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In this study, a TEC variation window value was selected based on the wavelet power spectrum method to analyze the seismic–ionospheric coupling relationship. In the full-time domain, a 27-day periodicity of the wavelet power spectrum was obtained that passed the 95% significance test. The sliding interquartile range method was used to analyze earthquakes above Mw6.5 in Japan from 2011 to 2022, excluding the hybrid effects between earthquakes close to one another. The sunspot number (SSN), 10.7 cm radio flux (F10.7), total solar irradiance (TSI), solar wind velocity (Vsw), geomagnetic activity index in the equatorial region (DST), and global geomagnetic activity index (KP) were used as indices representing solar and geomagnetic activity. After removing solar and geomagnetic interference from ionospheric anomaly changes using the sliding interquartile range method, the TEC anomaly changes before the earthquake were verified as being caused by the earthquake and analyzed. The statistical analysis of ionospheric total electron content (TEC) anomalies showed that earthquake magnitude was positively correlated with the amplitude of TEC anomalies but not linearly. The occurrence time of ionospheric anomalies lagged behind to some extent with the increase in earthquake magnitude. Additionally, abnormal changes on the 29th day (15 February 2022) before the 20th earthquake did not conform to previous research rules. According to the lithosphere–atmosphere–ionospheric coupling (LAIC) mechanism and global ionospheric map (GIM) studies, the TEC anomaly was consistent with the vertical projection of the epicenter with obvious regularity. The results show that these TEC anomalies may be related to earthquakes. [ABSTRACT FROM AUTHOR]

This paper evaluated the performance of OECD's Composite Leading Indicator using the Continuous Wavelet Transform. We used various wavelet tools ‐ wavelet coherency, wavelet phase and wavelet phase difference ‐ to assess the co‐movement between the Composite Leading Indicator and three macroeconomic variables ‐ Industrial Production Index, unemployment rate, and real GDP Growth ‐ at each frequency and moment. We also explored the lead–lag relationship between each pair of variables across time and frequency. We concluded that OECD's Composite Leading Indicator is a useful leading indicator of the Industrial Production Index and can be suited for forecasting the unemployment rate. However, it exhibited poor performance regarding GDP growth. [ABSTRACT FROM AUTHOR]

In this study, a TEC variation window value was selected based on the wavelet power spectrum method to analyze the seismic–ionospheric coupling relationship. In the full-time domain, a 27-day periodicity of the wavelet power spectrum was obtained that passed the 95% significance test. The sliding interquartile range method was used to analyze earthquakes above Mw6.5 in Japan from 2011 to 2022, excluding the hybrid effects between earthquakes close to one another. The sunspot number (SSN), 10.7 cm radio flux (F10.7), total solar irradiance (TSI), solar wind velocity (Vsw), geomagnetic activity index in the equatorial region (DST), and global geomagnetic activity index (KP) were used as indices representing solar and geomagnetic activity. After removing solar and geomagnetic interference from ionospheric anomaly changes using the sliding interquartile range method, the TEC anomaly changes before the earthquake were verified as being caused by the earthquake and analyzed. The statistical analysis of ionospheric total electron content (TEC) anomalies showed that earthquake magnitude was positively correlated with the amplitude of TEC anomalies but not linearly. The occurrence time of ionospheric anomalies lagged behind to some extent with the increase in earthquake magnitude. Additionally, abnormal changes on the 29th day (15 February 2022) before the 20th earthquake did not conform to previous research rules. According to the lithosphere–atmosphere–ionospheric coupling (LAIC) mechanism and global ionospheric map (GIM) studies, the TEC anomaly was consistent with the vertical projection of the epicenter with obvious regularity. The results show that these TEC anomalies may be related to earthquakes.

