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Quantifying Carbon and Water Dynamic...

Qu, Yang.

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Quantifying Carbon and Water Dynamics of Terrestrial Ecosystems At High Temporal And Spatial Resolutions Using Process-Based Biogeochemistry Models and in situ and Satellite Data.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Quantifying Carbon and Water Dynamics of Terrestrial Ecosystems At High Temporal And Spatial Resolutions Using Process-Based Biogeochemistry Models and in situ and Satellite Data./
Author:	Qu, Yang.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	118 p.
Notes:	Source: Dissertations Abstracts International, Volume: 81-01, Section: B.
Contained By:	Dissertations Abstracts International81-01B.
Subject:	Atmospheric sciences. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10790835
ISBN:	9781085561686

Quantifying Carbon and Water Dynamics of Terrestrial Ecosystems At High Temporal And Spatial Resolutions Using Process-Based Biogeochemistry Models and in situ and Satellite Data.
Qu, Yang.

Quantifying Carbon and Water Dynamics of Terrestrial Ecosystems At High Temporal And Spatial Resolutions Using Process-Based Biogeochemistry Models and in situ and Satellite Data. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 118 p.

Source: Dissertations Abstracts International, Volume: 81-01, Section: B.

Thesis (Ph.D.)--Purdue University, 2018.

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

To better understand the role of terrestrial ecosystems in the global carbon cycle and their feedbacks to the global climate system, process-based ecosystem models that are used for quantifying net carbon exchanges between the terrestrial biosphere and the atmosphere need to be improved. My research objective is to improve the model from following aspects: 1) Improving parameterization and model structure for carbon and water dynamics, 2) improving regional model simulations at finer spatial resolutions (from 0.5 degree to 0.05 degree or finer), 3) developing faster spin-up algorithms, and 4) evaluating high performance model simulations using fast spin-up technique deployed on various computing platforms. I improved the leaf area index (LAI) modeling in a terrestrial ecosystem model (TEM) for North America. The evaluated TEM was used to estimate ET at site and regional scales in North America from 2000 to 2010. The estimated annual ET varies from 420 to 450 mm yr-1 with the improved model, close to MODIS monthly data with root-mean-square-error less than 10 mm month-1 for the study period. Alaska, Canada, and the conterminous US accounts for 33%, 6% and 61% of the regional ET, respectively. I then used new algorithm for a fast spin-up for TEM. With the new spin-up algorithm, I showed that the model reached a steady state in less than 10 years of simulation time,while the original method requires more than 200 years on average of model run. Lastly, I conducted simulations under both original resolution and high resolution in the conterminous US. The high-resolution simulation predicts slightly higher average annual gross primary production (GPP) (~2%) from 2000 to 2015 in the conterminous US than original version of TEM. From the improved TEM simulation, I estimated that regional GPP is between 7.12 and 7.69 Pg C yr-1 and NEP is between 0.09 and 0.75 Pg C yr-1.

ISBN: 9781085561686Subjects--Topical Terms:

3168354
Atmospheric sciences.
Subjects--Index Terms:

Biogeochemistry model

Quantifying Carbon and Water Dynamics of Terrestrial Ecosystems At High Temporal And Spatial Resolutions Using Process-Based Biogeochemistry Models and in situ and Satellite Data.
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520 $a To better understand the role of terrestrial ecosystems in the global carbon cycle and their feedbacks to the global climate system, process-based ecosystem models that are used for quantifying net carbon exchanges between the terrestrial biosphere and the atmosphere need to be improved. My research objective is to improve the model from following aspects: 1) Improving parameterization and model structure for carbon and water dynamics, 2) improving regional model simulations at finer spatial resolutions (from 0.5 degree to 0.05 degree or finer), 3) developing faster spin-up algorithms, and 4) evaluating high performance model simulations using fast spin-up technique deployed on various computing platforms. I improved the leaf area index (LAI) modeling in a terrestrial ecosystem model (TEM) for North America. The evaluated TEM was used to estimate ET at site and regional scales in North America from 2000 to 2010. The estimated annual ET varies from 420 to 450 mm yr-1 with the improved model, close to MODIS monthly data with root-mean-square-error less than 10 mm month-1 for the study period. Alaska, Canada, and the conterminous US accounts for 33%, 6% and 61% of the regional ET, respectively. I then used new algorithm for a fast spin-up for TEM. With the new spin-up algorithm, I showed that the model reached a steady state in less than 10 years of simulation time,while the original method requires more than 200 years on average of model run. Lastly, I conducted simulations under both original resolution and high resolution in the conterminous US. The high-resolution simulation predicts slightly higher average annual gross primary production (GPP) (~2%) from 2000 to 2015 in the conterminous US than original version of TEM. From the improved TEM simulation, I estimated that regional GPP is between 7.12 and 7.69 Pg C yr-1 and NEP is between 0.09 and 0.75 Pg C yr-1.
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