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Irrigation, risk aversion, and water right priority under water supply uncertainty.

ABSTRACT: This paper explores the impacts of a water right's allocative priority-as an indicator of farmers' risk-bearing ability-on land irrigation under water supply uncertainty. We develop and use an economic model to simulate farmers' land irrigation decision and associated economic returns in eastern Idaho. Results indicate that the optimal acreage of land irrigated increases with water right priority when hydroclimate risk exhibits a negatively skewed or right-truncated distribution. Simulation results suggest that prior appropriation enables senior water rights holders to allocate a higher proportion of their land to irrigation, 6 times as much as junior rights holders do, creating a gap in the annual expected net revenue reaching up to $141.4 acre^-1 or $55,800 per farm between the two groups. The optimal irrigated acreage, expected net revenue, and shadow value of a water right's priority are subject to substantial changes under a changing climate in the future, where temporal variation in water supply risks significantly affects the profitability of agricultural land use under the priority-based water sharing mechanism.

SUBMITTER: Li M

PROVIDER: S-EPMC5698760 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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Irrigation, risk aversion, and water right priority under water supply uncertainty.

Li Man M Xu Wenchao W Rosegrant Mark W MW

Water resources research 20170914 9

This paper explores the impacts of a water right's allocative priority-as an indicator of farmers' risk-bearing ability-on land irrigation under water supply uncertainty. We develop and use an economic model to simulate farmers' land irrigation decision and associated economic returns in eastern Idaho. Results indicate that the optimal acreage of land irrigated increases with water right priority when hydroclimate risk exhibits a negatively skewed or right-truncated distribution. Simulation resu ...[more]

PMID: 29200529

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Project description:This study presents a new risk-averse multi-stage stochastic epidemics-ventilator-logistics compartmental model to address the resource allocation challenges of mitigating COVID-19. This epidemiological logistics model involves the uncertainty of untested asymptomatic infections and incorporates short-term human migration. Disease transmission is also forecasted through a new formulation of transmission rates that evolve over space and time with respect to various non-pharmaceutical interventions, such as wearing masks, social distancing, and lockdown. The proposed multi-stage stochastic model overviews different scenarios on the number of asymptomatic individuals while optimizing the distribution of resources, such as ventilators, to minimize the total expected number of newly infected and deceased people. The Conditional Value at Risk (CVaR) is also incorporated into the multi-stage mean-risk model to allow for a trade-off between the weighted expected loss due to the outbreak and the expected risks associated with experiencing disastrous pandemic scenarios. We apply our multi-stage mean-risk epidemics-ventilator-logistics model to the case of controlling COVID-19 in highly-impacted counties of New York and New Jersey. We calibrate, validate, and test our model using actual infection, population, and migration data. We also define a new region-based sub-problem and bounds on the problem and then show their computational benefits in terms of the optimality and relaxation gaps. The computational results indicate that short-term migration influences the transmission of the disease significantly. The optimal number of ventilators allocated to each region depends on various factors, including the number of initial infections, disease transmission rates, initial ICU capacity, the population of a geographical location, and the availability of ventilator supply. Our data-driven modeling framework can be adapted to study the disease transmission dynamics and logistics of other similar epidemics and pandemics.

Project description:BackgroundAt the start of the COVID-19 pandemic, guidance was needed more than ever to direct frontline healthcare and national containment strategies. Rigorous guidance based on robust research was compromised by the emergence of the pandemic and the urgency of need for guidance. Rather than aiming to "get guidance right", guidance developers needed to "get guidance right now".AimTo examine how guidance developers have responded to the need for credible guidance at the start of the COVID-19 pandemic.MethodsAn exploratory mixed-methods study was conducted among guidance developers. A web-based survey and follow-up interviews were used to examine the most pertinent challenges in developing COVID-19 guidance, strategies used to address these, and perspectives on the implications of the COVID-19 pandemic on future guidance development.ResultsThe survey was completed by 46 guidance developers. Survey findings showed that conventional methods of guidance development were largely unsuited for COVID-19 guidance, with 80% (n = 37) of respondents resorting to other methods. From the survey and five follow-up interviews, two themes were identified to bolster the credibility of guidance in a setting of extreme uncertainty: (1) strengthening end-user involvement and (2) conjoining evidence review and recommendation formulation. 70% (n = 32) of survey respondents foresaw possible changes in future guidance production, most notably shortening development time, by reconsidering how to balance between rigour and speed for different types of questions.Conclusion"Getting guidance right" and "getting guidance right now" are not opposites, rather uncertainties are always part of guidance development and require guidance developers to balance scientific robustness with usability, acceptability, adequacy and contingency. This crisis points to the need to acknowledge uncertainties of scientific evidence more explicitly and points to mechanisms to live with such uncertainty, thus extending guidance development methods and processes more widely.

Dataset Information

Irrigation, risk aversion, and water right priority under water supply uncertainty.

Publications

Irrigation, risk aversion, and water right priority under water supply uncertainty.

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