Evaluating the effects of climate extremes on crop yield, production and price using multivariate distributions: A new copula application

Alidoost, Fakhereh; Su, Zhongbo; Stein, Alfred

doi:10.1016/j.wace.2019.100227

Cited by 31 publications

(21 citation statements)

References 32 publications

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“…With the prolongation of the drought, the small and medium reservoirs (with storage capacity below 75 hm³ according to Ceará State's declaration no. 23.068/1994 [62]) started to collapse, both in terms of quantity and quality, enhancing the costs of capturing and distributing water at longer distances. The water shortage also affected the water quality of those reservoirs, especially regarding eutrophication and an increase in the concentration of salts due to the low inflow periods, higher evaporation, and anthropogenic actions.…”

Section: The 2012-2018 Droughtmentioning

confidence: 99%

“…It enables the identification of current drought exceptionality and permits the use of this information as a preparation tool for the mitigation of future droughts.The univariate approach has traditionally dominated the drought frequency analysis. However, multiple aspects of drought characteristics present a dependence structure that can be entirely ignored by the univariate approach, resulting in an incomplete representation of the phenomenon [21][22][23]. For instance, drought with the same duration could present completely different impacts, depending on their respective severity.…”

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confidence: 99%

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Copula-Based Multivariate Frequency Analysis of the 2012–2018 Drought in Northeast Brazil

Filho

Martins

et al. 2020

Water

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The 2012–2018 drought was such an extreme event in the drought-prone area of Northeast Brazil that it triggered a discussion about proactive drought management. This paper aims at understanding the causes and consequences of this event and analyzes its frequency. A consecutive sequence of sea surface temperature anomalies in the Pacific and Atlantic Oceans, at both the decadal and interannual scales, led to this severe and persistent drought. Drought duration and severity were analyzed using run theory at the hydrographic region scale as decision-makers understand impact analysis better at this scale. Copula functions were used to properly model drought joint characteristics as they presented different marginal distributions and an asymmetric behavior. The 2012–2018 drought in Ceará State had the highest mean bivariate return period ever recorded, estimated at 240 years. Considering drought duration and severity simultaneously at the level of the hydrographic regions improves risk assessment. This result advances our understanding of exceptional events. In this sense, the present work proposes the use of this analysis as a tool for proactive drought planning.

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Section: The 2012-2018 Droughtmentioning

confidence: 99%

mentioning

confidence: 99%

Copula-Based Multivariate Frequency Analysis of the 2012–2018 Drought in Northeast Brazil

Filho

Martins

et al. 2020

Water

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“…Theoretical studies adopted copula, for instance, to gain a better understanding of the dependence of various variables, such as temperature and precipitation (Cong & Brady, 2012) or to generate synthetic hydroclimatic data (Lee et al, 2008). Applied studies adopted copula to examine, for example, sediment transport (e.g., Shojaeezadeh et al, 2020), heatwave mortality (e.g., Mazdiyasni et al, 2017), droughts (e.g., Brunner, Liechti, et al, 2019; Hao & AghaKouchak, 2013; Madadgar & Moradkhani, 2014; Ribeiro et al, 2019; L. Wang et al, 2019), river flooding (e.g., Brunner et al, 2017; Brunner, Furrer, et al, 2019; Serinaldi & Kilsby, 2017), or coastal flooding (e.g., Didier et al, 2019), crop yield (e.g., Alidoost et al, 2019; Zscheischler et al, 2017), and variability of groundwater quality (e.g., Bárdossy, 2006). In addition, numerous climate‐change impact studies applied copulas to bias‐correct future projections of climate models (e.g., Bárdossy & Pegram, 2012; Piani & Haerter, 2012; Räty et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Copulas for hydroclimatic analysis: A practice‐oriented overview

