Trend analysis using discrete wavelet transform (DWT) for long-term precipitation (1851–2006) over India

Pandey, Brij Kishor; Tiwari, Harinarayan; Khare, Deepak

doi:10.1080/02626667.2017.1371849

Cited by 51 publications

(21 citation statements)

References 57 publications

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“…Analysis confirmed that the rainfall in the coconut growing areas have shown a statistically significant (at 5 per cent level) decrease over the long-run. This trend has also been evident in the neighbouring India for a long-term precipitation series (Pandy et al, 2017). The number of rainy days also followed a similar downward trend.…”

Section: Rainfall Anomalysupporting

confidence: 65%

Weather variability and coconut production in Sri Lanka: state-contingent analysis

Jayalath¹

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Weather variability presents an external impediment that increases the uncertainty in crop production potential specially for perennial crops under rain-fed cultivation. In particular, increasing rainfall variability that has accompany climate change in the tropics is increasing production uncertainty exposing farmers to a range of risks. Producer behavior under uncertainty is theoretically advanced in Chambers and Quiggin (2000) with the development of the state-contingent approach. This theory advocates farmers manage uncertainty by allocating inputs into different states of nature. This will enhance farmers' capacity to choose variable production technologies to manage uncertainty associated with unexpected events as state-contingent production technologies are naturally more flexible. This thesis examines the application of a state-contingent model for coconut production to assess the impact of a changing climate on coconut production with a view to assess adaptation options for a production system involving long-lived crops. The analysis of production of perennial crops encounters the treatment of long gestation periods, gradual increase in yield over an extended maturation period and yield stabilization following maturity. In considering optimal resource allocation from an agricultural production system perspective, a) the nature of resource constraints, b) the appropriateness of production technology and c) decision makers managerial behaviour needs to be considered together. The analysis in this thesis integrates these three aspects, with a special focus on managing uncertainty in coconut production in Sri Lanka. Climate is a primary determinant of crop choice and weather variability is the major factor influencing agricultural productivity and uncertainty in production outcomes. To examine the impact of changes in climatological variables, rainfall and temperature, on coconut production, a production time series over 1950-2014 was used to identify variations and trends over time and their statistical significance. The graphical and statistical analyses undertaken proved useful in gaining a more complete understanding of how the production environment is changing in the short run as well as over the long run. It offered a sound basis to further consider effective adaptation options in agricultural production, especially long-lived assets such as coconuts, in response to changing states of climate, representing the natural environment in which production takes place. It was revealed that while climate change impacts are observable over longer time horizons, understanding the nature of such changes may allow farmers to adapt to more observable variations in weather over narrow time periods.

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Section: Rainfall Anomalysupporting

confidence: 65%

Weather variability and coconut production in Sri Lanka: state-contingent analysis

Jayalath¹

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“…It has no assumptions for the distribution of variables and is interfered with by few outliers compared with the LR test. Since Kendall [21] put forward the MK test based on the rank correlation method of Mann [22] in 1975, the MK test has been an especially suitable hydrometeorological trend test [23,24]. Li et al [21] adopted the MK test, the moving t-test and the precipitation-runoff double cumulative curve to analyze the impact of climate variation and human activities on the runoff of the Songhua River Basin over the past fifty years.…”

Section: Introductionmentioning

confidence: 99%

“…The time-frequency analysis methods mainly include wavelet transform [1,22], empirical mode decomposition (EMD) series [23], partial spectral analysis [24,25] and singular spectrum analysis [26,27]. The main principle of time-frequency analysis methods is to decompose the signal into a low-frequency component, which reveals the long-term trend, and several high-frequency components, which reveal their periodic characteristics.…”

Section: Introductionmentioning

confidence: 99%

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Intra- and Inter-Annual Variability of Hydrometeorological Variables in the Jinsha River Basin, Southwest China

2019

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In this study, the intra- and inter-annual variability of three major elements in the water system, temperature, precipitation and streamflow, from 1974 to 2010 in the Jinsha River Basin, China, were analyzed. An exploratory data analysis method, namely, moving average over shifting horizon (MASH), was introduced and combined with the Mann–Kendall (MK) test and Sen’s slope estimation to analyze the intra- and inter-annual variations. The combination of MASH with the MK test and Sen’s slope estimation demonstrated that the annual temperature, precipitation and streamflow from 1974 to 2010 showed, on average, an increasing trend. The highest change in temperature was detected in early January, 0.8 ℃, that of precipitation was detected in late June, 0.4 mm/day, and that of streamflow was detected mid-August, 138 mm/day. Sensitivity analysis of the smoothing parameters on estimated trends demonstrated that Y parameters smaller than 2 and w parameters smaller than 6 were not suitable for trend detection when applying the MASH method. The correlation between the smoothed data was generally greater than that between the original hydrometeorological data, which demonstrated that the application of MASH could eliminate the influence of periodicity and random fluctuations on hydrometeorological time series and could facilitate regularity and the detection of trends.

