Selection of Optimal Palmer Predictors for Increasing the Predictability of the Danube Discharge: New Findings Based on Information Theory and Partial Wavelet Coherence Analysis

Mares, Ileana; Mares, Constantin; Dobrică, Venera; Demetrescu, Camil

doi:10.3390/e24101375

Cited by 4 publications

(11 citation statements)

References 86 publications

(156 reference statements)

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“…Regarding the multiple wavelet coherence (mwc) developed by Hu and Si [96], the authors of this study applied it to find multiple coherence between different large-scale climate indices and the Danube discharge and between climate indices and indicators of solar/geomagnetic variability [65][66][67]82].…”

Section: Methodsmentioning

confidence: 99%

“…Special attention is given to the investigation by means of wavelet which assumes the validity of the principle of superposition even in the case of non-linear links [61,62], as is the case with most terrestrial phenomena under solar/geomagnetic impact [38,51,[63][64][65][66][67]. Multiple advantages of using the wavelet, especially in detailing the information, in the analysis of time series can be found in [38,[68][69][70][71][72].…”

Section: Introductionmentioning

confidence: 99%

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Solar Signature in Climate Indices

et al. 2022

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The influence of solar/geomagnetic activity on climate variables still remains a fully unclarified problem, although many scientific efforts have been made to better understand it. In order to bring more information to this open problem, in the present study, we analyze the connection between solar/geomagnetic activity (predictors) and climate variables (predictands) by applying elements from information theory and wavelet transform analysis. The solar activity was highlighted by the Wolf number and geomagnetic activity was quantified by the aa index. For the climate variables, we considered seven Climate Indices (CIs) that influence atmospheric circulation on regional or global scales, such as the Greenland-Balkan Oscillation Index (GBOI), North Atlantic Oscillation Index (NAOI), Arctic Oscillation (AO), Atlantic Multidecadal Oscillation (AMO), Southern Oscillation Index (SOI), Bivariate ENSO Timeseries (BEST) and Trans-Niño Index (TNI). By using the difference between synergy and redundancy, a few cases were found where the two predictors can be considered together for CIs’ estimation. Coherence analysis through the wavelet transform for three variables, both through multiple and partial analysis, provides the time intervals and bands of periods, where the two considered predictors can be used together or separately. The results differ depending on the predictand, the season and the considered lags. Significant information is brought out by using the two predictors together, namely the summer season, for GBOI and NAOI, when the predictors were taken 2 years before, and the winter season, as AMO responds to the variations of both solar and geomagnetic activity after 4 years.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Solar Signature in Climate Indices

et al. 2022

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“…Two large-scale atmospheric variables taken into consideration were the North Atlantic Oscillation Index (NAOI) and the Greenland-Balkan Oscillation Index (GBOI), which has been shown to play an important role in the climate of southeastern Europe [24,26,27]. The NAOI data are available from the Climate Research Unit (https://crudata.uea.ac.uk/cru/ data/nao/values.html, accessed on 9 February 2023).…”

Section: Datamentioning

confidence: 99%

“…The behavior of the Danube discharge in relation to indices defining the atmospheric circulation or those quantifying solar activity has been evaluated in many studies [19][20][21][22][23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

“…The GBO reflects the direct polar circulation from Greenland to Southeastern Europe, which drives the consistent precipitation in the Lower Danube basin, hence the closer correlation with the Lower Danube discharge [51] than with the NAO. The PHDI gives the closest correlation with the Lower Danube discharge at the Orsova station [26]. Yin (1993) [52] described how the use of annual Palmer index data is preferable over monthly/seasonal data in trend analyses because it eliminates some problems typically associated with the Palmer indices, primarily bimodality.…”

Section: Introductionmentioning

confidence: 99%

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The Combined Effect of Atmospheric and Solar Activity Forcings on the Hydroclimate in Southeastern Europe

