Long-term trend analysis using discrete wavelet components of annual precipitations measurements in Marmara region (Turkey)

Partal, Turgay; Küçük, Murat

doi:10.1016/j.pce.2006.04.043

Cited by 203 publications

(140 citation statements)

References 25 publications

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“…In the literature, numerous approaches and applications of DWT can be found: the analysis of trends in streamflow and precipitation (Nalley et al 2012;Partal and Kucuk 2006), the assessment of long-term trends in geomagnetic activity (De Artigas et al 2006), the detection and location of seismic events d'Auria et al 2010;Galiana-Merino et al 2007Hafez et al 2010), and the analysis of geophysical data series (Percival 2008).…”

Section: Discrete Wavelet Transformmentioning

confidence: 99%

Assessment of CO2 dynamics in subsurface atmospheres using the wavelet approach: from cavity–atmosphere exchange to anthropogenic impacts in Rull cave (Vall d′Ebo, Spain)

et al. 2016

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Section: Discrete Wavelet Transformmentioning

confidence: 99%

Assessment of CO2 dynamics in subsurface atmospheres using the wavelet approach: from cavity–atmosphere exchange to anthropogenic impacts in Rull cave (Vall d′Ebo, Spain)

et al. 2016

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“…Some important features of DWT are as follows: (i) the wavelet spectra generated are in discrete steps and give a very compact representation of the signal; and (ii) results of transformations using DWT remove the redundant information within the wavelet coefficients in order to better identify drought characteristics contained in signals (Nalley et al 2012). DWT adopts the following form (Partal and Küçük 2006):…”

Section: Cluster Analysis Of Drought Via Discrete Wavelet Transform (mentioning

confidence: 99%

“…This is the DWT dyadic grid arrangement (i.e., integer powers of two; logarithmic scaling of the translations and dilations). If a time series exhibits discrete properties, with a value of x i , occurring at a discrete time t, the wavelet coefficient (Ww(m, n)) for the DWT becomes (Partal and Küçük 2006;Nalley et al 2012):…”

Section: Cluster Analysis Of Drought Via Discrete Wavelet Transform (mentioning

confidence: 99%

“…The wavelet coefficient for the DWT is calculated at scale s = 2 m and location c = 2 m n, revealing the variation of signals at different scales and locations (Partal and Küçük 2006;Nalley et al 2012).…”

Section: Cluster Analysis Of Drought Via Discrete Wavelet Transform (mentioning

confidence: 99%

“…Discrete wavelet transform (DWT) is well suited to the study of multi-scale and non-stationary precipitation properties over finite spatial and temporal domains (Aldrich 2012;Kumar and Foufoula-Georgiou 1997;Nalley et al 2012). Partal and Küçük (2006) suggested that DWT plays an important role in analyzing precipitations data as its local analysis and multi-resolution decomposition make the analysis process more efficient and accurate. Considering the dynamic characteristics and non-uniform distribution of precipitation data and the need for homogeneous regions in water resource management, cluster analysis based on DWT is employed for exploring the temporal-spatial characteristics of drought time series.…”

Section: Introductionmentioning

confidence: 99%

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Spatial and temporal characteristics of droughts in the Northeast China Transect

et al. 2014

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In this study, drought trends and change magnitudes of the Northeast China Transect (NECT) were analyzed using the Mann-Kendall test and Theil-Sen's slope estimator. Meteorological data from 20 meteorological stations of NECT region from 1957 to 2012 were used. Results demonstrated that five stations had significant negative trends in precipitation. The magnitudes of the significant negative trends at the 95 % confidence level varied from -2.41 ± 1.05 mm year -1 at Tonghe station to -1.11 ± 0.51 mm year -1 at Qianguoerluosi station. Analysis of the seasonal precipitation series showed a mix of negative and positive trends. Many stations also exhibited strong contrasting seasonal trends that counterbalanced one other at the yearly level. In addition, cluster analysis based on discrete wavelet transform (DWT) was applied to the standard precipitation index (SPI) series. Results revealed three different and spatially well-defined subregions (east, center and west regions of NECT). Due to the decrease in precipitation from the east to the west, land use varies from forest regions in the east, to agriculture in the center, to pastoral areas in the west. Characteristics of drought events for each representative station of different subregions are explored using temporal evolution of the SPI values. Results showed that severe or extreme droughts occurred in 2001, 2003 in Tonghe, 1980 in Tongliao and 2005 123Nat Hazards (2015) 76:601-614 DOI 10.1007/s11069-014-1507 has great potential for examining spatial coherence of regional drought, which was consistent with not only the precipitation spatial distribution but also the characteristics of land use in the study area. This study not only provides important information on drought variability in the NECT, but also provides useful information for improving water management strategies and planning agricultural practices.

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Temporal and spatial characteristics of pan evaporation trends and their attribution to meteorological drivers in the Three‐River Source Region, China

Wang

Zhao

et al. 2015

JGR Atmospheres

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Pan evaporation is an important indicator of atmospheric evaporative demand, and its long-term variation is of much concern in studies of climate change. Based on data from 33 meteorological stations from 1962 to 2012, this work considered the temporal and spatial trends of pan evaporation and the meteorological variables that affect them in the Three-River Source Region (TRSR) of southwestern China. Pan evaporation in the TRSR has decreased significantly since 1988 with an obvious abrupt change from 1993 to 2003. Furthermore, a 27 year period of oscillation has existed over the past 51 years. Pan evaporation reflects the combined effects of four meteorological variables: net radiation (R n ), wind speed (u 2 ), actual vapor pressure (e a ), and air temperature (T a ). Based on this research, a number of conclusions were drawn. (1) The pace of climate change increased after 1980 and pan evaporation decreased at a rate of À13.3 mm/a 2 from 1980 to 2012, which is much faster than the rate of À1.2 mm/a 2 from 1962 to 1979. (2) For the decrease of pan evaporation from 1980 to 2012, the quantifying contributions of R n , u 2 , e a , and T a were À8.7, À6.4, À1.8, and +3.6 mm/a 2 , respectively. Thus, it was established for the TRSR that "global dimming" was the main reason, and "wind stilling" was a close second to global dimming for the decrease in pan evaporation. (3) Different regions of the TRSR are affected differently by the effects of the meteorological variables. Low-elevation regions in the TRSR are more susceptible to the effects of net radiation and wind speed, whereas high-elevation regions are affected more by actual vapor pressure and air temperature.

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Long-term trend analysis using discrete wavelet components of annual precipitations measurements in Marmara region (Turkey)

Cited by 203 publications

References 25 publications

Assessment of CO2 dynamics in subsurface atmospheres using the wavelet approach: from cavity–atmosphere exchange to anthropogenic impacts in Rull cave (Vall d′Ebo, Spain)

Assessment of CO2 dynamics in subsurface atmospheres using the wavelet approach: from cavity–atmosphere exchange to anthropogenic impacts in Rull cave (Vall d′Ebo, Spain)

Spatial and temporal characteristics of droughts in the Northeast China Transect

Temporal and spatial characteristics of pan evaporation trends and their attribution to meteorological drivers in the Three‐River Source Region, China

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