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Managing variability of water resources in river basins for enhanced food and livelihoods security
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Water for a food-secure world
MANAGING VARIABILITY OF WATER RESOURCES IN
RIVER BASINS FOR ENHANCED FOOD AND
LIVELIHOODS SECURITY
Smakhtin, V., Pavelic, P., McCartney, M., Amarnath, G., Eriyagama, N.,
Bharati, L., Cai, X.
International Water Management Institute
Sri Lanka – Laos – Nepal –South Africa
GWSP Conference: Water in the Anthropocene. May 2013. Bonn, Germany
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Water for a food-secure world
Variability increases with climate change
Source: Dartmouth Flood
Observatory
Variability increases water scarcity
Variability has important positive implications
Variability determines flood risk and damages
FACETS OF WATER RESOURCES VARIABILITY
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Water for a food-secure world
RESEARCH DIRECTIONS
• Technical and economic design and appraisal of diverse water storage
“portfolios”
• Quantifying and considering ecosystem services of natural storage
“infrastructure” in basin development
• Detailed characterization and mapping of flood and drought risks and
hot spots
• Exploring underground solutions for conjunctive management of
floods and droughts in a same basin
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Water for a food-secure world
EVALUATING STORAGE OPTIONS
Storage – Drought Deficit Index (how much of the long-
term annual hydrological drought deficit is satisfied by
the existing large storage capacity in a county)
Livestock storage need (based on
livestock density per cell, poverty
levels, water availability, etc)
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Water for a food-secure world
EVALUATING REGULATING ROLE OF NATURAL STORAGE
(example - wetlands in Zambezi basin)
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Water for a food-secure world
AGRICULTURE;
PAGE (2005)
POPULATION
CIESIN - 2010
FLOOD EXPOSED
GDP
WB, 2010
• Globally - 90 grid cells of 100 km with catastrophic flood occurrence > 5,
during 1900-2010
• Damage analysis
ANALYSING FLOOD HOT SPOTS AND FLOOD MAPPING
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Water for a food-secure world
ANALYSING FLOODING HOT SPOTS AND FLOOD MAPPING
Ganges Basin
11. ANALYSING FLOODING HOT SPOTS AND FLOOD MAPPING
Flooded area (2000 – 2011)
2010
2010
8-days maps of inundation extent
Annual maps of maximum inundation
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Water for a food-secure world
CONJUNCTIVE FLOOD AND DROUGHT MANAGEMENT
Current Situation – Dry Season
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Water for a food-secure world
CONJUNCTIVE FLOOD AND DROUGHT MANAGEMENT
Current Situation – Wet Season
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Water for a food-secure world
CONJUNCTIVE FLOOD AND DROUGHT MANAGEMENT
Underground Taming of Floods for Irrigation (UTF-I);
Wet season
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Water for a food-secure world
CONJUNCTIVE FLOOD AND DROUGHT MANAGEMENT
Underground Taming of Floods for Irrigation (UTF-I);
Dry season
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Water for a food-secure world
CONJUNCTIVE FLOOD AND DROUGHT MANAGEMENT
UTF-I in plan view
17. CONJUNCTIVE FLOOD AND DROUGHT MANAGEMENT
UTF-I desktop case study: Chao Phraya basin, Thailand
Source: DFO
Land Use Regular Flooding Alluvium aquifers
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Water for a food-secure world
CONJUNCTIVE FLOOD AND DROUGHT MANAGEMENT
UTF-I desktop case study: Chao Phraya basin, Thailand
• Harvest water only exceptionally wet years. 25% of the MAR is harvestable
• 100 km2 dedicated land to flood harvesting needed (< 1% of the Basin area)
• Additional 270,000 ha of irrigation possible
• $150 mill / year - income to smallholder farmers
• Cost < $ 1 Bill. Payback time – under 7 years
• No analogs so far exist
• Scaling up this solution to SA and SEA; needs:
– Detailed design (should be as clear as the current design of surface reservoirs)
– Identification of areas in a larger region that are suitable for piloting (flood hot
spots, hydrogeology, etc)
– Several pilot experiments
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Water for a food-secure world
CONCLUSIONS
• Water resources Variability is a primary issue in basin water
resources planning and management globally
• Management of temporal Variability should consider a variety
of storage options and their combinations, large and small,
man-made and natural ones
• Understanding and characterization of Variability hot spots
may help target related research and investments
• Subsurface solutions for water resources Variability
management may have significant potential for flood and
drought mitigation, increasing irrigation water availability and
improving livelihoods. This potential is still to be realized
Could be either too much or too little. This can be in the same place/ basin. Temporal and spatial variability. In water management – spatial variability was dealt with by interbasin transfers, and temporal – by surface storage reservoirs
More arid areas also often have more variable rainfall And increases with CC ! Need to mention it or combine slides or both . Also- to “complicate things” – variability has positive aspects too.- mention This is why it was identified for intensive research under WLE program….
Mentioned only 3 basins, but in principle the problem of uncoordinated modelling is more or less the same in all WLE basins
Every time we build an artificial structure, it affects natural ecosystem services (e.g. regulating service). This needs to be factored into development
Mention – Global economic damage from natural disasters 165 bill / a. The damage globally is more than current aid flow ,M ay rise to over $450 bill by 2030, floods take lion share of thisFlood hotspots are areas that are highly flood prone having frequent flood events; causing several damages both in livelihood and infrastructure / crop lossPAGE? - Pilot Analysis of Global Ecosystems (PAGE)Giriraj, Amarnath; Ameer, Mohamed; Aggarwal, Pramod; Smakhtin, Vladimir. 2012. Detecting spatio-temporal changes in the extent of seasonal and annual flooding in South Asia using multi-resolution satellite data. In Civco, D. L.; Ehlers, M.; Habib, S.; Maltese, A.; Messinger, D.; Michel, U.; Nikolakopoulos, K. G.; Schulz, K. (Eds.). Earth resources and environmental remote sensing/GIS applications III: proceedings of the International Society for Optics and Photonics (SPIE), Vol.8538, Amsterdam, Netherland, 1-6 July 2012. Bellingham, WA, USA: International Society for Optics and Photonics (SPIE). 11p.
Example mapping products …The left map is for 2010 year example shows different classes of “water” “Mixed crops” class refer to areas that are inundated in cropland, meaning reflectance value has mixed values from vegetation and water content
Need to mention Ganges Machine and other Machines somewhere