Exploratory modeling for analyzing coupled human-natural systems under uncertainty

Moallemi, Enayat A.; Kwakkel, Jan H.; Haan, Fjalar J. de; Bryan, Brett A.

doi:10.1016/j.gloenvcha.2020.102186

Cited by 98 publications

(77 citation statements)

References 182 publications

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“…Bankes (1993) moved away from enumerating the number of use cases, and simply spoke of exploratory modeling. Since these formative ideas from the early 1990s, a large body of literature on exploratory modeling has emerged (see, e.g., Kwakkel, 2017;Moallemi et al, 2020 for detailed overviews). Perhaps most importantly, exploratory modeling has become one of the cornerstones of the field of decision-making under deep uncertainty (Marchau et al, 2019).…”

Section: E Xplor Atory Modeling Is Alre Ady Well E S Tab Lis Hedmentioning

confidence: 99%

Reaction: A commentary on Lustick and Tetlock (2021)

Kwakkel

Auping

2021

Futures & Foresight Science

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Lustick and Tetlock (2021) argue for the use of theory-guided simulation for aiding geopolitical planning and decision-making. This is a very welcome contribution, as in our experience, geopolitical analyses are often qualitative in nature and prone to group think. The complexities of geopolitical issues, however, really call for the use of theory-guided simulations. These issues are too complex for mental simulation (Atkins et al., 2002;Sterman, 1989Sterman, , 1994. Dynamic simulations, if properly grounded in appropriate theories and wellmotivated assumptions, can derive the possible dynamics from interacting nonlinear processes, and thus aid human reasoning about system behavior (Sterman, 2002). Moreover, since geopolitical issues are subject to uncertainty, scarce data, and conflicting information, using an ensemble modeling approach is appropriate. An ensemble

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Section: E Xplor Atory Modeling Is Alre Ady Well E S Tab Lis Hedmentioning

confidence: 99%

Reaction: A commentary on Lustick and Tetlock (2021)

Kwakkel

Auping

2021

Futures & Foresight Science

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“…Exploration: models are used for exploratory analysis, to explore different possibilities within the model space (Moallemi et al, 2020). That is, exploring a range of possible:…”

Section: Motivationmentioning

confidence: 99%

Evaluating Catchment Models as Multiple Working Hypotheses under Uncertainty

Khatami

2020

Preprint

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Catchment models are conventionally evaluated in terms of their response surface or likelihood surface constructed from model runs using different sets of model parameters. Model evaluation methods are mainly based upon the concept of the equifinality of model structures or parameter sets. The operational definition of equifinality is that multiple model structures/parameters are equally capable of producing acceptable simulations of catchment processes such as runoff. Examining various aspects of this convention, in this thesis I demonstrate their shortcomings and introduce improvements including new approaches and insights for evaluating catchment models as multiple working hypotheses (MWH). First (Chapter 2), arguing that there is more to equifinality than just model structures/parameters, I propose a theoretical framework to conceptualise various facets of equifinality, based on a meta-synthesis of a broad range of literature across geosciences, system theory, and philosophy of science. I distinguish between process-equifinality (equifinality within the real-world systems/processes) and model-equifinality (equifinality within models of real-world systems), explain various aspects of each of these two facets, and discuss their implications for hypothesis testing and modelling of hydrological systems under uncertainty. Second (Chapter 3), building up on this theoretical framework, I propose that characterising model-equifinality based on model internal fluxes — instead of model parameters which is the current approach to account for model-equifinality — provides valuable insights for evaluating catchment models. I developed a new method for model evaluation — called flux mapping — based on the equifinality of runoff generating fluxes of large ensembles of catchment model simulations (1 million model runs for each catchment). Evaluating the model behaviour within the flux space is a powerful approach, beyond the convention, to formulate testable hypotheses for runoff generation processes at the catchment scale. Third (Chapter 4), I further explore the dependency of the flux map of a catchment model upon the choice of model structure and parameterisation, error metric, and data information content. I compare two catchment models (SIMHYD and SACRAMENTO) across 221 Australian catchments (known as Hydrologic Reference Stations, HRS) using multiple error metrics. I particularly demonstrate the fundamental shortcomings of two widely used error metrics — i.e. Nash–Sutcliffe efficiency and Willmott’s refined index of agreement — in model evaluation. I develop the skill score version of Kling–Gupta efficiency (KGEss), and argue it is a more reliable error metric that the other metrics. I also compare two strategies of random sampling (Latin Hypercube Sampling) and guided search (Shuffled Complex Evolution) for model parameterisation, and discuss their implications in evaluating catchment models as MWH. Finally (Chapter 5), I explore how catchment characteristics (physiographic, climatic, and streamflow response characteristics) control the flux map of catchment models (i.e. runoff generation hypotheses). To this end, I formulate runoff generating hypotheses from a large ensemble of SIMHYD simulations (1 million model runs in each catchment). These hypotheses are based on the internal runoff fluxes of SIMHYD — namely infiltration excess overland flow, interflow and saturation excess overland flow, and baseflow — which represent runoff generation at catchment scale. I examine the dependency of these hypotheses on 22 different catchment attributes across 186 of the HRS catchments with acceptable model performance and sufficient parameter sampling. The model performance of each simulation is evaluated using KGEss metric benchmarked against the catchment-specific calendar day average observed flow model, which is more informative than the conventional benchmark of average overall observed flow. I identify catchment attributes that control the degree of equifinality of model runoff fluxes. Higher degree of flux equifinality implies larger uncertainties associated with the representation of runoff processes at catchment scale, and hence pose a greater challenge for reliable and realistic simulation and prediction of streamflow. The findings of this chapter provides insights into the functional connectivity of catchment attributes and the internal dynamics of model runoff fluxes.

