Advancing coupled human-water systems analysis by agent-based modeling

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Department of Electronics, Information, and Bioengineering Doctoral Program in Information Technology

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DVANCING

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OUPLED

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UMAN

-W

ATER

S

YSTEMS

A

NALYSIS BY

A

GENT

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BASED

MODELING

Doctoral Dissertation of:

Yu Li

Supervisor:

Prof. Andrea F. Castelletti

Tutor:

Prof. Carlo Piccardi

The Chair of the Doctoral Program:

Prof. Andrea Bonarini

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Acknowledgment

First of all, I would thank to my family in China for supporting me all the time on my back, even though they could not manage to share the joy of many special moments with me here.

I would like to thank my supervisor, Prof. Andrea Castelletti, for giv-ing me the opportunity to undertake the Ph.D program in this beautiful country, and also for being a great example of a passion and dedication to the academic activities. I am particularly grateful for his encouragement and enlightenment on my research during my Ph.D career, as well as for his guidance on my technical skills in communicating and presenting the work.

I would also thank to all the people with whom I worked with. In par-ticular, Dr. Daniela Anghileri for helping me a lot in my early Ph.D life and guiding me into a discreet and rigorous scientific attitudes. Dr. Matteo Giuliani for supporting my research projects in every single perspective, and for always being available to discuss any doubt and results, and timely pointing out the space for improvements. Prof. Patrick Reed from Cornell University, for hosting me in his excellent research group during my vis-iting period in U.S., and for supervising my work and sharing his unique idea and insightful perspective.

Thanks to all the colleagues in my office, who shared many important moments and Ph.D experience with me. Rafael Schmitt, Simona Denaro, Emanuele Manson, Andrea Cominola, Francesca Ricanati and Dr. Dinh Nhat Quant, who are all brilliant and friendly, and set a great example of being an enthusiastic young researcher. I should specially thank for those

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Italian colleagues who kindly adapt to my weakness in communication, and are so generous to share their lives with me.

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Abstract

Most of the hydrological models developed in the last decades, and ordinar-ily used for decision support in water system, focus on the natural compo-nent in the water cycle whilst human activities are predominantly regarded as external forces that marginally impact the hydrological system. This is, however, contradict to the presence of human signature in many river basins worldwide which are prevalent nowadays, and whose increasing impacts are rivaling with the natural forces themselves in conditioning the natural processes and transforming the hydrosphere. The feedback between the human system and the natural ones also imply such process a complex co-evolutionary one, with the projected non-stationary climate change further exacerbating our ability to predict the future conditions of these systems. Therefore, there is a need to shift from traditional human-excluded model-ing practices to a more integrated approach, where the human related enti-ties have to be included in the modeling framework to address their internal feedback and the joint dynamics under changing conditions. Recent stud-ies in water systems analysis are increasingly advocate such holistic view by treating the human and natural subsystems as a whole, the so-called Coupled Human and Natural Systems (CHNS), where characterizing and modeling co-evolution of CHNS is key to building reliable medium-to-long term projections and, ultimately, to designing management and adaptation strategies to mitigate water stress and crises under climate change context.

To this end, we developed a decision-analytic framework, namely the DistriLake framework, to analyze and characterize the co-evolution of cou-pled human water systems (descriptive component), and to design and

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sess alternative management strategies (prescriptive component). The ob-jective is to build a mathematical model of the co-evolving CHNS under changing climate and socio-economic conditions. The DistriLake inte-grates traditional hydrological process equations for the natural compo-nent, and behavioral models to characterize the human decision-making processes and their effects on the water cycle. Different climate and socio-economic scenarios will be used as boundary conditions. The behavioral modeling of stakeholders’ decision-making process is the key component to approach the projection of such co-evolutionary process. The modeling framework is essentially based on Multi Agent Systems (MAS), which are state-of-the-art tools to characterize heterogeneous human agents, where agents’ behaviors are modeled using normative approach assuming rational agents.

The proposed modeling framework was applied on Lake Como water system located in north Italy as a pilot study area, and also as a represen-tative coupled human water system. In the first application, the framework was shown to successfully capture current situation, and the system may, however, be exposed under a considerable risk under projected drought sce-narios. Results show that timely co-adaptation to such changing conditions will be valuable to mitigate the negative impact from climate changes. In the second study, we applied the DistriLake framework to investigate the robustness of current system in face of deep uncertain scenarios. Results highlight the potential multi-stakeholders’ conflicts implicit to traditional robustness based assessments, often with presumption of social-planner’s view. The last study adopted the proposed modeling framework to as-sess the optional value of long-term climate forecast products in supporting farmers’ crop planning decisions. Results show that integrating the end-users’ decision making process into the assessment framework may provide more insightful thoughts on the poor adoption of the long-term forecast, given the current status of forecast quality.

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Riassunto

La maggior parte dei modelli idrologici sviluppati negli ultimi decenni, e di solito utilizzati per il supporto decisionale nel sistema idrologico, si con-centrano sulla componente naturale nel ciclo dell’acqua, mentre le attività umane sono prevalentemente considerato come disturbi esterni che influis-cono marginalmente il sistema idrologico. Questo è, tuttavia, contraddice alla presenza di firma umano in molti bacini fluviali che sono prevalenti al giorno d’oggi, e il cui impatto crescente sono rivaleggiando con le forze naturali stessi per condizionare i processi naturali e trasformare l’idrosfera. Il feedback tra il sistema umano e naturali implicano anche tale processo un complesso co-evoluzione, con il cambiamento climatico non stazionari ulteriore esacerbando la nostra capacità di prevedere le future condizioni di questi sistemi. Pertanto, vi è la necessità di passare da pratiche di model-lazione tradizionale umano-escluso ad una più approccio integrato, in cui le entità correlate umani devono essere incluso nella schema di modellazione per affrontare la loro feedback interno e le dinamiche congiunte in con-dizioni mutevoli. Recenti ricerche in analisi sistema idrico sono sempre sostengono tale visione olistica trattando i sottosistemi umani e naturali nel suo complesso, il cosiddetto Coupled Human Nature Systems (CHNS), dove la caratterizzazione e modellazione co-evoluzione di CHNS è la chi-ave per costruire affidabili proiezioni a medio-lungo termine e, in ultima analisi, a la progettazione di strategie di gestione e di adattamento per mit-igare lo stress idrico e crisi sotto contesto del cambiamento climatico.

Per questa ragione, abbiamo sviluppato un modello analitico-decisionale, cio`e il DistriLake, per analizzare e caratterizzare la co-evoluzione dei

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tema idrico umani accoppiati (componente descrittiva), e di progettare e va-lutare strategie di gestione alternativa (componente prescrittiva). L’obiettivo `e quello di costruire un modello matematico delle CHNS co-evoluzione sotto cambiamento del clima e delle condizioni socio-economiche. Il Dis-triLake integra le equazioni di processo idrologica tradizionali per la com-ponente naturale, e modelli di comportamento per caratterizzare i processi decisionali umani e dei loro effetti sul ciclo dell’acqua. Diversi scenari del climatico e socio-economici saranno utilizzati come condizioni al contorno. La modellazione del comportamento del processo decisionale delle parti interessate `e il componente fondamentale di avvicinarsi alla proiezione di tale processo co-evolutivo. La schema di modellazione si basa essen-zialmente sui Sistemi Multi-Agente (MAS), che sono state-of-the-art stru-menti per caratterizzare agenti umani eterogenei, in cui i comportastru-menti degli agenti vengono modellati utilizzando approccio normativo assumendo agenti razionali.

