Contemporary Decision Methods for Agricultural, Environmental, and Resource Management and Policy

Johnson, David R.; Geldner, Nathan B.

doi:10.1146/annurev-resource-100518-094020

Cited by 18 publications

(14 citation statements)

References 111 publications

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“…Scholars have certainly made significant advances in understanding and modeling uncertainty in the Anthropocene System using a variety of methods that take seriously the complex and adaptive dynamics of the Anthropocene (80,226,296,(336)(337)(338). Despite these advances, sustainability science still has only a modest ability to predict future shocks and surprises, let alone recommend optimal development pathways over the multi-generational timescales relevant to sustainability (275).…”

Section: Confronting Uncertaintymentioning

confidence: 99%

Sustainability Science: Toward a Synthesis

Clark

Harley

2020

Annu. Rev. Environ. Resour.

259

163

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This review synthesizes diverse approaches that researchers have brought to bear on the challenge of sustainable development. We construct an integrated framework highlighting the union set of elements and relationships that those approaches have shown to be useful in explaining nature–society interactions in multiple contexts. Compelling evidence has accumulated that those interactions should be viewed as a globally interconnected, complex adaptive system in which heterogeneity, nonlinearity, and innovation play formative roles. The long-term evolution of that system cannot be predicted but can be understood and partially guided through dynamic interventions. Research has identified six capacities necessary to support such interventions in guiding development pathways toward sustainability. These are capacities to ( a) measure sustainable development, ( b) promote equity, ( c) adapt to shocks and surprises, ( d ) transform the system into more sustainable development pathways, ( e) link knowledge with action, and ( f ) devise governance arrangements that allow people to work together in exercising the other capacities. Expected final online publication date for the Annual Review of Environment and Resources, Volume 45 is October 19, 2020. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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Section: Confronting Uncertaintymentioning

confidence: 99%

Sustainability Science: Toward a Synthesis

Clark

Harley

2020

Annu. Rev. Environ. Resour.

259

163

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“…DMDU approaches are well‐suited for making decisions regarding urban infrastructure to manage flooding under climate change because there are multiple facets of deep uncertainty associated with the future of flood management and urban infrastructure (Johnson & Geldner, 2019). Despite advances in modeling techniques, projections of precipitation and resulting pluvial flooding are deeply uncertain (Dittes et al., 2018; Lopez‐Cantu et al., 2020; Rözer et al., 2019).…”

Section: Dmdu Approachesmentioning

confidence: 99%

“…A number of literature reviews have identified DMDU approaches used to make decisions about flood and water management infrastructure (Dittrich et al., 2016; Johnson & Geldner, 2019; Marchau et al., 2019). Applications span a number of different infrastructural adaptations, including reservoirs or water management (e.g., Culley et al., 2016; Lempert & Groves, 2010; Tingstad et al., 2014), dikes or levees (e.g., Ceres et al., 2019; Ciullo et al., 2019; Raso, Jan, & Timmermans, 2019), dams or barrages (e.g., Fu et al., 2015; Shortridge & Guikema, 2016), infrastructure retrofits (e.g., Lempert, Kalra, et al., 2013; Radhakrishnan, Nguyen, et al., 2018; van Veelen et al., 2015) and land use change or green infrastructure (e.g., Fischbach et al., 2017, 2020; Tariq et al., 2017).…”

Section: Dmdu Approachesmentioning

confidence: 99%

“…For example, stakeholders in Florida deciding on the optimal coastal protection for urban settlements may choose the strategy with the lowest cost across all scenarios (Genovese & Green, 2015). While this is a dominant metric to interpret and communicate risk and resilience, the system must be well understood and uncertainties must be well characterized to represent robustness using this concept (Johnson & Geldner, 2019). Multi-objective analyses, such as those used in MORDM, commonly use a weighting scheme or use Pareto optimization to evaluate over competing objectives.…”

Section: Dmdu Metrics For Performancementioning

confidence: 99%

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A Review of Decision Making Under Deep Uncertainty Applications Using Green Infrastructure for Flood Management

