Investigation of hydrological variability in the Korean Peninsula with the
ENSO teleconnections

Yoon, Sun‐Kwon; Lee, Taesam

doi:10.5194/piahs-374-165-2016

Cited by 7 publications

(7 citation statements)

References 19 publications

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“…Also, several MI metrics were developed to assist in predictor selection for hydroclimatic forecasting [ 48 , 49 ]. More importantly, MI was able to detect the presence of strong nonlinear associations of streamflow, rainfall, and global mean temperature with several large-scale climatic variables [ 31 , 50 , 51 , 52 , 53 , 54 ].…”

Section: Introductionmentioning

confidence: 99%

Information Entropy Suggests Stronger Nonlinear Associations between Hydro-Meteorological Variables and ENSO

Mishra

Konapala

2018

Entropy

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Abstract:Understanding the teleconnections between hydro-meteorological data and the El Niño-Southern Oscillation cycle (ENSO) is an important step towards developing flood early warning systems. In this study, the concept of mutual information (MI) was applied using marginal and joint information entropy to quantify the linear and non-linear relationship between annual streamflow, extreme precipitation indices over Mekong river basin, and ENSO. We primarily used Pearson correlation as a linear association metric for comparison with mutual information. The analysis was performed at four hydro-meteorological stations located on the mainstream Mekong river basin. It was observed that the nonlinear correlation information is comparatively higher between the large-scale climate index and local hydro-meteorology data in comparison to the traditional linear correlation information. The spatial analysis was carried out using all the grid points in the river basin, which suggests a spatial dependence structure between precipitation extremes and ENSO. Overall, this study suggests that mutual information approach can further detect more meaningful connections between large-scale climate indices and hydro-meteorological variables at different spatio-temporal scales. Application of nonlinear mutual information metric can be an efficient tool to better understand hydro-climatic variables dynamics resulting in improved climate-informed adaptation strategies.

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Section: Introductionmentioning

confidence: 99%

Information Entropy Suggests Stronger Nonlinear Associations between Hydro-Meteorological Variables and ENSO

Mishra

Konapala

2018

Entropy

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“…The values of the two correlation coefficients are presented in Figure 9, simultaneously, with lags from 1 to 5 of the SSN before the terrestrial variables. As has been shown in several studies [77,80,83], if NLR > |R|, the relationship between the respective variables is non-linear. As can be seen in Figure 9, for all terrestrial variables considered by annual average values, the relationship with the SSN is non-linear.…”

Section: Relationship Between Solar Activity and Hydroclimatic Variablesmentioning

confidence: 81%

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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“…In order to address the inherent nonlinearity in the relationship of the climate indices and hydrologic variables, several studies have used nonlinear approaches like mutual information (Knuth et al ., ; Yoon and Lee, ), cross‐wavelet analysis (Labat, ; Agarwal et al ., ) or PC analysis (Bethere et al ., ), etc. However, the objective of this study is only to highlight the spatial variability in the strength of interrelationship between watershed‐scale drought and climate indices; the inferences are derived from linear correlation analysis and R ‐square of the least‐square model fit between hydrologic variables and DIs.…”

Section: Methodsmentioning

confidence: 99%

Effect of hydroclimatological teleconnections on the watershed‐scale drought predictability in the southeastern United States

Sehgal

Sridhar

2018

Intl Journal of Climatology

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Large‐scale atmospheric circulation patterns have a strong influence on hydrologic variability in the southeastern United States (SEUS). These climatic indices are often linked with anomalous climatic conditions and thus can be useful to forecast either water surplus or deficit conditions over the region. This study provides an assessment of the watershed‐scale influence of hydroclimatological teleconnections in the context of drought predictability. The interrelationship between several climate indices is assessed with the monthly percentiles of soil water storage (SW), precipitation (PCP), surface run‐off (SURQ), and potential evapotranspiration (PET) for 50 watersheds in the South Atlantic Gulf region of the SEUS. The hydrologic variables are simulated by implementing SWAT models for each watershed at a HUC‐12 resolution for a period of January 1982 through December 2013. The study highlights the strong correlation between the climate indices and watershed‐scale hydrologic variables and provides important insights on the effect of seasonality and the dynamics of water balance components on the predictability of drought at watershed‐scale. Among all hydrologic variables evaluated, soil moisture shows a stronger relationship with the climate indices compared to PCP, SW, and SURQ. The interrelationship between watershed hydrology and climate indices is found to be stronger during fall (September–November) and winter seasons (December–February) with high correlation of SW and PCP with the climate indices, especially in the Carolinas, Georgia, and parts of Florida. Simulated SW corresponds strongly with the Palmer drought severity index (PDSI) in terms of its response to climate indices, indicating that SW can be an effective predictor of drought in the region.

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Investigation of hydrological variability in the Korean Peninsula with the ENSO teleconnections

Cited by 7 publications

References 19 publications

Information Entropy Suggests Stronger Nonlinear Associations between Hydro-Meteorological Variables and ENSO

Information Entropy Suggests Stronger Nonlinear Associations between Hydro-Meteorological Variables and ENSO

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

Effect of hydroclimatological teleconnections on the watershed‐scale drought predictability in the southeastern United States

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