Use of Teleconnection Indices for Water Management in the Cantareira System - São Paulo – Brazil

Calado, Gabrielle Gomes; Valverde, María Cleofé; Baigorria, Guillermo A.

doi:10.1007/s40710-019-00368-9

Cited by 7 publications

(5 citation statements)

References 25 publications

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“…The 2014/2015 drought severely affected SP (Coelho et al, 2015), resulting in one of the most important water crises ever recorded in the São Paulo metropolitan area. The lack of rain in 2014/2015 resulted in the lowest water levels in the Cantareira Water Production System, a phenomenon that had not been recorded in the last 40 years (Calado et al, 2019).…”

Section: Resultsmentioning

confidence: 99%

Heat waves in São Paulo State, Brazil: Intensity, duration, spatial scope, and atmospheric characteristics

Valverde

Rosa

2023

Intl Journal of Climatology

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Heat waves (HWs) are atmospheric events of synoptic and global scale that negatively impact productive sectors and the population. This study aims to investigate HWs in the São Paulo (SP) State, Brazil, and describe their intensity, duration, spatial coverage and atmospheric characteristics. HWs were identified using observed data from 65 weather stations for the period 2000–2020. The NCEP‐DOE Reanalysis was used to determine synoptic and global scale circulation characteristics. The results showed that SP experiences different effects of thermal stress on maximum temperatures (Tmax), with the north and northwest sectors reaching the highest average values (38°C). The average temperature of HWs in the state was 34.9°C, with an average duration of 5.3 days and 92% of events taking place between 2010 and 2020. A greater number of events occurred in 2015, 2016 and 2019, mainly in the austral summer and spring. The following atmospheric characteristics were found to be associated with HWs in SP: an anomalous semistationary anticyclonic circulation at 500 hPa over the hinterland regions of Brazil, a thermal low at 1000 hPa over Paraguay, a Rossby wave train originating in the South Pacific Ocean and spreading to centre‐south Brazil, a positive sea surface temperature anomaly in the South Atlantic Ocean near the coast of Brazil and another over the South Pacific, and a negative pattern circling Antarctica. Apparently, there is a certain degree of coupling between sea surface temperature anomalies and the Rossby train, the former being the trigger of the event.

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Section: Resultsmentioning

confidence: 99%

Heat waves in São Paulo State, Brazil: Intensity, duration, spatial scope, and atmospheric characteristics

Valverde

Rosa

2023

Intl Journal of Climatology

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“…Australian rainfall has a distinct nature as coastal regions experience wetter winter while inland west encounters less rainfall at that time. Several studies around the world explored teleconnections between the climate indices with regional rainfalls (Ghamariadyan and Imteaz 2021;Calado et al 2019). Around Australia, primarily IOD and SAM have been found as influential drivers for rainfall generation in south-eastern and western parts, Blocking Highs for southern parts, and ENSO Modoki and Madden Julian Oscillation (MJO) for north-western and northern parts (Ashok et al 2003;Marshall and Hendon 2014;Risbey et al 2009).…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Approach for Predicting Western Australia’s Seasonal Rainfall Variability

Islam

Imteaz

2022

Water Resour Manage

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In this paper, 100 years of uninterrupted rainfall data for 12 rainfall stations (four rainfall stations from each region) in Western Australia were analyzed against respective dominant climate indices, and representative prediction models were developed using ARIMAX, GEP, and a hybrid technique (GEP-ARIMAX). Statistical performance evaluators such as Pearson correlation $$(r)$$ ( r ) , root mean square error $$(RMSE)$$ ( R M S E ) , mean absolute error ($$MAE$$ MAE ), and refined Willmot index of agreement ($${d}_{r}$$ d r ) were used to evaluate the prediction performance of the developed models. These models demonstrated their capability to predict up to four months in advance with Pearson correlation $$(r)$$ ( r ) values ranging from 0.53 to 0.83, 0.75 to 0.85, and 0.87 to 0.95 for ARIMAX, GEP, and hybrid (GEP-ARIMAX) models respectively. While compared, the hybrid (GEP-ARIMAX) model showed superior prediction performance in both calibration and validation periods with Pearson correlation $$(r)$$ ( r ) and refined Willmot index of agreement ($${d}_{r}$$ d r ) values were as high as 0.96 and 0.84 respectively. This paper demonstrated a novel hybrid GEP-ARIMAX model showing significantly good rainfall forecasting capability than conventional linear and non-linear models.

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“…The effect of climate teleconnection patterns, such as the North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), and El NiÑo–Southern Oscillation (ENSO), on streamflow has been widely studied, including in the United States and Canada (Hsieh et al ., 2003; Tootle et al ., 2005; Gobena et al ., 2013; Kalra et al ., 2013; Mukhopadhyay et al ., 2018), Brazil (Calado et al ., 2019), Australia (Sen et al ., 2004), Europe (Hidalgo‐Muñoz et al ., 2015), and Iran (Saghafian et al ., 2013). Yang et al .…”

Section: Introductionmentioning

confidence: 99%

Long‐range reservoir inflow forecasts using large‐scale climate predictors

Moradi

Dariane

Yang

et al. 2020

Intl Journal of Climatology

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Identifying significant large‐scale climate indicators has the potential to improve long‐range streamflow forecasts. In this research, we develop streamflow forecasts for Lake Urmia basin, Iran, specifically for inflow into the Boukan and Mahabad reservoirs. In doing so, two types of inflow forecast models are considered: a single site univariate model ignoring the cross correlation between streamflow at different stations, and a multi‐site multivariate forecast model which takes into consideration the cross correlations among stations. Predictor selection is performed through a principal component analysis and an adaptive‐network‐based fuzzy inference system is used to forecast streamflow. Forecast performance is investigated by employing different combinations of large‐scale climatic information and hydrologic data. We found that gridded ocean‐atmospheric circulation variables, including surface precipitation rate and omega (pressure vertical velocity), have the highest correlations (about 0.7) with annual streamflow. In general, multivariate models are able to better preserve the annual cross‐correlations between streamflow at different stations, as expected, without sacrificing forecast skill as compared to the univariate forecast model approach. Additionally, as compared with the baseline feed‐forward artificial neural network‐ and traditional multiple linear regression‐forecast models, the results were approximately the same. This similarity in the forecast performance between the linear and nonlinear models is likely due to the short of data (44‐sample record).

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Use of Teleconnection Indices for Water Management in the Cantareira System - São Paulo – Brazil

Cited by 7 publications

References 25 publications

Heat waves in São Paulo State, Brazil: Intensity, duration, spatial scope, and atmospheric characteristics

Heat waves in São Paulo State, Brazil: Intensity, duration, spatial scope, and atmospheric characteristics

A Novel Hybrid Approach for Predicting Western Australia’s Seasonal Rainfall Variability

Long‐range reservoir inflow forecasts using large‐scale climate predictors

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