[Day 2] Center Presentation: IWMI
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Presented by Mir Matin at the CGIAR-CSI Annual Meeting 2009: Mapping Our Future. March 31 - April 4, 2009, ILRI Campus, Nairobi, Kenya

Presented by Mir Matin at the CGIAR-CSI Annual Meeting 2009: Mapping Our Future. March 31 - April 4, 2009, ILRI Campus, Nairobi, Kenya

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    [Day 2] Center Presentation: IWMI [Day 2] Center Presentation: IWMI Presentation Transcript

    • GIS, Remote sensing and Data Management at IWMI Mir Matin 1
    • Structural Change in IWMI Thematic structure where projects  are under one of the four themes GIS/RS application in research  projects CPWF GIS/RS IDIS IWMI 2 GIS/RS/Data
    • Outline Water Availability  Water productivity  Spatial model for site selection  Data harmonization and sharing  Future activities  3
    • Changes in Water Availability at Sub basin level - BFP IGB Luna Bharati, Priyantha Jayakody 4
    • Gorai River Catchment - Bangladesh 5
    • Methods: SWAT Model Description Conceptually, SWAT divides a watershed into sub  watersheds. Each sub watershed is connected through a stream channel and further divided in to Hydrologic Response Unit (HRU). HRU is a unique combination of soil and  vegetation type in a sub watershed, and SWAT simulates water balances, vegetation growth, and management practices at the HRU level. The subdivision of the watershed enables the  model to reflect differences in evapo-transpiration for various crops and soils. Runoff is predicted separately for each HRU and  routed to obtain the total runoff for the watershed. 6
    • Method : input data Spatial Data  • Digital Elevation Model • Land Use Map • Soil Map and Soil Properties • River network Time Series Data  • Meteorological data ( Rainfall , Maximum and minimum temperature, Relative humidity, Sunshine hours, Wind speed ) • Flow data 7
    • Water Balance Results 4000 2000 Input/output (mm) 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 -2000 -4000 Average annual RF (mm) Average annual ET (mm) Average annual RO (mm) Balance closer (mm) Water balance at each sub basin during 1965 to 1975 (1 to 22 are sub basin numbers) 4000 2000 Input/Output (mm) 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 -2000 -4000 8 Average annual RF (mm) Average annual ET (mm) Average annual RO (mm) Balance closer (mm) Water balance at each sub basin during 1990 to 1997
    • Actual ET from the two time periods: •As expected, ET changes can be linked to the landuse changes in the catchment. •ET has decreased in Sub- basins where landcover has changed from Forest to Agriculture and increased where rice has replaced some of the traditional agricultural crops 9
    • Model Performance Evaluation Observed and simulated flow during the calibration period for the 4th sub basin outlet. r2 is 0.96 Observed and simulated flow during the validation period for the 4th sub basin out let. r2is 0.94 10
    • Water Productivity Mapping Cai Xueliang 11
    • Water productivity – the concept Water productivity (WP) is “the physical mass of production or the economic value of production measured against gross inflow, net inflow, depleted water, process depleted water, or available water” (Molden, 1997, SWIM 1). It measures how the systems convert water into goods and services. The generic equation is: Output derived from water use (kg/m2 or $/m2 ) Water Productivity (kg/m or $/m )  3 3 Water input (m 3 /m 2 ) 12
