
- Meera, V.; Mansoor Ahammed, M. Water quality of rooftop rainwater harvesting systems: A review. J. Water Supply Res. Technol. AQUA 2006, 55, 257–268. [Google Scholar]
- Sazakli, E.; Alexopoulos, A.; Leotsinidis, M. Rainwater harvesting, quality assessment and utilization in Kefalonia Island, Greece. Water Res. 2007, 41, 2039–2047. [Google Scholar] [CrossRef] [PubMed]
- Mendez, C.B.; Klenzendorf, J.B.; Afshar, B.R.; Simmons, M.T.; Barrett, M.E.; Kinney, K.A.; Kirisits, M.J. The effect of roofing material on the quality of harvested rainwater. Water Res. 2011, 45, 2049–2059. [Google Scholar] [CrossRef] [PubMed]
- Byrne, J.; Taminiau, J.; Kurdgelashvili, L.; Kim, K.N. A review of the solar city concept and methods to assess rooftop solar electric potential, with an illustrative application to the city of Seoul. Renew. Sustain. Energy Rev. 2015, 41, 830–844. [Google Scholar] [CrossRef]
- Williams, N.; Quincey, D.; Stillwell, J. Automatic classification of roof objects from aerial imagery of informal settlements in Johannesburg. Appl. Spat. Anal. Policy 2015, 9, 269–281. [Google Scholar] [CrossRef]
- Veljanovski, T.U.; Kanjir, U.; Pehani, P.; Oštir, K.; Kovačič, P. Object-based image analysis of VHR satellite imagery for population estimation in informal settlement Kibera-Nairobi, Kenya. In Remote Sensing-Applications; InTech Europe: Rijeka, Croatia, 2012; pp. 407–434. [Google Scholar]
- Yates, D.; Sieber, J.; Purkey, D.; Huber-Lee, A. WEAP21—A demand-priority and preference-driven water planning model Part 1: Model characteristics. Water Int. 2005, 30, 487–500. [Google Scholar] [CrossRef]
- Yates, D.; Purkey, D.; Sieber, J.; Huber-Lee, A.; Galbraith, H. WEAP21—A demand-priority and preference-driven water planning model. Part 2: Aiding freshwater ecosystem service evaluation. Water Int. 2005, 30, 501–512. [Google Scholar] [CrossRef]
- Lévite, H.; Sally, H.; Cour, J. Testing water demand management scenarios in a water-stressed basin in South Africa: Application of the WEAP model. Phys. Chem. Earth 2003, 28, 779–786. [Google Scholar] [CrossRef]
- Mutiga, J.K.; Mavengano, S.T.; Zhongbo, S.; Woldai, T.; Becht, R. Water allocation as a planning tool to minimize water use conflicts in the Upper Ewaso Ng’iro North Basin, Kenya. Water Resour. Manag. 2010, 24, 3939–3959. [Google Scholar] [CrossRef]
- Falkenmark, M.; Lundquist, J.; Widstrand, C. Macro-scale Water Scarcity Requires Micro-scale Approaches: Aspects of Vulnerability in Semi-arid Development. Nat. Resour. Forum 1989, 13, 258–267. [Google Scholar] [CrossRef] [PubMed]
- WRMA. Integrated Water Resources Management and Water Efficiency Plan for Kenya; Water Resources Management Authority (WRMA): Nairobi, Kenya, 2009.
- Droogers, P.; Butterfield, R.; Dyszynski, J. Climate Change and Hydropower, Impact and Adaptation Costs: Case Study Kenya; Future Water: Wageningen, The Netherlands, 2009. [Google Scholar]
- Taylor, K.E.; Stouffer, R.J.; Meehl, G.A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 2012, 93, 485–498. [Google Scholar] [CrossRef]
- Overland, J.E.; Wang, M.; Bond, N.A.; Walsh, J.E.; Kattsov, V.M.; Chapman, W.L. Considerations in the selection of global climate models for regional climate projections: The Arctic as a case study. J. Clim. 2011, 24, 1583–1597. [Google Scholar] [CrossRef]
- MWI. Practice Manual for Water Supply Services in Kenya; Ministry of Water and Irrigation (MWI): Nairobi, Kenya, 2005.
- Mombasa County. Mombasa County Government First County Integrated Development Plan (2013–2017); Government of Kenya: Mombasa, Kenya, 2014.
- Kenya National Bureau of Statistics. Kenya 2009 Population and Housing Census; Government of Kenya: Mombasa, Kenya, 2009.
- TAHAL/Bhundia Consultants. Water Supply Master Plan for Mombasa and Other Towns within Coast Province; Coast Water Services Board: Mombasa, Kenya, 2013. [Google Scholar]
- WASREB. A Performance Review of Kenya’s Water Services Sector 2012–2013 (Impact Report Issue No. 7); Water Services Regulatory Board (WASREB): Nairobi, Kenya, 2014.
- Lillesand, T.M.; Kiefer, R.W. Remote Sensing and Image Interpretation; John Wiley & Sons: New York, NY, USA, 1994. [Google Scholar]
- CES Consultants. Feasibility Study, Preliminary and Detailed Engineering Designs of Development of Mwache Multi-Purpose Dam Project along Mwache River—Hydrology Report; Ministry of Regional Development: Nairobi, Kenya, 2013.
