1

GIS, Remote sensing and Data

Management at IWMI

Mir Matin

2

Structural Change in IWMI

IDIS GIS/RS

GIS/RS/Data

Management

(part of Information and

Knowledge group)

IWMI

CPWF

Thematic structure where projects are under one of the four themes

GIS/RS application in research projects

3

Outline

Water Availability

Water productivity

Spatial model for site selection

Data harmonization and sharing

Future activities

4

Changes in Water

Availability at Sub basin

level - BFP IGB

Luna Bharati, Priyantha Jayakody

5

Gorai River Catchment -

Bangladesh

6

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.

7

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

8

-4000

-2000

0

2000

4000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

Inp

ut/

Ou

tpu

t (m

m)

Average annual RF (mm) Average annual ET (mm) Average annual RO (mm) Balance closer (mm)

Water Balance Results

-4000

-2000

0

2000

4000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

Inp

ut/

ou

tpu

t (m

m)

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)

Water balance at each sub basin during 1990 to 1997

9

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

10

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

11

Water Productivity

Mapping

Cai Xueliang

12

Water productivity – the

conceptWater 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:

)/m(m inputWater

)$/m or (kg/muse waterfrom derived utputO)$/m or (kg/moductivityPrWater

23

2233

13

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.

14

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)?

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?

Scope for improvement – how much potential for, where?

15

The Methodology

1. Data collection: production, weather data, MODIS NDVI and Land Surface Temperature (LST) products,

existing LULC maps and GIS layers, GT points;

2. Crop dominance map synthesizing;

3. Land productivity:1. district/state wise agricultural productivity map

from census;

2. Interpolating to pixel wise productivity using MODIS NDVI indices;

4. ET mapping:1. Potential ET map with FAO approach;

2. Actual ET estimation using SSEB model;

5. Water productivity mapping.

16

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

17

Crop Dominance MapSynthesizing existing maps to a crop dominance map with

GT data500m, IWMI, 2003

500m, IWMI, 20051km, USGS, 1992-1993

Legend

00 Ocean and other areas

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

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

state boundary

18

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 IGB map.

Crop Dominance Map

19

Crop ProductivityStep 1. District wise productivity map using census data

IGB paddy rice yield map of 2005 Crop GVP map of India and Nepal

for 2005 Kharif season

20

Crop ProductivityStep 2. Pixel wise rice productivity map interpolation using MODIS data

paddy rice yield map of 2005NDVI composition

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.

21

Actual ET EstimationStep 1. Potential ET calculation (2005-09-21 as example)

Daily data from 58 weather stations

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

potential ET map (2005 Sept 21)

22

Actual ET EstimationStep 2. Actual ET calculation by Simplified Surface Energy Balance (SSEB) approach

Seasonal actual ET map

(2005 Jun 10 – Oct 15)

potential ET map (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.

CH

xHf

TT

TTET

fpa ETETET

SSEB

ET fraction map (2005 Sept 21)

MODIS LST 2005 Sept 21

23

Water Productivity MapsRice productivity (kg/m3)

Mean AVG SDV Min Max

0.618 0.618 0.306 0.09 2.5

24

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

Nepal Mechi 0.964

Average 0.701 Average 0.603

Results: Water Productivity MapsRice productivity (kg/m3)

25

Aral

Sea

Toktogul

Kirgizstan

Galaba study site Kuva study site

Kazakhstan

Tajikistan

Lakes

Basin boundaries

Administrative provinces

Rivers

Canals

# Test sites

Uzbekistan

Irrigated area

Water productivity mapping in Central Asia

Satellite sensor data• MODIS • IRS• Landsat• Quickbird

Ground measurements• NDVI, LAI, Spectra-radiometer

reflectance• Biomass (wet, dry), yield, crop

height, canopy cover• Soil moisture, irrigation, outflow• Weather data

26

Alfalfa

Cotton

Fallow

Home garden

Orchard

Rice

Settlements

Legend

LULC Areas share

ha %

Alfalfa 858.5 8.7

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

LULC and the areas

in Galaba site

Crop type mapping

27

Spectro-biophysical and yield modeling

Best bands Best indices

Crop Parameter Sensorsample

sizeBest

model band

R-square

Best model

band combinati

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 0.506Dry 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 Yield IRSA 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 0.309LAI IRS 18 Quadratic 4 0.8 Multi-linear 1,3; 2,3 0.465

