optimizing irrigation water management on the global change context in a mediterranean region
DESCRIPTION
Optimizing Irrigation Water Management on the Global Change Context in a Mediterranean RegionTRANSCRIPT
CO
NS
EJE
RÍA
DE A
GR
ICU
LTU
RA
Y P
ES
CA
Em
pre
sa P
úblic
a D
esa
rrollo
Agra
rio y
Pesq
uero
Optimizing Irrigation Water Optimizing Irrigation Water Management on the Global Management on the Global
Change Context in a Change Context in a Mediterranean RegionMediterranean Region
Optimizing Irrigation Water Optimizing Irrigation Water Management on the Global Management on the Global
Change Context in a Change Context in a Mediterranean RegionMediterranean Region
CO
NS
EJE
RÍA
DE A
GR
ICU
LTU
RA
Y P
ES
CA
Em
pre
sa P
úblic
a D
esa
rrollo
Agra
rio y
Pesq
uero
Objetive
To analyse the impact of Water Framework Directive, the Common Agricultural Policy Reform and the Climate Change on the management, the productivity and the economic efficiency of irrigation at farm level.
CO
NS
EJE
RÍA
DE A
GR
ICU
LTU
RA
Y P
ES
CA
Em
pre
sa P
úblic
a D
esa
rrollo
Agra
rio y
Pesq
ueroLocation:
Central Part of the Guadalquivir Valley
Location:
Central Part of the Guadalquivir Valley
Crop:
Irrigated Grain Maize
Crop:
Irrigated Grain Maize
CO
NS
EJE
RÍA
DE A
GR
ICU
LTU
RA
Y P
ES
CA
Em
pre
sa P
úblic
a D
esa
rrollo
Agra
rio y
Pesq
uero
MethodologyMethodology
Crop DataCrop Data
Irrigation DataIrrigation Data
Socioeconomic DataSocioeconomic Data
Climatic DataClimatic Data
WADI Political Scenarios
CAP+WFD
WADI Political Scenarios
CAP+WFD
Climate Change Scenarios
Climate Change Scenarios
DSSAT ModelDSSAT Model
Hydraulic Irrigation Model
Hydraulic Irrigation Model
Seasonal Economic Optimization ModelSeasonal Economic Optimization Model
GCM Model &
Downscaling
GCM Model &
Downscaling
INT
ER
FA
CE
INT
ER
FA
CE
CO
NS
EJE
RÍA
DE A
GR
ICU
LTU
RA
Y P
ES
CA
Em
pre
sa P
úblic
a D
esa
rrollo
Agra
rio y
Pesq
uero
Crop, Soil and Climatic DataCrop, Soil and Climatic Data
CO
NS
EJE
RÍA
DE A
GR
ICU
LTU
RA
Y P
ES
CA
Em
pre
sa P
úblic
a D
esa
rrollo
Agra
rio y
Pesq
uero
Irrigation Optimisation ModelIrrigation Optimisation Model
Seasonal Model of Irrigation Management
Soil Water Balance Model
Farm Irrigation Model
Crop Production Model
Economic Optimisation Model
Net Profit (€/ha)
Irrigation Productivity (Kg/m3)
Economic Efficiency (€/m3)
CO
NS
EJE
RÍA
DE A
GR
ICU
LTU
RA
Y P
ES
CA
Em
pre
sa P
úblic
a D
esa
rrollo
Agra
rio y
Pesq
uero
Seasonal Model of Irrigation Management
Soil Water Balance Model
Farm Irrigation Model
Crop Production Model
Economic Optimisation Model
Net Profit (€/ha)
Irrigation Productivity (Kg/m3)
Economic Efficiency (€/m3)
The model proposed by Allen et al. (1998) was used to calculate a daily water balance in the soil-plant-atmosphere complex. Potential and actual evapotranspiration were estimated by the method of dual crop coefficients, taking into account the water stress conditions.
The model proposed by Allen et al. (1998) was used to calculate a daily water balance in the soil-plant-atmosphere complex. Potential and actual evapotranspiration were estimated by the method of dual crop coefficients, taking into account the water stress conditions.
Irrigation Optimisation ModelIrrigation Optimisation Model
CO
NS
EJE
RÍA
DE A
GR
ICU
LTU
RA
Y P
ES
CA
Em
pre
sa P
úblic
a D
esa
rrollo
Agra
rio y
Pesq
uero
Seasonal Model of Irrigation Management
Soil Water Balance Model
Farm Irrigation Model
Crop Production Model
Economic Optimisation Model
Net Profit (€/ha)
Irrigation Productivity (Kg/m3)
Economic Efficiency (€/m3)
A mathematical model was developed in order to simulate all phases (advance, storage, depletion and recession) of furrow irrigation with free runoff. For drip irrigation system modelling, an Application Efficiency of 90%, a drip discharge of 2.3 L/h and a density of 6666 drips/ha were assumed.