    • Why mapping water productivity The overarching goal of Water Productivity assessment is to identify opportunities to improve the net gain from water by either • increasing the productivity for the same quantum of water; or • reduce the water input without or with little productivity decrease. 13
    • Basin WP Analysis – What to Care? Magnitude – what’s the current status?  Spatial Variation – how does it vary within and among  regions? Causes – why is WP varying (both high and low)?  Scope for improvement – how much potential for,  where? Irrigated vs. rainfed – what’s the option for  sustainable development under water scarcity and food deficit condition? Crop vs. livestock and fisheries – how is livestock and  fisheries contributing to water use outputs? 14
    • The Methodology Data collection: production, weather data, MODIS 1. NDVI and Land Surface Temperature (LST) products, existing LULC maps and GIS layers, GT points; Crop dominance map synthesizing; 2. Land productivity: 3. 1. district/state wise agricultural productivity map from census; 2. Interpolating to pixel wise productivity using MODIS NDVI indices; ET mapping: 4. 1. Potential ET map with FAO approach; 2. Actual ET estimation using SSEB model; Water productivity mapping. 5. 15
    • Data collection A ground truth mission was conducted in India from 8th -17th Oct, 2008 Across Indus and  Gangetic river basin >2700km covered  175 samples  • LULC • Cropping pattern • Agricultural productivity (cut and farmer survey) • Water use (rainfed, surface/GW) • Social-economic survey 16
    • Crop Dominance Map Synthesizing existing maps to a crop dominance map with GT data 500m, IWMI, 2005 1km, USGS, 1992-1993 500m, IWMI, 2003 Legend 01Irrigated, surfacewater, rice, single crop 02 Irrigated, surfacewater, rice, double crop 03 Irrigated, surfacewater, rice-other crops, single crop 04 Irrigated, surfacewater, rice-other crops, double crop 05 Irrigated, surfacewater, rice-other crops, continuous crop 06 Irrigated, conjunctive use, mixed forest, rice-other crops, continuous crop 07 Irrigated, surfacewater, wheat-other crops, single crop 08 Irrigated, surfacewater, wheat-other crops, double crop 09 Irrigated, surfacewater, wheat-other crops, continuous crop 10 Irrigated, surfacewater, sugarcane-other crops, single crop 11 Irrigated, surfacewater, mixed crop, single crop 12 Irrigated, surfacewater, mixed crops, double crop 13 Irrigated, groundwater, rice-othercrops, single crop 14 Irrigated, groundwater, rice-othercrops, double crop 15 Irrigated, groundwater, cotton-other crops, single crop 16 Irrigated, groundwater, cotton, wheat-other crops, double crop 17 Irrigated, groundwater, cotton, soyabean-other crops, continuous crop 18 Irrigated, groundwater, sugarcane-other crops, single crop 19 Irrigated, groundwater, mixed crops, single crop 20 Irrigated, groundwater, plantations-other crops, continuous crop 21 Irrigated, conjunctive use, rice-other crops, single crop 22 Irrigated, conjunctive use, rice, wheat-other crops, double crop 23 Irrigated, conjunctive use, wheat, rice-other crops, double crop 24 Irrigated, conjunctive use, rice, sugarcane-other crops, continuous crop 25 Irrigated, conjunctive use, wheat-other crops, single crop 17 26 Irrigated, conjunctive use, cotton-other crops, single crop 27 Irrigated, conjunctive use, cotton, wheat-other crops, double crop 28 Irrigated, conjunctive use, sugarcane-other crops, single crop 29 Irrigated, conjunctive use, soyabean, wheat-other crops, double crop 30 Irrigated, conjunctive use, mixed crops, single crop 00 Ocean and other areas