Yield IRS 12 Linear 2, 3 0.67MaizeD Wet Biomass IRS 19 Power 2 0.815 Power 2, 3 0.871

Dry Biomass IRS 17 Exp 2 0.928 Power 2, 3 0.903LAI 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 0.600Dry 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 0.234Alfalfa Wet Biomass IRS 21 Power 2 0.838 Quadratic 1, 2 0.853

QB 8 Multi-linear 2, 4 0.772 Multi-linear1,2; 2,3;

3,40.887

Dry Biomass IRS 21 Power 2 0.817 Exp 1, 2 0.812

QB 8 Multi-linear 2, 4 0.732 Multi-linear1,2; 2,3;

3,40.867

LAI IRS 21 Power 3 0.499 Exp 3, 4 0.639QB 8 Multi-linear 1, 3, 4 0.927 Multi-linear 1,3; 3,4 0.858

The best models for determining biomass, LAI and yield of 5 crops using IRS and QB data

28

2006-04-24 2006-05-10 2006-06-11

2006-07-29 2006-08-14 2006-10-01

0 2 41 km 0 2 41 km 0 2 41 km

0 2 41 km 0 2 41 km 0 2 41 km

Crop water use mapping Evapotranspiration using Landsat ETM+ thermal data

29

Legend

<0.3

0.3-0.4

>0.4

Kg/m3

With an average value

of 0.3 kg/m3, the water

productivity map shows

explicit scope for

improvement: where

and how much.

Water productivity map

30

Spatial models for best site selections

of inland valley wetlands for rice

cultivation in Ghana

Muralikrishna Gumma, Prasad S. Thenkabail, and Fujii Hideto

31

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.

32

Methodology

33

Model Development

Factor

Factor

weight

Score

range

Maximu

m score

Scores

given Weighted score Factor

Factor

weight

Score

range

Maximum

score

Scores

given Weighted score

01-Annual-rainfall 1.89 1 - 5 3 3 (1.89*3)=5.67 01-Annual-rainfall 1.89 1 - 5 3 3, ( 1.89 * 3 ) = 5.67

02-PET 1.47 1 - 5 3 3,2 (1.47*3)=4.41 02-PET 1.47 1 - 5 3 3,2 ( 1.47 * 3 ) = 4.41

03-LPG 2.05 1 - 5 5 5 (2.05*5)=10.25 03-LPG 2.05 1 - 5 3 3,2 ( 2.05 * 3 ) = 6.15

04-specificdischarge 1.89 1 - 5 5 5,4,3,2,1 (1.89*5)=9.45 05-Stream order 2.05 1 - 5 3 3,2,1 ( 2.05 * 3 ) = 6.15

05-Stream order 2.05 1 - 5 5 5,4,3,2,1 (2.05*5)=10.25 07-Slope-percent 2.95 1 - 5 5 5,4,3,2,1 ( 2.95 * 5 ) = 14.75

07-Slope-percent 2.95 1 - 5 5 5,4,3,2,1 (2.95*5)=14.75 08-Lulc 1.37 1 - 5 5 5,4,3,2,1 ( 1.37 * 5 ) = 6.85

08-Lulc 1.37 1 - 5 5 5,3,2,1 (1.37*5)=6.85 09-Soils 1.53 1 - 5 3 3,2,1 ( 1.53 * 3 ) = 4.59

12-Experience in rice cultivation 1.42 1 - 5 5 5,4 (1.42*5)=7.1 10-Soil depth 1.68 1 - 5 5 5,4,3,2,1 ( 1.68 * 5 ) = 8.4

13-Agro., technology (yield) 1.11 1 - 5 4 4,3 (1.11*4)=4.44 11- Soil fertility 2.32 1 - 5 5 5,4,3,2,1 ( 2.32 * 5 ) = 11.6

14-Watermangement tech,. 1.68 1 - 5 2 2,1 (1.68*2)=3.36 16a-Major settlement 1.5 1 - 5 5 5,4,3,2,1 ( 1.5 * 5 ) = 7.5

