Discriminant Analysis of the Solar Input on the Danube’s Discharge in the Lower Basin
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...
Published in: | Atmosphere |
---|---|
Main Authors: | , , , |
Format: | Text |
Language: | English |
Published: |
Multidisciplinary Digital Publishing Institute
2023
|
Subjects: | |
Online Access: | https://doi.org/10.3390/atmos14081281 |
id |
ftmdpi:oai:mdpi.com:/2073-4433/14/8/1281/ |
---|---|
record_format |
openpolar |
spelling |
ftmdpi:oai:mdpi.com:/2073-4433/14/8/1281/ 2023-09-05T13:19:56+02:00 Discriminant Analysis of the Solar Input on the Danube’s Discharge in the Lower Basin Constantin Mares Ileana Mares Venera Dobrica Crisan Demetrescu agris 2023-08-13 application/pdf https://doi.org/10.3390/atmos14081281 EN eng Multidisciplinary Digital Publishing Institute Climatology https://dx.doi.org/10.3390/atmos14081281 https://creativecommons.org/licenses/by/4.0/ Atmosphere; Volume 14; Issue 8; Pages: 1281 NAO GBO Palmer index Schwabe and Hale solar cycles Danube discharge filter wavelet transform deep learning machine Text 2023 ftmdpi https://doi.org/10.3390/atmos14081281 2023-08-20T23:52:12Z 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. Text Greenland North Atlantic North Atlantic oscillation MDPI Open Access Publishing Greenland Hale ENVELOPE(-86.317,-86.317,-78.067,-78.067) Nash ENVELOPE(-62.350,-62.350,-74.233,-74.233) Sutcliffe ENVELOPE(-81.383,-81.383,50.683,50.683) Atmosphere 14 8 1281 |
institution |
Open Polar |
collection |
MDPI Open Access Publishing |
op_collection_id |
ftmdpi |
language |
English |
topic |
NAO GBO Palmer index Schwabe and Hale solar cycles Danube discharge filter wavelet transform deep learning machine |
spellingShingle |
NAO GBO Palmer index Schwabe and Hale solar cycles Danube discharge filter wavelet transform deep learning machine Constantin Mares Ileana Mares Venera Dobrica Crisan Demetrescu Discriminant Analysis of the Solar Input on the Danube’s Discharge in the Lower Basin |
topic_facet |
NAO GBO Palmer index Schwabe and Hale solar cycles Danube discharge filter wavelet transform deep learning machine |
description |
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. |
format |
Text |
author |
Constantin Mares Ileana Mares Venera Dobrica Crisan Demetrescu |
author_facet |
Constantin Mares Ileana Mares Venera Dobrica Crisan Demetrescu |
author_sort |
Constantin Mares |
title |
Discriminant Analysis of the Solar Input on the Danube’s Discharge in the Lower Basin |
title_short |
Discriminant Analysis of the Solar Input on the Danube’s Discharge in the Lower Basin |
title_full |
Discriminant Analysis of the Solar Input on the Danube’s Discharge in the Lower Basin |
title_fullStr |
Discriminant Analysis of the Solar Input on the Danube’s Discharge in the Lower Basin |
title_full_unstemmed |
Discriminant Analysis of the Solar Input on the Danube’s Discharge in the Lower Basin |
title_sort |
discriminant analysis of the solar input on the danube’s discharge in the lower basin |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2023 |
url |
https://doi.org/10.3390/atmos14081281 |
op_coverage |
agris |
long_lat |
ENVELOPE(-86.317,-86.317,-78.067,-78.067) ENVELOPE(-62.350,-62.350,-74.233,-74.233) ENVELOPE(-81.383,-81.383,50.683,50.683) |
geographic |
Greenland Hale Nash Sutcliffe |
geographic_facet |
Greenland Hale Nash Sutcliffe |
genre |
Greenland North Atlantic North Atlantic oscillation |
genre_facet |
Greenland North Atlantic North Atlantic oscillation |
op_source |
Atmosphere; Volume 14; Issue 8; Pages: 1281 |
op_relation |
Climatology https://dx.doi.org/10.3390/atmos14081281 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/atmos14081281 |
container_title |
Atmosphere |
container_volume |
14 |
container_issue |
8 |
container_start_page |
1281 |
_version_ |
1776200705892155392 |