Il modello proposta è stata applicata sul sistema idrico Lago di Como si trova nel nord Italia come area di studio pilota, e anche come rappresen-tante CHNS. Nella prima applicazione, il framework è stato mostrato per catturare correttamente situazione attuale, e il sistema può, tuttavia, essere esposto sotto un rischio considerevole in scenari siccità proiettate. I risul-tati mostrano che puntuale co-adattamento a tali condizioni mutevoli sarà prezioso per mitigare l’impatto negativo dei cambiamenti climatici. Nel secondo studio, abbiamo applicato il DistriLake framework di indagare la robustezza del sistema attuale di fronte a scenari incerti profonde. I risultati mettono in evidenza i conflitti potenziali fra parti interessate implicito alle valutazioni tradizionali a base di robustezza, spesso con la presunzione di vista socio-planner. L’ultimo studio ha adottato il framework proposto per valutare il valore opzionale di prodotti di previsione del clima a lungo ter-mine nel sostenere decisioni di pianificazione delle colture degli agricoltori. I risultati mostrano che l’integrazione di processo decisionale rendendo il utenti finali nella schema di valutazione può fornire pensieri più penetranti sui poveri adozione del lungo periodo di previsione, dato lo stato attuale della qualità del tempo.

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Acronyms

A

ABM Agent Based Model.

ANN Artificial Neural Network. C

CanSIPS The Canadian Seasonal to Inter-annual Pre-diction System.

CART Classification and Regression Tree.

CCCma Canadian Centre for Climate modeling and analysis.

CDF Cumulative Density Function. CFS The Climate Forecast System. CHNS Coupled Human-Natural System.

CMIP Coupled Model Intercomparison Project. E

ECMWF European Center of Meteorologic Weather Forecast.

EMODPS Evolutionary Multi-Objective Direct Policy Search.

Emp2Ave Empirical forecast by averaging the past two years’ observations.

EmpClima Climatological forecast.

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Acronyms

EmpPast Empirical forecast by duplicating the past year’s observations.

G

GCM Global Circulation Model. GPC Global Producing Center. H

HBM Health Belief Model.

HBR Human Behavioral Representation. I

IPCC Intergovermental Panel on Climate Change.

IWRM Integrated Water Resources Management. L

LHS Latin Hypercube Sampling. M

MAS Multi-Agent System.

MASH Moving Average over Shifting Horizon. MCDA Multi-Criterion Decision Analysis. MCF Monte Carlo Filtering.

MME Multi-Model Ensemble.

MOEA Multi-Objective Evolutionary Algorithm. N

NCEP National Centers for Environmental Predic-tion.

NEF Number of Function Evaluations. O

OAGCM Ocean-Atmosphere General Circulation Model.

P

PB Pareto Boundary.

PMT Protection Motivation Theory. PRIM Patient Rule Induction Method.

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Acronyms PRUDENCE Prediction of Regional scenarios and

Un-certainties for Defining EuropeaN Climate change risks and Effects.

R

RBF Radial Basis Function. RCM Regional Climate Model.

RCP Representative Concentration Pathway. RDA Robust Decision Analysis.

S

SCT Social-Cognitive Theory. T

TPB Theory of Planned Behavior. TRA Theories of Reasoned Action. W

WMO World Meteorological Organization.

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Notations

A

ai weight of the radial basis neuron i in the

linear output neuron. B

bj,i the base of the i-th radial basis function for

j-th input. C

cj,i the center of the i-th radial basis function

for j-th input.

c(γk) crop production cost as a function of crop

choice. D

Dmax Kolmogorov Smirnov statistic for two

given samples. F

F_iP,corr change factor for biac-correcting the monthly estimates of precipitation hind-casts in i-th month.

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Notations

∆F_iT ,corr change factor for biac-correcting the monthly estimates of temperature hind-casts in i-th month.

F_idry,perm change factor for perturbing the dry spell from precipitation hindcasts in i-th month. F_iP,perm change factor for perturbing the

precipita-tion estimates of hindcasts in i-th month. ∆F_iT,perm change factor for perturbing the monthly

estimates of temperature hindcasts in i-th month.

F_iwet,perm change factor for perturbing the wet spell from precipitation hindcasts in i-th month. G

gfa step cost of flooded area.

gfd step cost of flooding days.

H

ht lake level at time t.

H total simulation horizon in days. J

JCo a vector of multiple objectives faced by

manager of Lake Como.

J_kD objective at demand side for k-th farmer. Jfa objective for flooded area.

Jfd objective for flooding days.

JS objective for water supply downstream. K

k index of farmer agent. ky crop yield response factor.

M

M total simulation years. N

N total number of farmer agents. Nin Total number of input variables.

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Notations Nr total number of radial basis function in the

network. O

θ parameter set of policy approximator. θ∗ optimal parameter set of policy

approxima-tor.

φi a constant term for the radial basis neuron.

ψi(·) radial basis function.

P

π operational policy of Lake Como.

πθ parameterized operational policy of Lake

Como.

π_θ∗ parameterized optimal operational policy of Lake Como.

π∗ optimal operational policy of Lake Como. P_i0 hindcasts of total precipitation in i-th

month.

P_i,j0 hindcasts of daily precipitation in i-th month at j-th day.

Pi observation of total precipitation in i-th

month.

Pi,j observation of daily precipitation in i-th

month at j-th day.

p(γk) crop sale price as a function of crop choice.

Q

qirr a vector of daily irrigation water supply.

qmef minimal environmental flow.

R

γk crop type selected by k-th farmer agent.

γ∗_k optimal crop type selected by k-th farmer agent.

~

γ a vector composed of each farmer agent’ crop choice.

Rf robustness criterion for flood protection. Rp,a _{robustness criterion for profit at agent level.}

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Notations Rp,s _{robustness criterion for profit at system}

level. S

S scenario that fails in meeting the perfor-mance threshold.

S scenario that succeeds in meeting the per-formance threshold.

st lake storage at time t.

T

Tb base temperature.

T_i0 hindcasts of mean temperature in i-th month.

Ti observation of mean temperature in i-th

month.

t daily time step. U

ut release decision at time t.

u∗_t optimal release decision at time t. W

wcrop a vector of crop water demand during

grow-ing period.

wt total water demand at time t.

X

χt a vector of input variable at time t.

Y

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CHAPTER

1

Introduction

1.1

Motivation

From ancient Mesopotamia culture to Chinese Yellow River civilization, since the emergence of human society along the river catchments, water has been playing a critical role in supporting the development of human civilization (e.g., Ashby, 1935; Bazza, 2007; Glick, 1970; Hassan, 2000). On the other hand, the natural variability of water resource directly or in-directly related to many disastrous natural hazards, stimulating humans’ adaptive nature (Potts, 1996), and leading us to try to understand the hy-drological cycle and to manage the water resource for the prosperity of human welfare. A history of water management might as well be a history of mankind’s socialization process.

