Post on 07-Jul-2015
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
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
Spot yield sampling locations based on variability OI6 Field
Jul 05, 2004 Jul 30, 2004Aug 31, 2004
Early Emergence Early Senescence – medium yield spot
Spot yield sampling locations based on variability OI6 Field
Late Emergence Late Senescence – medium yield spot
July 08,2005 August 05,2005 August 25,2005
Spot yield sampling locations based on variability OI1 Field
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
)
Estimated Production (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
)
Estimated Production (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
)
Estimated Production (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
100
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
111
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!