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PhD Candidate: MSc. Malena Orduña Alegría
Supervisor: Prof. Niels Schütze
Resilient Optimization of Agricultural Water Networks Under Water Scarcity
Conditions.
Resilient Optimization of Agricultural Water Networks Under Water Scarcity Conditions.
Content
• Introduction
• Problem
• Objectives
• Methodology Implemented
• Methodology in development
• Outlook
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Resilient Optimization of Agricultural Water Networks Under Water Scarcity Conditions.
Joint initiative of TU Dresden, Helmholtz-Centre for Environmental Research (UFZ) with their Center of Advanced Water Research (CAWR), Purdue University (USA) and University of Florida (USA).
The scientific focus is the structural and functional analyses of complex dynamic networks for the understanding of their performance, flexibility and resilience.
IRTG
Quality in the water cycle
Urban Water Systems
Data collection and information processingWater governance
Water quantity and scarcity
Societal and climate change
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Resilient Optimization of Agricultural Water Networks Under Water Scarcity Conditions.
Agrohydrological Networks
According to the International Water Management Institute, agricultureaccounts for about 70% of global water withdrawals.
Photo: © FAO Photo: © FAO
Photo: © Seibert et al. 2013
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Resilient Optimization of Agricultural Water Networks Under Water Scarcity Conditions.
Agrohydrological Networks
Rainfed agriculture: 80% of the land farmed around the world and it contributes about 58% to the global food basket.
Photo: © SIWI• Hydroclimatic variability.• Soil properties.
Photo: © USDA
Irrigated agriculture: 20% of the land farmed around the world and it contributes about 42% to the global food basket.
• Crop – Water Efficiency.• Source and availability.• Quality.
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Resilient Optimization of Agricultural Water Networks Under Water Scarcity Conditions.
Objectives
Optimization of Irrigation Strategies
Stakeholder’s Behavior
Water Policy
Ob
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Soil conditionsOrganization
Cooperation
Hydroclimatic variability
Hydroclimatic variability impacts.
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Resilient Optimization of Agricultural Water Networks Under Water Scarcity Conditions.
Tragedy of Commons
“How individually rational economic decisions can lead to environmental ruin.”
(Hardin 1968)
Photo: © USU
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Resilient Optimization of Agricultural Water Networks Under Water Scarcity Conditions.
Tragedy of Groundwater Commons
Photo: © People and the Commons
Pumping groundwater game
Photo: © Madani 2010
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Free-riding behavior: Individual rationality leads to an outcome that is not rational from the perspective of the group (Gardner et al., 1990).
Resilient Optimization of Agricultural Water Networks Under Water Scarcity Conditions.
Tragedy of Groundwater Commons
Farmer B
Fa
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Low High
Lo
w 3 1
3 4
Hig
h 4 2
1 2
Pumping groundwater game
Best Strategy
Free-riding
Nash Equilibrium
Photo: © Madani 2010
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Resilient Optimization of Agricultural Water Networks Under Water Scarcity Conditions.
Corn Agriculture in the U.S.
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Adaptations
Sustainable and Resilient Irrigation
Irrigation Investment
Climate Change
Rain Abundant Dryland
Rising Temperatures
Flash floods
Droughts
Corn Agriculture
Maize(Zea mays L.)
36%
World Corn Production
(2018 USDA)U.S.ChinaOtherBrazilEuropeArgentinaMexicoUkraineIndiaCanadaRussia
U.S. Corn Belt
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Resilient Optimization of Agricultural Water Networks Under Water Scarcity Conditions.
Location of Study Sites
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W7
W5W4
W9
W8
W2
W6
W1
W3
E4
E6
E5
E8
E7 E3
E2
E1
Irrigated Area [% of total harvested area]
0 5 20 40 70 90 100 %
Sites Location
W in the Western Corn Belt
E in the Eastern Corn Belt
Resilient Optimization of Agricultural Water Networks Under Water Scarcity Conditions.