The efforts spent in learning and managing water are never halted, but are continuously amplified thanks to the innovations of modern technolo-gies and the accumulation of scientific wisdom. Stepping into the 21st century, human beings are in a new era with enormous potential to modify, manage and regulate the water systems, with engineering artifacts unequiv-ocally showing the power of mankind. Some well known examples are, for instance, the Three Gorges Dam featuring a total electric power generating

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Chapter 1. Introduction

capacity of 22,500 MW, and the Ras Al Khair desalination plant that is able to produce 1,025,000 m3 fresh water per day (in 2014). In fact, the water system and human society have been twisted together for quite a history, forming an intimate relationship. On one hand, the use of water resource is not anymore limited to watering crops in agricultural sectors, but extends into hydro-power generation, industrial production and the urban lifestyle. In other word, the use of water resource has penetrated into every angle of modern human life and the availability (or unavailability) of water will manifest greater impact on our strategies of survival and well-being. On the other hand, the day-to-day activities within human society are equally impacting water bodies in terms of both quantity and the quality.

N

H

external disturbances

feedback dynamicsinternal internal

dynamics

systems' responses

CHNS

Figure 1.1: The conceptualized diagram of CHNS (Polhill et al., 2016). The Human (H) and Natural (N) systems are represented by two separated boxes, while the dashed boarder implies the modeling framework integrating the human and natural systems as a coupled one.

Given the close bond and direct feedback between water systems and human society, it makes an intuitive sense to treat the human entities as endogenous components within the water system where the society resides and interacts with, rather than as exogenous forcings that only unilaterally impact the water bodies. In fact, such affinity between the human society and water systems is only one of the many examples of Coupled Human-Natural System (CHNS; J. Liu, Dietz, S. R. Carpenter, et al. 2007, see Section 1.2 for more details), or Socio-Ecological Systems (SES; Redman, Grove, and Kuby 2004), in which the humans are the basic components of social systems, while the hydrological sphere represents the natural sys-tems. A conceptualization of general CHNS diagram can then be

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1.1. Motivation sented as Figure 1.1. Many theoretical studies have already suggested such holistic view on the coupled human and water system (e.g., M. Falkenmark, 1997a; M. Sivapalan, H. H. G. Savenije, and G. Bl¨oschl, 2012a; Wagener et al., 2010). Moreover, the dynamic mutual interaction implies the two sys-tems are interconnected and co-evolve in time. Particularly, the evolution of natural water systems with evident human footprints are resulting from not only the natural environmental processes but also the anthropogenic ac-tivities, where latter are rivaling with former with deepening and widening influences (e.g., Y. Liu et al., 2014; Ma et al., 2008; Nilsson et al., 2005; H. Wang et al., 2007).

On the other hand, climate projections suggest a non-stationary trend of the boundary conditions—in contrary to traditional stationary assumption— to most of water systems (Milly et al., 2007), asking for new methods and tools to study systems’ evolution in the future and to explore the space of adaptation measures for improvement. Despite a number of studies which have extensively worked on resolving the non-stationary trend in water systems analysis (e.g., Cunderlik and Burn, 2003; Holman et al., 2011; Teutschbein and Seibert, 2013; Vaze et al., 2010), most of them only focus on natural systems, ignoring or over simplifying the feedback from human systems, and the co-evolution of coupled human-water system is rarely ad-dressed. Instead, studies on CHNS would require a comprehensive frame-work that captures the internal dynamics of both human and natural sys-tems, as well as the coupling and feedbacks between the two (e.g., Binder et al., 2013; J. Liu, Dietz, S. Carpenter, et al., 2007; J. Liu, Dietz, S. R. Carpenter, et al., 2007; Polhill et al., 2016; Redman, Grove, and Kuby, 2004)

The effort for achieving this methodologically can be twofold. First, modeling frameworks have to find ways to glue the anthropogenic sphere with the hydrological systems such that the feedback between the human activities and hydrological cycles can be addressed internally. Thanks to the advance in computer technology and information science, there are now a number of ways to accomplish this task via, e.g., Agent Based Model (ABM; Ferber 1999), in which each agent represents an active decision-maker who lives on the common environment and interacts within. A few examples of applications of agent-based modeling in water systems can be found in, e.g., Ng et al., 2011, Barthel et al., 2008, Becu et al., 2003, Giu-liani and Castelletti, 2013, and a comprehensive discussions about utility of agent-based modeling in water resource management can be found in Giuliani, 2014. It is worth to mention that with ABM, not only the feed-back between agents and physical environment can be modeled but also the

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social network can be represented through agents interactions.

Second but most important, given the increasing impact of anthropogenic activities, traditional view on human components as external drivers to the water systems is not feasible, which ignores the important interactions and feedback that shape the long-term dynamics of CHNS. Neither, simple models such as rule-based cellular automata (Balmann, 1997) will be in-sufficient to describe humans’ behaviors, since the latter are influenced by many factors (e.g., personal belief, monetary constraint, others’ opinions) and hence are characterized by high non-linearity and uncertainties There-fore, the ‘Human’ box in Figure 1.1 should be replenished with behavioral modeling technique from extant knowledge of other disciplinary, such as cognitive science or decision-making theory, in order to better describe hu-mans’ decision making processes and their feedback with natural systems.

To summarize, how to better model humans’ behaviors and incorporate them into a coupled modeling framework in a flexible and meaningful way represents the major challenges in approaching the holistic view of modern CHNS analysis, which motivates this research. In this Ph.D dissertation, we contribute a novel decision-analytic framework by modeling CHNS using ABM framework and explicitly describing the processes of human decision-making as impacting on the natural processes, and the feedbacks of these latter on humans’ sectors.

In the following sections, a brief discussion about the concept of CHNS and human behavioral models will be given to frame the main context of the present thesis. We will use ‘human entity’ or a particular occupation, e.g., farmers or water regulator, to refer to an individual mankind, while use ‘agent’ instead for more general cases in which agent could be of higher abstraction, such as group of humans or organizations. Moreover, if is not specifically pointed out, the word ‘decision-making’ will be equivalent to the meaning of ‘behavior’ (or action) in this case, as the emphasis of be-havioral models in the dissertation is on the decisions through which agents interact with the environment. This idea is in contrary to many cognitive studies where the focus is on the mental states or intentions of the human entity.

1.2

Coupled human and natural systems (CHNS)

Interactions between humans and nature have long been recognized (e.g., M. Falkenmark, 1977; Heichelheim, 1956; Marsh and Lowenthal, 1965; Redman, 1999; Thomas, 1956; Turner, 1988), and during the development

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1.2. Coupled human and natural systems (CHNS) of natural science there were visionaries who proactively suggested holistic perspective on the partnership between the human and natural systems from time to time (e.g., Holling, 1978; Kates and Clark, 1999; Redman, 1999; Vitousek et al., 1997). Regardless of the voice from those advocates, few studies are really practicing such idea in developing their methodologies. This might partly be due to the intrinsic complexity of either natural sys-tem or human syssys-tem that already poses a number of unsolved questions for scientific community, let alone their interactions which are even more com-plicated (J. Liu, Dietz, S. Carpenter, et al., 2007). In addition, human activ-ities might not be as pervasive as they are now and hence their impacts on the natural systems are arguably small to produce profound affects. Dually, the potential risk from environment hazards, e.g., flood or drought events, were still manageable through engineering approaches for sufficiently long time, given the diminishing validity of stationarity assumption on natural variability.

