Post on 16-Dec-2014
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Big data, big productivity gains – is it so simple in water management?
Water for Food Seattle, October 2014
Jeremy Bird International Water Management Institute
Photo: IWMI
How transferable are ‘first-world’ solutions…… is there scope for ‘leapfrogging’
UNITING AGRICULTURE AND NATURE FOR POVERTY REDUCTION
Drones for monitoring
Photos: CIP
Photo: IWMI GRanD Unit
Satellite controlled water management?
UNITING AGRICULTURE AND NATURE FOR POVERTY REDUCTION
Pennan Chinnasamy, IWMI
GRACE – Similar opportunities in emerging economiesMonitoring of recharge dams, Gujarat
UNITING AGRICULTURE AND NATURE FOR POVERTY REDUCTION
Drought Monitoring in Sri Lanka
• SPEI (Standardised Precipitation –Evapotranspiration Index) to determine the onset, duration and magnitude of drought conditions
• Potential to inform inter-basin transfers , crop choice ….
Drought Index using SPEI
IWMI - 2014 Drought in Kurunegala District
Big data will make a big difference … …but many of the intractable problems on
water management need a better understanding of the changing institutional and socio-cultural context
Informal canal diversion around automatic gate
Photo: IWMI Cairo
‘Illegal’ interventions
Photos: IWMI Cairo
Participatory Irrigation Management - mixed success and failure
• PIM – was the paradigm for irrigation management
• emerging evidence that PIM schemes are failing when financial support is withdrawn
Region Success Failure
S Asia 18 20
E Asia 7 2
SE Asia 12 24
C Asia 4 14Source: Mukherji, IWMI
Over abstraction of groundwater
Perverse subsidies on electricity for agriculture led to over- abstraction of groundwater
1950-51
1954-55
1958-59
1962-63
1966-67
1970-71
1974-75
1978-79
1982-83
1986-87
1990-91
1994-95
1998-99
2002-03
2006-07(p)0
5000
10000
15000
20000
25000
30000
35000
40000
45000
Groundw
ater
Canal
Tanks
Tushaar Shah, IWMI
Points to the need for questioning what data we need to inform change….
….its relevance, intensity and quality….not just data because it is there
Photo: Hamish John Appleby
UNITING AGRICULTURE AND NATURE FOR POVERTY REDUCTION
Merti Aquifer, Kenya
Identify uncertainties and risk in decision variables
Compute value of additional information
Probabilistic outcomes for different groups
What data and how much?
Keith Shepherd, ICRAF
Infographic: Rachel Cramer, IWMI
Canal monitoring in Pakistan – on the ground data
Photos, IWMI Pakistan
Ground truthing and understanding the reasons behind good or poor performance
Aditi Mukherji, IWMI
Understanding trends : Feminization and ageing of agricultural population
1 million Nepali migrants in 2004 - 97% were male. World Bank. 2009
26% of Nepalese households are headed by females. 2011 Census
World’s farming population is ageing – average age approaching 60 Trends towards consolidation of land in China, Korea, Malaysia…
IWMI, Nepal; WLE
Some options for…
….incorporating gender in planning ….irrigation management….natural resources management….flood and drought management….transboundary management
UNITING AGRICULTURE AND NATURE FOR POVERTY REDUCTION
Incorporate gender diverse perceptions into landscape modeling
Photo: Scott Miller
3-D participatory mapping
Gender Mapper and Gender Profiles
UNITING AGRICULTURE AND NATURE FOR POVERTY REDUCTIONPhoto: Birhanu Zemadim
Citizen Scientists
2006
Nepal
2012
Global irrigation mapping: dramatic improvements in resolution
Salman Siddiqui, et a
IFPRI, 2012
FAO, 2012
Altchenko and Villholth, 2014
Resource potential (e.g., groundwater)
INCREASING WATER AND LAND PRODUCTIVITYInvesting in sustainable access to supplementary irrigation - data underpins business models and policy change
Photo: IWMI
UNITING AGRICULTURE AND NATURE FOR POVERTY REDUCTION
K. Umarov WUA, Fergana Province
Using SMS to improve irrigation scheduling procedures - Uzbekistan
Photos: IWMI
UNITING AGRICULTURE AND NATURE FOR POVERTY REDUCTION
Analyze irrigation system bureaucracies and pathways to reform.
Reconfigure irrigation benchmarking
Explore roles of ‘new data’ in irrigation management
Re-examine storage options – surface, subsurface
Test alternative financing models
Build on successful public/private sector models
Not just about data – need to re-examine public irrigation models
Tushaar Shah, S Prathapar IWMI
• Groundwater reserves in Africa are many times greater than surface water
• Mapping techniques plus ground truthing show potential
Big data approaches could help with the development of sustainable groundwater use in Africa
Source: Karen Villholth , IWMI
Big data supports an ecosystemsapproach to landscape management
• Sudd wetland in South Sudan - twice the size of Spain
• Upstream hydrological construction could impact floods, which feed the Sudd
Photo: JAXA; Analysis: Rebelo, IWMI
Historic flood analysis informs future flood planning
Historic analysis available from 2000 Giriraj Amarnath, IWMI
Concept: Index Based Flood Insurance
Peoples Participation
Flood map
Scaled for Depth
Scaled for Duration
Final IndexMap
Flood Indexing Concept
Flood Hazard Model
Flood Loss Model
Flood Insurance Policy
Partner: IWM
Mobile Apps– Operational Flood Information Management
“Project outcomes to target thousands of
farmers get access to right information at right time
on flood risks and opportunities from flood
recession agriculture”
Photo: Thor Windham-Wright, IWMI
• IWMI, UNESCO-IHE, FAO partnership• public domain data repository for
monthly water accounts of river basins. • range of ‘Accounting’ sheets such as
agricultural production, ecosystem services, useable flows and groundwater depletion
Product – Water Accounting Plus
Dry season water levels, Chiang Saen Nov ‘13-May ’14 Source: MRC
How can ‘big data’ improve transboundary cooperation?
Urban nutrient balance ….waste as an asset
UNITING AGRICULTURE AND NATURE FOR POVERTY REDUCTION
CGIAR Research Program Water Land and Ecosystems
Sustainable intensification providing a pathway for agriculture productivity, human development and resilient landscapes
WLE - Thematic and focal region focus
www.iwmi.orghttp://wle.cgiar.org
IWMI - looking forward to cooperation with DWFI