resilient optimization of agricultural water networks ... · resilient optimization of agricultural...

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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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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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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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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Objectives

Optimization of Irrigation Strategies

Stakeholder’s Behavior

Water Policy

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Soil conditionsOrganization

Cooperation

Hydroclimatic variability

Hydroclimatic variability impacts.

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Tragedy of Commons

“How individually rational economic decisions can lead to environmental ruin.”

(Hardin 1968)

Photo: © USU

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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).


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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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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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


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)


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


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


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


Objectives



Water Policy

Ob

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Soil conditionsOrganization

Cooperation



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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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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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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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Incorporation of Social Sciences

Photo: © Lu et al. 2018

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Incorporation of Social Sciences

Socioeconomic Data Fieldwork (Interviews)

Photo: © SBCC

Theory of Planned Behavior

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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.


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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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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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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Unique characteristics:• Climate change awareness• Cooperation level• Optimization objectives

Current research: MAHIZ – ABM: Decision Making Process

Farmers

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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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Current research: MAHIZ – ABM: Modelling Approaches

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DIT APPM

GAMA

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Assessment, Prognosis, Planning and Management tool Grundmann, Schütze,

Schmitz et al. (2012).


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APPM

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GAMA is a modeling and simulation development environment for building

spatially explicit agent-based simulations.


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GAMA

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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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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


Current research: MAHIZ – ABM in GAMA

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ImplementingReceiving PromotingClosed Happy

Scenario 2: Decentralized InnovationScenario 1: Centralized Innovation

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Soil conditions

Thank you for your attention!

Questions or Suggestions?

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Water Policy

Organization

Cooperation




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resilient optimization of agricultural water networks ... · resilient optimization of agricultural...

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