Figure 1.2: Adapted from IPCC AR4 report, which shows the trend of increasing temper-ature on the global scale from the observed data, with and without the anthropogenic forces. The comparison highlights the strong influence of human impacts from the past.

To date, we are in a critical period in which individual humans are weaved into a gigantic web due to the globalization, and the affects of anthropogenic activities, particularly the industrial productions, have

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cu-Chapter 1. Introduction

mulatively manifested profound impacts on the earth. Global warming phenomenon, in particular, has been reported to feature a strong human footprint in the trend (see Figure 1.2, IPCC 2007). The projected future climate shows an increase of at least around 2 degree by the end of this century (Stocker et al., 2013), and the changed climate will alter the statisti-cal distributions of many hydro-climatic variables, accompanied with more extreme events and ultimately weaken the stationarity assumption (Milly et al., 2007). More concerns on conventional decoupled analysis of hu-man/natural systems are arising under such climate change context, which informs current interests in studying the CHNS.

However, CHNS are not merely alignments of human and natural sys-tems, but rather an integrated system in which humans interact with the environment. The focus of CHNS lies on studying the patterns and pro-cesses that link human and natural systems, and the reciprocal feedback between the two across spatial and temporal scales (J. Liu, Dietz, S. R. Carpenter, et al., 2007). The term ‘coupling’ implies the interlacing of the two systems at lower levels, which can be conceptualized as the entities with nested hierarchies (Gunderson, 2001). The dynamic feedback loops in the coupled system will either accelerate or hinder the rate of changes of the components in the human or natural systems, producing unexpected consequences or anticipated results but at a more rapid pace (e.g. J. Liu, Dietz, S. Carpenter, et al., 2007; Steffen et al., 2006), which might be un-derestimated with traditional approaches.

Although the idea of CHNS is not completely new (e.g., Integrated Wa-ter Resources Management (IWRM); see e.g., Castelletti and Soncini-Sessa 2006; Castelletti and Soncini-Sessa 2007; Fang, Bao, and Huang 2007; Jønch Clausen and Fugl 2001; Medema, McIntosh, and Jeffrey 2008; B. Mitchell 2005; Soncini-Sessa, Weber, and Castelletti 2007; Van der Zaag 2005 ), this time it quickly sparked many relevant research activities. In the hydrological domain topics such as “Socio-Hydrology” (M. Sivapalan, H. H. G. Savenije, and G. Bl¨oschl, 2012a), “Water Diplomacy” (Islam and Susskind, 2012) and the “Panta Rhei” (Montanari et al., 2013) project are actively pushing the applications of CHNS and advance the understandings of human and natural interactions. By and large, most of the works in cou-pling human and natural systems take two paths, i.e., a conceptual lumped model where differential equations are used to represent stylized parsimo-nious coupled systems with human-nature interactions incorporated as in-ternal parameters, whose values will change as the interactions go on (e.g. D. Liu et al., 2015; Van Emmerik et al., 2014), or agent-based models in which the anthropogenic elements are modeled as distributed individual

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1.3. Behavioral modeling agents acting within a common environment (e.g., Ducrot et al., 2004; Oel et al., 2010; Ralha et al., 2013). Despite the simplistic form of the lumped models, agent-based models are generally more flexible for constructing CHNS due to their abilities of representing the heterogeneity of human entities and to explore the decentralized solutions. Moreover, not only the human-nature feedbacks but also the social interactions can be mod-eled within agent-based framework, therefore a more realistic panorama of CHNS can be sketched (see Figure 1.3).

Nevertheless, applications of ABM framework for human-natural sys-tems, particularly hydrological syssys-tems, are still in their infancy. First, constructing the ABM for CHNS models requires substantial parameters to be defined and calibrated in order to represent a fully distributed het-erogeneous environment, including the human sphere (Bazghandi, 2012). Secondly, because ABM is intrinsically composed by a number of modules (e.g., agent models, environmental layers), leading to highly non-linear dy-namics of model behavior, it is not easy to understand the results nor to validate them (Ligtenberg et al., 2010).

Last but not the least, it is still not clear enough about how humans be-have and interact with the environment, even for some specific decisions, e.g., farmers’ crop choices. In addition, despite the considerable works in expanding the knowledge base on the natural processes, there are arguably incomparable efforts even in exploiting extant knowledge on the social sys-tems, let alone the studies on the coupled one. Therefore, new techniques about behavioral models and more multi-disciplinary efforts are needed to enrich a more comprehensive ABM framework, which should contribute new knowledge in studying the CHNS. In the following sections a review of theoretical progress and modeling techniques regarding to humans’ be-havioral models will be provided.

1.3

Behavioral modeling

The impact of human societies on the natural system is mainly through the anthropogenic activities, in which people make decisions on how to react to external conditions towards certain goal. On a minimal level such deci-sion could be, e.g. the choice of whether to save water or use more water for entertainment, while in other cases the decision may be more critical and influential, such as the decision of constructing nuclear power plant or hydroelectric reservoir. Therefore understanding how human behaves will provide precious insights and serve as foundations for conceptualizing the behavioral models, which in turn is the key to build an effective and

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reli-Chapter 1. Introduction

Natural System Human System

ABM framework

natural process

modeling behavioral modeling

Figure 1.3: A conceptual representation of ABM framework in modeling CHNS.

able representation of social system, and should eventually benefit a more comprehensive CHNS model.

Modeling human behavior, however, is rather a non-trivial task. Human behavior is well recognized as a complex non-linear, multi-variate process due to the high heterogeneity and uncertainties in human cognition and de-cision making processes (e.g., George, 2008; Gluck and Pew, 2006). By nature, human are nothing but intelligent entities who have control over their actions and internal states in order to achieve goals. In psychologi-cal literature, human behaviors are under the control of motivation, while motivation comes from humans’ needs. From the motivation based behav-ioral mechanism it has developed diverse theories, noticeably in the stud-ies of cognitive science (e.g., Bobrow, 2014; Miller, 2003) and behavioral decision-making (Simon, 1959). This section will review some critical as-pects of the behavioral modeling in the literature.

