Neale presentation unl

Geospatial Technologies for Mapping and

Monitoring Irrigated Agricultural Areas

Christopher M. U. Neale

Professor

Dept. of Civil and Environmental Engineering

Irrigation Engineering Division

Utah State University

Outline of the Presentation

Describe an international project: the

development of a digital cadastre of irrigation

water users in the Dominican Republic

Airborne remote sensing of agricultural and

natural resources

Estimation of Crop Evapotranspiration and

Yield with Remote Sensing

Estudios Básicos Para El Manejo de Los

Sistemas de Riego

Programa de Administración de Sistemas de

Riego por los Usuarios (PROMASIR)

Préstamo BID 905/OC-DR

Instituto Nacional de Recursos Hidráulicos

INDRHI

Dominican Republic

Basic Studies for Management of Irrigation Systems

• Contract between INDRHI and (Utah State University) funded by the Inter-American Development Bank

• $7.9 million dollars

• Duration: 4 ½ years

• Comprised 4 studies:

1. Aerial Photography of the Entire Country

2. Cadastre of Water Users (Padrón de Usuarios)

3. Hydro-Agricultural Information System

4. Monitoring of Salinity and Drainage Problems

These studies had the objective of providing basic information required for the transfer of Operation and Maintenance of 30 canal-based irrigation systems from the government control (INDRHI) to newly formed Irrigation Water User Associations

Purpose of the studies:

• In the Dominican Republic water is still distributed in fixed amounts and charged according to area served and not based on volume. Therefore a good estimate of irrigated area for each property is needed for proper assessment of system operation and maintenance fees.

• An up-to-date cadastre of irrigators is necessary in the transfer of publicly operated system to private irrigator cooperatives so that appropriate Irrigation Systesm Operation and Maintenance charges can be estimated

• Problems with salinity needed to be identified prior to the transfer in order to be included in the civil works and mitigation plans

Final Results of the Aerial Photography Campaign99.6 % of the Country covered with color photographs at 1:20000 scale

Remote Sensing based Products

Color aerial photographs at 1:20,000 scale of the entirecountry in digital format

Color contact prints of the aerial photography at 1:20,000

Color and Black and White diapositives at 1:20,000

Digital orthophotoquads at 1:5000 for the irrigated areas ofthe country: (4400 Km2)

Contour curves at 1-meter intervals, of the irrigated areas(ortofotos)

High resolution multispectral image mosaics of theirrigated areas

Digital Cadastre of Irrigation Water Users and InformationDatabase for all the irrigation systems of the country

Digital Aerial Photograph at 1:20,000 scale

The Basic Product

Resulting from the

scanning of the

negative with

software producing a

color digital positive

Contact Prints at 1:20,000

Printed on photographic

quality paper

Diapositive of the original photo:

Used in analytical stereoplotters for the production of

orthophotos and updating of topographic maps

Diapositives are transparent

photos (like a slide)

Surveying of Ground Control Points used in the

aerial triangulation and production of orthophotos

High Precision Dual frequency GPS

systems were used to obtain the

ground control points for mapping

Installation of GPS base station networkto support aerial photography and ground control activities

Data from the base stations were used to correct the positions of the

mobile units based on differential correction techniques, resulting in high

positional accuracy. The network of GPS base stations installed was similar

to the CORS network in the US.

Location of the GPS Base Station in the DRSan Cristóbal, Barahona, San Juan, Villa Vázquez, La

Vega, Nagua, Higuey

Digital Orthophoto with Contour Lines of the irrigated areasRepública Dominicana

Project Design before this Project:

Traditional design drawing and cartography

After the development of the digital products, planning for irrigation of new

systems was done digitally, such as the Azua II Irrigation Project

Digital Orthophoto Mosaic with Contour Curves at 1-meter intervals

Detail of a Digital Orthophoto with Contours

Digital Orthophotoscovering 4430 Km2 of irrigated areas in the country

What is a Digital Cadastre of Irrigators?