    • Crop Dominance Map A “crop dominance map” of namely year 2008 shows major crops rice and wheat area, and other mixed croplands. Watering sources are also given for 18 IGB map.
    • Crop Productivity Step 1. District wise productivity map using census data Crop GVP map of India and Nepal IGB paddy rice yield map of 2005 for 2005 Kharif season 19
    • Crop Productivity Step 2. Pixel wise rice productivity map interpolation using MODIS data NDVI composition paddy rice yield map of 2005 of 29 Aug – 5 Sept 2005 for rice area MODIS 250m NDVI at rice heading stage was used to interpolate yield from district average to pixel wise employing rice yield ~ NDVI linear relationship. 20
    • Actual ET Estimation Step 1. Potential ET calculation (2005-09-21 as example) Daily data from 58 weather stations potential ET map (2005 Sept 21) Steps: 1. Hargreaves equation for reference ET. 2. Kc approach for potential ET. Note: Kc (FAO56) was determined by maximum Kc values of major crop of the month 21
    • Actual ET Estimation Step 2. Actual ET calculation by Simplified Surface Energy Balance (SSEB) approach SSEB TH  Tx ET f  TH  TC ETa  ET p  ET f MODIS LST 2005 Sept 21 ETa – the actual Evapotranspiration, mm. ETf – the evaporative fraction, 0-1, unitless. ET0 – Potential ET, mm. Tx – the Land Surface Temperature (LST) of pixel x from thermal data. TH/TC – the LST of hottest/coldest pixels. ET fraction map (2005 Sept 21) potential ET map (2005 Sept 21) Seasonal actual ET map 22 (2005 Jun 10 – Oct 15)
    • Water Productivity Maps Rice productivity (kg/m3) Mean AVG SDV Min Max 23 0.618 0.618 0.306 0.09 2.5
    • Results: Water Productivity Maps Rice productivity (kg/m3) Rice water productivity for 4 major IGB countries (unit: kg/m3) Country ADMIN_NAME WP_MEAN Country ADMIN_NAME WP_MEAN Bangladesh Chittagong 0.445 Pakistan North-west Frontier 0.451 Bangladesh Dhaka 0.496 Pakistan FAT 0.525 Bangladesh Barisal 0.533 Pakistan Azad Kashmir 0.580 Bangladesh Khulna 0.796 Pakistan Baluchistan 0.657 Bangladesh Rajshahi 0.856 Pakistan Sind 0.732 Pakistan Punjab 0.755 Average 0.625 Average 0.617 Nepal Lumbini 0.542 India Madhya Pradesh 0.393 Nepal Sagarmatha 0.556 India Himachal Pradesh 0.407 Nepal Janakpur 0.578 India Bihar 0.408 Nepal Bagmati 0.583 India Jammu & Kashmir 0.430 Nepal Gandaki 0.607 India Uttar Pradesh 0.560 Nepal Seti 0.699 India West Bengal 0.718 Nepal Bheri 0.713 India Rajasthan 0.720 Nepal Rapti 0.715 India Haryana 0.746 Nepal Narayani 0.754 India Delhi 0.818 Nepal Mahakali 0.792 India Punjab 0.833 Nepal Kosi 0.904 24 Nepal Mechi 0.964 Average 0.701 Average 0.603
    • Water productivity mapping in Central Asia Satellite sensor data  • MODIS • IRS • Landsat • Quickbird Ground measurements  • NDVI, LAI, Spectra-radiometer Kazakhstan reflectance • Biomass (wet, dry), yield, crop Aral height, canopy cover Sea • Soil moisture, irrigation, outflow • Weather data Test sites # Toktogul Rivers Canals Uzbekistan La kes Kirgizstan Irrigated area Administrative provinces Basin boundaries Tajikistan 25 Galaba study site Kuva study site
    • Crop type mapping Legend LULC and the areas Alfalfa Cotton in Galaba site Fallow Home garden LULC Areas share Orchard ha % Rice Alfalfa 858.5 8.7 Settlements Cotton 4414.9 44.5 Fallow 1853.8 18.7 Home garden 90.3 0.9 Orchard 1.4 0.0 Rice 361.8 3.6 Settlement 573.9 5.8 other 1769.9 17.8 Sum 9924.5 100.0 26