et al. 2022

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A warming climate is associated with increasing hydroclimatic extremes, which are often interconnected through complex processes, prompting their concurrence and/or succession, and causing compound extreme events. It is critical to analyze the risks of compound events, given their disproportionately high adverse impacts. To account for the variability in two or more hydroclimatic variables (e.g., temperature and precipitation) and their dependence, a rising number of publications focuses on multivariate analysis, among which the notion of copula‐based probability distribution has attracted tremendous interest. Copula is a mathematical function that expresses the joint cumulative probability distribution of multiple variables. Our focus is to re‐emphasize the fundamental requirements and limitations of applying copulas. Confusion about these requirements may lead to misconceptions and pitfalls, which can potentially compromise the robustness of risk analyses for environmental processes and natural hazards. We conducted a systematic literature review of copulas, as a prominent tool in the arsenal of multivariate methods used for compound event analysis, and underpinned them with a hydroclimatic case study in Sweden to illustrate a practical approach to copula‐based modeling. Here, we (1) provide end‐users with a didactic overview of necessary requirements, statistical assumptions and consequential limitations of copulas, (2) synthesize common perceptions and practices, and (3) offer a user‐friendly decision support framework to employ copulas, thereby support researchers and practitioners in addressing hydroclimatic hazards, hence demystify what can be an area of confusion. This article is categorized under: Science of Water > Hydrological Processes Science of Water > Methods

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“…Both variables also strongly affect the water cycle: precipitation as a direct component of the water cycle, and temperature as a proxy for energy availability, which in turn is of key interest for estimating evapotranspiration as well as snow accumulation and melt, both of which exert influence on the amount and timing of runoff. As a result, the spatiotemporal trends of precipitation and temperature greatly affect environmental processes, control natural hazards such as floods and droughts, and influence water quality and availability (Alidoost et al, 2019;Ribeiro et al, 2019). Therefore, future socio-economic development and sustainable use of water resources is highly dependent on reliable representations and modeling of these two variables (Alexandrov and Hoogenboom, 2000;Phiri et al, 2019;Satgé et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Applied studies adopted the copula-based methodology to examine e.g. agricultural droughts (Wang et al 2019;Ribeiro et al 2019) or joint effects of temperature and precipitation extremes on vegetation growth (Alidoost et al, 2019;Cong and Brady, 2012). In addition, numerous climate-change impact studies applied copulas to bias-correct future projections by global or regional climate models (Piani and Haerter 2012;Li et al 2014;Gennaretti, Sangelantoni, and Grenier 2015;Vrac and Friederichs 2015;Rana, https://doi.org/10.5194/hess-2020-306 Preprint.…”

Section: Introductionmentioning

confidence: 99%

Copulas for hydroclimatic applications – A practical note on common misconceptions and pitfalls

Tootoonchi

Haerter

Räty

et al. 2020

Preprint

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Abstract. For most hydroclimatic applications, precipitation and temperature are of particular interest as they strongly affect the water cycle, can easily be measured and are often readily available from many meteorological stations worldwide. To account for precipitation and temperature variability, their co-dependence and their correlation, several multivariate analysis methods have been adopted in the hydroclimatic literature in recent years. In line with the steadily rising number of publications on this topic, the notion of copula-based probability distribution has also attracted tremendous interest to deal with the complexity of compound events in the multidimensional context. A copula is a function that connects a multivariate distribution to its one-dimensional margins, which allows for a joint distribution of random variables with great flexibility for the marginal distribution. However, there seems to be a lack of comprehensive understanding of the fundamental requirements of the copula concept such as the strength and significance of correlation between variables, autocorrelation effects and the choice of representative copula families, which potentially compromises the robustness of projections of future environmental processes and natural hazards. Therefore, by combining a systematic literature review with a specific hydroclimatic case study in Sweden, we illustrate a practical approach to copula-based modeling, which (1) provides end-users with an overview of necessary requirements, statistical assumptions and consequential limitations of copulas, (2) highlights possible pitfalls and misconceptions, and (3) offers a decision support framework for the application of copulas to support researchers and decision makers in addressing climatological hazards and sustainable development, thereby demystifying what is currently an area of great confusion.

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Evaluating the effects of climate extremes on crop yield, production and price using multivariate distributions: A new copula application

Cited by 31 publications

References 32 publications

Copula-Based Multivariate Frequency Analysis of the 2012–2018 Drought in Northeast Brazil

Copula-Based Multivariate Frequency Analysis of the 2012–2018 Drought in Northeast Brazil

Copulas for hydroclimatic analysis: A practice‐oriented overview

Copulas for hydroclimatic applications – A practical note on common misconceptions and pitfalls

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