It is a database containing information on all the property owners and irrigation water users within an irrigation project or system. It consists of:

An up-to-date property boundary map containing information on every property owner or water user

Information on total area and irrigated area of each property

Information on the canal system that delivers water to the property

=> Because of the geographic and distributed nature of the information as well as the requirement to have an associated database, USU opted in using Geographic Information System (GIS) technology to develop thisproduct.

Digital Orthophoto at 1:5000 scaleThe map base for the cadastre of irrigation water users

The orthophoto is a

digital photograph with

geographic coordinates

that has been adjusted

to the terrain so that

areas can be correctly

measured

Printed Orthophoto at 1:4000 scale were used for field

verification of property boundaries

Field brigades used printed and

laminated maps to identify the

property boundaries together with

the land owner or a local facilitator

that has a good knowledge of the

irrigation system (president of a

water association, ditch rider)

A brigade consisted of 4 to 6

trained cartographers with one 4x4

dual cabin pickup and 3 or 4 cross-

country motorbikes

Survey Form used in Information Gathering by the Cartographers

The cartographers conduct the survey

with the property owner to obtain

basic information on the water user

(complete name, nickname, address)

and on the property (delivery canal,

water distribution problems, crops

planted, salinity or drainage problems)

Information was digitized using an

Access DB application to automate

the data entry. This work was done in

the Dominican Republic at a Lab in

USU’s local office.

On-screen digitizing of the

parcel boundaries in

ArcInfo using

the digital ortho as a

backdrop

Most of the digitizing was

done at USU using

battalions of graduate and

undergraduate student

technicians. Also the

merging with survey

database and Quality

Control

Observation: We were very good

clients of FEDEX!

Some characteristics of the digital

cadastre of irrigation water users

• Union of the geographic layer containing the property

boundaries with the information in the surveys

• The geographic coordinates come from the digital

orthophotos

• The property area is exact

• The database is complete because all properties within

the command area of a main canal or irrigation system

are included

• Can be readily and easily updated

• The property boundaries can be updated if a

consolidation or division of properties occurs

The Water Users Database Prior to the

Estudios Basicos Project

Essentially a listing of users => Rapidly became obsolete, areas were not

correct, water users were repeated, owners of multiple properties

sometimes were not registered and did not pay for the water. Some

irrigation systems had property boundary maps but were static in time.

Delivered Products Water Users Database and Property Boundary Layers

Study Goals Goals Accomplished

Cadastre maps for

81,000 properties over

4400 Km2 of

orthophotos of

irrigated areas

Cadastre maps for

84,500 properties over

4460 Km2 of

orthophotos (net) of

irrigated areas

Irrigator database for

all the irrigation

systems within 4400

Km2 of orthophotos

of irrigated areas

Irrigator database for

278 irrigation systems

over 5400 Km2 of

orthophotos (gross)

The Hydro-Agricultural Information System was

created to allow easy access to the Cadastre of Water

Users Database and to Manage the Information

Software developed in MapObjects and Visual Basic

Allowed the search of irrigators by first and last name,nickname etc.

Visualize the irrigation control structures for maintenancepurposes

GIS Layers: Cadastre (property boundary), canal and drainsystem, hydraulic control structures, crop and land uselayer, soil layers

Allowed for the updating of the crop agricultural statistics

Estimates the irrigation water demand through ETcalculations on a canal command area level and also bylateral and entire project

Product installed at the Water Users Associations andIrrigation Districts

Example: Search for a Water User or Irrigator

Digital Database

Information can be easily updated

Information on irrigation infrastructure for Marcos A. Cabral System

Irrigation Water Control Structures

Canal Distribution GIS Layer

Before Estudios Basicos Project:

