Digital Filters to Eliminate or Separate Tidal Components in Groundwater Observation Time-Series Data
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- SHIRAHATA Katsushi
- Renewable Resources Engineering Research Division, National Institute for Rural Engineering (NIRE), National Agriculture and Food Research Organization (NARO)
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- YOSHIMOTO Shuhei
- Renewable Resources Engineering Research Division, National Institute for Rural Engineering (NIRE), National Agriculture and Food Research Organization (NARO)
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- TSUCHIHARA Takeo
- Renewable Resources Engineering Research Division, National Institute for Rural Engineering (NIRE), National Agriculture and Food Research Organization (NARO)
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- ISHIDA Satoshi
- Renewable Resources Engineering Research Division, National Institute for Rural Engineering (NIRE), National Agriculture and Food Research Organization (NARO)
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説明
<p>This paper discusses digital low-pass filters for application to tidally fluctuated groundwater observation data. Three types of filters that are commonly used, mainly for oceanography, and newly produced filters are comparatively evaluated with a focus on their ability to eliminate major diurnal and semidiurnal tidal components. All the digital filters presented are the nonrecursive type that can easily be used with spreadsheet software. Newly produced low-pass filters are excellent tide-killer filters with a length of 241 hours applicable to hourly sampled time-series data. The new filters suppress eight major diurnal and semidiurnal tides to practically negligible magnitudes (10–8 order input), with longer-period components (longer than two days) being nearly completely preserved. High-pass filters transformed from these new tide-killer low-pass filters can separate the components of semidiurnal to diurnal tidal periods from other longer-period components, keeping approximately the same magnitude as in the input data for eight major tides. Therefore, the use of the new high-pass filters prior to quantitative analysis of major tidal components in groundwater observation data should effectively improve the accuracy of analysis.</p>
収録刊行物
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- Japan Agricultural Research Quarterly: JARQ
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Japan Agricultural Research Quarterly: JARQ 50 (3), 241-252, 2016
国立研究開発法人 国際農林水産業研究センター
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詳細情報 詳細情報について
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- CRID
- 1390282679678156928
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- NII論文ID
- 130005254692
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- ISSN
- 21858896
- 00213551
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- 本文言語コード
- en
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- 資料種別
- journal article
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- データソース種別
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- JaLC
- Crossref
- CiNii Articles
- KAKEN
- OpenAIRE
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- 抄録ライセンスフラグ
- 使用不可