scholarly journals Spectral Analysis of Vertical Temperature Profile Time‐Series Data in Yellowstone Lake Sediments

2021 ◽  
Vol 57 (4) ◽  
Author(s):  
Robert A. Sohn ◽  
Robert N. Harris
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Temperature Profile ◽  
Lake Sediments ◽  
Time Series Data ◽  
Series Data ◽  
Vertical Temperature ◽  
Vertical Temperature Profile ◽  
Yellowstone Lake

scholarly journals Assessing Stream-Aquifer Connectivity in a Coastal California Watershed

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 416
Author(s):  
Bwalya Malama ◽  
Devin Pritchard-Peterson ◽  
John J. Jasbinsek ◽  
Christopher Surfleet
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Water Level ◽  
Time Series Data ◽  
Series Data ◽  
Low Permeability ◽  
Pumping Tests ◽  
Stream Flows ◽  
The Impact

We report the results of field and laboratory investigations of stream-aquifer interactions in a watershed along the California coast to assess the impact of groundwater pumping for irrigation on stream flows. The methods used include subsurface sediment sampling using direct-push drilling, laboratory permeability and particle size analyses of sediment, piezometer installation and instrumentation, stream discharge and stage monitoring, pumping tests for aquifer characterization, resistivity surveys, and long-term passive monitoring of stream stage and groundwater levels. Spectral analysis of long-term water level data was used to assess correlation between stream and groundwater level time series data. The investigations revealed the presence of a thin low permeability silt-clay aquitard unit between the main aquifer and the stream. This suggested a three layer conceptual model of the subsurface comprising unconfined and confined aquifers separated by an aquitard layer. This was broadly confirmed by resistivity surveys and pumping tests, the latter of which indicated the occurrence of leakage across the aquitard. The aquitard was determined to be 2–3 orders of magnitude less permeable than the aquifer, which is indicative of weak stream-aquifer connectivity and was confirmed by spectral analysis of stream-aquifer water level time series. The results illustrate the importance of site-specific investigations and suggest that even in systems where the stream is not in direct hydraulic contact with the producing aquifer, long-term stream depletion can occur due to leakage across low permeability units. This has implications for management of stream flows, groundwater abstraction, and water resources management during prolonged periods of drought.

FRACTAL ANALYSIS OF DIFFERENT EASTERN AND WESTERN MUSICAL INSTRUMENTS

Fractals ◽  
2006 ◽  
Vol 14 (03) ◽  
pp. 165-170 ◽  
Author(s):  
ATIN DAS ◽  
PRITHA DAS
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Fractal Dimension ◽  
Time Series Data ◽  
Musical Instruments ◽  
Series Data ◽  
Data Sets ◽  
Different Types ◽  
Frequency Regime ◽  
Musical Sounds

In this paper, we attempt musical analysis by measuring fractal dimension (D) of musical pieces played by several musical instruments. We collected solo performances of popular instruments of Western and Eastern origin as samples. We attempted usual spectral analysis of the selected clips to observe peaks of fundamental and harmonics in frequency regime. After appropriate processing, we converted them into time series data sets and computed their fractal dimension. Based on our results, we conclude that instrumental musical sounds may have higher Ds than those computed from vocal performances of different types of Indian songs.

scholarly journals PERAMALAN CURAH HUJAN MENGGUNAKAN METODE ANALISIS SPEKTRAL

2016 ◽  
Vol 5 (4) ◽  
pp. 183
Author(s):  
NI PUTU MIRAH SRI WAHYUNI ◽  
I WAYAN SUMARJAYA ◽  
I GUSTI AYU MADE SRINADI
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Time Series Data ◽  
Complete Information ◽  
Series Data ◽  
Analysis Method ◽  
Maximum Rainfall ◽  
Spectral Analysis Method ◽  
Large Peak ◽  
Analysis Of Time Series

The purpose of this research is the model of forecasting rainfall using spectral analysis method. To obtain complete information on characteristics of time series data we need to examine periodicity of the data. Examining the periodicity of time series data in the frequency domain is called spectral analysis. The results of spectral analysis show that periodogram is clearly dominated by a very large peak at frequency . This frequency corresponds to period of 12 cycle per month. Based on the results of analysis of time series data rainfall is SARIMA (0,1,1)(0,1,1)12 where the model can be written as The result indicates minimum rainfall happen in January and maximum rainfall happen in August.

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