When can decision analysis improve climate adaptation planning? Two procedures to match analysis approaches with adaptation problems

Shi, Rui; Hobbs, Benjamin F.; Jiang, Huai

doi:10.1007/s10584-019-02579-3

Cited by 16 publications

(8 citation statements)

References 30 publications

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“…(2019) proposed deeply uncertain pathways for multi-city regional water supply infrastructure investment and portfolio management. Shi et al. (2019) proposed two adaptive screening procedures to identify when comprehensive decision analysis methods, such as DMDU methods introduced here, should be implemented.…”

Section: Regional Sustainability and Decision Making Under Deep Uncermentioning

confidence: 99%

A perspective on the role of uncertainty in sustainability science and engineering

Diwekar

Amekudzi-Kennedy

Bakshi

et al. 2021

Resources, Conservation and Recycling

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Section: Regional Sustainability and Decision Making Under Deep Uncermentioning

confidence: 99%

A perspective on the role of uncertainty in sustainability science and engineering

Diwekar

Amekudzi-Kennedy

Bakshi

et al. 2021

Resources, Conservation and Recycling

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“…A multi-scenario analysis with adaptive options including a decision-tree analysis, real options analysis (Woodward et al, 2014;Sturm et al, 2017), dynamic adaptive policy pathways, and multi-stage stochastic programming (MSP) has been considered. In this approach, adaptation strategies can be modified dynamically and progressively based on updated information (Shi et al, 2019). A Bayesian analysis is widely used in the multiscenario analysis with adaptive options.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Decision Model for Optimum Investment and Design of Low-Impact Development in Urban Stormwater Infrastructure and Management

Wang¹,

Zhang²,

Zheng³

et al. 2021

Front. Environ. Sci.

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Uncertainties concerning low-impact development (LID) practices over its service life are challenges in the adoption of LID. One strategy to deal with uncertainty is to provide an adaptive framework which could be used to support decision-makers in the latter decision on investments and designs dynamically. The authors propose a Bayesian-based decision-making framework and procedure for investing in LID practices as part of an urban stormwater management strategy. In this framework, the investment could be made at various stages of the service life of the LID, and performed with deliberate decision to invest more or suspend the investment, pending the needs and observed performance, resources available, anticipated climate changes, technological advancement, and users’ needs and expectations. Variance learning (VL) and mean-variance learning (MVL) models were included in this decision tool to support handling of uncertainty and adjusting investment plans to maximize the returns while minimizing the undesirable outcomes. The authors found that a risk-neutral investor tends to harbor greater expectations while bearing a higher level of risks than risk-averse investor in the VL model. Constructed wetlands which have a higher prior mean performance are more favorable during the initial stage of LID practices. Risk-averse decision-makers, however, could choose porous pavement with stable performance in the VL model and leverage on potential technological advancement in the MVL model.

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“…Firstly, this generates region speci c knowledge for policy and adaptation measures or priorities in relation to crop and climate change (Challinor et al, 2009;Leng, 2017). For instance, agronomic management such as planting dates, varieties selection, irrigation and residue management have been evaluated as climate change adaptation measures in speci c regions (Brüssow et al, 2019;Challinor et al, 2014;Karapinar and Özertan, 2020;Shi et al, 2019). Secondly, insight in the impacts of climate change on water limited yield is also relevant as it is often used to derive rainfed target yield levels (mostly set at 80% of Yw) for use in fertilizer recommendations (Sherene et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…In addition to nutrient management, it is also important to evaluate the possible adaptation options such as altering planting dates, varieties, fertilizer application, irrigation and other agronomic management practices, given their potential to counteract climate induced changes in crop yield (Brüssow et al, 2019;Challinor et al, 2014;Knox et al, 2012;Shi et al, 2019). Temperatures and precipitation changes in the short-term can be mitigated by adjusting planting dates and switching varieties as adaption (Liu et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Climate change impacts on rainfed maize yields in Zambia under conventional and optimized crop management

Siatwiinda

Supit

Hove

et al. 2021

Preprint

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Maize production in Zambia is characterized by significant yield gaps attributed to nutrient management and climate change threatens to widen these gaps unless agronomic management is optimized. Insights in the impacts of climate change on maize yields and the potential to mitigate negative impacts by crop management is currently lacking for Zambia. Using five Global Circulation models and the WOFOST crop model, we assessed expected climate change and the impacts on maize yields at a 0.5° × 0.5° spatial resolution for RCP 4.5 and RCP 8.5 scenarios. Impacts were assessed for two future periods (i.e. near future: 2035–2066 and far future: 2065–2096) in comparison with a reference period (1971–2001). The average surface temperature and summer days (above 30°C) are projected to increase strongly in the southern and western regions. Precipitation is expected to decline, except in the northern regions while the number of wet days decline everywhere, indicating a shortening growing season. The risk of crop failure in western and southern regions increases due to dry spells and heat stress while crops in the northern regions will be threatened by flooding or waterlogging due to heavy precipitation. The simulated decline in the water limited and water- and nutrient- limited maize yields varied from ca 15–20% in the near future and from ca 20–40% in the far future, mainly due to the expected temperature increases. Optimizing management by adjusting planting dates and maize varieties can counteract these impacts by 6–29%. Quantitatively, the existing gaps between water limited yields and nutrient limited maize yields are substantially larger than the expected yield decline due to climate change. Improved nutrient management is therefore crucial to avoid crop yield decline and might even increase crop yields in Zambia.

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When can decision analysis improve climate adaptation planning? Two procedures to match analysis approaches with adaptation problems

Cited by 16 publications

References 30 publications

A perspective on the role of uncertainty in sustainability science and engineering

A perspective on the role of uncertainty in sustainability science and engineering

A Bayesian Decision Model for Optimum Investment and Design of Low-Impact Development in Urban Stormwater Infrastructure and Management

Climate change impacts on rainfed maize yields in Zambia under conventional and optimized crop management

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