A mathematical model was developed in order to simulate all phases (advance, storage, depletion and recession) of furrow irrigation with free runoff. For drip irrigation system modelling, an Application Efficiency of 90%, a drip discharge of 2.3 L/h and a density of 6666 drips/ha were assumed.
Irrigation Optimisation ModelIrrigation Optimisation Model
CO
NS
EJE
RÍA
DE A
GR
ICU
LTU
RA
Y P
ES
CA
Em
pre
sa P
úblic
a D
esa
rrollo
Agra
rio y
Pesq
uero
Seasonal Model of Irrigation Management
Soil Water Balance Model
Farm Irrigation Model
Crop Production Model
Economic Optimisation Model
Net Profit (€/ha)
Irrigation Productivity (Kg/m3)
Economic Efficiency (€/m3)
The Jensen’s model (Jensen, 1968) was used to estimate the actual crop yield:
In order to relate the yield response factors Kyi, calibrated from DSSAT results, to the sensitivity index of Jensen’s model, a polynomial function proposed by Kipkorir and Raes (2002) was used.
The Jensen’s model (Jensen, 1968) was used to estimate the actual crop yield:
In order to relate the yield response factors Kyi, calibrated from DSSAT results, to the sensitivity index of Jensen’s model, a polynomial function proposed by Kipkorir and Raes (2002) was used.
N
i i
i
p
a
i
ETp
ETa
Y
Y
1
Irrigation Optimisation ModelIrrigation Optimisation Model
CO
NS
EJE
RÍA
DE A
GR
ICU
LTU
RA
Y P
ES
CA
Em
pre
sa P
úblic
a D
esa
rrollo
Agra
rio y
Pesq
uero
Seasonal Model of Irrigation Management
Soil Water Balance Model
Farm Irrigation Model
Crop Production Model
Economic Optimisation Model
Net Profit (€/ha)
Irrigation Productivity (Kg/m3)
Economic Efficiency (€/m3)
Dynamic Programming was implemented as the method for economic optimisation, in which each irrigation event was considered as a stage of the process. As objective function, the maximization of net profit of agricultural production was defined.
Dynamic Programming was implemented as the method for economic optimisation, in which each irrigation event was considered as a stage of the process. As objective function, the maximization of net profit of agricultural production was defined.
Irrigation Optimisation ModelIrrigation Optimisation Model
CO
NS
EJE
RÍA
DE A
GR
ICU
LTU
RA
Y P
ES
CA
Em
pre
sa P
úblic
a D
esa
rrollo
Agra
rio y
Pesq
uero
1. The irrigation optimisation model used the daily values of reference evapotranspiration calculated in DSSAT for each climatic data series.
2. Daily evolution of basal crop coefficient was determined as the relation between potential transpiration and reference evapotranspiration calculated by DSSAT model.
3. Successive DSSAT simulations were run, by introducing different water stress levels in every phase of crop development. The values of yield response factors Kyi were determined through lineal regression between the relative yield and the relative evapotranspiration obtained by DSSAT for each individual period of crop growth.
1. The irrigation optimisation model used the daily values of reference evapotranspiration calculated in DSSAT for each climatic data series.
2. Daily evolution of basal crop coefficient was determined as the relation between potential transpiration and reference evapotranspiration calculated by DSSAT model.
3. Successive DSSAT simulations were run, by introducing different water stress levels in every phase of crop development. The values of yield response factors Kyi were determined through lineal regression between the relative yield and the relative evapotranspiration obtained by DSSAT for each individual period of crop growth.
Integration: DSSAT Model and the Irrigation Optimisation Model
Integration: DSSAT Model and the Irrigation Optimisation Model
CO
NS
EJE
RÍA
DE A
GR
ICU
LTU
RA
Y P
ES
CA
Em
pre
sa P
úblic
a D
esa
rrollo
Agra
rio y
Pesq
uero
•The climate change scenario forecasted for the year 2020 were taken from the CGCM2 model outputs, provided by the Canadian Centre for Climate Modelling and Analysis.
•The IPCC SRES A2 scenario for greenhouse gases emissions (IPCC, 2001) was considered.
•In order to downscaling the forecasted climatic data, the outputs of LARS-WG weather generator were perturbing according to the CGCM2 results.
•The climate change scenario forecasted for the year 2020 were taken from the CGCM2 model outputs, provided by the Canadian Centre for Climate Modelling and Analysis.
•The IPCC SRES A2 scenario for greenhouse gases emissions (IPCC, 2001) was considered.
•In order to downscaling the forecasted climatic data, the outputs of LARS-WG weather generator were perturbing according to the CGCM2 results.
