Assessment of trends in point rainfall using Continuous Wavelet Transforms

Rashid, M. M.; Beecham, Simon; Chowdhury, R. K.

doi:10.1016/j.advwatres.2015.04.006

Cited by 82 publications

(36 citation statements)

References 49 publications

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“…Both analyses are based on a series of smoothed auto-and crosswavelet power spectra (Grinsted, Moore, & Jevrejeva, 2004;Kumar & Foufoula-Georgiou, 1997;Torrence & Compo, 1998), which require the calculation of wavelet coefficients at different scales and locations for each variable. We only briefly introduce the equations related to the bivariate wavelet coherency and MWC in this section, and readers can refer to previous studies for the details (Carey et al, 2013;Das & Mohanty, 2008;Graf et al, 2014;Grinsted et al, 2004;Hu & Si, 2016;Mullon, Chang, Yang, & Weiss, 2013;Perez-Valdivia, Sauchyn, & Vanstone, 2012;Rashid, Beecham, & Chowdhury, 2015;Schaefli, Maraun, & Holschneider, 2007;Si, 2008;Torrence & Webster, 1999).…”

Section: Theorymentioning

confidence: 99%

Temporally stable patterns but seasonal dependent controls of soil water content: Evidence from wavelet analyses

Cheng

Biswas

et al. 2017

Hydrological Processes

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Scale‐ and location‐dependent relationships between soil water content (SWC) and individual environmental factors have been widely explored. SWC is controlled by multiple factors concurrently; however, the multivariate relationship is rarely explored at different scales and locations. Multivariate controls of SWC at different scales and locations in two seasons within a hummocky landscape of North America were identified using bivariate wavelet coherency and multiple wavelet coherence. Results showed that depth to CaCO3 layer, which was correlated with elevation over all locations at scales of 36–144 m and cos(aspect), provided the best individual factor for explaining SWC variations in spring (May 2) and summer (August 23), respectively. Although spatial patterns of SWC were temporally stable, different topographic indices affected spatial distribution of SWC in different seasons (elevation in spring and aspect in summer) due to different dominating hydrological processes. These varying hydrological processes also resulted in the distinct role of soil organic carbon (SOC) content in different seasons: a positive correlation in spring and a negative correlation in summer. Multiple wavelet coherence identified a combination of depth to CaCO3 layer and SOC in spring and a combination of cos(aspect) and SOC in summer that controlled SWC at different scales and locations, respectively. This indicated a combined effect of soil and topographic properties on SWC distribution and a clear need for these two factors in developing scale‐dependent prediction of SWC in the hummocky landscape of North America.

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Section: Theorymentioning

confidence: 99%

Temporally stable patterns but seasonal dependent controls of soil water content: Evidence from wavelet analyses

Cheng

Biswas

et al. 2017

Hydrological Processes

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“…Trend analysis is usually conducted to investigate the long-term systematic changes in climatic variables (Wang et al, 2006;Dinpashoh et al, 2011;Sonali and Nagesh Kumar, 2013;Goyal, 2014;Li et al, 2015;Rashid et al, 2015;Zhang et al, 2015;Araghi et al, 2018;Şen, 2018). Different trend detection methods (e.g., parametric, nonparametric and Bayesian) have been developed to assess trends in hydroclimatic time series (Reeves et al, 2007;Khaliq et al, 2009;Sonali and Nagesh Kumar, 2013;Dabanlı et al, 2016) which include Mann-Kendall (MK) test, Sen's slope estimator, Spearman's rho tests, linear regression and innovative trend test (Şen method).…”

Section: Introductionmentioning

confidence: 99%

Spatial assessment of meteorological drought features over different climate regions in Iran

Sharafati

Nabaei

Shahid

2019

Intl Journal of Climatology

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Iran has a predominantly arid and semi‐arid climate where the drought hazards and their variability are the crucial concerns for water resources management. This study assesses the characteristics and trends of meteorological droughts features in different climate regions of Iran. Monthly rainfall data are analysed using Standardized Precipitation Index (SPI) to reconstruct meteorological droughts for different time steps (1, 3, 6, 9 and 12). The drought events are decomposed into three features, namely Severity (S), Duration (D) and Peak (P) to assess their spatial variations in different climate regions. The Mann–Kendall (MK) test is used to detect the trends in drought features in different climate regions. The percentage area (PA) of increasing/decreasing trend in drought features is computed to assess the spatial variations of trends in each region. Besides, SPI is compared with Standardized Precipitation Evapotranspiration Index (SPEI) to understand the importance of evapotranspiration on meteorological droughts. The high correlations between SPI and SPEI indicate that the results obtained using SPI would not vary if a similar analysis is conducted using SPEI. The results obtained using SPI reveal that short‐duration droughts (6 months or less) are more frequent and severe in the northwest and the north along the Caspian plains while the longer‐duration droughts (more than 6 months) are more frequent and severe in the south, southeast and southwest. Overall, the northwest region of Iran is found most susceptible to droughts in terms of S, P and D. Moderate (40% < PA < 60%) or significant (PA > 60%) increase in the drought features are observed in all climate regions for shorter SPI (3 months or less) except northwest, while no drought feature is found to increase significantly for SPI‐12. Overall, the higher increase in drought S, P and D are found in the central arid region and at higher altitudes.

