Trend analysis of rainfall in four meteorological subdivisions of southern India using nonparametric methods and discrete wavelet transforms

Adarsh, S.; Reddy, M. Janga

doi:10.1002/joc.4042

Cited by 114 publications

(58 citation statements)

References 57 publications

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“…The SQMK test estimates a sequence of MK values (for different time instants), and the intersection of progressive and retrograde series indicates a trend turning point. More details of this test can be found elsewhere (Adarsh & Janga Reddy, ; Gerstengarbe & Werner, ; Modarres & Sarhadi, ). The results of SQMK test of the SPI3 series for the three subdivisions are presented in Figure .…”

Section: Resultsmentioning

confidence: 99%

“…The monthly rainfall data of Kerala, Telangana, and Orissa subdivisions for the period 1871–2012 was collected from Indian Institute of Tropical Meteorology Pune (htpp://http://www.tropmet.res.in) and used for determining SPI3 values of the three regions. In the past, several researchers have used these datasets for spatio‐temporal trend analysis of rainfall and droughts over different regions in India (Adarsh & Janga Reddy, ; Ganguli & Janga Reddy, ; Joshi et al, ; Thomas & Prasannakumar, ). The time series of the monthly rainfall data of the three subdivisions are presented in Figure .…”

Section: Study Area and Datasetsmentioning

confidence: 99%

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Evaluation of trends and predictability of short‐term droughts in three meteorological subdivisions of India using multivariate EMD‐based hybrid modelling

Adarsh

Reddy

2018

Hydrological Processes

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This paper presented trend analysis of droughts in Kerala, Telangana, and Orissa meteorological subdivisions in India and proposed a framework for drought prediction by employing the Empirical Mode Decomposition (EMD)‐based prediction models. The study used 3‐month standardized precipitation index (SPI3) for drought analysis. The trend analysis of SPI3 series for the period 1871–2012 using Mann–Kendall method showed statistically significant increasing trend in Kerala and Telangana subdivisions and a decreasing trend in Orissa subdivision. In addition, the non‐linear trend component extracted from EMD showed statistically significant changes in all the three subdivisions. Then, the study proposed a hybrid approach for prediction of short‐term droughts by coupling multivariate extension of EMD (MEMD) with stepwise linear regression (SLR) and genetic programming (GP) methods. First, the multivariate dataset comprising the SPI3 series of current and lagged time steps are decomposed using the MEMD. Then, SLR/GP models are developed to predict each subseries of SPI3 of desired time step considering the subseries of predictor variables at the corresponding timescales as inputs. The resulting models at different timescales are recombined to obtain the SPI3 values of the desired time step. The method is applied for prediction of short‐term droughts in the three subdivisions. The results obtained by the hybrid models are compared with that obtained by conventional prediction models using M5 Model Trees and GP. The rigorous performance evaluation based on multiple statistical criteria clearly exhibited the superiority of the hybrid approaches (i.e., prediction models with MEMD‐based decomposition over models without decomposition) for prediction of SPI3 in three subdivisions. Further, the study found that MEMD‐GP model performs marginally better than the MEMD‐SLR model due to its efficacy in modelling high frequency modes.

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Section: Resultsmentioning

confidence: 99%

Section: Study Area and Datasetsmentioning

confidence: 99%

Evaluation of trends and predictability of short‐term droughts in three meteorological subdivisions of India using multivariate EMD‐based hybrid modelling

Adarsh

Reddy

2018

Hydrological Processes

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“…In the past, several studies conducted to analyze the rainfall trends over different regions of India (Mohapatra et al 2003;Kumar et al 2010;Krishnakumar et al 2009;Jain et al 2013;Adarsh and Janga Reddy 2015a). Few studies also dealt with the characterization and forecasting of monsoon rainfall over India (Walker 1933;Shukla and Paolino 1983;Kripalani and Kulkarni 1997;Bhattcharya and Narasimha 2007).…”

Section: Related Workmentioning

confidence: 97%

“…moisture, and groundwater reserves (Jain et al 2013;Adarsh and Janga Reddy 2015a). In recent past, several studies reported that climate is changing with warming of the atmosphere and ocean system, and resulting in changes in rainfall amounts and hydrological extremes and causing increase of floods and droughts worldwide (IPCC 2007).…”

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confidence: 99%

Time–frequency characterization of sub-divisional scale seasonal rainfall in India using the Hilbert–Huang transform