This study investigates the temporal variability of the northeast monsoon rainfall (NEMR) in India and its relationship with El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) using wavelet analysis. The results show that NEMR is highly variable, with a coefficient of variation of about 25%, which is due to its dependence on dynamic systems with a wide range of spatial and temporal scales. The temporal variability of NEMR is at different time scales ranging from intra-annual to interannual to decadal time scales. The large-scale circulation features, ENSO and IOD, also exhibit variability at different time scales. El Niño events have a typical frequency range of 2 to 7 years, with a prolonged impact that can last for several months, spreading across the globe. Wavelet transforms of SOI and Nino 3.4 have revealed variability at the time scale of 2–8 years, which is in line with the observed ENSO cycle. The wavelet transform of DMI displays significant variability in short time scales, typically less than 2–4 years. The study also finds a strong negative relationship between NEMR and SOI, recorded in September, especially over Tamil Nadu, Kerala, and Coastal Andhra Pradesh during the periods 1900–1920 and 1980–2000. This correlation is strong at time scales of 2–4 years and 4–8 years during the periods 1880–1920, possibly related to the ENSO cycle and the oscillation of anomalous SST over the Indian Ocean. The study highlights the need for further studies to understand the physical processes that contribute to the observed variability in El Niño and other ocean–atmosphere interactions. [ABSTRACT FROM AUTHOR]

Key points 1. To explain the data of the 2021 earthquake swarm in Hualien 2. To describe the Morlet wavelet and fluctuation methods 3. Results show that the memory effect is stronger for the time sequence of magnitudes than for that of inter-event times and higher for M L ≥ 3 events than for M L ≥ 4 ones; and the short- term corrected memory effect was operative in the Hualien swarms

The gravest challenge for economic sustainability is the undetermined growth in the financial and economic risks of the nation, which need to be overcome with adequate measures without compromising economic growth. The uncertainty of economic factors produces fluctuations in the financial sector and makes them more vulnerable. However, the existing literature has not significantly focused on the economic and financial risk challenge for sustainable economic growth. Therefore, to fill the gap, an in-depth study is imperative to explore the association between these risks. To do so, this study incorporates both economic and financial risk to determine how risks are interconnected across time (frequency) and how they are linked by utilizing quarterly data from 1984-Q1 to 2020-Q4 and by applying both the “wavelet power spectrum (WPS)” and “wavelet coherence (WTC)” approaches, to examine the time-frequency dependency of each variable on the other. The findings of WTC revealed that the economic and financial risks have a positive dependency on each other in India at high, medium, and low frequencies. Likewise, the wavelet power spectrum outcomes reflect the high economic and financial risks vulnerability during 1991, 1992, and 1996. In addition, for the robustness check, the study employed both the “quantile regression (QR)” and “quantile-on-quantile regression (QQR)”. Both the QQR and QR endorsed the positive association between FR and ER. Hence, our paper is the first research of its kind for the Indian economy, and it extends to the existing literature by examining the link between the two most significant indicators in terms of both time and frequency dependency. The findings in our paper offer excellent perspectives for investors and policymakers to assess prospects for investment and policy changes if necessary.

The Bank of Zambia has one of the highest central bank turnover averages in Africa and the inflationary effects of the low central bank autonomy are yet to be empirically quantified. This study develops an index of central bank turnover for the Zambian economy extending from 1984 to 2020 and applies continuous wavelet coherence techniques to examine its co-movement with inflation in a time-frequency domain. We find negative co-movements between the variables across the entire time window with higher (lower) frequency correlations being dominant in the pre-2000 (post-2000) period. Moreover, the phase difference dynamics indicate that central bank turnover has led inflation in all periods except during the 1990–1995 and 2016–2020 periods where reverse causality is observed. Altogether, our study paints a detailed narrative on the relationship between the variables for the Zambian economy and offers policy advice aimed at strengthening the independence and credibility of the country’s central bank.

