The combined effect of climate oscillations in producing extremes: the 2020 drought in southern Brazil

Grimm, Alice M.; Almeida, Arlan Scortegagna; Beneti, Cesar; Leite, Eduardo Alvim

doi:10.1590/2318-0331.252020200116

Cited by 21 publications

(23 citation statements)

References 26 publications

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“…As rice is a major staple food in Brazil, in order to ensure food security and supply logistics for the Brazilian population, we cannot rely solely on rice produced in the subtropical ecosystem, which is located in the extreme south of the country. An example of this vulnerability is the deleterious effect of the “ENSO” phenomenon on subtropical rice yields in Rio Grande do Sul due to extreme events of rainfalls or drought (Cai et al, 2020; Grimm et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Data-driven machine learning for pattern recognition supports environmental quality prediction for irrigated rice in Brazil

Costa‐Neto

Matta

Fernandes

et al. 2022

Preprint

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The sustainability of irrigated rice (Oryza sativa L.) production systems in Brazilian tropical region highly depends on the success of developing stable cultivars. To achieve this goal, many steps in product development must address the environmental variability and genotype by environment interactions (GE), which makes difficult the design and development of local-specific adapted cultivars. Thus, the adoption of new strategies for characterizing environmental-phenotype relations are the key for optimizing this process. In addition, it could also benefit post-breeding stages of seed production. To overcome this situation, we implemented a data-driven approach to link environmental characterization to yield clustering using historical data (1982-2017, 31 locations, 471 genotypes), 42 envirotyping covariables and machine learning (ML), combining two unsupervised (K-means and decision tree models, DTC) algorithms. Additionally, linear mixed models (LMM) were applied to explore the relations between the outcomes of our approach and GE analysis for irrigated rice yield in Brazilian tropical region. Four environments were identified: Very Low Yield (1.7 Mg.ha-1), Low Yield (5.1 Mg.ha-1), High Yield (7.2 Mg.ha-1), and Very High Yield (9.0 Mg.ha-1), considering all genotypes and regions. Our approach allows the prediction of environments (yield clusters) for a diverse set of growing conditions and revealed geographic and climatic causes of environmental quality, which differ according to each region and genotype group. From the LMM analysis, we found that the current relation between genetics (G), environmental variation (E), and GE for rainfed rice in Brazil is 1:6:2, but when we introduced our data-driven clusters (ME), the ratio decreased to 1:5:1. Consequently, the selection reliability for local adaptability across an extensive region increases. Our approach helps to identify mega-environments in Brazil that could be used as a target population of environments (TPE) of breeding programs. Additionally, it helps to identify more productive and stable seed production fields.

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Section: Introductionmentioning

confidence: 99%

Data-driven machine learning for pattern recognition supports environmental quality prediction for irrigated rice in Brazil

Costa‐Neto

Matta

Fernandes

et al. 2022

Preprint

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“…In recent years, the state of Rio Grande do Sul has suffered from increasingly frequent droughts and floods (Viana et al, 2009;Grimm et al, 2020), and the Guaíba hydrographic region is the area that serves more than half of the state population, so it is essential to know the probabilities and return periods of extreme precipitation and flow events, especially considering the changing climate, which can mean new scenarios of droughts and floods, and result in environmental and socioeconomic impacts.…”

Section: Introductionmentioning

confidence: 99%

Analysis of hydrological extremes in the Guaíba hydrographic region: an application of extreme values theory

Vieira

Osório

Quevedo

2022

Rev. Bras. Ciênc. Ambient.

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Knowing the behavior of extreme hydrological phenomena is essential so that the impacts resulting from these natural events are minimized. Rio Grande do Sul has frequently been hit by extreme events such as droughts and floods, and these events are associated with several consequences, such as energy or water rationing, urban flooding and damage to hydraulic structures. In this context, the analysis of historical series extremes of hydrometeorological data through the Extreme Values Theory (EVT) is one of the ways to determine the variability due to climate change, enabling the modeling of extreme events. EVT makes it possible to know the frequency with which extreme events occur, allowing extrapolation beyond the historical series, generating occurrence probabilities of such an event. Therefore, the purpose of this work was to apply the Extreme Values Theory in hydrological the data historical series of flow and precipitation in the Guaíba hydrographic region and to carry out occurrence probabilities of intense events return, helping in the planning of the hydrographic watersheds that are in this region, as well as to verify whether the EVT has return periods similar to the climate projections of CMIP5 models. The results demonstrate that the values of flow and precipitation, in the historical series used, have already presented changes regarding the volume and frequency of extreme events occurrence and, in the future, for some stations, values can be expected both above and below the extremes already observed in the historical series.

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“…Despite these relationships between ENSO events and rainfall in NEB being relatively well established, no two El Niño/La Niña episodes have exactly the same characteristics (Cai et al 2020). The mechanisms responsible for different rainfall conditions between events need to be well understood, especially those that result in anomalies whose sign is contrary to that expected for a given ENSO phase (as in Grimm et al 2020). Sectors requiring rainfall information to plan their activities will benefit greatly from this knowledge.…”

Section: Introductionmentioning

confidence: 99%

On the occurrence of a less usual rainfall pattern in northeastern Brazil during the 2019 El Niño

Godoi¹,

Andrade

2021

Preprint

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El Niño is generally associated with below-average rainfall in northeastern Brazil (NEB). In 2019, however, the opposite rainfall pattern was observed during an El Niño episode. Here, we explore the mechanisms that overwhelmed typical El Niño-related conditions and resulted in above-average rainfall in NEB. We focus on the austral autumn, when El Niño is most related to rainfall anomalies in the region. The analysis of rainfall data from weather stations and the Global Precipitation Climatology Project, upper- and lower-level circulation reanalysis data, sea surface temperature data, outgoing longwave radiation data, and modelled data allowed us to identify that the autumn 2019 heavy rainfall in NEB was likely associated with three combined factors; these are: (1) the weak intensity of the 2019 El Niño; (2) local and remote diabatic heating anomalies, especially over the western South Pacific and tropical South Atlantic, which resulted in anticyclonic and cyclonic circulations in the upper and lower troposphere, respectively, over the tropical South Atlantic; and (3) sub-seasonal atmospheric convection anomalies over the western South Pacific, which reinforced the low-frequency convection signal over that region. This latter factor suggests the influence of the Madden–Julian Oscillation on rainfall in NEB during the first ten days of March. We discuss these mechanisms in detail and provide evidence for their associations with the anomalously heavy rainfall in NEB. Our results may assist in the planning of several crucial activities, such as water resources management and agriculture.

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The combined effect of climate oscillations in producing extremes: the 2020 drought in southern Brazil

Cited by 21 publications

References 26 publications

Data-driven machine learning for pattern recognition supports environmental quality prediction for irrigated rice in Brazil

Data-driven machine learning for pattern recognition supports environmental quality prediction for irrigated rice in Brazil

Analysis of hydrological extremes in the Guaíba hydrographic region: an application of extreme values theory

On the occurrence of a less usual rainfall pattern in northeastern Brazil during the 2019 El Niño

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