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Spatial and Temporal Patterns of Soi...

Dong, Jingnuo.

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Spatial and Temporal Patterns of Soil Moisture: A Study on Soil Moisture Observation and Modeling.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Spatial and Temporal Patterns of Soil Moisture: A Study on Soil Moisture Observation and Modeling./
Author:	Dong, Jingnuo.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	121 p.
Notes:	Source: Dissertations Abstracts International, Volume: 82-06, Section: B.
Contained By:	Dissertations Abstracts International82-06B.
Subject:	Soil sciences. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27960378
ISBN:	9798557008617

Spatial and Temporal Patterns of Soil Moisture: A Study on Soil Moisture Observation and Modeling.
Dong, Jingnuo.

Spatial and Temporal Patterns of Soil Moisture: A Study on Soil Moisture Observation and Modeling. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 121 p.

Source: Dissertations Abstracts International, Volume: 82-06, Section: B.

Thesis (Ph.D.)--Oklahoma State University, 2020.

This item must not be sold to any third party vendors.

This dissertation addressed three research questions related to observing and modeling soil moisture spatial and temporal patterns. 1) What is the spatial pattern like for soil moisture at the mesoscale? A mobile device was used to measure soil moisture along a 150-km transect 18 times over 13 months. Spatial structures of soil moisture, sand content, and precipitation were characterized and compared. Soil texture turned out to exert a stronger influence than precipitation on mesoscale soil moisture patterns. 2) How can we effectively upscale in situ soil moisture measurements to a larger scale? A phase space analysis method was applied to upscale soil moisture from the point-scale to field-scale, and its performance was evaluated and compared to traditional scaling methods - linear regression and CDF matching. The phase space method was not able to improve the prediction accuracy compared to the two traditional scaling methods. 3) How can we discover and model hidden structures in soil moisture temporal dynamics? An approach from computational mechanics, called epsilon-machines, was applied to symbolized soil moisture time series and their first and second order derivatives. Based on the reconstructed epsilon-machines, soil moisture time series exhibit a degree of complexity hidden structures, and unpredictability. Statistical complexities tend to increase with the orders of derivatives for soil moisture processes.

ISBN: 9798557008617Subjects--Topical Terms:

2122699
Soil sciences.
Subjects--Index Terms:

Computational mechanics

Spatial and Temporal Patterns of Soil Moisture: A Study on Soil Moisture Observation and Modeling.
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245 1 0 $a Spatial and Temporal Patterns of Soil Moisture: A Study on Soil Moisture Observation and Modeling.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 121 p.
500 $a Source: Dissertations Abstracts International, Volume: 82-06, Section: B.
500 $a Advisor: Ochsner, Tyson E.
502 $a Thesis (Ph.D.)--Oklahoma State University, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a This dissertation addressed three research questions related to observing and modeling soil moisture spatial and temporal patterns. 1) What is the spatial pattern like for soil moisture at the mesoscale? A mobile device was used to measure soil moisture along a 150-km transect 18 times over 13 months. Spatial structures of soil moisture, sand content, and precipitation were characterized and compared. Soil texture turned out to exert a stronger influence than precipitation on mesoscale soil moisture patterns. 2) How can we effectively upscale in situ soil moisture measurements to a larger scale? A phase space analysis method was applied to upscale soil moisture from the point-scale to field-scale, and its performance was evaluated and compared to traditional scaling methods - linear regression and CDF matching. The phase space method was not able to improve the prediction accuracy compared to the two traditional scaling methods. 3) How can we discover and model hidden structures in soil moisture temporal dynamics? An approach from computational mechanics, called epsilon-machines, was applied to symbolized soil moisture time series and their first and second order derivatives. Based on the reconstructed epsilon-machines, soil moisture time series exhibit a degree of complexity hidden structures, and unpredictability. Statistical complexities tend to increase with the orders of derivatives for soil moisture processes.
590 $a School code: 0664.
650 4 $a Soil sciences. $3 2122699
650 4 $a Environmental studies. $3 2122803
650 4 $a Information technology. $3 532993
650 4 $a Climate change. $2 bicssc $3 2079509
653 $a Computational mechanics
653 $a Cosmic-ray neutron rover
653 $a Nonlinear dynamics
653 $a Soil moisture
653 $a Upscaling
653 $a Spatial patterns
653 $a Temporal patterns
690 $a 0481
690 $a 0477
690 $a 0489
690 $a 0404
710 2 $a Oklahoma State University. $b Plant & Soil Sciences. $3 3554018
773 0 $t Dissertations Abstracts International $g 82-06B.
790 $a 0664
791 $a Ph.D.
792 $a 2020
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27960378