Reddy

Adarsh

2015

Stoch Environ Res Risk Assess

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Time-frequency characterization is useful in understanding the nonlinear and non-stationary signals of the hydro-climatic time series. The traditional Fourier transform, and wavelet transform approaches have certain limitations in analyzing non-linear and non-stationary hydro-climatic series. This paper presents an effective approach based on the Hilbert-Huang transform to investigate time-frequency characteristics, and the changing patterns of sub-divisional rainfall series in India, and explored the possible association of monsoon seasonal rainfall with different global climate oscillations. The proposed approach integrates the complete ensemble empirical mode decomposition with adaptive noise algorithm and normalized Hilbert transform method for analyzing the spectral characteristics of two principal seasonal rainfall series over four meteorological subdivisions namely Assam-Meghalaya, Kerala, Orissa and Telangana subdivisions in India. The Hilbert spectral analysis revealed the dynamic nature of dominant time scales for two principal seasonal rainfall time series. From the trend analysis of instantaneous amplitudes of multiscale components called intrinsic mode functions (IMFs), it is found that both intra and inter decadal modes are responsible for the changes in seasonal rainfall series of different subdivisions and significant changes are noticed in the amplitudes of inter decadal modes of two seasonal rainfalls in the four subdivisions since 1970s. Further, the study investigated the links between monsoon rainfall with the global climate oscillations such as Quasi Bienniel Oscillation (QBO), El Nino Southern Oscillation (ENSO), Sunspot Number (SN), Atlantic Multidecadal Oscillation (AMO) etc. The study noticed that the multiscale components of rainfall series IMF1, IMF2, IMF3, IMF4 and IMF5 have similar periodic structure of QBO, ENSO, SN, tidal forcing and AMO respectively. As per the seasonal rainfall patterns is concerned, the results of the study indicated that for Assam-Meghalaya subdivision, there is a likelihood of extreme rare events at *0.2 cycles per year, and both monsoon and pre-monsoon rainfall series have decreasing trends; for Kerala subdivision, extreme events can be expected during monsoon season with shorter periodicity (*2.5 years), and monsoon rainfall has statistically significant decreasing trend and post-monsoon rainfall has a statistically significant increasing trend; and for Orissa subdivision, there are chances of extremes rainfall events in monsoon season and a relatively stable rainfall pattern during post-monsoon period, but both monsoon and post-monsoon rainfall series showed an overall decreasing trend; for Telangana subdivision, there is a likelihood of extreme events during monsoon season with a periodicity of *4 years, but both monsoon and post-monsoon rainfall series showed increasing trends. The results of correlation analysis of IMF components of monsoon rainfall and five climate indices indicated that the association is expressed well only for low frequency modes wit...

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“…Wavelet analysis has been extensively employed to determine the non-stationary trends and periodicities in the analysis of various hydrological and meteorological variables [34][35][36][37]. In order to better analyze the trends and the fluctuating patterns of the hydrological variables, the wavelet transform has been recently used in conjunction with the MK test [20,[38][39][40]. Partal and Küçük (2006) [22] firstly co-utilized the wavelet transform and the original MK test to find which periodicities are mainly responsible for the trends of the annual total precipitation series and found that the trend analysis on detailed components of the precipitation time series resulting from the discrete wavelet transform (DWT) can clearly explain the trend structure of data.…”

Section: Introductionmentioning

confidence: 99%

Investigating Trends in Streamflow and Precipitation in Huangfuchuan Basin with Wavelet Analysis and the Mann-Kendall Test

Chen

Guan

Guang-hua

et al. 2016

Water

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Abstract:This study aims to investigate trends in streamflow and precipitation in the period 1954-2010 in a semiarid region of the Yellow River watershed, Huangfuchuan basin, China. The combination of the wavelet transform and different Mann-Kendall (MK) tests were employed to figure out the basic trends structure in streamflow and precipitation and what time scales are affecting the observed trends. The comparative analysis with five MK test methods showed that the modified MK tests with full serial correlation structure performed better when significant autocorrelations exhibited for more than one lag. Three criteria were used to determine the optimal smooth mother wavelet, the decomposition level and the extension mode used in the discrete wavelet transform (DWT) procedure. The first criteria referred to the relative error of the wavelet approximated component and the original series. The second one was the relative error of MK Z-values of approximation component and the original series. Additionally, a new criterion (Er), based on the relative error of energy between the approximate component and the original series, was proposed in this study, with better performance than the previous two criteria. Further, a new powerful index, the energy of the hydrological time series, was proposed to verify the dominant periodic components for the observed trends. The analysis indicated that all monthly, seasonal and annual streamflow showed significant decreasing trends, while no significant trends were found in precipitation. Results from the DWT and MK tests revealed that the main factors influencing the trends in the monthly and seasonal series in Huangfuchuan watershed are intra-annual cycles, while the leading factors affecting the trends in the annual series are decadal events. Different driving factors (e.g., seasonal cycles, solar activities, etc.) related to the periodicities identified in these data types resulted in this discrepancy.

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Trend analysis of rainfall in four meteorological subdivisions of southern India using nonparametric methods and discrete wavelet transforms

Cited by 114 publications

References 57 publications

Evaluation of trends and predictability of short‐term droughts in three meteorological subdivisions of India using multivariate EMD‐based hybrid modelling

Evaluation of trends and predictability of short‐term droughts in three meteorological subdivisions of India using multivariate EMD‐based hybrid modelling

Time–frequency characterization of sub-divisional scale seasonal rainfall in India using the Hilbert–Huang transform

Investigating Trends in Streamflow and Precipitation in Huangfuchuan Basin with Wavelet Analysis and the Mann-Kendall Test

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