While we can say that there is a global market for crude oil, we cannot say the same for natural gas. There is a strand of literature that argues that, in the last decades, gas markets have become less regional and more global. We use wavelets to test this hypothesis and conclude otherwise: although the European and Japanese gas markets are significantly synchronized, they are much less than the oil markets, which we take as the benchmark. We also show that the North American gas market fluctuations are independent of the other gas markets. Finally, we show that the existing synchronization between gas markets almost vanishes once one filters out the effect of oil price variations, suggesting that it is the global oil market that connects the regional gas markets. [ABSTRACT FROM AUTHOR]

Wavelet power spectrum (WPS) techniques were used to evaluate the temporal structure of the clearness index and relative sunshine duration, using monthly values of clearness index and relative sunshine duration between 1995 and 2010. The significant years are featured by high wavelet coefficients of both the clearness index and relative sunshine duration. Low wavelet coefficients noticed during the selected years might emerge from the increase in turbidity and cloudiness, indicating that cloudiness has the greater impact on the solar radiation resulting in weak sky condition. Bootstrap method was also used to acquire regression correlations between clearness index and relative sunshine duration. The correlation coefficient between the monthly values of clearness index and relative sunshine duration for ABK (0.32), ENU (0.31), JOS (0.40), MAID (0.46), OWE (0.25), PH (0.46), SOK (0.65), YOL (0.58). The climatic condition and weather patterns of locations have an impact on the amount solar energy received.

The wavelet power spectrum and nonlinear dynamics time series techniques were used to examine the ionospheric electric current disturbance (Diono) during the geomagnetic storms of 9–11 November 2004 and 14–16 May 2005. These storms were characterized by high solar wind speed from coronal mass ejections (CMEs). The time series of the solar wind parameters (interplanetary magnetic field, IMF Bz and solar wind speed, Vx) and geomagnetic indices (Symmetric disturbance field, SYM-H, eastward electrojet, AU and westward electrojet AL), were used to monitor solar wind conditions and the geomagnetic perturbation, respectively. The horizontal component of the geomagnetic field obtained from six ground-based magnetometer stations was also analyzed. The high latitudes stations (ESK, NUR, LER and SOD) show higher energy wavelet coefficient of Diono with high frequencies, due to the existence of ionospheric currents at high latitudes during geomagnetic storm. The mid and low latitude stations (HER and AAE) display low energy wavelet coefficient with low frequency. The low latitude geomagnetic stations display high values of Lyapunov exponent (LE) during 9–11 November 2004 and 14–16 May 2005 of the geomagnetic storm. The low values of Lyapunov exponent might be as result of the internal dynamics restructuring during geomagnetic storm. The electrodynamics of the magnetosphere-ionosphere due to solar wind influenced the chaotic signature of Diono. The solar wind is a stochastic driver which contributes in the generation of geomagnetic disturbance in the Earth's magnetosphere due to CMEs that generates the geomagnetic storms. [ABSTRACT FROM AUTHOR]

An earthquake sequence that occurred in the Hualien area during April 7 to August 30, 2021 has been recognized as two swarms by the Central Weather Bureau. Its earthquakes with ML ≤ 6.2 (ML = local magnitude) and focal depths ≤ 25 km were located in an area from 23°46’ N to 24°04’ N and from 121°25’ E to 121°42’ E. The Morlet wavelet technique is applied to analyze the dominant periods of temporal variations in numbers of daily events for the earthquake sequence in two magnitude ranges, i.e., ML ≥ 3 and ML ≥ 4. Results show that the dominant periods are 30.8 and 38.0 days when ML ≥ 3; while the dominant period does not exist when ML ≥ 4. The fluctuation analysis technique in the natural time domain is used to study the memory effect in the swarm for two magnitude ranges, i.e., ML ≥ 3 and ML ≥ 4. Calculated results show that the memory effect is stronger for the time sequence of magnitudes than for that of inter-event times and higher for ML ≥ 3 earthquakes than for ML ≥ 4 events. Consequently, only the short-term corrected memory effect was operative in the earthquake sequence of the Hualien swarms.Key points: To explain the data of the 2021 earthquake swarm in Hualien To describe the Morlet wavelet and fluctuation methods Results show that the memory effect is stronger for the time sequence of magnitudes than for that of inter-event times and higher for ML ≥ 3 events than for ML ≥ 4 ones; and the short- term corrected memory effect was operative in the Hualien swarms [ABSTRACT FROM AUTHOR]