1.3.1 Descriptive versus normative

Countless attempts in modeling humans’ behaviors have long been given in isolated fields, particularly in cognitive science and behavioral economics, but in general those approaches can be categorized into two broad paradigms, namely descriptive method or normative one. In the first case, descrip-tive modeling of human behavior strives on describing and explaining the mechanism of how humans make choices. Studies as such can mostly be found in many cognitive science literature, which investigates the mental structure and routes of processing information in the brain, seeking to

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1.3. Behavioral modeling derstand the mechanisms of how human thinks and reasons. Theories de-veloped so far has involved into a number of Human Behavioral Repre-sentation models (HBR; Mavor and Pew 1998), such as the Health Belief Model (HBM; Rosenstock, Strecher, and Becker 1988), Social-Cognitive Theory (SCT; Bandura 2001), Theories of Reasoned Action (TRA; Fish-bein and Ajzen 1975) and Theory of Planned Behavior (TPB; Netemeyer et al. 1991), and Protection Motivation Theory (PMT; Rogers 1975). Appli-cations can be found largely in the health education and promotion studies, where researchers adopt HBR to ensure the adherence of medical treatment of patients. It is also worth mentioning that artificial intelligence was de-rived from this field (McCarthy et al., 2006).

On the other hand, the classic normative approach does not concern on the internal states of the decision makers, but focuses on providing the guid-ance to lead them towards the ‘optimal’ solutions. The normative paradigm is frequently seen in the neoclassical economics and engineering fields, where the idea is to find the best alternative for improving the performance of the target (e.g., financial market) under investigation. It is built on the assumption of rational decision maker, whose decision-making process is goal oriented and seeks on attaining optimal solution in order to maximize (minimize) some explicit or measurable criteria. Typically, the utility func-tion is used for providing such measures, where people use it to rank the preference of different alternatives. In other word, the decision-making process in this case is intrinsically an optimization problem. Variations from this discipline includes Multi-Criterion Decision Analysis (MCDA; Zionts 1979), in which decision-maker is facing many objectives and has to make a trade-off, and Game Theory (Von Neumann and Morgenstern, 1944), where interactive decisions may take place among multiple decision makers.

Apart from its popularity, the normative paradigm constantly suffers from the critics about rational agent assumption, which is often contra-dicted with reality as humans’ behaviors might not be entirely rational (e.g., Baron, 1998; Nickerson, 1998). Such rational agent assumption, however, can be relaxed by assuming different types of rationalities, such as the bounded rationality, in which decision maker has incomplete access of in-formation and hence results into suboptimal decision (Simon, 1957). Other people defend the full rationality as the consequence of natural evolution, as only rational actors can survive during the natural selection (Capra and Rubin, 2011). Although the rationality assumption is somehow debatable, what can not be reduced in the assumption is the functional behavior for-mulation which connect the means to the end.

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Despite the large applications of both paradigms, it is difficult to say one is superior than the other. On one hand, the descriptive representation of human behavior is more intuitive. Many HBR models require observed samples for model calibration, and hence is in principle more close to re-ality and also easy to be validated. However, given the high heterogeneity of sample data it is often impossible to parameterize the model for each individual nor can it be easily generalized to develop common knowledge. On the other hand, the normative paradigm is less context dependent. If one can accept the rationality assumption, there are well-defined axioms for constructing the utility and formulating the problem. And there are numbers of algorithm available for searching possible alternatives and solv-ing such optimization problem effectively via, e.g. dynamic programmsolv-ing, evolutionary algorithm, etc. Nevertheless, one has to pay attention to the formulation of the behavioral optimization problems, particularly the ob-jective function, because trade-off persists under many obob-jectives or even within a single objective problem but considering short-run or/and long-run strategies. In addition, it is difficult to identify all possible alternatives leading to suboptimal performance. One may consider the outcome from normative formation of behavior models as an equilibrium state, and pos-tulates that a rational actor will eventually behave in an optimal way as resource and knowledge is becoming available, which also give the norma-tive approach somewhat the predicnorma-tive capacity, while the descripnorma-tive one can be used as an explicit approach in explaining the internal state transi-tions before the equilibrium state is reached. Therefore, the choice between the two paradigms depends on the characteristic of the problems.

The architecture of human behavioral models is reflective of the be-havioral paradigm, as was described above. From modeling perspective, there are two broad ways of approaching human behavior, namely Explicit method and Implicit method (or optimization based).

As rooted in artificial intelligence and cognitive science, explicit models usually take a structured design focusing on describing the procedures of how human acts to make a decision, characterized by an incomplete repre-sentations of reality as analogous to the incapability of humans in capturing the world accurately, and is highly dynamic as representative of humans’ adaptive nature. The Modeling frameworks can be grouped in the follow-ing three classes: 1) Decision-tree models or rule-set based models, where each non-leaf node represents a triggering condition or operation, and leaf node represents the behavioral decisions (e.g., Arentze et al., 2000; Bres-felean, 2007; Oh and H.-A. Park, 2004). 2) Task network models in which the operator goals are decomposed into their component tasks (e.g.,

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1.3. Behavioral modeling haene and Changeux, 1997; Mu and Li, 2010). Therefore, to achieve the final goal requires the accomplishment of series of tasks. It is necessary to point out that in this case the decision-making process concerns with how tasks are represented, and less emphasis is put on the final outcome. In other word, the actor is interested in the ‘rational’ decisions (so called in-strumental rationality, see Heidegger, 1954) to achieve the task rather than obtain the optimal outcome. Therefore, it cuts off the connection between the means to the ends, while the latter in this case are rather an endoge-nous component of the model of the environment. 3) Cognitive models where mental states upon which reasoning, decision making, and behavior are explored and constructed (e.g., Aquino et al., 2009; Davis, 2001; Ferry, Fouad, and Smith, 2000; Fridman and Kaminka, 2007). It is considered as most realistic framework for modeling humans’ behavior, providing the cognitive foundations for other framework, but the downsides are the high complexity and intensive computation cost.

In general, Explicit models can be graphically represented as hierar-chical set of nodes and edges with links standing for the control flows, and decision makers follow simple rules (also referred as condition-action, stimulus-response rules or if-then rules) that guide their decision-making process. Apart from the cognitive models, it is also relatively easy to val-idate these models as in each branch one can simply check whether the decision maker do follow certain path or not, by interviewing target or/and expertise. However, constructing a decision tree is not a trivial task, as the researcher needs to identify not only the most important behavioral factors, but also the correct sequence. Moreover, in dealing with large number of entities, which may involve thousands of decision-makers, it will be unfea-sible to derive the behavioral rules for all the targets considered.

In Implicit modeling, decision-makers are assumed as fully rational and very often economic initiative, represented as a computational agent who is able to reason his/her utility function and do the math. The final decision is the one that maximize the utility function, derived from a range of feasible alternatives. The utility function can be a monetary performance or other risk-based indicator such as regret. Different from the explicit approach, the implicit models focus on the best outcome achievable from all possi-ble alternatives, while pays less interest on the explicit state transitions of reaching such results. In addition, the computation engine incorporated in the behavioral model for solving optimization problems postulates that the decision-makers have all information necessary for doing complex math, and the computation cost is usually free or negligible. These assumptions reflect the prescriptive nature, where the models provide means for

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iden-Chapter 1. Introduction

tifying the behavioral inefficacy components and guidance toward optimal outcome. As was mentioned above, the normative paradigm depicts the equilibrium state assuming the decision-maker will ultimately make the best choice given sufficient resource and time. If the rational assumption can hold, then the implicit model also acquires the predictive skill in de-scribing what may occur in long-run.