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“…Systems models are often used to better deal with the complexity and uncertainty inherent in understanding the long-term progress towards interacting goals and their synergies and trade-offs (3). Several specific modelling approaches, such as input-output analysis (4), computational general equilibrium (5), system dynamics (6), and agent-based modelling (7), as well as broader model-based paradigms such as integrated assessment modelling (8), transitions modelling (9), and exploratory modelling (10)(11)(12), have been used to systematically analyse sustainability in various ways.…”

Section: Introductionmentioning

confidence: 99%

A review of systems modelling for local sustainability

Bertone¹,

Eker²,

Gao³

et al. 2022

Preprint

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The 17 sustainable development goals (SDGs) represent a holistic and ambitious agenda for transforming the world towards societal well-being, economic prosperity, and environmental protection. Achieving the SDGs is, however, challenged by the performance of interconnected sectors and the complexity of their interactions which drive non-linear system responses, tipping points, and spillover effects in progress towards sustainability. Systems modelling, as an integrated way of thinking about and modelling multisectoral dynamics, can help explain how feedback interactions within and among different sectors can lead to broader system transformation and progress towards the SDGs. Here, we review how system dynamics, as one of prominent systems modelling approaches, can inform and contribute to sustainability research and implementation, framed by the SDGs. We systematically analyse 357 system dynamics studies undertaken at the local scale (e.g., communities, cities), published between 2015 (i.e., inception of the SDGs) and 2020. We analyse the studies to illuminate strengths and limitations in four key areas: diversity of scope; interdisciplinarity of the approaches; the role of stakeholder participation; and the analysis of SDG interactions. We assess strengths and limitations of the reviewed literature, and propose related research priorities in four areas: better consideration of societal aspects of sustainable development in modelling efforts; integrating with new interdisciplinary methods; improving genuine stakeholder engagement; a more in-depth analysis of feedback interactions underlaying the SDGs (i.e., synergies and trade-offs). The review provides a comprehensive knowledge base of existing works that can guide future applications and set a new research agenda for filling existing gaps in the application of systems modelling to sustainability.

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Exploratory modeling for analyzing coupled human-natural systems under uncertainty

Cited by 98 publications

References 182 publications

Reaction: A commentary on Lustick and Tetlock (2021)

Reaction: A commentary on Lustick and Tetlock (2021)

Evaluating Catchment Models as Multiple Working Hypotheses under Uncertainty

A review of systems modelling for local sustainability

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