This study examined the ionospheric variation over the African low-latitude region using Total Electron Content (TEC) and its wavelet power spectrum (WPS). TEC data were obtained from five Global Positioning System (GPS) stations located in the trough and crests of the equatorial ionization anomaly (EIA) in both hemispheres during quiet periods of the maximum phase of solar cycle 24 (2012–2014). Our results revealed a striking equinoctial asymmetry (EA) with stronger TEC and higher solar flux in September equinox than March equinox of 2012–2013, with the reverse being the case in 2014. As such, the difference in solar flux between both equinoxes might have played a dominant role in driving the EA. Furthermore, we found a hemispheric asymmetry in TEC with higher value towards the crest in the northern hemisphere (southern hemisphere) in September equinox (March equinox) of 2012–2013. In 2014, nevertheless, TEC was remarkably higher towards the crest located in the southern hemisphere in September equinox only. The chaotic time series methods used to examine the TEC variations showed positive Lyapunov exponents (LE) at all stations. This indicated the presence of chaoticity in the African EIA. Nevertheless, stations located towards the southern crest (Eldoret and Dodoma) had the maximum LE values while those at the magnetic equator (Adis Ababa) and northern crest (Nama and Al Wajh) had the minimum values. Consequently, the changes in the internal dynamics of the equatorial ionosphere have impacts on its chaotic behavior. • The nonlinear time series were used to study the ionospheric variation using TEC data. • The TEC over the African region during quiet days between 2012 and 2014 were examined. • Analysis of TEC over Africa stations gives theoretical studies in estimating weather system. [ABSTRACT FROM AUTHOR]

Introduction : Hemorrhagic fever with renal syndrome (HFRS) is a life-threatening public health problem in China, accounting for ~90% of HFRS cases reported globally. Accurate analysis and prediction of the HFRS epidemic could help to establish effective preventive measures.Materials and Methods : In this study, the geographical information system (GIS) explored the spatiotemporal features of HFRS, the wavelet power spectrum (WPS) unfolded the cyclical fluctuation of HFRS, and the wavelet neural network (WNN) model predicted the trends of HFRS outbreaks in mainland China.Results : A total of 209,209 HFRS cases were reported in mainland China from 2004 to 2019, with the annual incidence ranged from 0 to 13.05 per 100,0000 persons at the province level. The WPS proved that the periodicity of HFRS could be half a year, 1 year, and roughly 7-year at different time intervals. The WNN structure of 12-6-1 was set up as the fittest forecasting model for the HFRS epidemic.Conclusions : This study provided several potential support tools for the control and risk-management of HFRS in China.

This paper demonstrates the implementation of R-CNN in terms of electromyography-related signals to recognize hand gestures. The signal acquisition is implemented using electrodes situated on the forearm, and the biomedical signals are generated to perform the signals preprocessing using wavelet packet transform to perform the feature extraction. The R-CNN methodology is used to map the specific features that are acquired from the wavelet power spectrum to validate and train how the architecture is framed. Additionally, the real-time test is completed to reach the accuracy of 96.48% compared to the related methods. This kind of result proves that the proposed work has the highest amount of accuracy in recognizing the gestures. [ABSTRACT FROM AUTHOR]

We use wavelet analysis to study the relationship between the yield curve and macroeconomic indicators in Canada. We rely on the Nelson–Siegel approach to model the zero-coupon yield curve and use the Kalman filter to estimate its time-varying factors: the level, the slope and the curvature. Apart from establishing a bidirectional yield–macro relation, the paper broadens the existing literature by exploring the link between the monetary policy and the yield curve. We reached several conclusions. First, the monetary policy variable, the bank rate, affects mainly short-run interest rates. Arguably, the main driver for economic activity is the long-run interest rate (instead of the short run), suggesting that monetary policy is mostly ineffective. Second, we concluded that concerning the inflation rate, the Bank of Canada is very proactive. Third, regarding the unemployment rate, we found that both the slope and the curvature are leading indicators for the long-run evolution of unemployment. Finally, our results suggest that the industrial production index leads the yield curve factors and not the other way. [ABSTRACT FROM AUTHOR]