Webber

Samaras

2022

Earth's Future

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Decision making under deep uncertainty (DMDU) approaches are well‐suited for making decisions about infrastructure to manage flooding exacerbated by climate change. One important system for climate resilience and flood management is green infrastructure, which refers to a network of natural and semi‐engineered areas that provides ecosystem functions. Green infrastructure is often characterized as a low‐regret strategy with multiple co‐benefits under uncertainty. These attributes enable green infrastructure to be an important adaptation strategy under DMDU frameworks for flood management. However, DMDU analyses that include green infrastructure are still relatively limited, perhaps due to computational or modeling complexity and other barriers. This paper identifies and reviews publications in the flood management literature that use DMDU frameworks and refer to green infrastructure adaptation strategies, in order to identify trends and inform future research. The reviewed publications are categorized according to a variety of performance metrics, climate change scenarios, DMDU metrics, and hydrologic modeling techniques, and represent several adaptation strategies applied to case studies on five continents using a range of data sources and assumptions. This paper highlights a number of solutions that can be employed to facilitate additional research at the intersection of these fields. Primary among these is the transparent documentation and use of open source models, methods, and data. Future research should also focus on communication among different stakeholders, particularly in ensuring definitions, assumptions, and data requirements are clear. These partnerships can facilitate effective application of robust strategies such as green infrastructure for urban adaptation to the effects of climate change.

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“…As noted by Ben-Haim (2012), the performance of a system under "most likely" conditions is a "distinct and independent attribute" from the sensitivity of its performance to deviations from those conditions. Risk scholars working in complex systems (including flood risk management) emphasize the need to design systems that are robust to uncertainty, arguing for satisfying performance over a wide range of scenarios rather than optimal performance in the most likely scenario (Lempert et al, 2003;Keller et al, 2008;Cox, 2012;Aven, 2013;Johnson and Geldner, 2019). In the context of managing flood risk, research has examined the impact of uncertainty in topography, roughness coefficients, and flow data on inundation maps (Jung and Merwade, 2012), but less attention has been given to uncertainty in rainfall.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic rainfall generator for tropical cyclones affecting Louisiana

Villarini

Zhang

Miller

et al. 2021

Intl Journal of Climatology

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This study focuses on the development of a probabilistic rainfall generator for tropical cyclones (TCs) affecting Louisiana. We consider 12 storms making landfall along the Louisiana coast during 2002-2017 and generate ensembles of high-resolution (~5 km and 20 min) TC-rainfall fields for each storm. We develop a data-driven multiplicative model, relating observed rainfall to the rainfall obtained from a parametric TC rainfall model (Interagency Performance Evaluation Task Force Rainfall Analysis [IPET]) through the product of a deterministic and a stochastic component; the former accounts for rain-dependent biases, while the latter for the stochastic nature of the rainfall processes. As a preliminary step, we describe the overall bias of the IPET model as a function of total TC rainfall within the state and maximum wind speed at landfall. We then estimate the rain-dependent bias using a cubic spline. Finally, we characterize the random errors in terms of their probability distribution and spatial correlation. We show that the marginal distribution of the logarithm of the random errors can be described by a mixture of four Gaussian distributions, and its spatial correlation is estimated based on the nonparametric Kendall's τ. We then present a methodology to generate ensembles of random fields with the specified statistical properties. Here, the generation of probabilistic rainfall comes from the statistical modelling of the uncertainties between IPET rainfall and observations. While these results are valid for Louisiana and the IPET model, the methodology can be generalized to other parametric rainfall models and regions, and it represents a viable tool to improve our quantification of the risk associated with TC rainfall.

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Contemporary Decision Methods for Agricultural, Environmental, and Resource Management and Policy

Cited by 18 publications

References 111 publications

Sustainability Science: Toward a Synthesis

Sustainability Science: Toward a Synthesis

A Review of Decision Making Under Deep Uncertainty Applications Using Green Infrastructure for Flood Management

Probabilistic rainfall generator for tropical cyclones affecting Louisiana

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