Mares,

Dobrica,

Demetrescu

et al. 2023

Atmosphere

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The purpose of this study was to analyze the influence of solar activity described by the sunspot number (SSN) on certain terrestrial variables that might impact the Southeastern European climate at different spatio-temporal scales (the North Atlantic Oscillation Index, NAOI, and the Greenland–Balkan Oscillation Index, GBOI—on a large scale; the Palmer Hydrological Drought Index, PHDI—on a regional scale; the Danube discharge at the Orsova (lower basin), Q, representative of the Southeastern European climate—on a local scale). The investigations were carried out for the 20th century using the annual and seasonal averages. To find the connections between terrestrial (atmospheric and hydrological) parameters and SSN, the wavelet coherence were used both globally and in the time–frequency domain. The analyses were carried out for the time series and considered simultaneously (in the same year or season), as well as with lags from 1 to 5 years between the analyzed variables. For the annual values, the type of correlation (linear/non-linear) was also tested using elements from information theory. The results clearly revealed non-linear links between the SSN and the terrestrial variables, even for the annual average values. By applying the wavelet transform to test the solar influence on the terrestrial variables, it was shown that the connections depend on both the terrestrial variable, as well as on the considered lags. Since, in the present study, they were analyzed using wavelet coherence, but only the cases in which the coherence was significant for almost the entire analyzed time interval (1901–2000) and the terrestrial variables were in phase or antiphase with the SSN were considered. Relatively few results had a high level of significance. The analysis of seasonal averages revealed significant information, in addition to the analysis of annual averages. Thus, for the climatic indices, the GBOI and NAOI, a significant coherence (>95%) with the solar activity, associated with the 22-year (Hale) solar cycle, was found for the autumn season for lag = 0 and 1 year. The Hale solar cycle, in the case of the PHDI, was present in the annual and summer season averages, more clearly at lag = 0. For the Danube discharge at Orsova, the most significant SSN signature (~95%) was observed at periods of 33 years (Brüuckner cycle) in the autumn season for lags from 0 to 3 years. An analysis of the redundancy–synergy index was also carried out on the combination of the terrestrial variables with the solar variable in order to find the best synergistic combination for estimating the Danube discharge in the lower basin. The results differed depending on the timescale and the solar activity. For the average annual values, the most significant synergistic index was obtained for the combination of the GBOI, PHDI, and SSN, considered 3 years before Q.

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Discriminant Analysis of the Solar Input on the Danube’s Discharge in the Lower Basin

Mares

Dobrica

et al. 2023

Atmosphere

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This paper presents the extent to which the combination of extra-atmospheric and hydroclimatic factors can be deciphered to record their contribution to the evolution and forecasting of the Danube discharge (Q) in the lower basin. A combination of methods such as wavelet filtering and deep learning (DL) constitutes the basic method for discriminating the external factors (solar activity through Wolf numbers) that significantly contribute to the evolution and prediction of the lower Danube discharge. An ensemble of some of the most important factors, namely, those representing the atmospheric components, i.e., the Greenland-Balkan Oscillation Index (GBOI) and the North Atlantic Oscillation Index (NAOI); the hydroclimatic indicator, the Palmer Hydrological Drought Index (PHDI); and the extra-atmospheric factor, constitutes the set of predictors by means of which the predictand, Q, in the summer season, is estimated. The external factor has to be discriminated in the Schwabe and Hale spectra to make its convolutional contribution to the Q estimation in the lower Danube basin. An interesting finding is that adding two solar predictors (associated with the Schwabe and Hale cycles) to the terrestrial ones give a better estimation of the Danube discharge in summer, compared to using only terrestrial predictors. Based on the Nash–Sutcliffe (NS) index, a measure of performance given by the extreme learning machine (ELM), it is shown that, in association with certain terrestrial predictors, the contribution of the Hale cycle is more significant than the contribution of the Schwabe cycle to the estimation of the Danube discharge in the lower basin.

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Selection of Optimal Palmer Predictors for Increasing the Predictability of the Danube Discharge: New Findings Based on Information Theory and Partial Wavelet Coherence Analysis

Cited by 4 publications

References 86 publications

Solar Signature in Climate Indices

Solar Signature in Climate Indices

The Combined Effect of Atmospheric and Solar Activity Forcings on the Hydroclimate in Southeastern Europe

Discriminant Analysis of the Solar Input on the Danube’s Discharge in the Lower Basin

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