Agricultural Statistics Compiled

Manually, with no map base

Presently: Crop Layer can be Easily Updated

Calculation of Agricultural Statistics

Airborne Multispectral Mosaics of the

Irrigated Areas

Acquired with the USU digital airbornemultispectral system

Used in the study of salinity and drainageproblems of the irrigation areas of the country

Used to obtain the crop cover and land use for thehydro-agricultural database system

Maps of areas with salinity impacts and drainageproblems

Multispectral Mosaic of

The Mao-Gurabo Irrigation

System

Multispectral Mosaic of the Manzanillo Area used

for Salinity Impact studies

Soil sampling sites

Results were verified with intensive soil sampling

Training Activities:

Creation of the Geomatics Laboratory at INDRHI

• Repository of the printed and digital products developed though the project

• Care and protect the information in a climatized, safe environment

• Use the products and technical staff of the laboratory to support the different Departments of INDRHI

• Maintain and update the water users cadastre database by supporting the water users associations

• Development of a methodology for the expansion of the available digital cadastre for the entire country

Present Situation

• A modern geomatics lab with state-of-the-art

hardware and software and well-trained

technical staff serving the institution

Training Strategy

Combination of short and medium term training

6 Technicians and Engineers were trained in Utah:Geographical Information Systems (GIS), Image Processing,Computer Programming (Visual Basic, Avenue),Development of the products: water users database and agro-hyrdological software

Additional technicians were trained in Santo Domingo byUSU

Side-by-side training with direct participation in theproduction

Responsibility for coordination with the Water UsersAssociations

Responsibility for future training

Training of Water User Association Personnel and

INDRHI Irrigation District Personnel

Basic Windows: 4

Use of the Agro-hydrological System software: 4

Updating the Water Users Database: 4

Updating the Agricultural Statistics: 4

Training of Personnel from the

Soil and Water Division of

INDRHI on the use of the

Hydro-agricultural

System Software

Services Offered by the Geomatics

Laboratory at INDRHI

Sale of contact prints of the aerial photographs tothe public

Support to the different Departments of INDRHIin the production of maps, geomatic products, GISand verification of cartografic information

Support to different institutions in the agriculturalsector of the country

Goal => Offer the products for sale over the internet,sale of the digital products, sale of value addedproducts to generate funds to keep the laboratory

equipment updated and protect products

Final Observations

The Geomatics Laboratory at INDRHI with itshighly trained technical personnel and serving as arepository for all digital and printed productssupports the different departments of INDRHI andthe general public and has the capacity to offerservices to other goverhment institutions.

Became a new Department at INDRHI called theDepartment of Digital Cartography

Final Observations

The cadastre was necessary information in thetransfer of the irrigation systems from governmentcontrol the Water Users Associations

USU facilitated the transfer of 30 irrigationsystems in the Dominican Republic, setting up thedemocratic structure, training the WUA staff onoperation and maintenance of the irrigationsystem, administration and governance

The use of the digital cadastre of irrigators andrelated database by the transferred Water UserAssociations increased their O&M assessmentincome by up to 70% in some cases

Airborne Multispectral Remote

Sensing for Agricultural and Natural

Resource Monitoring

Remote Sensing Services Laboratory - RSSL

Detail of Multispectral Cameras

USU Cessna TU206

Remote Sensing Aircraft

USU Multispectral Digital System

equipment rack with FLIR SC640

thermal infrared camera in the

foreground.

Green Red

NIR 3bands

USU Airborne multispectral system merged with LASSI Lidar

Details on MS Camera System

– Four co-boresighted multispectral cameras

• 16 megapixels each for IR, R, G, B bands

• 64 megapixels total

• Integrated with lidar

• Calibrated

Sample of Our Custom 16 mp System (R,G,B channels shown)

Camera Hardware

ROW 3D Mapping System

– 3D Lidar• Based on Riegl Q560

• 150,000 measurements/second (300 kHz laser)

• 25 mm range accuracy at any range

• 31 mm footprint @ 100 m range

• 60 degree swath angle

• Integrated with cameras

Helicopter swath through Utah State University from 200 m using Riegl Q560 lidar system

Helicopter swath through Bonanza Power Plant, Uintah Basin, Utah

LASSI Lidar Image of Tropical Rainforest

LIDAR classified point cloud data.