Periodicity correlations between roadside PM $_{2.5}$ concentration and traffic volume are difficult to explore due to the complicated process of production and diffusion of PM $_{2.5}$ : a reaction of vehicle emission with atmosphere, dependence with wind speed, pavement width, vehicle speed, vehicle type, etc. We propose a framework to explore the periodicity correlation between roadside PM $_{2.5}$ concentration and traffic volume using wavelet transform. The framework utilizes wavelet spectrum to obtain the characteristic periods of PM $_{2.5}$ concentration and traffic volume, and utilizes cross-wavelet spectrum to explore the hysteresis response of PM $_{2.5}$ concentration with respect to traffic volume. Different from traditional research work which handle pollutants and traffic data of macro scales, our study examines the micro-scale relationship of PM $_{2.5}$ concentration and traffic volume. We conduct experiments from four urban streets of Beijing, and reveal two micro-scale rules: (a) the characteristic period of PM $_{2.5}$ concentration approximates the characteristic period of traffic volume, and both of them are significantly affected by the traffic signal cycle; (b) PM $_{2.5}$ concentration exhibits a time delay within 0.3–0.9 minutes with respect to traffic volume. The research will benefit the further study on the evolution of PM $_{2.5}$ concentration and traffic volume, and provide a reference for the mitigation of PM $_{2.5}$ pollution by traffic control as well as practical traffic pollutant regulations in metropolises. [ABSTRACT FROM AUTHOR]

The solar quiet (Sq) variations of horizontal and vertical (SqH and SqZ) components of the geomagnetic field obtained from both the Northern Hemisphere and Southern Hemisphere of the International Real-Time Magnetic Observatory Network (INTERMAGNET) during solar maximum year 2001 were investigated. The results show enlargement of the SqH component of the geomagnetic field during the daytime, attributed to equatorial electrojet (EEJ) current closer to the geomagnetic equator at the electrojet stations (BNG and MBO), which are produced from large eastward flow of the current. It was observed that SqZ is positive at the southward and negative at the northward hemispheres. SqZ is amplified at HER and HBK around the daytime. Wavelet power spectrum based approach was employed to analyse the SqH, SqZ, and rate of induction (SqZ/SqH) time series in a sequence of time scaling from January to December. The higher energy of SqH and SqZ of the wavelet coefficients is noticeable at high frequency. The monthly variation rate of induction (SqZ/SqH) analyses during the Sq variations are associated with the influence of equatorwards penetration of electric fields from the field-aligned current, Earth conductivity, effect of the ocean, and ionospheric conductivity. [ABSTRACT FROM AUTHOR]

By means of the Continuous Morlet Wavelet Transform fostering covariance/correlation, lead-lag causal relationship as well as coherency via the wavelets analysis, we explore the two indices on the Ghana Stock Exchange, the larger Ghana Stock Exchange Composite Index (GSE-CI) and the smaller Ghana Stock Exchange Financial Services Index (GSE-FSI) with the US dollar and Euro. Using daily data from January 2011 to December 2016 we confirm that there is mixed interplay of lead-lag relationships, mostly strong at lower frequencies, among the indices and the two most important exchange rates in Ghana. This paper serves as the first of its kind in the literature owing to its rich methodology and variables employed. Our study implies that investing selectively in either GSE-CI or GSE-FSI is very important and differences in co-movement of GSE-CI and GSE-FSI with the exchange rates. We reveal that there is narrowly identifiable lead-lag relationship between GSE-CI and USD/GHS and GSE-FSI and USD/GHS. Investors as revenue maximisation agents should consider the time and frequency spaces of the GSE-CI and GSE-FSI in their investment decisions involving diversification with the USD and EUR both in the short- and medium-terms (up to four years). Further, any policy meant to influence performance on the Ghana Stock Exchange should consider the time and frequency domains of the equities traded on the exchange.