Full climate scenarios of 17 sites
Trend Analysis and Aridity Index
Performance metrics and Stochastic analysis
Deficit Irrigation Toolbox
Hydroclimatic Analysis
Irrigation Strategy Evaluation
Results Aggregation
Corn Agriculture in the U.S.
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2,760 Simulation results for each time series
Grouping of results by West (9 sites) and East Corn Belt (8 sites)
Resilient Optimization of Agricultural Water Networks Under Water Scarcity Conditions.
DIT Modeling Framework
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Crop models
• Cropwat (FAO)
• Aquacrop OS (FAO)
• Daisy
• Apsim
Climate variability
• Climate stations
• Historic and future simulations
• Meteorological forecasts
Soil variability
• Soil generator
Features
• Probabilistic framework (SCWPF)
• Parallel computation
• Visualization tools for performance analysis
• Manual, examples and tutorial
Resilient Optimization of Agricultural Water Networks Under Water Scarcity Conditions.
DIT Modeling Framework
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Irrigation strategies
1. No irrigation.
2. Full supplemental
3. Simple deficit
4. Constant supplemental in a fixed schedule
5. Optimized deficit with decision table
6. Optimized deficit with phenological stages
7. Optimized deficit with GET-OPTIS
Resilient Optimization of Agricultural Water Networks Under Water Scarcity Conditions.
Optimal Irrigation Strategies
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W7
W5W4
W8
W9
W6
W1
W2
W3
E4
E6
E5 E7
E8
E3
E2
E1
Initial soil moisture [%]
40%
Code
10%
20%30%
Irrigation Strategy
S7_GO
S6_ODTph
S5_ODT
S4_CFS
S3_DI
S1_RF
Resilient Optimization of Agricultural Water Networks Under Water Scarcity Conditions.
Objectives
Optimization of Irrigation Strategies
Stakeholder’s Behavior
Water Policy
Ob
jecti
ve
Soil conditionsOrganization
Cooperation
Hydroclimatic variability
Hydroclimatic variability impacts.
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Resilient Optimization of Agricultural Water Networks Under Water Scarcity Conditions.
Socio-hydrology
Multidisciplinary field that studies the complex inter-relationships and co-evolution of combined human and water systems to build reliable strategies for water resources management and planning.
Photo: © Pouladi et al. 2019
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Resilient Optimization of Agricultural Water Networks Under Water Scarcity Conditions.
Modelling Approaches
Centralized Decentralized
Decision Process Command-and-Control Bottom-up Procedure
Public Participation Low High
Efficiency More * Less *
Information Exchange
Complete - Easy Partial - Difficult
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Resilient Optimization of Agricultural Water Networks Under Water Scarcity Conditions.
Agent Based Modelling (ABM)
ABM are built as microscale models (Gustafsson & Sternad, 2010),operational on an agent level, but their study allows conclusions to bedrawn at a larger scale, following the process of emergence.
Photo: © Rebaudo et al. 2018 Photo: © Yang et al. 2018
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Resilient Optimization of Agricultural Water Networks Under Water Scarcity Conditions.
Incorporation of Social Sciences
Photo: © Lu et al. 2018
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Resilient Optimization of Agricultural Water Networks Under Water Scarcity Conditions.
Incorporation of Social Sciences
Socioeconomic Data Fieldwork (Interviews)
Photo: © SBCC
Theory of Planned Behavior
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Resilient Optimization of Agricultural Water Networks Under Water Scarcity Conditions.
Companion Modelling
The methodology is based on the companion modelling approach(Barreteau et al. 2003). It calls for continuous and iterative confrontation between theory and reality.
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Rules-based modelling and simulation of the
interdependencies of hydroclimatic, crop, economic
and social parameters.
Agent-Based Model
Emulate behaviours in a controlled and
safe environment.
Serious game
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Companion modelling facilitates collective decision-making processes by identifying the various viewpoints and subjective criteria to which the different stakeholders refer implicitly or even unconsciously.
Resilient Optimization of Agricultural Water Networks Under Water Scarcity Conditions.
Serious Games
When I hear, I forget.When I see, I remember.When I do, I understand.
Chinese Proverb
Serious Games appeal to three basic motivational human needs• Relatedness• Autonomy• Competence.