Quality control: Check for mismatch at the overlap region.

Create ortho-rectified tiles.

Evapotranspiration Analysis of

Saltcedar and Other Vegetation in the

Mojave River Floodplain, 2007 and 2010

Mojave Water Agency Water Supply Management Study

Phase 1 Report

Study Overview

• Analyses included:

– 2007 and 2010 classification of native

and non-native vegetation

– Vegetation evapotranspiration

modeling

– Lidar elevation map development

– Groundwater mapping

– Water evapotranspiration cost

calculations

• Results are presented as a

whole and also by Mojave

Water Agency Alto, Alto

Transition, Centro, and Baja

subarea boundaries.

Saltcedar (Tamarix)

Multispectral

Ortho Imagery

Block 1 and 2

Ortho-rectification

using direct geo-

referencing with Lidar

point cloud data

Multispectral Image DetailPixel resolution: 0.35 meter (1 foot)

Thermal infrared Imagery

20 – 30 C

30 – 35 C

35 – 40 C

40 – 45 C

34 – 50 C

50 – 55 C

55 – 60 C

> 60 C

Temperature

Analysis Conducted within an Area of

Interest (AOI) Polygon

SEBAL ModelBastiaansen et al (1995)

One-layer models

Uniquely solve for H using the dT method, a linear

relationship between air temperature and surface temperature

obtained through a linear transformation generated from surface

temperatures observed over selected “hot” and “cold” pixel in the

satellite image.

Advantages:

Do not require absolute calibration of the thermal imagery

Well suited for irrigated areas under well watered conditions

Provides actual evapotranspiration of the crops

Disadvantages:

Requires experienced operator to identify the “hot and cold” pixels

May not work well in water limited, semi-arid natural vegetation

Two-Source ModelNormal et al (1995)

Kustas et al (1999)

Li et al (2005)

Advantages:

Well suited for modeling sparse canopies either in the agricultural or

natural vegetation context where water could be limited

Has a more diverse ecosystem area of application

Provides actual evapotranspiration of the vegetation

Works better with higher spatial resolution thermal infrared imagery

Disadvantages:

Requires carefully calibrated and atmospherically corrected satellite or

airborne imagery

More complex to program

TRAD( ) ~ fc( )Tc + [1-fc( )]Ts

(two-source approximation)Norman, Kustas et al. (1995)

Provides information onsoil/plant fluxes and stress

TRAD( ) ~ fc( )Tc + [1-fc( )]Ts

Accommodates off-nadirthermal sensor view angles

Treats soil/plant-atmosphere coupling differences explicitly

Two-Source Energy Balance Model (TSEB)

TAERO

Interpreting thermal remote sensing data

RN

System and Component Energy Balance

= H + E + G

RNC = HC + EC

RNS = HS + ES + G

= = =

+ + +

TS

TCTAERO

SY

ST

EM

CA

NO

PY

SO

IL

Derived fluxes

Derived states

TRAD

SEBAL ET Results for Block 1

0–1 mm/day

1–2 mm/day

2–3.3 mm/day

3.4–7.1 mm/day

7.1–9 mm/day

>9 mm/day

0–1 mm/day

1–2 mm/day

2–3.3 mm/day

7.1–9 mm/day

>9 mm/day

Seasonal ET Estimation using ET fractions (crop coefficients)