Climate Change ScenarioClimate Change Scenario
CO
NS
EJE
RÍA
DE A
GR
ICU
LTU
RA
Y P
ES
CA
Em
pre
sa P
úblic
a D
esa
rrollo
Agra
rio y
Pesq
uero
Agricultural and Water Policies Combined Scenarios
Agricultural and Water Policies Combined Scenarios
CO
NS
EJE
RÍA
DE A
GR
ICU
LTU
RA
Y P
ES
CA
Em
pre
sa P
úblic
a D
esa
rrollo
Agra
rio y
Pesq
uero
Irrigation Modernization Scenarios
Irrigation Modernization Scenarios
CO
NS
EJE
RÍA
DE A
GR
ICU
LTU
RA
Y P
ES
CA
Em
pre
sa P
úblic
a D
esa
rrollo
Agra
rio y
Pesq
uero
Climate Change and Crop Water Requirements
Climate Change and Crop Water Requirements
0
5
10
15
20
25
30
35
40
45
0 20 40 60 80 100
120
140
160
180
200
220
240
260
280
300
320
340
360
380
DOY
Me
an
Da
ily T
em
pe
ratu
re (
ºC)
Forecasted Weather DataYear: 2020
100 Realizations
Historical Weather DataYears: 1961-2004
44 Years
Minimum Temperature
Maximum Temperature
0
5
10
15
20
25
30
35
40
45
0 20 40 60 80 100
120
140
160
180
200
220
240
260
280
300
320
340
360
380
DOY
Me
an
Da
ily T
em
pe
ratu
re (
ºC)
Forecasted Weather DataYear: 2020
100 Realizations
Historical Weather DataYears: 1961-2004
44 Years
Minimum Temperature
Maximum Temperature
CO
NS
EJE
RÍA
DE A
GR
ICU
LTU
RA
Y P
ES
CA
Em
pre
sa P
úblic
a D
esa
rrollo
Agra
rio y
Pesq
uero
Climate Change and Crop Water Requirements
Climate Change and Crop Water Requirements
1,00
2,00
3,00
4,00
5,00
6,00
7,00
8,00
9,00
0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360 380
DOY
ET
o (
mm
/d)
Forecasted Weather DataYear: 2020
100 Realizations
Historical Weather DataYears: 1961-2004
44 Years
1,00
2,00
3,00
4,00
5,00
6,00
7,00
8,00
9,00
0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360 380
DOY
ET
o (
mm
/d)
Forecasted Weather DataYear: 2020
100 Realizations
Historical Weather DataYears: 1961-2004
44 Years
CO
NS
EJE
RÍA
DE A
GR
ICU
LTU
RA
Y P
ES
CA
Em
pre
sa P
úblic
a D
esa
rrollo
Agra
rio y
Pesq
uero
Climate Change and Crop Water Requirements
Climate Change and Crop Water Requirements
0
50
100
150
200
250
300
350
400
450
500
550
600
0 20 40 60 80 100
120
140
160
180
200
220
240
260
280
300
320
340
360
380
DOY
Ac
cu
mu
late
d M
ea
n D
aily
Pre
cip
ita
tio
n (
mm
)
Forecasted Weather DataYear: 2020
100 Realizations
Historical Weather DataYears: 1961-2004
44 Years
0
50
100
150
200
250
300
350
400
450
500
550
600
0 20 40 60 80 100
120
140
160
180
200
220
240
260
280
300
320
340
360
380
DOY
Ac
cu
mu
late
d M
ea
n D
aily
Pre
cip
ita
tio
n (
mm
)
Forecasted Weather DataYear: 2020
100 Realizations
Historical Weather DataYears: 1961-2004
44 Years
CO
NS
EJE
RÍA
DE A
GR
ICU
LTU
RA
Y P
ES
CA
Em
pre
sa P
úblic
a D
esa
rrollo
Agra
rio y
Pesq
uero
Climate Change and Crop Water Requirements
Climate Change and Crop Water Requirements
0,00
1,00
2,00
3,00
4,00
5,00
6,00
7,00
8,00
9,00
10,00
90 100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 250
DOY
Ne
t Ir
rig
ati
on
Re
qu
ire
me
nts
(m
m/d
)
Forecasted Weather DataYear: 2020
100 Realizations
Historical Weather DataYears: 1961-2004
44 Years
0,00
1,00
2,00
3,00
4,00
5,00
6,00
7,00
8,00
9,00
10,00
90 100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 250
DOY
Ne
t Ir
rig
ati
on
Re
qu
ire
me
nts
(m
m/d
)
Forecasted Weather DataYear: 2020
100 Realizations
Historical Weather DataYears: 1961-2004
44 Years
CO
NS
EJE
RÍA
DE A
GR
ICU
LTU
RA
Y P
ES
CA
Em
pre
sa P
úblic
a D
esa
rrollo
Agra
rio y
Pesq
uero
Crop Production FunctionCrop Production Function
y = 0.389x
R2 = 0.857 Initial Stage
0
0.025
0.05
0.075
0.1
0.125
0.15
0.175
0.2
0.225
0.25
0.275