    • Spectro-biophysical and yield modeling The best models for determining biomass, LAI and yield of 5 crops using IRS and QB data Best bands Best indices R- sq band sample Best ua Best combinati Crop Parameter Sensor size model band re model on R-square Cotton Wet Biomass IRS 140 Exp 2 0.697 Power 2, 3 0.834 QB 41 Multi-linear 1, 4 0.813 Multi-linear 1,4; 3,4 0.506 Dry Biomass IRS 136 Power 2 0.620 Power 2, 3 0.821 QB 41 Exp 2 0.521 Exp 1, 2 0.661 LAI IRS 135 Multi-linear 3, 4 0.634 Power 1, 3 0.725 QB 41 Multi-linear 2, 4 0.511 Quadratic 2, 4 0.574 IRSA Yield 14 Linear 2, 3 0.753 QBB 7 Linear 3, 4 0.610 Wheat Wet Biomass IRS 9 Quadratic 2 0.425 Quadratic 1, 3 0.678 Dry Biomass IRS 14 Quadratic 1 0.205 Quadratic 3, 4 0.309 LAI IRS 18 Quadratic 4 0.8 Multi-linear 1,3; 2,3 0.465 Yield IRS 12 Linear 2, 3 0.67 MaizeD Wet Biomass IRS 19 Power 2 0.815 Power 2, 3 0.871 Dry Biomass IRS 17 Exp 2 0.928 Power 2, 3 0.903 LAI IRS 19 Multi-linear 1, 3 0.777 Multi-linear 1,2; 2,3 0.839 RiceE Wet Biomass QB 10 Multi-linear 1, 2 0.535 Multi-linear 1,2; 2,4 0.600 Dry Biomass QB 10 Multi-linear 1, 2 0.395 Multi-linear 1,3; 2,3 0.414 LAI QB 10 Multi-linear 2, 4 0.879 Quadratic 2, 3 0.234 Alfalfa Wet Biomass IRS 21 Power 2 0.838 Quadratic 1, 2 0.853 1,2; 2,3; QB 8 Multi-linear 2, 4 0.772 Multi-linear 0.887 3,4 Dry Biomass IRS 21 Power 2 0.817 Exp 1, 2 0.812 1,2; 2,3; QB 8 Multi-linear 2, 4 0.732 Multi-linear 0.867 3,4 LAI IRS 21 Power 3 0.499 Exp 3, 4 0.639 27 QB 8 Multi-linear 1, 3, 4 0.927 Multi-linear 1,3; 3,4 0.858
    • Crop water use mapping Evapotranspiration using Landsat ETM+ thermal data 0 1 2 4 km 0 1 2 4 km 0 1 2 4 km 2006-04-24 2006-05-10 2006-06-11 0 1 2 4 km 0 1 2 4 km 0 1 2 4 km 28 2006-07-29 2006-08-14 2006-10-01
    • Water productivity map With an average value of 0.3 kg/m3, the water productivity map shows explicit scope for improvement: where and how much. Legend Kg/m3 <0.3 0.3-0.4 29 >0.4
    • Spatial models for best site selections of inland valley wetlands for rice cultivation in Ghana Muralikrishna Gumma, Prasad S. Thenkabail, and Fujii Hideto 30
    • Approach Identify critical spatial data layers needed for the land  suitability model for inland valley (IV) wetland rice cultivation; Provide weightages to spatial data layers and for classes  within each spatial data layer based on expert knowledge; Develop spatial model that will provide answers to relevant  questions and identify best sites (e.g., IV wetland rice cultivation) based on the spatial data layers and their weightages. 31
    • Methodology 32
    • Model Development Summary of Variables considered in Model runs: variable weights for layers and variable weights for classes Kumasi Tamale Factor Score Maximu Scores Factor Score Maximum Scores Factor weight range m score given Weighted score Factor weight range score given Weighted score 1.89 1 -5 3 3 (1.89*3)=5.67 1.89 1-5 3 01-Annual-rainfall 01-Annual-rainfall 3, ( 1.89 * 3 ) = 5.67 1.47 1 -5 3 3,2 (1.47*3)=4.41 1.47 1-5 3 02-PET 02-PET 3,2 ( 1.47 * 3 ) = 4.41 2.05 1 -5 5 5 (2.05*5)=10.25 2.05 1-5 3 03-LPG 03-LPG 3,2 ( 2.05 * 3 ) = 6.15 04-specificdischarge 1.89 1 -5 5 5,4,3,2,1 (1.89*5)=9.45 2.05 1-5 3 05-Stream order 3,2,1 ( 2.05 * 3 ) = 6.15 2.05 1 -5 5 5,4,3,2,1 (2.05*5)=10.25 2.95 1-5 5 05-Stream order 07-Slope-percent 5,4,3,2,1 ( 2.95 * 5 ) = 14.75 2.95 1 -5 5 5,4,3,2,1 (2.95*5)=14.75 1.37 1-5 5 07-Slope-percent 08-Lulc 5,4,3,2,1 ( 1.37 * 5 ) = 6.85 1.37 1 -5 5 5,3,2,1 (1.37*5)=6.85 1.53 1-5 3 08-Lulc 09-Soils 3,2,1 ( 1.53 * 3 ) = 4.59 1.42 1 -5 5 5,4 (1.42*5)=7.1 1.68 1-5 5 12-Experience in rice cultivation 10-Soil depth 5,4,3,2,1 ( 1.68 * 5 ) = 8.4 1.11 1 -5 4 4,3 (1.11*4)=4.44 2.32 1-5 5 13-Agro., technology (yield) 11- Soil fertility 5,4,3,2,1 ( 2.32 * 5 ) = 11.6 1.68 1 -5 2 2,1 (1.68*2)=3.36 1.5 1-5 5 14-Watermangement tech,. 