derived from remotely sensed ET

Phenology Dates Code Greenup Begins Peak ET Senescence Begins Senescence Ends

Salt Cedar (Tamarisk) SC 3/1 5/1 9/1 11/1

Mesquite MS 4/1 5/15 8/1 9/15

Cottonwood CW 4/1 5/15 9/15 11/1

Desert Scrub DS 3/1 4/15 7/1 8/1

Decadent Vegetation VD 4/1 5/15 8/1 9/15

Mesophytes MP 4/1 5/15 7/1 8/1

Conifer CO 3/1 5/15 10/1 11/15

Arundo AR 4/1 6/1 10/1 11/1

Kc = ETa / ET0

ETa = Actual ET from

Energy Balance Model

ET0 = Reference ET from

CIMMIS Weather Station

Classified Lidar point clouds to obtain canopy height at 1-

meter grid cells

Block 1 Seasonal ET results for Tamarisk using both energy

balance models

The Two-source model was selected for all estimates due to processing

speed and expediency

Results for other blocks on

downstream side

Potato Yield Model Development Using High

Resolution airborne ImageryDifferent areas of interest are selected in the field for sampling based

on rate of growth and duration of the crop

Empirical Relationship

Time

SAVI

Time

LAI

LAD => Yield

Yield = f(VI)

3 date ISAVI

8 date ISAVI

Spot yield sampling location Site OI6 Field

• Locations marked by flags

• Four rows (10 *12 ft)

• GPS readings recorded

Jul 05, 2004Jul 30, 2004

Aug 31, 2004

Late Emergence Early Senescence – low yield spot

Spot yield sampling locations based on variability OI6 Field

Jul 05, 2004 Jul 30, 2004Aug 31, 2004

Early Emergence Late Senescence – high yield spot


Jul 05, 2004 Jul 30, 2004Aug 31, 2004

Early Emergence Early Senescence – medium yield spot


Late Emergence Late Senescence – medium yield spot

July 08,2005 August 05,2005 August 25,2005


y = 16.03x - 1.427

R² = 0.810

3.00

4.00

5.00

6.00

7.00

8.00

0.300 0.350 0.400 0.450 0.500

Act

ual

yie

ld k

g/s

qm

ISAVI

3.00

4.00

5.00

6.00

7.00

3.00 4.00 5.00 6.00 7.00

Act

ual Y

ield

Kg/s

qm

Estimated Yield Kg/sqm

R-square = 0.89

MBE = 0.07 Kg/sqm

RMSE = 0.24

• model developed using 16 hand

dug samples from two center pivots

2004 season (7/05, 7/30, 8/31 2004)

• SE of yield estimate of 0.41 kg/sq.m

• ISAVI significant parameter (p< 0.05)

• 18 hand dug samples from two center

pivots 2005 season (7/08, 8/04,8/25)

• Model slightly over predicted at higher

end

Model development and Validation

2004

2005

3.00

4.00

5.00

6.00

7.00

8.00

3.00 4.00 5.00 6.00 7.00 8.00

Act

ual

yie

ld k

g/s

qm

Estimated yield kg/sqm

2004 Whole field data

R-square = 0.89

MBE = 0.16 kg/sqm

RMSE = 0.29

5

6

7

8

9

5 6 7 8 9

Act

ual

Yie

ld k

g/s

qm

Estimated Yield kg/sqm

2005 Whole field data

R-square = 0.81

MBE = -0.15 kg/sqm

RMSE = 0.25

• 2004 3-date ISAVI model applied to 2004 and 2005 whole field data

• Differences in yield prediction - ISAVI model less dates

• More number of images covering emergence to vine kill

500

1000

1500

2000

2500

3000

3500

4000

4500

500 1000 1500 2000 2500 3000 3500 4000 4500

Act

ual

Pro

du

ctio

n (

MT

)

Estimated Production (MT)

2004 Whole field data in MT

R-square = 0.95

MBE = 66.47 MT

RMSE = 144.63

500

1500

2500

3500

4500

5500

500 2500 4500

Act

ual

Pro

du

ctio

n (

MT

)