Serious Games can create positive user experiences:✓ Enable social good.✓ Improve knowledge retention.✓ Enable new problem-solving ideas.✓ Enable real-time data and analysis.
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Resilient Optimization of Agricultural Water Networks Under Water Scarcity Conditions.
Current research: MAHIZ – Board game
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Role-playing board game for 2-4 players, designed toanalyze the people and farmers’ behaviors regardingclimate change, policy implementations, andtechnological adaptations in maize agriculture.
A simplified representation using a cooperative andcompetitive mechanics to emulate the socio-hydrological dynamics to find an integrative solutionto the tragedy of commons.
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Resilient Optimization of Agricultural Water Networks Under Water Scarcity Conditions.
Current research: MAHIZ – Behavior Analysis
Behavior Description
Homo economicus Completely rational.
Bounded Rationality Limited by available information and cognitive capacity.
Planned Mediated by intentions and perceived behavioural control.
Habitual learningBehavioural learning that originates in the classical (Pavlov, 1927) and operant (Skinner, 1953) conditioning theories.
Descriptive Norm Social norms: influence of perceiving what other people do.
Prospect TheoryImportant aspects from cognitive psychology: willingness to seek or avoid risk.
Behavior Identification
MoHuB (Modelling Human Behavior) by M. Schlüter et al (2017)
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Resilient Optimization of Agricultural Water Networks Under Water Scarcity Conditions.
Unique characteristics:• Climate change awareness• Cooperation level• Optimization objectives
Current research: MAHIZ – ABM: Decision Making Process
Farmers
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Resilient Optimization of Agricultural Water Networks Under Water Scarcity Conditions.
Current research: MAHIZ – ABM: Decision Making Process
Farmers
Financial situation
Resources situation
Social situation
Calculating the level of intention towards
water conservation behavior
Memory Bank Satisfaction Analysis
Optimize by selecting the strategy with maximum profit
Optimize by selecting the strategy with maximum ware
savings
Using the results of MAHIZ
Manager
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Resilient Optimization of Agricultural Water Networks Under Water Scarcity Conditions.
Current research: MAHIZ – ABM: Modelling Approaches
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DIT APPM
GAMA
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Resilient Optimization of Agricultural Water Networks Under Water Scarcity Conditions.
Assessment, Prognosis, Planning and Management tool Grundmann, Schütze,
Schmitz et al. (2012).
Current research: MAHIZ – ABM: Modelling Approaches
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APPM
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Resilient Optimization of Agricultural Water Networks Under Water Scarcity Conditions.
GAMA is a modeling and simulation development environment for building
spatially explicit agent-based simulations.
Current research: MAHIZ – ABM: Modelling Approaches
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GAMA
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Resilient Optimization of Agricultural Water Networks Under Water Scarcity Conditions.
Current research: MAHIZ – ABM: Modelling Approaches
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DIT APPM
GAMA
• Irrigation strategies• SCWPFs• Yield
• Groundwater levels• Quality levels• Multi-criteria
optimization
• ABM Multilevel network dispersion model
• Decision making processes
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Resilient Optimization of Agricultural Water Networks Under Water Scarcity Conditions.
20 Farmers randomly located and linked
Current research: MAHIZ – ABM in GAMA
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Scenario 2: Decentralized InnovationScenario 1: Centralized Innovation
Few Friends + Big CollectiveLow trust values of the collective
More Friends + Small CollectiveHigher trust values of collective
Resilient Optimization of Agricultural Water Networks Under Water Scarcity Conditions.
Current research: MAHIZ – ABM in GAMA
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ImplementingReceiving PromotingClosed Happy
Scenario 2: Decentralized InnovationScenario 1: Centralized Innovation
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Resilient Optimization of Agricultural Water Networks Under Water Scarcity Conditions.
Ou
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Soil conditions
Thank you for your attention!
Questions or Suggestions?
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Optimization of Irrigation Strategies
Stakeholder’s Behavior
Water Policy
Organization
Cooperation
Hydroclimatic variability
Hydroclimatic variability impacts.