- TWDB. The Texas Manual on Rainwater Harvesting; Texas Water Development Board (TWDB): Austin, TX, USA, 2005.
- Stehman, S.V. Selecting and interpreting measures of thematic classification accuracy. Remote Sens. Environ. 1997, 62, 77–89. [Google Scholar] [CrossRef]
- Grote, A.; Rottensteiner, F. Assessing the impact of digital surface models on road extraction in suburban areas by region-based road subgraph extraction. Int. Arch. Photogramm. Remote Sens. and Spat. Inf. Sci. 2009, 38, 27–34. [Google Scholar]
- Hirschmüller, H. Stereo processing by semiglobal matching and mutual information. IEEE Trans. Pattern Anal. Mach. Intell. 2008, 30, 328–341. [Google Scholar] [CrossRef] [PubMed]
|Model Name||RCP 4.5||RCP 8.5||Climate Modelling Institution/Centre|
|MIROC-ESM||−12.70||−15.70||National Institute for Environmental Studies, Japan|
|CNRM-CM5||−8.00||−8.10||Centre National de Recherches Meteorologiques, France|
|CAN-ESM2||−3.80||−2.30||Canadian Centre for Climate Modelling and Analysis|
|FGOALS-S2||−13.80||−19.70||Institute of Atmospheric Physics, Chinese Academy of Sciences|
|BNU-ESM||−16.00||−15.00||Beijing Normal University|
|MIROC5||9.20||8.90||National Institute for Environmental Studies, Japan|
|GFDL-ESM2G||0.40||2.70||Geophysical Fluid Dynamics Laboratory, USA|
|MIROC-ESM-CHEM||−15.50||−15.40||National Institute for Environmental Studies, Japan|
|GFDL-ESM2M||−1.20||−1.70||Geophysical Fluid Dynamics Laboratory, USA|
|MRI-CGCM3||26.90||22.20||Japan Meteorological Agency|
|BCC-CSM1-1||−10.90||−10.70||Beijing Climate Centre|
|Category||Persons||Water Use Rate||Remarks|
|High Class Houses (HCH)||5.00%||250||Poverty level was 37.6% in 2013, remaining 62.4% assumed as 5% for HCH and 57.4% for MCH|
|Medium Class Houses (MCH)||57.40%||150|
|Low Class Houses with individual connections (LCH_IC)||18.80%||75|
|Low Class Houses without individual connections (LCH_WIC)||18.80%||20|
|Sensor Name||Acquisition Date||Cloud Cover (%)||Multispectral Bands||Off-Nadir (°)||Spatial Resolution (m)|
|Roof||Crop coefficient, Kc||0.1||Lower than bare soil (0.3 from FAO Paper 56)|
|Effective rainfall, Peff||Iron-10%|
Tile and concrete-20%
|Groundwater sources: Baricho, Mzima, Tiwi, Marere, Ind. Wells||Storage Capacity|
Initial Storage (MCM)Max Withdrawal (MCM)
80, 82, 7.3, 7.3, 16
Same as initial storage
83, 405, 21, 15, 23
Mumma and Lane, 2010; ; JBG Gauff Ingenieure, 1995; Samez Consultants, 2008; Sincat/Atkins Consultants, 1994; Fichtner/Wanjohi Consultants, 2014
|Mwache Dam||Storage capacity (MCM)|
|Effective rainfall, Peff|
Crop coefficient, Kc
|Transmission||Loss in transmission links||47%|||
|Loss in RRWH transmission||20%||Reasonable assumptions|
|High Population Growth (HPG) rate||4.2%||Kenya National Bureau of Statistics (2009), BCEOM/Mangat (2011) and Mombasa County (2014)|
|Low Population Growth (LPG) rate||1.9%|
|Increased water consumption due to better standard of living||116 LPCD to 155 LPCD|
|Source||Capacity||Current||Phase I||Phase II||Phase III|
|NWS/RRWH_4||Existing system with new water sources developed (NWS) and RRWH_4 (using all new roofs) implemented|
|NWS/NRWS||Existing system with NWS and non-revenue water (NRWS) reduction strategy implemented|
|RRWH_4/NRWS||Existing system with both RRWH_4 and NRWS strategy implemented|
|NWS/EWU||Existing system with new water sources developed and water use efficiency (EWU) improved|
|RRWH_4/NRWS/EWU||Existing system without new water sources developed but RRWH_4, NRWS, and EWU implemented|
|NWS/NRWS/EWU||Existing system with new water sources developed and NWRS and EWU implemented, but no RRWH_4|
|NWS/RRWH_4/NRWS/EWU||All strategies implemented (new water sources, RRWH, non-revenue water reduction, and efficient water use)|
|Class in Results (Automatic Classification) (All Values in %)|
|Class in Reference (Digitized)||Background||83.3||1.7||1.0||3.6||89.6||92.9|
|Supply/Demand Strategy||Total Supplied (MCM)||Supply Increase (MCM)|
|New water sources (NWS)||1055||470|
|All existing and new buildings (RRWH_5)||880||295|
|Non-Revenue Water Reduction (NRWS)||837||252|
|All existing buildings (RRWH_1)||804||219|
|Selected existing and all new buildings (RRWH_4)||696||111|
|Only new buildings (RRWH_2)||661||76|
|Selected existing buildings (RRWH_3)||619||34|
|Efficient Water Use (EWU)||585||0|
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