Based on the collected wheat yields in the years of 2008-2013 in the Guanzhong Plain, China and the drought monitoring results of vegetation temperature condition index(VTCI), the Morlet, Mexican Hat and Paul(m=4) were used to study droughts. Wavelet power spectra of the three non-orthogonal wavelet functions were applied to analyze the multi-time scale characteristics and the cross-correlation degrees of the wheat yields and the VTCIs at the main growth stages of winter wheat. Linear regression models between the yields and the weighted VTCIs at the main growth stages were compared for selecting a better wavelet function for assessing drought impact. The results show that the oscillation energy of the VTCIs using the same wavelet function is different. There are differences of the main oscillation periods determined by three wavelet functions at the same growth stage of wheat, and further there are differences in the wavelet cross-correlation coefficients. The time series VTCIs at the four growth stages of wheat all have a 6-year main oscillation period. The Paul(m=4) wavelet is most applicable to analyze the multi-scale correlation between the VTCIs and wheat yields, and assess the multi-scale drought impact. [ABSTRACT FROM AUTHOR]

Over the last century, the vast majority of peatlands in Europe have experienced substantial transformation as a result of drainage works that led to an imbalance in the natural hydrologic regime as well as changes in vegetation composition. The ongoing study aims to reconstruct the natural hydrologic regime of peatlands and restore their typical vegetation communities. In this study, we examine the variability of groundwater levels and groundwater temperature in raised bogs located in the Bieszczady Mts. in southern Poland. Both groundwater table levels and groundwater temperature serve to characterise the hydrology of peatlands, which in turn is critical for plant growth and rates of relevant biochemical processes. Our objective is to determine the predominant scale of intra-annual variability in time series and identify their potential sources by assessing the adaptive response of peat bogs to key changes in weather conditions. For this purpose, data obtained from 9 monitoring wells located in peat bogs, with a varying degree of degradation, were used. Fluctuations in time series and potential linkages between selected variables were analysed in the frequency domain using the continuous wavelet transform. The results show that peat bogs exhibit a relatively high stability of groundwater table levels and groundwater temperature despite meaningful changes in weather conditions. The most visible response of peat bogs to weather conditions was observed in summer and autumn. Our study demonstrates that degraded peat bogs experience the largest decrease in groundwater table levels and more frequent fluctuations. In contrast, groundwater temperature remained stable throughout the year at all the studied bog sites. [ABSTRACT FROM AUTHOR]

This paper investigates equity market risk and co-movements between the Lithuanian stock market and the Central European stock markets. We cover the equity market returns both in time and frequency domains. We focus our studies on the changes of the market risk and co-movements of the Lithuanian and the Central European markets returns during the period of 2000-2018. The wavelet analysis was applied to segregate the returns across different time horizons (frequencies). Our findings corroborate the findings from other authors, namely that crisis periods have a great impact on the interrelations of the Central European and Lithuanian markets. We discover that volatility is concentrated in the medium and long periods (medium and low frequencies) from 1 to 3,5 years for all the markets under consideration. The absolute maximum of volatility is achieved at the period of the frequencies corresponding to the period of 3 years. We found that the co-movements with Poland, the Czech Republic and Hungary are slightly lower after the announcement of the introduction of the euro in Lithuania by the European Commission. From the investment diversification point of view, the investment horizon plays a crucial role for the level of co-movements. Our conclusion is that for Lithuanian investors, diversification with Central European markets is not useful for long horizons, because of the high co-movements. The benefit of the diversification can be achieved for the investors with time horizons less than 1 year. [ABSTRACT FROM AUTHOR]

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In three different ways of lead–lag causal relationship, covariance/correlation and coherence, we apply the wavelets analysis via the Continuous Morlet Wavelet Transform to delineate the significant frequency–time domain lead–lag relationships for the West African Monetary Zone member countries for real US dollar exchange rates and their absolute log returns from January 2001 to April 2015. The results indicate that lead–lag associations at different periodicities vary across the countries. No one country comes off as leading conveniently for both real and absolute returns of the exchange rates. Our results corroborate other evidences of non-convergence of exchange rates in the monetary zone, which hinders the eventual implementation of the single currency in the ECOWAS region.