15-Postharvest tech., 1.05 1 - 5 5 5,4,3,2,1 (1.05*5)=5.25 16b-Minor settlement 1.5 1 - 5 5 5,4,3,2,1 ( 1.5 * 5 ) = 7.5

16a-Major settlement 1.5 1 - 5 5 5,4,3,2,1 (1.5*5)=7.5 17a-Major roads 1.7 1 - 5 5 5,4,3,2,1 ( 1.7 * 5 ) = 8.5

16b-Minor settlement 1.5 1 - 5 5 5,4,3,2,1 (1.5*5)=7.5 17b-Minor roads 1.7 1 - 5 5 5,4,3,2,1 ( 1.7 * 5 ) = 8.5

17a-Major roads 1.7 1 - 5 5 5,4,3,2,1 (1.7*5)=8.5 18a-Major markets 1.4 1 - 5 5 5,4,3,2,1 ( 1.4 * 5 ) = 7

17b-Minor roads 1.7 1 - 5 5 5,4,3 (1.7*5)=8.5 18b-Minor markets 1.4 1 - 5 5 5,4,3,2,1 ( 1.4 * 5 ) = 7

18-Markets 1.4 1 - 5 5 5,4,3,2,1 (1.4*5)=7 25-Malaria 0.41 1 - 5 2 2,1 ( 0.41 * 2 ) = 0.82

19-Land tenure 1.74 1 - 5 5 5,4,3,2,1 (1.74*5)=8.7 Total Score 115.39

20-Labour force 1.53 1 - 5 5 5,4,3,2,1 (1.53*5)=7.65

21-Crdit system 1.58 1 - 5 3 3,2,1 (1.58*3)=4.74

22-Extension system 1.05 1 - 5 5 5,4 (1.05*5)=5.25

24-Incentives_net benfit 1.37 1 - 5 3 3,4,5 (1.37*3)=4.11

25-Malaria 0.41 1 - 5 3 3,4 (0.41*3)=1.23

Total score 152.46

Kumasi Tamale

Summary of Variables considered in Model runs: variable weights for layers and variable weights for classes

34

Model Output

35

Irrigated Area Mapping

for China

Cai Xueliang

36

Irrigated area mapping for China

MODIS 500m monthly NDVI as main dataset

37

Ground truth collection

±

0 680 Km

140°E

140°E

130°E

130°E

120°E

120°E

110°E

110°E

100°E

100°E

90°E

90°E

80°E

80°E

50°N 50°N

40°N 40°N

30°N 30°N

20°N 20°N

Gu F. X., Sun D.B.(84 GT)

who? CAAS(370 GT)

Hao, W.P., Gu F.

X., Xu J.(156 GT)Gu F. X., Liu S., Sun

D.B. (138 GT)

Xu J., Liu Q.(197 GT)

Liu S.,Liu Q.,

Huang(200 GT)

38

Ideal Spectral Data Bank on Irrigated crops of China

Irrigated-SW-Rice-SC

Irrigated-GW-Rice-DC Irrigated-SW-Rice-DC

0.40

0.50

0.60

0.70

0.80

0.90

1.00

Jan-05 Feb-05 Mar-05 Apr-05 May-05 Jun-05 Jul-05 Aug-05 Sep-05 Oct-05 Nov-05 0.00

0.20

0.40

0.60

0.80

1.00

1.20

Jan-05 Feb-05 Mar-05 Apr-05 May-05 Jun-05 Jul-05 Aug-05 Sep-05 Oct-05 Nov-05

0.00

0.20

0.40

0.60

0.80

Jan-05 Feb-05 Mar-05 Apr-05 May-05 Jun-05 Jul-05 Aug-05 Sep-05 Oct-05 Nov-05 Dec-05

0.00

0.20

0.40

0.60

0.80

1.00

Jan-05 M ar-05 M ay-05 Jul-05 Sep-05 Nov-05

Irrigated-SW-Sugarcane-SC

39

Data Harmonization and

Sharing

40

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

41

IDIS – Improved Interface

42

Browse Available Data

43

Time series Data Download

44

Download Survey Data

45

IWMIDSP Data Access

http://www.iwmidsp.org/

River Basins

Nations

Regions

Entire Globe

Web access ftp access

46

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

47

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

48

Discussion

49

Thanks!