Resilient Optimization of Agricultural Water Networks Under Water Scarcity Conditions.
Re
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Grundmann, J.; Schütze, N.; Schmitz, G.H.; Al-Shaqsi, S. Towards an Integrated Arid Zone Water Management Using Simulation-basedOptimization. Environmental Earth Sciences 2012, 65, 1381–1394. doi:https://doi.org/10.1007/s12665-011-1253-z.
Prokopy, L.S.; Carlton, J.S.; Haigh, T.; Lemos, M.C.; Mase, A.S.; Widhalm, M. Useful to Usable: Developing Usable Climate Science for Agriculture. Clim. Risk Manage. 2017, 15, 1–7. doi:10.1016/j.crm.2016.10.004.
Takle, E.S.; Anderson, C.J.; Adresen, J.; Angel, J.; Elmore, R.W.; Gramig, B.M.; Guinan, P.; Hilberg, S.; Kluck, D.; Massey, R.; Niyogi, D.; Schneider, J.M.; Schulski, M.D.; Todey, D.; Widhalm, M. Climate Forecast for Corn Producer Decision Making. Earth Interact 2014, 18, 1–8. doi:10.1175/2013ei000541.1
Niyogi, D.; Liu, X.; Andresen, J.; Song, Y.; Jain, A.K.; Takle, O.K.E.S.; Doering, O.C. Crop Models Capture the Impacts of Climate Variability on Corn Yield. Geophys. Res. Lett. 2015, 42. doi:10.1002/2015gl063841.
Dobermann, A.; Nelson, R.; Beever, D.; Bergvinson, D.; Crowley, E.; Denning, G.; Griller, K.; d’Arros Hughes, J.; Jahn, M.; Lynam, J. Solutions for Sustainable Agriculture and Food Systems - Technical Report for the Post-2015 Development Agenda. Technical report, The United Nations Sustainable Development Solutions Network, 2013.
Niyogi, D.; Kellner, O. Climate Variability and the U.S. Corn Belt: Enso and AO Episode-dependent Hydroclimatic Feedbacks to Corn Production at Regional and Local Scales. Earth Interact 2015, 6, 1–32. doi:10.1175/ei-d-14-0031.1.
Alter, R.E.; Douglas, H.C.; Winter, J.M.; Elfatih, A.B.E. Twentieth Century Regional Climate Change during the Summer in the Central United States Attributed to Agricultural Intensification. Geophys Res Lett 2017. doi:10.1002/2017gl075604.
Pryor, S.C.; Scavia, D.; Downer, C.; Gaden, M.; Iverson, L.; Nordstrom, R.; Patz, J.; Robertson, G.P. Midwest. Climate Change Impacts in the United States. Technical report, National Climate Assessment, 2014.
Van Dop, M.; Gramig, B.M.; Sesmero, J.P. Irrigation Adoption, Groundwater Demand and Policy in the U.S. Corn Belt, 2040-2070. mathesis, Purdue University, 2016.
Schütze, N.; Schmitz, G.H. OCCASION: New Planning Tool for Optimal Climate Change Adaption Strategies in Irrigation. J. Irrig. Drain. Eng. 2010, 136, 836–846. doi:10.1061/(asce)ir.1943-4774.0000266
Schütze, N.; Wöhling, T.; de Paly, M.; Schmitz, G. Global Optimization of Deficit Irrigation Systems Using Evolutionary Algorithms. Proceedings of the XVI International Conference on Computational Methods in Water Resources; , 2006. doi:10.4122/1.1000000744.
Giulia, V.; Amilcare, P. From Rainfed Agriculture to Stress-avoidance Irrigation: I. a Generalized Irrigation Scheme with Stochastic Soil Moisture. Adv. Water Resour. 2011, 34, 263–271. doi:10.1016/j.advwatres.2010.11.010.
Rao, N.H.; Sarma, P.B.S.; Chander, S. Real-time Adaptive Irrigation Scheduling under a Limited Water Supply. Agric. Water Manage. 1992, 20, 267–279. doi:10.1016/0378-3774(92)90002-e.
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