As deeply rooted in the normative paradigm, the implicit behavioral models are also criticized for its strong assumptions, such as full rationality, super computation capacity, and full information access. Many assump-tions, in fact, can be compromised by carefully identifying the problem nature and adapting the models accordingly. For example, the utility func-tion can be constrained to reflect the limited ability of the decision-maker in terms of resource and information. Therefore, inefficient decisions are ascribed to the infrastructural defect, such as the lack of accessibility of crop price in the market due to the failure of information sharing.

Nevertheless, the domains between explicit and implicit models are yet exclusive to each other, but rather a continuum in between. Therefore, it is expected a third hybrid form, which could draw on the advantage of im-plicit form, as flexible and content independent, but at same time assimilate the knowledge derived from explicit method, such as behavioral strategies or parameters, as the framework of behavioral model or as constraints to bound the objective function

1.3.2 Abstract agent versus real person

The target entity of behavioral models does not necessarily take the form of individual human beings, but may also be the organizations or infrastruc-tures where humans’ decisions play the major roles in their functionalities, such as the association of farm households or the regulated reservoir with central management committee. Therefore, modeling the behavioral enti-ties can be approached at different dimensions, ranging from large scale organizational ‘agent’ to micro scale real actors. It is usually intuitive to justify the choice of such scale issue according to the specific problems that one is targeting. For instance, studies of economics tend to apply be-havioral models on the high abstraction level of bebe-havioral entities such as firms, or on governmental institutions, while cognitive researches tend to be more interested in studying an individual behaviors.

However, by and large it is easier to conduct the behavioral modeling for the high level-of-analysis than for a particular entity. On one hand, for the normative paradigm rational assumption becomes more valid as those

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1.4. Objective and organization of the dissertation havioral entities generally are interested in maximizing the profit or social welfare, and the utility functions can be well defined for solving optimiza-tion problem. In addioptimiza-tion, the prescriptive soluoptimiza-tions feature the decision-aid for helping such entities to overcome the inefficacy of current operation and move towards more efficient one as long as they acquire sufficient resource, which is also more predictable behavior. On the other hand, it is also more manageable for explicit behavioral models to describe the decision-making process at organizational level, since in such a way that the organizational operations appears to be the key predictor for explaining the overall behav-ioral pattern (Krueger, 2002). Instead, when the scope is narrowed down to an individual decision-maker, the number of uncertainty factors in deter-mining the predictant behavior increases dramatically, and the possibility of deviating from the rational performance is high (e.g., Baron, 1998; Nicker-son, 1998; Tversky and Kahneman, 1974).

1.4

Objective and organization of the dissertation

The present Ph.D dissertation offers several contributions to CHNS stud-ies, starting from an innovative modeling framework of constructing a fully coupled CHNS, followed by a series of demonstrations to assess the po-tential of proposed framework. In particular, we will focus on a coupled human-water system as subset of CHNS, namely Lake Como (Italy) water system, where the natural subsystems are lake and agricultural irrigation ar-eas, while the human subsystems are composed of lake operator operating the lake, and farmer stakeholders engaged in agricultural practices, respec-tively. By applying an ABM we construct a fully coupled model for Lake Como CHNS, denoted as DistriLake framework, in which the hydrological sphere are described by traditional processed based models, while the hu-mans’ behaviors are represented by implicit/normative, optimization based behavioral models.

The aim of this dissertation is to demonstrate that, through a number of examples of applications, an ABM framework with behavioral modeling approach can be a valuable tool for the following purposes:

1. to describe the current and projected situations in the system as a con-sequence of changing boundary conditions;

2. to explore the vulnerability of the system in facing the deep uncertain scenarios, discover any asymmetry of performance due to the hetero-geneity of stakeholders’ characteristics, and assess the potentials of different adaptation alternatives;

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3. to assess the operational value of technological improvement at end-users’ perspective accounting for their behavioral pattern and risk at-titudes.

This dissertation will be organized as follows. Chapter 2 first provides a comprehensive description about Lake Como water system as our pilot study area, based on which the DistriLake framework has been constructed and applied. Meanwhile, in the same chapter we will explain the architec-ture of DistriLake and its constituent parts, namely the models of natural processes and agents’ behaviors, respectively.

Chapter 3 presents the first application, in which we show how the pro-posed DistriLake framework is able to capture the current situation of the Lake Como CHNS, and to provide insights about the vulnerability of the system in projected future, when changing climate boundary conditions may impose severe damages on systems’ performance. In addition, we assess different policy alternatives to tackle with the projected drought sce-narios, and show how strategical and timely interactions (co-adaptation) between the lake operator and farmers is promising in enhancing the effi-ciency of agricultural water management practices and foster the crop pro-duction, as well as to mitigate climate change adverse impacts. The work derived from this chapter has been under the review for Water Resource Research.

In Chapter 4, we advance traditional robustness decision analysis with Distrilake modeling framework, by replacing the centralized social planner with a bottom-up, agent-based approach. With artificially generated deep uncertain scenarios representing co-varying climate and socio-economic conditions, we evaluate the robustness of the Lake Como CHNS at sys-tem and agent level, respectively, as well as possible adaptation options (e.g., improved irrigation efficiency or changes in the lake operating rules). We show how the DistrkLake framework enables a more explicit explo-ration of the major stressors to the system, as well as potential inequities and asymmetries in the distribution of the system-wide benefit due to the stakeholders’ context. The contribution from this work is under preparation and will be delivered as a journal paper for Global Environmental Change. In Chapter 5, we apply the DistriLake framework to assess a number of state-of-the-art long-term weather forecast products. We focus on examin-ing the value of long-term weather forecast as a potential tool to improve farmers’ performance, in which the DistriLake framework serves as a valu-able tool to offer an additional dimension regarding to the value of forecast products, by embedding forecast information into farmers’ behavioral

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1.4. Objective and organization of the dissertation els. This allows a more comprehensive assessment of the forecast products mediated by the end-users’ perspective, including farmers’ risk attitudes and behavioral pattern. We show that, given the current skill of long-term forecast, not all state-of-the-art forecast products may generate beneficial effects on farmers’ performance, as the ineligible forecast errors may be amplified due to farmers’ decision-making processes and risk attitudes. The contribution will be organized in a paper for Journal of Agricultural Water Management.

Final remarks and the suggested directions for future improvements will be given Chapter 6.

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CHAPTER

2

Materials and Methods

2.1

Study site

In this dissertation, the three methodological contributions were developed for the same study site, namely Lake Como water system, Italy. This is a complex water system which can be represented by three interrelated com-partments (see Figure 2.1): the upper watershed, the regulated lake and the irrigation areas in the lower part, where the Muzza irrigation district will be used as a paradigmatic example. This system can be considered as a typical CHNS, characterized by multiple stakeholders and decision makers, and it is undergoing a transition in hydrological conditions with increasingly se-vere droughts challenging current water management.

2.1.1 Lake Como

Lake Como is the third largest Italian lake located in northern Italy close to Switzerland (see Figure 2.1), and receives water from a catchment of around 4,500 km2_{characterized by a highly varying terrain elevation, which}

provides a huge hydro-power potential exploited through a series of small to medium artificial reservoirs for a total storage capacity of 545 Mm3_.

