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Niels Schütze and Oleksandr MialykTU Dresden, University of Twente
The value of information for the management ofdeficit irrigation systemsEGU General Assembly 2020, 6. Mai 2020
Key messages• the value of information about irrigation control strategies, future climatedevelopment, soil characteristics, and initial moisture conditions isevaluated for an arid and a semi-arid irrigation site• as a stochastic framework the Deficit Irrigation Toolbox (DIT) is used• value of information =̂ costs for compensation of uncertainty in units ofadditional water requirements• results are:
– costs of climate uncertainty (variability) are higher for the semi-arid site– costs of soil uncertainty (variability) are higher for the arid site, and evenhigher for wet initial conditions– costs of soil uncertainty (variability) can be reduced by proper soil analysis– but highest added value of information have: knowledge about theproper deficit irrigation strategy and information about initial conditionsManagement of deficit irrigation systemsTU Dresden, University of Twente // Niels Schütze and Oleksandr MialykEGU General Assembly 2020, 6. Mai 2020 Folie 2 von 29
OutlineIntroductionControlled deficit irrigationDIT modeling frameworkApplication to different climate regionsResultsImpact of climate and soil variabilityImpact of deficit irrigation strategyConclusions and Outlook
Management of deficit irrigation systemsTU Dresden, University of Twente // Niels Schütze and Oleksandr MialykEGU General Assembly 2020, 6. Mai 2020 Folie 3 von 29
Introduction
Management of deficit irrigation systemsTU Dresden, University of Twente // Niels Schütze and Oleksandr MialykEGU General Assembly 2020, May 6, 2020 Slide 4 of 29
OutlineIntroductionControlled deficit irrigationDIT modeling frameworkApplication to different climate regionsResultsImpact of climate and soil variabilityImpact of deficit irrigation strategyConclusions and Outlook
Management of deficit irrigation systemsTU Dresden, University of Twente // Niels Schütze and Oleksandr MialykEGU General Assembly 2020, May 6, 2020 Slide 5 of 29
Focus on controlled deficit irrigation (CDI) forimproving water productivityFocus on water productivity
• More crop per drop: Improvement of Water Productivity (WP)– Improvement of crop growth– Efficient and sustainable irrigation– Preservation of farmland through better cultivation practices
• WP = gain over expenses– (Marketable) yield over total irrigation amount or evapotranspiration
• Typical ranges of WPET
Maize: Wheat: Rice: 0.3 – 1.7 kg m-3 0.6 – 1.5 kg m-3 0.4 – 1.4 kg m-3
Management of deficit irrigation systemsTU Dresden, University of Twente // Niels Schütze and Oleksandr MialykEGU General Assembly 2020, May 6, 2020 Slide 6 of 29
Theoretical framework of CDI:The crop water production function (CWPF)
18
Focus on water productivitysources of improvements in water productivity –deficit irrigation strategy -
norm
alized
norm
alized
Management of deficit irrigation systemsTU Dresden, University of Twente // Niels Schütze and Oleksandr MialykEGU General Assembly 2020, May 6, 2020 Slide 7 of 29
What is unknown?1) future weather development Focus on water productivityconsidering climate variability and food security
19
Variability in yield for rainfed, supplemental and full irrigation for maize (Montpellier, France)If the site is known:optimized simulations of 500 possible growing seasons for different irrigation water amounts available.Management of deficit irrigation systemsTU Dresden, University of Twente // Niels Schütze and Oleksandr MialykEGU General Assembly 2020, May 6, 2020 Slide 8 of 29
What is unknown?2) soil variability and / or 3) initial soil moisture
0.8 0.6 0.4 0.2 0
0.8
0.6
0.4
0.2
0 0.8
0.6
0.4
0.2
0
sand
siltclay
S lS saL
LsiL
Si
L
cL
saCL
siCL
saCsiC
C
sand
siltclay
triangular rangesynthetic samples
0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0
0.9
0.8
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0
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0.8
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0
0.920.920.92
0.92
0.92
1.13
1.13
1.13
1.13
1.13
1.351.351.35
1.35
1.571.571.57
1.57
1.791.79
1.79
22.222.442.652.872.87
sand
siltclay
soil texture log (Ks)If nothing about the soil is known:sampling of 500 texture realizations and translated saturated conductivity values by the ROSETTA-PTF.