2005 Whole field data in MT

R-square = 0.96

MBE = -60.59 MT

RMSE =105.98

3-Date ISAVI model to 2004 and 2005 whole field

July 05, 2004 July 30, 2004 August 31, 2004

July 05, 2004 July 30, 2004 August 31, 2004

FCC Aerial Images of HF19 field (Top) and WSA09 (Bottom) showing soil and crop growth

variations during the year 2004

HF 19 WSA 9 OI10

Yield map showing the variability in yields for field HF19, WSA9

during 2004 and OI10 during 2005 season

Actual yield 2607 MT Actual yield 2637 MT Actual yield 2956 MT

Estimated yield 2765 MT Estimated yield 2714 MT Estimated yield 2901 MT

Improvements to 3-date model

Path/Row 40 30

Path/Row 39 31

Land Sat TM 5 Scene of the Study area

Layout of Cranney Farm potato fields for the year 2004

Layout of Cranney Farm potato fields for the year 2005

Soil Water Balance in the Study Area

• The soil water balance model:

SWj+1 = SWj + I +P – ETc – DP

SW represents the available soil water,

j and j+1 - soil water content in the current time and a succeeding time

j+1.

I represents the irrigation amount applied

P - precipitation. ETc represents the crop evapotranspiration, and DP

is the deep percolation.

• Reference ET - 1982 Kimberly-Penman method

Contd..

Canopy-Based Reflectance Method

• Kcrf linear transformation of SAVI corresponding to bare soil and

SAVI at effective full cover with the basal crop coefficient (Kcb)

values corresponding to bare soil and effective full cover (EFC).

• The resulting transformation is:

Kcrf = 1.085 x SAVI + 0.0504 (Jayanthi et al. 2007 )

• Basal crop coefficient method

ETcrop = Kc * ETref

Kc = Kcb * Ka + Ks

Ka = ln(Aw + 1)/ln(101)

Ks = (1-Kcb) [1-(tw/td)0.5] * fw

Actual ET calculation

Incorporating Actual ET into Yield model

• Actual ET computed for all hand dug samples from 2004

season

• ISAVI values extracted for all the AOIs

• Multiple linear regression (MLR)

• ISAVI and ET independent variable

• Yield dependent variable

• Significance of variables analyzed using ANOVA

• MLR model applied to 2004 and 2005 satellite images

and tested

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00

10.00

75 100 125 150 175 200 225 250 275 300

ET

(m

m)

DOY

LEES

EEES

EELS

LELS

R² = 0.87

2.00

3.00

4.00

5.00

6.00

7.00

8.00

620 640 660 680 700 720

Act

ual Y

ield

Kg/s

qm

ET (mm)

Y = 5.56 * ISAVI + 0.0359 * ET – 21.397

R2 = 0.88

Yield model with Actual ET and ISAVI

3 date ISAVI model to Satellite images

1500

2000

2500

3000

3500

4000

1500 2000 2500 3000 3500 4000

Act

ual

Pro

du

ctio

n (

MT

)


R-square = 0.92

MBE = 90.51 MT

RMSE = 203.74

2000

2500

3000

3500

4000

4500

5000

2000 2500 3000 3500 4000 4500 5000

Act

ual

Pro

du

ctio

n (

MT

)


R-square = 0.61

MBE = -150.33 MT

RMSE = 247.09

June 30, Aug

01, Sep11, 2004

July 03, Aug 04, Sep05

2005

MLR model validation using Satellite images

1500

2000

2500

3000

3500

4000

1500 2000 2500 3000 3500 4000

Act

ual

Pro

dcu

tion

(M

T)

Estimated Prodcution (MT)

R-square = 0.97

MBE = 44.13 MT

RMSE = 146.20

2000

2500

3000

3500

4000

4500

5000

2000 2500 3000 3500 4000 4500 5000

Act

ual

Pro

dcu

tio

n (

MT

)

Estimated Prodcution (MT)