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“…For example, Torrence and Compo [26] operated continuous wavelet transform and Labat [27] and Schaefli et al [28] conducted overviews or applications of wavelet analysis from different aspects or research fields. Rashid et al [29] used continuous wavelet transform to assess trends in point rainfall. Kuang et al [30] used continuous wavelet transform to reveal the multiscale structure, break point, change and distribution of periodic variation in runoff series of the Yangtze River basin in China.…”

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confidence: 99%

Spatiotemporal Variability of Monthly and Annual Snow Depths in Xinjiang, China over 1961–2015 and the Potential Effects

Liu

et al. 2019

Water

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The spatiotemporal variability of snow depth supplies important information for snow disaster prevention. The monthly and annual snow depths and weather data (from Xinjiang Meteorological Observatory) at 102 meteorological stations in Xinjiang, China over 1961-2015 were used to analyze the spatiotemporal characteristics of snow depths from different aspects. The empirical orthogonal function (EOF), the modified Mann-Kendall method, Morlet wavelet, Daubechies wavelet decomposition and cross wavelet transform were applied to investigate the trend and significance, spatial structure, periods, decomposed series and coherence of monthly and annual snow depths. The results indicated that: (1) The value of EOF first spatial mode (EOF1) of the monthly and annual snow depths in north Xinjiang were larger than south Xinjiang, indicating greater variability of snow depths in north Xinjiang. (2) The change points of annual snow depth mainly occurred during 1969-1979 and 1980-1990. The annual snow depth of most sites showed increasing trends, but with different slope magnitudes. (3) The sites that had main periods of 2-8 and 9-14 years of monthly and annual snow depths (detected by the Morlet wavelet) mainly distributed in northern Xinjiang. The sites that had main periods of 15-20 years of monthly and annual snow depths mainly distributed in southwestern Xinjiang. (4) By using the Daubechies wavelet, the decomposed annual snow depth in entire Xinjiang tended to increase. (5) Through the cross wavelet transform, annual snow depths in entire Xinjiang had good correlations with annual precipitation or relative humidity, and showed a low negative correlation with minimum temperature or sunshine hours. In conclusion, the monthly and annual snow depths had comprehensive spatiotemporal variability but had overall increasing trend during 1961-2015.There have been several research projects on spatial and temporal variation analysis, simulation and remote sensing analysis of snow depth, conducted at daily, monthly, or annual scales and in different parts of the world [7,8]. Liu et al. [9] used wavelet analysis to analyze the periodic characteristics of maximum snow depth in northern Xinjiang over 1961-2008. They found that the maximum snow depth had a main period of 7-11 years in northern Xinjiang. Hu et al. [10] analyzed the correlation coefficient between the annual snow depth and the annual precipitation (or average temperature) in Xinjiang over 1960-2011. The annual snow depth had a good correlation with the annual precipitation, but a low correlation with the average temperature. Xu et al. [11] used a linear model to investigate the spatiotemporal characteristics of daily snow depth and the snow-cover days in the Qinghai-Tibetan Plateau over 1961-2010. They revealed that snow cover was controlled by multiple climate factors rather than a single variable. Ding et al. [6] used the modified Mann-Kendall test to analyze trends and significance of daily snow depth at 105 stations (spread in the whole area) in Xinjiang, China over 19...

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Assessment of trends in point rainfall using Continuous Wavelet Transforms

Cited by 82 publications

References 49 publications

Temporally stable patterns but seasonal dependent controls of soil water content: Evidence from wavelet analyses

Temporally stable patterns but seasonal dependent controls of soil water content: Evidence from wavelet analyses

Spatial assessment of meteorological drought features over different climate regions in Iran

Spatiotemporal Variability of Monthly and Annual Snow Depths in Xinjiang, China over 1961–2015 and the Potential Effects

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