16a-Major settlement 5,4,3,2,1 ( 1.5 * 5 ) = 7.5 1.05 1 -5 5 5,4,3,2,1 (1.05*5)=5.25 1.5 1-5 5 15-Postharvest tech., 16b-Minor settlement 5,4,3,2,1 ( 1.5 * 5 ) = 7.5 1.5 1 -5 5 5,4,3,2,1 (1.5*5)=7.5 1.7 1-5 5 16a-Major settlement 17a-Major roads 5,4,3,2,1 ( 1.7 * 5 ) = 8.5 1.5 1 -5 5 5,4,3,2,1 (1.5*5)=7.5 1.7 1-5 5 16b-Minor settlement 17b-Minor roads 5,4,3,2,1 ( 1.7 * 5 ) = 8.5 1.7 1 -5 5 5,4,3,2,1 (1.7*5)=8.5 1.4 1-5 5 17a-Major roads 18a-Major markets 5,4,3,2,1 ( 1.4 * 5 ) = 7 1.7 1 -5 5 5,4,3 (1.7*5)=8.5 1.4 1-5 5 17b-Minor roads 18b-Minor markets 5,4,3,2,1 ( 1.4 * 5 ) = 7 1.4 1 -5 5 5,4,3,2,1 (1.4*5)=7 0.41 1-5 2 18-Markets 25-Malaria 2,1 ( 0.41 * 2 ) = 0.82 1.74 1 -5 5 5,4,3,2,1 (1.74*5)=8.7 115.39 19-Land tenure Total Score 1.53 1 -5 5 5,4,3,2,1 (1.53*5)=7.65 20-Labour force 1.58 1 -5 3 3,2,1 (1.58*3)=4.74 21-Crdit system 1.05 1 -5 5 5,4 (1.05*5)=5.25 22-Extension system 1.37 1 -5 3 3,4,5 (1.37*3)=4.11 24-Incentives_net benfit 0.41 1 -5 3 3,4 (0.41*3)=1.23 25-Malaria 152.46 Total score 33
    • Model Output 34
    • Irrigated Area Mapping for China Cai Xueliang 35
    • Irrigated area mapping for China MODIS 500m monthly NDVI as main dataset 36
    • Ground truth collection 80°E 90°E 100°E 110°E 120°E 130°E 140°E Hao, W.P., Gu F. X., Xu J.(156 GT) ± Gu F. X., Liu S., Sun D.B. (138 GT) Liu S.,Liu Q., Huang(200 GT) who? CAAS(370 GT) 0 680 Km Gu F. X., Sun D.B.(84 GT) 37 Xu J., Liu Q.(197 GT)
    • Ideal Spectral Data Bank on Irrigated crops of China Irrigated-SW-Rice-SC 0.80 0.60 0.40 0.20 0.00 Jan-05 Feb-05 Mar-05 Apr-05 May-05 Jun-05 Jul-05 Aug-05 Sep-05 Oct-05 Nov-05 Dec-05 Irrigated-SW-Sugarcane-SC 1.00 0.80 0.60 0.40 0.20 0.00 Jan-05 M ar-05 M ay-05 Jul-05 Sep-05 Nov-05 Irrigated-SW-Rice-DC Irrigated-GW-Rice-DC 1.20 1.00 1.00 0.90 0.80 0.80 0.70 0.60 0.60 0.40 0.50 0.20 38 0.40 0.00 Jan-05 Feb-05 Mar-05 Apr-05 May-05 Jun-05 Jul-05 Aug-05 Sep-05 Oct-05 Nov-05 Jan-05 Feb-05 Mar-05 Apr-05 May-05 Jun-05 Jul-05 Aug-05 Sep-05 Oct-05 Nov-05
    • Data Harmonization and Sharing 39
    • Progress Research data management policy and procedure  for projects IDIS user interface improved  Implementing open source version of IDIS to  replicate at regional offices for localized access Integrating IDIS and IWMIDSP for one stop data  portal for all data Develop frame work for water resources audit –  case study for Sri Lanka IWMI Geo Network updated to version 2.2 with  PostGreSQL 40
    • IDIS – Improved Interface 41
    • Browse Available Data 42
    • Time series Data Download 43
    • Download Survey Data 44
    • IWMIDSP Data Access River Basins Web access ftp access Nations Regions Entire Globe 45 http://www.iwmidsp.org/
    • IDIS – IWMIDSP Integration Provide one stop access to all IWMI  data and metadata Data access by category, location,  country, basin, project, sources Decentralized across regions  46
    • Water Resources Audit Framework Organize data and information to  support water resources assessment for a country Provide access to data and maps on  water availability, productivity, poverty, quality, disaster and governance First case study for Sri Lanka  47
    • Discussion 48
    • Thanks! 49