This paper aims to assess the low-frequency dynamics of energy inflation's volatility across a sample of six OECD advanced countries. The traditional argument attributes a transitory effect to energy inflation putting less attention on the longer-term economic consequences. Using the wavelet power spectrum, we provide evidence that most of energy inflation volatility is mainly concentrated at the frequency bands between approximately 1.5 to 5.5 years. Potentially, this result can have nontrivial consequences on how monetary policy should face changes in energy prices. • Energy inflation's volatility is not concentrated in the short-run. • According to power spectra, it operates at frequencies between 1.5 and 5.5 years. • Monetary policy cannot dismiss energy inflation as a transitory phenomenon. [ABSTRACT FROM AUTHOR]

Blind image quality assessment (BIQA) can assess the perceptual quality of a distorted image without a prior knowledge of its reference image or distortion type. In this paper, a novel BIQA model is developed in wavelet domain. Considering the multi-resolution and band-passing characteristics of discrete wavelet transform (DWT), an improvement over the power spectrum is put forward, i.e., dubbed wavelet power spectrum (WPS) estimation. Then, the concept of directional WPS is applied to simplify the calculation. Moreover, a rotationally symmetric modulation transfer function (MTF) of human visual system (HVS) is integrated as a filter, which makes the metric to be consistent with the human vision perception and more discriminative. Experiments are conducted on the LIVE databases and three other databases, and the results show that the proposed metric is highly correlated with subjective evaluations and it competes well with other state-of-the-art metrics in terms of effectiveness and robustness. [ABSTRACT FROM AUTHOR]

Hydrologic regionalization deals with the investigation of homogeneity in watersheds and provides a classification of watersheds for regional analysis. The classification thus obtained can be used as a basis for mapping data from gauged to ungauged sites and can improve extreme event prediction. This paper proposes a wavelet power spectrum (WPS) coupled with the self-organizing map method for clustering hydrologic catchments. The application of this technique is implemented for gauged catchments. As a test case study, monthly streamflow records observed at 117 selected catchments throughout the western United States from 1951 through 2002. Further, based on WPS of each station, catchments are classified into homogeneous clusters, which provides a representative WPS pattern for the streamflow stations in each cluster. [ABSTRACT FROM AUTHOR]

The study explored droughts using the Standardized Precipitation Index (SPI) in the northwestern region of Bangladesh, which is the drought prone area. In order to assess the trend and variability of monthly rainfall, as well as 3-month scale SPI, non-parametric Mann-Kendall (MK) tests and continuous wavelet transform were used respectively. The effect of climatic parameters on the drought in this region was also evaluated using SPI, with the Southern Oscilation Index (SOI) by means of the wavelet coherence technique, a relatively new and powerful tool for describing processes. The MK test showed no statistically significant monthly rainfall trends in the selected stations, whereas the seasonal MK test showed a declining rainfall trend in Bogra, Ishurdi, Rangpur and Sayedpur stations respectively. Sen's slope of six stations also provided a decreasing rainfall trend. The trend of the SPI, as well as Sen's slope indicated an increasing dryness trend in this area. Dominant periodicity of 3-month scale SPI at 8 to 16 months, 16 to 32 months, and 32 to 64 months were observed in the study area. The outcomes from this study contribute to hydrologists to establish strategies, priorities and proper use of water resources. [ABSTRACT FROM AUTHOR]