Management of deficit irrigation systemsTU Dresden, University of Twente // Niels Schütze and Oleksandr MialykEGU General Assembly 2020, May 6, 2020 Slide 9 of 29
OutlineIntroductionControlled deficit irrigationDIT modeling frameworkApplication to different climate regionsResultsImpact of climate and soil variabilityImpact of deficit irrigation strategyConclusions and Outlook
Management of deficit irrigation systemsTU Dresden, University of Twente // Niels Schütze and Oleksandr MialykEGU General Assembly 2020, May 6, 2020 Slide 10 of 29
Evaluation of the value of information using:The Deficit irrigation toolbox (DIT) – http://bit.ly/TUD-DIT
Crop models• Cropwat (FAO)• Aquacrop OS∗
(FAO)• Daisy• Apsim• . . .
Irrigation strategies (full and deficit)• no irrigation• full irrigation• open-loop control (fixed)• closed-loop (feedback) control• open-loop control (calendar)• . . .
Management of deficit irrigation systemsTU Dresden, University of Twente // Niels Schütze and Oleksandr MialykEGU General Assembly 2020, May 6, 2020 Slide 11 of 29
DIT uncertainty framework:parameters, initial and boundary conditions
Climate variabilityusing a weathergenerator, e.g.LARS-WG• based on climatestations, e.g. NOAA• generatesrealisations ofhistoric and futuregrowing seasons• and meteorologicalforecasts
Soil variability• using a soil texturegenerator
Initial conditions• θ0 = PWP• θ0 = FC• θ0 = PWP . . . θs
Management of deficit irrigation systemsTU Dresden, University of Twente // Niels Schütze and Oleksandr MialykEGU General Assembly 2020, May 6, 2020 Slide 12 of 29
DIT modeling framework v1.0Features• probabilistic framework(SCWPF)• parallel computation(works on HPC)• visualization tools forperformance analysis• manual, examples,tutorial
Software development• uses softwareengineering tools
– Unit tests– FusionForge– Code revision control• provided as open source
– http://bit.ly/TUD-DIT
Management of deficit irrigation systemsTU Dresden, University of Twente // Niels Schütze and Oleksandr MialykEGU General Assembly 2020, May 6, 2020 Slide 13 of 29
Application to different climate regions
Management of deficit irrigation systemsTU Dresden, University of Twente // Niels Schütze and Oleksandr MialykEGU General Assembly 2020, May 6, 2020 Slide 14 of 29
Considered climate regionsArid site: Seeb, Oman Semi-arid site: Montpellier, France
Temperature (100 years)
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan0
10
20
30
40C°
Growing seasonAverage TmaxMeanAverage Tmin
Climatic water balance (100 years)
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan0
75
150
225
300
mm Growing season
ET 10% quantileET 50% quantileET 90% quantileP 10% quantileP 50% quantileP 90% quantile
Temperature (100 years)
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan0
10
20
30
40
C°
Growing seasonAverage TmaxMeanAverage Tmin
Climatic water balance (100 years)
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan0
75
150
225
300
mm
Growing seasonET 10% quantileET 50% quantileET 90% quantileP 10% quantileP 50% quantileP 90% quantile
Management of deficit irrigation systemsTU Dresden, University of Twente // Niels Schütze and Oleksandr MialykEGU General Assembly 2020, May 6, 2020 Slide 15 of 29
Experimental setup:32 combinations of varying levels of information in
Climate variability2 levels of informationfrom 2 climate stations:Seeb, Montpellier• climate series of 100generated years• average year (perfectknowledge)
Soil variability2 levels of informationfrom2 texture samplings:soil triangle,USDA class: sandy loam• 100 soil samples• cendroid of eachsample (perfectknowledge)
Initial conditions2 cases of initialcondition:• dry: θ0 = PWP• wet: θ0 = FC
Management of deficit irrigation systemsTU Dresden, University of Twente // Niels Schütze and Oleksandr MialykEGU General Assembly 2020, May 6, 2020 Slide 16 of 29
Experimental setup:32 combinations of varying levels of information in
Climate variability2 levels of informationfrom 2 climate stations:Seeb, Montpellier• climate series of 100generated years• average year (perfectknowledge)
Soil variability2 levels of informationfrom2 texture samplings:soil triangle,USDA class: sandy loam• 100 soil samples• cendroid of eachsample (perfectknowledge)
