Comparative study of statistical methods to identify a predictor for discharge at Orsova in the Lower Danube Basin

Dobrică

et al. 2021

Sci Rep

Self Cite

The present study aims to investigate the possible influence of solar/geomagnetic forcing on climate variables, such as the drought index, Danube discharge and large-scale atmospheric indices. Our analysis was performed separately for each season for two time periods, 1901–2000 and 1948–2000. The relationship between terrestrial variables and external indices was established based on the application of (1) information theory elements, namely, synergy, redundancy, total correlation, transfer entropy and (2) wavelet coherence analysis. Bandpass filtering has also been applied. The most significant signature of the solar/geomagnetic forcing in the climate variables was obtained for the data smoothed by the bandpass filter. According to our results, significant solar/geomagnetic forcing appears in the terrestrial variables with a delay of 2–3 years.

Section: Methodsmentioning

confidence: 96%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 90%

See 1 more Smart Citation

Assessing the solar variability signature in climate variables by information theory and wavelet coherence

Dobrică

et al. 2021

Sci Rep

Self Cite

“…Apart from using MI to estimate TE (equation 2), this measure is used in different applications such as calculating the nonlinear correlation coefficient. Thus in [52] nonlinear connections between the five atmospheric predictors and the Danube discharge were tested using the nonlinear correlation coefficient (NLR) based on mutual information. Significant connections between the predictor and the predictand indicated a nonlinear link between them, the NLR being generally higher than the linear correlation coefficient.…”

Section: H-information Entropy Of the Variables X And Y [47])mentioning

confidence: 99%

Quantification of the Direct Solar Impact on Some Components of the Hydroclimatic System

Mares¹,

Mares²,

Dobrica³

et al. 2021

Preprint

This study addresses the causal links between external factors and the main hydro-climatic variables. There is a gap in the literature on the description of a complete chain in addressing the structures of direct causal links of solar activity on the terrestrial variables. This is why, the present study uses the extensive facilities of the application of information theory in view of recent advances in different fields. Also, by other methods (e.g. neural networks) first are tested the existence non-linear links of solar-terrestrial influences on hydro-climate system. The results are promising related to the solar impact on terrestrial phenomena which is discriminant in space-time domain. The implications prove robust for determining the causal measure of climate variables under direct solar impact which makes it easier to consider solar activity in climate models, by appropriate parametrizations. This study found that hydro-climatic variables are sensitive to solar impact only for certain frequencies (periods) and these have a coherence with the Solar-Flux only for some lags of the Solar-Flux (in advance).

“…Recently, Hu and Si (2016) have extended the BWC to multiple wavelet coherence (MWC) that can be used to untangle multivariate (≥3 variables) relationships in multiple scale-location domains. This method has been successfully used in hydrology (Gu et al, 2020;Hu et al, 2017;Mares et al, 2020;Nalley et al, 2019;Su et https://doi.org/10.5194/hess-2020-315 Preprint. Discussion started: 7 August 2020 c Author(s) 2020.…”

Section: Introductionmentioning

confidence: 99%

Technical Note: Partial wavelet coherency for improved understanding of scale-specific and localized bivariate relationships in geosciences

2020

Preprint

Abstract. Bivariate wavelet coherency is widely used to untangle the scale-specific and localized bivariate relationships in geosciences. However, it is well-known that bivariate relationships can be misleading when both variables are correlated to other variables. Partial wavelet coherency (PWC) has been proposed, but is limited to one excluding variable and presents no phase information. The objective of this study was to develop a new PWC method that can deal with multiple excluding variables and presents phase information for the PWC. Tests with both stationary and non-stationary artificial datasets verified the known scale- and localized bivariate relationships after eliminating the effects of other variables. Compared with the previous PWC method, the new method has the advantages of capturing phase information, dealing with multiple excluding variables, and producing more accurate results. The new method was also applied to two field measured datasets. Results showed that the coherency between response and predictor variables was usually less affected by excluding variables when predictor variables had higher correlation with the response variable. Application of the new method also confirmed the best predictor variables for explaining temporal variations in free water evaporation at Changwu site in China and spatial variations in soil water content in a hummocky landscape in Saskatchewan Canada. We suggest the PWC method to be used in combination with previous wavelet methods to untangle the scale-specific and localized multivariate relationships in geosciences. Matlab codes for the PWC were developed and are provided in the supplement.