scholarly journals A hybrid wavelet analysis-cloud model data-extending approach for meteorologic and hydrologic time series

2015 ◽  
Vol 120 (9) ◽  
pp. 4057-4071 ◽  
Author(s):  
Dong Wang ◽  
Hao Ding ◽  
Vijay P. Singh ◽  
Xiaosan Shang ◽  
Dengfeng Liu ◽  
...  
Keyword(s):  
Time Series ◽  
Wavelet Analysis ◽  
Cloud Model ◽  
Model Data ◽  
Hydrologic Time Series

Detecting groundwater anthropogenic extraction with cyclicity results of wavelet models

2020 ◽  
Author(s):  
Nuria Naranjo-Fernández ◽  
Carolina Guardiola-Albert ◽  
Héctor Aguilera ◽  
Ana Fernandez-Ayuso ◽  
Esperanza Montero-González
Keyword(s):  
Time Series ◽  
Wavelet Analysis ◽  
Water Source ◽  
Piezometric Level ◽  
Latent Factors ◽  
Crop Fields ◽  
Hydrologic Time Series ◽  
Sw Spain ◽  
Legal Control ◽  
Hydrological Cycles

<p>Groundwater is the main water source for irrigation in arid and semi-arid areas. Unfortunately, it has been proven very difficult to prevent unauthorized extractions. The present work studies the application of wavelet analysis to detect and quantify the unfavorable effects of these extractions on the piezometry.</p><p>Wavelets have been widely applied for hydrologic time series analysis since the 1990s, with increasing popularity in recent years. This method can be applied to hydrologic series to reveal complex hydrological processes and evaluate complex latent factors, such as seasonal crop irrigation, controlling groundwater level fluctuations.</p><p>Records of the piezometric level from more than 150 piezometers were studied from 1975 to 2016 in the Almonte-Marismas aquifer (SW Spain). The majority of these time series presented periodicities between 11-12 months, which corresponded to hydrological cycles of recharge and discharge. Nevertheless, in some areas close to crop fields, periodicities of 2-3 and 4-6 months have been detected. In these cases, wavelet analysis could be used as a tool to prevent damage in areas in need of deeper legal control.</p>

An Application of Elman Networks In Treatment and Prediction of Hydrologic Time Series

2011 ◽  
Vol 9 (3) ◽  
pp. 148-156
Author(s):  
Leonardo G. Tampelini ◽  
Clodis Boscarioli ◽  
Sarajane M. Peres ◽  
Silvio C. Sampaio
Keyword(s):  
Time Series ◽  
Hydrologic Time Series

scholarly journals The correlation between three teleconnections and leptospirosis incidence in the Kandy District, Sri Lanka, 2004–2019

2021 ◽  
Vol 49 (1) ◽  
Author(s):  
N. D. B. Ehelepola ◽  
Kusalika Ariyaratne ◽  
A. M. S. M. C. M. Aththanayake ◽  
Kamalanath Samarakoon ◽  
H. M. Arjuna Thilakarathna
Keyword(s):  
Time Series ◽  
Sri Lanka ◽  
Wavelet Analysis ◽  
Southern Oscillation ◽  
Population Data ◽  
Enso Modoki ◽  
Cross Correlation Analysis ◽  
Teleconnection Indices ◽  
Weather Parameters ◽  
Time Lagged

Abstract Background Leptospirosis is a bacterial zoonosis. Leptospirosis incidence (LI) in Sri Lanka is high. Infected animals excrete leptospires into the environment via their urine. Survival of leptospires in the environment until they enter into a person and several other factors that influence leptospirosis transmission are dependent upon local weather. Past studies show that rainfall and other weather parameters are correlated with the LI in the Kandy district, Sri Lanka. El Niño Southern Oscillation (ENSO), ENSO Modoki, and the Indian Ocean Dipole (IOD) are teleconnections known to be modulating rainfall in Sri Lanka. There is a severe dearth of published studies on the correlations between indices of these teleconnections and LI. Methods We acquired the counts of leptospirosis cases notified and midyear estimated population data of the Kandy district from 2004 to 2019, respectively, from weekly epidemiology reports of the Ministry of Health and Department of Census and Statistics of Sri Lanka. We estimated weekly and monthly LI of Kandy. We obtained weekly and monthly teleconnection indices data for the same period from the National Oceanic and Atmospheric Administration (NOAA) of the USA and Japan Agency for Marine-Earth Science and Technology (JAMSTEC). We performed wavelet time series analysis to determine correlations with lag periods between teleconnection indices and LI time series. Then, we did time-lagged detrended cross-correlation analysis (DCCA) to verify wavelet analysis results and to find the magnitudes of the correlations detected. Results Wavelet analysis displayed indices of ENSO, IOD, and ENSO Modoki were correlated with the LI of Kandy with 1.9–11.5-month lags. Indices of ENSO showed two correlation patterns with Kandy LI. Time-lagged DCCA results show all indices of the three teleconnections studied were significantly correlated with the LI of Kandy with 2–5-month lag periods. Conclusions Results of the two analysis methods generally agree indicating that ENSO and IOD modulate LI in Kandy by modulating local rainfall and probably other weather parameters. We recommend further studies about the ENSO Modoki and LI correlation in Sri Lanka. Monitoring for extreme teleconnection events and enhancing preventive measures during lag periods can blunt LI peaks that may follow.

scholarly journals Shift Detection in Hydrological Regimes and Pluriannual Low-Frequency Streamflow Forecasting Using the Hidden Markov Model

Water ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 2058 ◽  
Author(s):  
Larissa Rolim ◽  
Francisco de Souza Filho
Keyword(s):  
Time Series ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Extreme Events ◽  
Hidden Markov ◽  
Low Frequency ◽  
Streamflow Forecasting ◽  
Hydrologic Time Series ◽  
Low Frequency Variability ◽  
The Impact

Improved water resource management relies on accurate analyses of the past dynamics of hydrological variables. The presence of low-frequency structures in hydrologic time series is an important feature. It can modify the probability of extreme events occurring in different time scales, which makes the risk associated with extreme events dynamic, changing from one decade to another. This article proposes a methodology capable of dynamically detecting and predicting low-frequency streamflow (16–32 years), which presented significance in the wavelet power spectrum. The Standardized Runoff Index (SRI), the Pruned Exact Linear Time (PELT) algorithm, the breaks for additive seasonal and trend (BFAST) method, and the hidden Markov model (HMM) were used to identify the shifts in low frequency. The HMM was also used to forecast the low frequency. As part of the results, the regime shifts detected by the BFAST approach are not entirely consistent with results from the other methods. A common shift occurs in the mid-1980s and can be attributed to the construction of the reservoir. Climate variability modulates the streamflow low-frequency variability, and anthropogenic activities and climate change can modify this modulation. The identification of shifts reveals the impact of low frequency in the streamflow time series, showing that the low-frequency variability conditions the flows of a given year.

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