R-square = 0.75

MBE = -39.34 MT

RMSE = 200.94

MBE 90.51 MT to 44.13 MT

MBE -150 MT to -39.34 MT

July 05, 2004 July 30, 2004 August 31, 2004

June30, 2004 August 01, 2004 September 11, 2004

FCC images of Airborne images (Top) comparing with LandSat TM5 (Bottom)

for WC4 field showing soil and crop growth variability during 2004 season

Yield map showing the variability in yields for field WC4 using airborne

images(Left) and Satellite images (Right) during 2004 season

Actual yield 2651 MT Actual yield 2651 MT

Estimated yield 2839 MT Estimated yield 2763 MT

Water Balance of an Irrigated Area:

Palo Verde Irrigation District, CA

98Contribution from Saleh Taghvaeian

Palo Verde Irrigation District (PVID)

• Location: imperial and Riverside counties, CA.

• Area: more than 500 km2.

• Elevation: 67 m at South to 88 m at North.

• 400 km of irrigation canals and 230 km of drains.

• Predominant crops: alfalfa, cotton, grains, melons.

• Alluvial soil with predominant sandy loam texture.

99

101

Water balance of irrigation schemes

I + P = ET + DP + RO + ΔS

I: Applied irrigation water;

P: Precipitation;

ET: Evapotranspiration;

DP: Deep percolation;

RO: Surface runoff; and,

ΔS: Change in soil water storage.

102

PVID

Groundwater

Dynamics

103

Over 260 piezometers

1 mile by 1 mile grid

Monthly measurements

104

Average Depth to Groundwater (m)

January 2007 to December 2009

105

Evapotranspiration

106

Remote Sensing of Energy Balance

Rn = H + G + LE

Rn: Net Radiation

H: Sensible Heat Flux

G: Soil Heat Flux

LE: Latent Heat Flux

Surface Energy Balance Algorithm for Land (SEBAL)

Developed by Dr. Wim Bastiaanssen, Wageningen, The Netherlands

107

Landsat TM5

imagery

CIMIS Weather

data

SEBAL

108

EToF

109Total volume of water consumption by PVID crops for 20 dates of Landsat overpass

110

Precipitation

112

Surface

Inflow & Outflow

113

0

400

800

1200

1600

2000

2400

J-08 M-08 M-08 J-08 S-08 N-08 J-09

Dail

y A

ver

age

Flo

w R

ate

(cf

s)

Main Canal

Outfall Drain

Operational Spills

114

Closing

Water Budget

115

0

3

6

9

12

15

18

J-0

8

F-0

8

M-0

8

A-0

8

M-0

8

J-0

8

J-0

8

A-0

8

S-0

8

O-0

8

N-0

8

D-0

8

J-0

9

F-0

9

Dep

th (

mm

)Precip.

Inflow

0

3

6

9

12

15

18

J-0

8

F-0

8

M-0

8

A-0

8

M-0

8

J-0

8

J-0

8

A-0

8

S-0

8

O-0

8

N-0

8

D-0

8

J-0

9

F-0

9

Dep

th (

mm

)

ETa

Outflow

Depth (mm) Percentage

Precipitation 71 3

Surface inflow 2479 97

Σ Inputs 2550 100

Canal Spills 284 11

Drainage 998 39

Evapotranspiration 1286 50

Σ Outputs 2568 100

Σ Inputs – Σ Outputs -18 -0.7

116

Depleted fraction (DF)

- DFg = ETa / (Pg + Vd)

- DFn = ETa / (Pg + Va)

0.0

0.2

0.4

0.6

0.8

1.0

DF

DFg

DFn Nilo Coelho:

DFn = 0.60

PVID:

DFn = 0.55

Final Observations

Geo-spatial technologies have facilitated the designand management of large irrigation systems

Estimating spatial ET from remote sensing isbecoming a common approach for irrigation watermanagement and basin water balance studies

Thank You!

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