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Chapter 2. Materials and Methods

Since 1946, Lake Como is regulated by Consorzio dell’Adda1, a consor-tium of downstream stakeholders mostly composed by farmers but also hydro-power companies and industries. Lake Como has an active storage of around 250 Mm3 _{regulated through a dam on the effluent Adda river}

(see Figure 2.1) and is operated for multiple purposes, including a num-ber of run-of-river hydro-electric power plants and several large agricul-tural districts. Besides water supply, the regulation of the lake aims urban flood protection of the lake shores, particularly in Como City. In fact, there was reported a progressively sinking of main square in Como City and sur-rounding areas since 1960’s, which poses substantial flooding risk during high inflow periods.

The hydrological regime is influenced by both spring snowmelt and pre-cipitation, resulting into a bi-modal peak (see Figure 2.2 green and blue plots): one more pronounced peak corresponding to the snow-melt season, in late spring, and a smaller but more variable one produced by autumn rains. The spring peak (from May to July) is the most important contri-bution to the seasonal storage, which is released during the summer, when the agricultural water demand is high (violet plot in Figure 2.2). While the upstream reservoirs are operated by private power companies, the lake regulator has to consider both water allocation and coastal flood protection characterized by two conflicting release strategies: according to its water allocation target, the Consorzio dell’Adda should exploit the potential ca-pacity of the reservoir for moving water volumes from the wet to the dry season, particularly in exceptional dry summers, as observed in 2003, 2005, 2012 and 2015. On the other hand, an appropriate operating strategy to avoid floods in the urban areas along the lake shore would require to keep a low water level in the wet season in order to be able to receive high in-flows and to buffer potential extreme events. The lake release decision must therefore trade-off among the fast-dynamic target of protecting the coastal population from flash floods while at the same time competing seasonal goal of providing appropriate supply to the irrigation districts (e.g., Anghi-leri et al., 2012; Castelletti, Galelli, et al., 2010; Galelli and Soncini-Sessa, 2010).

While water availability has not been an issue for many years, in re-cent decade more drought events have been reported. Figure 2.3 visualizes the trend in the inflows observed over the last 60 years using the a tool called Moving Average over Shifting Horizon (MASH; Anghileri, Pianosi, and Soncini-Sessa 2014), a trend analysis technique that aims to identify non-stationary changes in hydro-climatic variables. As shown in Figure

1_{See online website: http://www.addaconsorzio.it.}

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2.1. Study site

elevation in [m] 4000 8

other irrigated area Muzza irrigation district

water bodies river networks

0 12.5 25 50 75 Kilometers

Ü

Adda River Como dam Milan Po River catchment reservoir irrigation district

Figure 2.1: Map of the study area: the upstream catchment, Lake Como, Adda River and the Muzza irrigation district. The schematic diagram of the entire system is shown on the top right corner with arrows indicating the direction of the water flow.

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Chapter 2. Materials and Methods water volume (x10 6 m 3) 0 500 1000 inflow (m 3/s) 0 200 400 600 800

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

inflow

nominal water demand snow water equivalent

regulated storage natural storage

Figure 2.2: Hydrological features of Lake Como estimated as the mean of statistics be-tween 2006 - 2013, and the nominal water demand trajectory is given by historical regulation policy. Notice that the natural storage estimates are obtained by regression method assuming no regulation imposed.

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2.1. Study site 2.3, there is a clear decreasing trend of inflow during the late spring and summer periods, which are the most critical for irrigated agriculture. In fact, in 2003 , 2005, 2006 and 2007 the system experienced severe drought events, which caused great losses to the agriculture (Anghileri, Pianosi, and Soncini-Sessa, 2014). If this tendency continues over next years, the sys-tem is likely to lose its designed functionality, and adoption of adaptation strategies will be indispensable.

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 50 100 150 200 250 300 Inflo w [m 3/s] [ 1946 - 1966 ] [ 1990 - 2010 ]

Figure 2.3: Trend analysis of the daily inflows over the time horizon 1946-2010, with colors of each line representing the average extra-annual inflow series from every 20-year moving average estimates.The average daily inflow is computed by means of a moving window that includes data over consecutive days in the same year and over the same days in consecutive years, with the horizon of consecutive years progressively shifted ahead to identify long-term trends.

2.1.2 Muzza agricultural district

The release from Lake Como serves a group of downstream cultivated areas totalling up to about 1,400 km2_{through the Adda River, the fourth longest}

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this dissertation we focus on the Muzza district, located southeast of the city of Milan (see Figure 2.1). This district was selected because it is the largest among the irrigation districts served by the Adda River (about 700 km2_{), and the one with the largest water concession. The Muzza canal}

derives water from the Adda river at Cassano d’Adda with 37 intakes, and the entire network is composed by open earth canals. In addition, flows along the canal can reach 110 m3/s at full capacity, which is about 70% of average daily water release from Lake Como. In fact, the Muzza canal is both the largest irrigation canal by capacity and the first artificial canal built in Norther Italy.

The average annual rainfall over the last 40 years (1960-2000) is about 900 mm. Major cultivated crops are maize (ca. 50% of the surface) and temporary grasslands (ca. 25% of the surface); minor crops include rice, soybean, wheat, tomato, and barley. The composition and the proportion of such land use persisted for many years with little change. An extensive irrigation network (more than 4,000 km in total length) feeds a number of irrigation units, each including a number of farms, receiving a continuous water supply, while traditional and low efficiency irrigation methods, e.g., border irrigation and flooding, are predominantly adopted in the area. Wa-ter distribution to the individual farms is on rotation with constant flow, i.e., each farmer receives his/her share of water by turn with approximately every 10-15 days.

Irrigation water supply from upstream Lake Como is the main determi-nant of high crop productivity in this area, with yields of 12 t/ha for maize and 50 t/ha for temporary grasslands (Pieri and Pretolani, 2013). On the other hand, the percolation of irrigation water also acts as a vital source of recharge for the underlying aquifer system and provides a significant return flow through drainage to the boundary rivers.

Historically, water availability has not been a major limiting factor for the development of regional water-related activities. As a consequence, his-torical management practices, even mostly uncoordinated, have generally satisfied all the competing demands and the opportunity of improving the system performances through better understanding and management of the system has long been overlooked. However, over the last decade Muzza has observed an increasing number of drought events characterized with low precipitation at local scale and insufficient water supply due to the de-creased inflow rate in the upstream lake, the pan-Europe drought in 2003 for instance (Ciais et al., 2005). Unavoidably, loss of crop production will be detriment to the profitability of agricultural activities, posing more burdens on the local farmers who are very vulnerable to the extreme climate

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2.2. The DistriLake framework tions even with the subsistence of European Union (Ackrill, 2000). Indeed, the historical land use already shows that in recent years more maize fields are given up and turned into temporary grassland for feeding livestock. The situations are akin to that in Lake Como upstream, both of which are con-sidered as a part of the symptom of climate change (Brunetti et al., 2006). Therefore, farmers are urged to make adaptive moves by, e.g., planning crop decision wisely or adopt new technologies to improve their robustness.