Initial conditions2 cases of initialcondition:• dry: θ0 = PWP• wet: θ0 = FC
Management of deficit irrigation systemsTU Dresden, University of Twente // Niels Schütze and Oleksandr MialykEGU General Assembly 2020, May 6, 2020 Slide 16 of 29
Experimental setup:32 combinations of varying levels of information in
Climate variability2 levels of informationfrom 2 climate stations:Seeb, Montpellier• climate series of 100generated years• average year (perfectknowledge)
Soil variability2 levels of informationfrom2 texture samplings:soil triangle,USDA class: sandy loam• 100 soil samples• cendroid of eachsample (perfectknowledge)
Initial conditions2 cases of initialcondition:• dry: θ0 = PWP• wet: θ0 = FC
Management of deficit irrigation systemsTU Dresden, University of Twente // Niels Schütze and Oleksandr MialykEGU General Assembly 2020, May 6, 2020 Slide 16 of 29
Experimental setup:varying knowledge / information about 5 irrigation strategies
Crop model• Aquacrop OS∗
(FAO)
Irrigation strategies• no irrigation• full irrigation• closed-loop control (decision table)• open-loop control (long term calendar)• open-loop control (perfect knowledge)
• 5 irrigation strategies x 32 scenarios → 160 combinations of informationManagement of deficit irrigation systemsTU Dresden, University of Twente // Niels Schütze and Oleksandr MialykEGU General Assembly 2020, May 6, 2020 Slide 17 of 29
Results
Management of deficit irrigation systemsTU Dresden, University of Twente // Niels Schütze and Oleksandr MialykEGU General Assembly 2020, May 6, 2020 Slide 18 of 29
OutlineIntroductionControlled deficit irrigationDIT modeling frameworkApplication to different climate regionsResultsImpact of climate and soil variabilityImpact of deficit irrigation strategyConclusions and Outlook
Management of deficit irrigation systemsTU Dresden, University of Twente // Niels Schütze and Oleksandr MialykEGU General Assembly 2020, May 6, 2020 Slide 19 of 29
Example plot:costs of uncertainty (additional water requirements) =̂ value of information
0 200 400 600 800 1,0000
2
4
6
8
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12
14
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Irrigation volume [mm]
Dry
yield[t/ha]
DI: 5% to 95% envelope FI: ensembleFI: averageDI: 90% quantileDI: medianDI: 10% quantile
∆ costs of uncertainty
Management of deficit irrigation systemsTU Dresden, University of Twente // Niels Schütze and Oleksandr MialykEGU General Assembly 2020, May 6, 2020 Slide 20 of 29
Climate variabilityEach season optimized (perfect knowledge of soil characteristics)
Montpellier (semi-arid) Seeb (arid)dry wet dry wet
0 200 400 600 800 1 ,0000
2
4
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12
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16
Irrigation volume [mm]
Dry
yield
[t/h
a]
0 200 400 600 800 1 ,0000
2
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Irrigation volume [mm]
Dry
yield
[t/h
a]
0 200 400 600 800 1 ,0000
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Irrigation volume [mm]
Dry
yield
[t/h
a]
0 200 400 600 800 1 ,0000
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Irrigation volume [mm]
Dry
yield
[t/h
a]
Management of deficit irrigation systemsTU Dresden, University of Twente // Niels Schütze and Oleksandr MialykEGU General Assembly 2020, May 6, 2020 Slide 21 of 29
Soil variability: Montpellier (semi-arid)Each season optimized (perfect knowledge of weather development)
no information sandy loamdry wet dry wet
0 200 400 600 800 1 ,0000
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Irrigation volume [mm]
Dry
yield
[t/h
a]
0 200 400 600 800 1 ,0000
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Irrigation volume [mm]
Dry
yield
[t/h
a]
0 200 400 600 800 1 ,0000
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Irrigation volume [mm]
Dry
yield
[t/h
a]
0 200 400 600 800 1 ,0000
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Irrigation volume [mm]
Dry
yield
[t/h
a]
Management of deficit irrigation systemsTU Dresden, University of Twente // Niels Schütze and Oleksandr MialykEGU General Assembly 2020, May 6, 2020 Slide 22 of 29