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7
(1-ETa/ETp)
(1-Y
a/Y
p)
Historical Weather Data
Forecast Weather Data
y = 0.502x
R2 = 0.843Crop Development Stage
0
0.025
0.05
0.075
0.1
0.125
0.15
0.175
0.2
0.225
0.25
0.275
0.3
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7
(1-ETa/ETp)
(1-Y
a/Y
p)
Historical Weather DataForecast Weather Data
y = 1.286x
R2 = 0.842Mid-Season Stage
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
0.55
0.6
0.65
0.7
0.75
0.8
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7
(1-ETa/ETp)
(1-Y
a/Y
p)
Historical Weather DataForecast Weather Data
y = 0.509x
R2 = 0.767Late Stage
0
0.025
0.05
0.075
0.1
0.125
0.15
0.175
0.2
0.225
0.25
0.275
0.3
0.325
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7
(1-ETa/ETp)
(1-Y
a/Y
p)
Historical Weather DataForecast Weather Data
i
ii
p
a
ETp
ETaKy
Y
Y11
i
ii
p
a
ETp
ETaKy
Y
Y11
CO
NS
EJE
RÍA
DE A
GR
ICU
LTU
RA
Y P
ES
CA
Em
pre
sa P
úblic
a D
esa
rrollo
Agra
rio y
Pesq
uero
Net Profit, Economic Efficiency...Net Profit, Economic Efficiency...
0
200
400
600
800
1000
1200
1400
1600
1800
Net
Pro
fit
(€/h
a)
BS GS WM
Scenario
0
0,05
0,1
0,15
0,2
0,25E
co
no
mic
Eff
icie
nc
y (
€/m
3)
BS GS WM
Scenario
CO
NS
EJE
RÍA
DE A
GR
ICU
LTU
RA
Y P
ES
CA
Em
pre
sa P
úblic
a D
esa
rrollo
Agra
rio y
Pesq
uero
Irrigation ManagementIrrigation Management
CO
NS
EJE
RÍA
DE A
GR
ICU
LTU
RA
Y P
ES
CA
Em
pre
sa P
úblic
a D
esa
rrollo
Agra
rio y
Pesq
uero
Irrigation ManagementIrrigation Management
0
20
40
60
80
100
120
140
160
92 102
112
122
132
142
152
162
172
182
192
202
212
222
232
DOY
Dep
th (
mm
)
Rain
Irrigation
Soil Water Depletion
Readily Available Water
Baseline Scenario
0
20
40
60
80
100
120
140
160
92 102
112
122
132
142
152
162
172
182
192
202
212
222
232
DOY
Dep
th (
mm
)
Rain
Irrigation
Soil Water Depletion
Readily Available Water
Global Sustainability Scenario
CO
NS
EJE
RÍA
DE A
GR
ICU
LTU
RA
Y P
ES
CA
Em
pre
sa P
úblic
a D
esa
rrollo
Agra
rio y
Pesq
uero
ConclusionsConclusions
1. Climate change scenario used in this study predicted an increment of net irrigation requirements in 40.2 mm.
2. Maize production experimented a remarkable loss in profitability and economic efficiency in the context of agricultural and water policies induced by the Global Sustainability and World Markets scenarios.
3. The irrigation systems based on medium levels of modernization were able of assimilate the new paradigm that transposition of the Water Framework Directive, the revision of the CAP and the climate change supposed.
1. Climate change scenario used in this study predicted an increment of net irrigation requirements in 40.2 mm.
2. Maize production experimented a remarkable loss in profitability and economic efficiency in the context of agricultural and water policies induced by the Global Sustainability and World Markets scenarios.
3. The irrigation systems based on medium levels of modernization were able of assimilate the new paradigm that transposition of the Water Framework Directive, the revision of the CAP and the climate change supposed.
CO
NS
EJE
RÍA
DE A
GR
ICU
LTU
RA
Y P
ES
CA
Em
pre
sa P
úblic
a D
esa
rrollo
Agra
rio y
Pesq
uero
Optimizing Irrigation Water Optimizing Irrigation Water Management on the Global Management on the Global
Change Context in a Change Context in a Mediterranean RegionMediterranean Region
Optimizing Irrigation Water Optimizing Irrigation Water Management on the Global Management on the Global
Change Context in a Change Context in a Mediterranean RegionMediterranean Region