Yet, the large number of physical and regulatory constraints posed by the complexity of the regional socio-economic system restricts the adaptive capacity of the system At present, on the water supply side, Lake Como is primarily operated for water supply downstream and flood control on the lake shores. The downstream nominal water demand (see Figure 2.2) is given by the aggregated water rights of the different water users as defined seventy years ago, while the quota of water supply assigned to each water users is also fixed and expected to hardly change in near future. On the wa-ter demand side, the cropping patwa-terns reflect the historical one, with most of the farmers cultivating maize and temporary grasslands. Although the persistence of the current settings in this system might suggest the success of historical operations, there is an increasing concerns about the system falling into a rigidity trap (Gunderson, 2001) as the past success may de-ceive the authoritative decision-maker from incoming challenges of the cli-mate change, and hence introducing the policy inertia or myopia that will hinder or degrade the value of adaptations to the future changing conditions.

2.2

The DistriLake framework

As anticipated in the introduction, we use an ABM framework to describe the Lake Como CHNS. An ABM framework, or Multi-Agent System (MAS; e.g., Shoham and Leyton-Brown 2009; Wooldridge 2009) offers several ad-vantages with respect to other modeling approaches such as traditional sys-tem dynamics models or bayesian networks (Bonabeau, 2002; Bousquet and Le Page, 2004): i) it provides a more natural description of a system, especially when it is composed of multiple, distributed, and autonomous decision-makers, ii) it relaxes the hypothesis of homogeneity in a popu-lation of actually heterogeneous individuals, iii) it allows an explicit rep-resentation of spatial variability, iv) it captures emergent global behaviors resulting from local interactions, and v) it allows a better representation of CHNS.

We denote this ABM framework as ‘DistriLake’ to honor the distributed nature of natural environment and behavioral agents in Lake Como CHNS.

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Figure 2.4 (top panel) shows the conceptual representation of Lake Como CHNS, with highlight of the components in human and natural systems, respectively. Recalling that the natural sphere of our system includes the catchment, Lake Como reservoir and the irrigation districts, connected by the water flow. On the societal side there is a lake operator representing an administrative decision-maker, and a number of farmer agents within the Muzza irrigation district as active stakeholders, who may periodically express their requirements and needs to the lake operator, meanwhile inter-act with each other from time to time. Moreover, the irrigation district and farmers as the recipient of water supply are denoted as demand side, while the upstream reservoir, the catchment and lake operator represent the supply side. Last but not least, the feedback between the natural and human sys-tem is realized as agents from societal syssys-tem are experiencing, observing and/or predicting the environmental conditions (water level, flood events, etc.) for decision-making, while the generated actions eventually impose the modifications on states of environment, e.g., lake level, and land use pattern.

As reflective to the settings of Lake Como CHNS, Figure 1.1 (bottom panel) shows the architecture of our DistriLake framework, with the natural processes described by a series of interconnected process-based modules, and human system being composed of two optimization schemes, which are essentially our implicit behavioral models. More descriptions about the modeling details of supply and demand side will be given in Section 2.2.2 and 2.2.3, respectively.

One main contribution of this work is about how we blend the agents’ behavioral models into the ABM framework. Many environmental stud-ies using ABM framework still follow the traditional routine in which the agents’ behaviors are modeled explicitly (i.e., descriptive approach; see e.g., Acosta-Michlik and Espaldon, 2008; Barreteau and Bousquet, 2000; Naivinit et al., 2010). However, it has been mentioned before (see Chap-ter 1.3) about the challenges in describing humans’ behavior explicitly, in which the descriptive behavioral models usually takes a simplistic form, such as if-then rules (e.g., Kanta and Zechman, 2014; Le, Seidl, and Scholz, 2012), and are often coarsely crafted. Moreover, the validation of these rules is generally a challenge given the presence of large number of agents (Ligtenberg et al., 2010).

In this work we use implicit (i.e. normative approach) model. We as-sume agents involved in the studies are rational with a given known util-ity function, and we determine their decisions by solving an optimization problem (Giuliani2013zambezi; e.g., Giuliani, Castelletti, Amigoni, et al.,

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2.2. The DistriLake framework Natural System Catchment Lake Como Muzza irrigation area Human System farmers laker operator supply side demand side Lake Como CHNS

Natural System Human System

supply side demand side water supply water demand HBV model dynamic mass balance heat unit water balance crop growth Crop Model DistriLake framework lake state control

crop and land states land use decisions

Figure 2.4: The schematic representation of Lake Como CHNS including each subsys-tem’s components (top panel); and the realization with DistriLake framework with the highlight of each module describing the natural processes, and the implicit behav-ioral models representing the agents’ decision making process and interactions (bot-tom panel). Notice that the external disturbance and systems’ responses are not shown here.

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2015; Shoham and Leyton-Brown, 2009; Yang, Cai, and Stipanovi´c, 2009). The rationale of using the implicit approach in our case study is based on two considerations. First, despite the fact that human full rationality assumption is questionable and frequently criticized (e.g., Baron, 1998; Nickerson, 1998), which is true looking at the individual behaviors where all the factors (psychological and/or sociological factors, etc.) can bias the rational hypothesis, most probably looking at institutional stakehold-ers representing group of interests (e.g., the lake operator as representative of Consorzio dell’Adda’s interests, or a group of farmers as association of farmers’ interests), the rationality hypothesis can be deemed to be more valid. Second, we want to describe the systems evolving in time driven by changing hydro-climatic and socio-techno-economical drivers using mod-eling based approach with reasonably prediction capability. This is diffi-cult to be achieved with explicit behavioral models, where the behavioral parameters are inferred and calibrated from observation data, and hence would require an extensive validation to justify the its applicability across the new observations that might be very different from the ones in the cali-bration stage. Instead, an implicit approach makes a milder assumption, as-suming that no matter the behavioral parameters the agent will derive, then from optimization the emphasis moves to the unity function. Of course, the utility function may also change in time, but the changes will be less essential given the consideration as mentioned above.

In the following session we will first discuss about the realization of lake operator and farmer agents’ behaviors using implicit modeling, and how their interaction can be described by cross-conditioning their behavioral models. Then each component in DistriLake framework will be covered thoroughly.

2.2.1 The architecture of human components in DistriLake

Driven by aforementioned concerns, in our DistriLake framework the agents are not associated one-to-one to single real human entities but to the group of them. Specifically, we consider a lake operator agent as the abstraction of the actual lake management committee, and 36 farmer agent representing an association of real farmer groups. The aggregated or abstracted agents can therefore be considered analogous to commercial companies who are more likely to be economic pursuers, and the rational assumptions on the agents are more valid. Each farmer agent differs not only in the geograph-ical attributes (e.g., elevation, coordinates, etc) but also in the soil fertility, farm size and, in particular, water allocation right as defined by the local

Advancing coupled human-water systems analysis by agent-based modeling

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Abstract

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Contents

Acronyms

Notations

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Introduction

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CHNS

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1.3

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CHAPTER

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Materials and Methods

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2.2