Soil variability: Seeb (arid)Each season optimized (perfect knowledge of weather development)
no information sandy loamdry wet dry wet
0 200 400 600 800 1 ,0000
2
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Irrigation volume [mm]
Dry
yield
[t/h
a]
0 200 400 600 800 1 ,0000
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Irrigation volume [mm]
Dry
yield
[t/h
a]
0 200 400 600 800 1 ,0000
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Irrigation volume [mm]
Dry
yield
[t/h
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0 200 400 600 800 1 ,0000
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Irrigation volume [mm]
Dry
yield
[t/h
a]
Management of deficit irrigation systemsTU Dresden, University of Twente // Niels Schütze and Oleksandr MialykEGU General Assembly 2020, May 6, 2020 Slide 23 of 29
OutlineIntroductionControlled deficit irrigationDIT modeling frameworkApplication to different climate regionsResultsImpact of climate and soil variabilityImpact of deficit irrigation strategyConclusions and Outlook
Management of deficit irrigation systemsTU Dresden, University of Twente // Niels Schütze and Oleksandr MialykEGU General Assembly 2020, May 6, 2020 Slide 24 of 29
Example plotDifferent irrigation strategies and costs of uncertainty in [mm] for 0.9 quantile
0 200 400 600 800 1 ,0000
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Irrigation volume [mm]
Dry
yield[t/ha]
FI: ensembleFI: averageDI: decision tableDI: irrigation calendarDI: perfect knowledge
∆
decision table irrigation calendarCosts in [mm]: 178 208
Management of deficit irrigation systemsTU Dresden, University of Twente // Niels Schütze and Oleksandr MialykEGU General Assembly 2020, May 6, 2020 Slide 25 of 29
Yield for maize for a semi-arid site: FranceCosts for dry conditions in [mm]
Montpellier (semi-arid) Seeb (arid)climate variability soil variability climate variability soil variability
0 200 400 600 800 1 ,0000
2
4
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12
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16
Irrigation volume [mm]
Dry
yield
[t/h
a]
0 200 400 600 800 1 ,0000
2
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Irrigation volume [mm]
Dry
yield
[t/h
a]
0 200 400 600 800 1 ,0000
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Irrigation volume [mm]
Dry
yield
[t/h
a]
0 200 400 600 800 1 ,0000
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Irrigation volume [mm]
Dry
yield
[t/h
a]
178 180 76 43 30 10 88 40
Management of deficit irrigation systemsTU Dresden, University of Twente // Niels Schütze and Oleksandr MialykEGU General Assembly 2020, May 6, 2020 Slide 26 of 29
An Example for Seeb for mixed variabilityStochastic Crop Water Production Function for wet conditions
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Irrigation volume [mm]
Dry
yield
[t/h
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Irrigation volume [mm]
Dry
yield
[t/h
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irrigation calendar simple deficit irrigationManagement of deficit irrigation systemsTU Dresden, University of Twente // Niels Schütze and Oleksandr MialykEGU General Assembly 2020, May 6, 2020 Slide 27 of 29
Conclusions and Outlook
Management of deficit irrigation systemsTU Dresden, University of Twente // Niels Schütze and Oleksandr MialykEGU General Assembly 2020, May 6, 2020 Slide 28 of 29
Conclusions• value of information =̂ costs for compensation of uncertainty in units ofadditional water requirements• costs of climate uncertainty (variability) are higher for the semi-arid sitecompared to arid climate conditions• costs of soil uncertainty (variability) are higher for the arid site, and evenhigher for wet initial conditions• costs of soil uncertainty (variability) can be reduced by proper soil analysis• but highest added value of information have: knowledge aboutdeficit irrigation strategy and information about initial conditions
Management of deficit irrigation systemsTU Dresden, University of Twente // Niels Schütze and Oleksandr MialykEGU General Assembly 2020, May 6, 2020 Slide 29 of 29