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“…It was initially proposed by Morlet et al [29,30] to analyze the seismic signals in geophysics and was popularized by Grossmann and Morlet [31] and Goupillaud et al [32]. Nowadays, WA becomes a new mathematical approach and is widely applied in geodesy and geophysics [33,34], remote sensing [35,36] and hydrology [37,38]. Unlike the Fourier transform (FT) [39] which maps a time series from time domain to frequency domain, the wavelet transform (WT) can simultaneously describe the time-frequency characteristics of a time series, which is one of the most important properties of the wavelets [40].…”

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confidence: 99%

Signal Extraction from GNSS Position Time Series Using Weighted Wavelet Analysis

Shen

Wang

2020

Remote Sensing

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The daily position time series derived by Global Navigation Satellite System (GNSS) contain nonlinear signals which are suitably extracted by using wavelet analysis. Considering formal errors are also provided in daily GNSS solutions, a weighted wavelet analysis is proposed in this contribution where the weight factors are constructed via the formal errors. The proposed approach is applied to process the position time series of 27 permanent stations from the Crustal Movement Observation Network of China (CMONOC), compared to traditional wavelet analysis. The results show that the proposed approach can extract more exact signals than traditional wavelet analysis, with the average error reductions are 13.24%, 13.53% and 9.35% in north, east and up coordinate components, respectively. The results from 500 simulations indicate that the signals extracted by proposed approach are closer to true signals than the traditional wavelet analysis.Remote Sens. 2020, 12, 992 2 of 16 filter (AWF) [28]. Wavelet analysis (WA) is one of the data-driven approaches to analyzing the non-linear time series. It was initially proposed by Morlet et al. [29,30] to analyze the seismic signals in geophysics and was popularized by Grossmann and Morlet [31] and Goupillaud et al. [32]. Nowadays, WA becomes a new mathematical approach and is widely applied in geodesy and geophysics [33,34], remote sensing [35,36] and hydrology [37,38]. Unlike the Fourier transform (FT) [39] which maps a time series from time domain to frequency domain, the wavelet transform (WT) can simultaneously describe the time-frequency characteristics of a time series, which is one of the most important properties of the wavelets [40]. As a time-frequency analysis method, the WT can decompose the time series into different components according to frequency, and then map the decomposed components at different scales to obtain the information of time series at different resolutions, which is named as multiresolution analysis (MRA) [41]. Due to its superior performance on signal processing, it has also been employed to analyze the GNSS position time series. For preprocessing of GNSS position time series, Wu et al. [42] proposed an improved 3σ method of outlier detection based on WA. Borghi et al. [43] introduced WT to detect discontinuity in GNSS position time series. For noise analysis, Kaczmarek and Kontny [44] identified the noise model using WA and demonstrated that the nature of noise did not depend on the method of modeling the time series. Wu et al. [45] proposed a wavelet-based hybrid approach to remove white noise and flicker noise originating from GNSS position time series. Li et al. [46] introduced a WT-based multiscale multiway principle component analysis (MSMPCA) to eliminate noise in high-rate GNSS data including high-frequency random noise, low-frequency errors and common mode error (CME). For signal analysis, Kaczmarek and Kontny [17] applied wavelet power spectrum analysis to position time series and found that annual signal is more significant than other pe...

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Trend analysis using discrete wavelet transform (DWT) for long-term precipitation (1851–2006) over India

Cited by 51 publications

References 57 publications

Weather variability and coconut production in Sri Lanka: state-contingent analysis

Weather variability and coconut production in Sri Lanka: state-contingent analysis

Intra- and Inter-Annual Variability of Hydrometeorological Variables in the Jinsha River Basin, Southwest China

Signal Extraction from GNSS Position Time Series Using Weighted Wavelet Analysis

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