Subject
Creator
- Bailu Zhao, 0000-0002-1326-4801 8
- Mingyang Guo, 0000-0002-3087-453X 4
- Licheng Liu, 0000-0002-9649-1056 3
- Seungbum Kim 3
- Steve Frolking, 0000-0001-6414-5004 3
- Xuan Xi, 0000-0003-1886-3085 3
- Zeli Tan 3
- L. Ruby Leung 2
- Narasinha Shurpali 2
Type
- Dataset 26
Date
subject: EAPS creator: Qianlai Zhuang, 0000-0002-4536-9851
Total is 26 Results
Data for Intercomparison of thermal regime algorithms in 1-D lake models
10.4231/8246-C724
Huaxia Yao
,
L. Ruby Leung
,
Malgorzata Golub
,
Mingyang Guo
,
Qianlai Zhuang
,
Zeli Tan
09/08/2020
This dataset includes the model intercomparison results using the Arctic Lake Biogeochemistry Model (ALBM) and codes for analysis.
EAPS global lake thermal dynamics lake model model intercomparison
Global CO Dynamics Model Output during 1901-2100
10.4231/JGZ8-9C75
07/05/2019
Global soil CO fluxes during 1901-2013, 2000-2013 and 2014-2100 from model simulations. Together with published paper, figures and tables.
Atmospheric Chemistry Modeling Biogeochemistry Carbon Monoxide Climate Climate Dynamics EAPS Earth and Atmospheric Sciences
Microbial decomposition processes and vulnerable arctic soil organic carbon in the 21st century
10.4231/ANMV-J384
07/25/2019
Various levels of representations of biogeochemical processes in current biogeochemistry models contribute to uncertainty in carbon budget quantification. Detailed microbial mechanisms were incorporated into TEM 5.0 (Terrestrial Ecosystem Model).
Arctic Region Biogeochemistry Carbon Dynamics Climate Change EAPS Earth and Atmospheric Sciences Microbial-Based Model Terrestrial Ecosystem Model (TEM)
Code and Data for Reduced net methane emissions due to microbial methane oxidation in a warmer Arctic
10.4231/Q3R8-SZ17
Bo Elberling
,
David Medvigy
,
Edward J. Dlugokencky
,
Gustaf Hugelius
,
Licheng Liu
,
Lisa R. Welp-Smith
,
Lori Bruhwiler
,
Ludovica D'Imperio
,
Maggie C.Y. Lau
,
Qianlai Zhuang
,
Tullis C. Onstott
,
Youmi Oh
02/04/2020
This publication contains code and data of a biogeochemistry model, XPTEM-XHAM, for paper "Reduced net methane emissions due to microbial methane oxidation in a warmer Arctic" by Oh et al. accepted in Nature Climate Change.
Arctic Region Biogeochemistry EAPS Earth and Atmospheric Sciences Methane Dynamics Model (MDM) Methane Emission Microbial-Based Model soil
Global CO Dynamics Model Output during 1901-2100
10.4231/XK25-7981
06/10/2020
Global soil CO fluxes during 1901-2013, 2000-2013 and 2014-2100 from model simulations. Together with published paper, figures and tables.
Atmospheric Chemistry Modeling Biogeochemistry Carbon Monoxide Climate Climate Dynamics EAPS Earth and Atmospheric Sciences
Modeling Holocene Peatland Soil Carbon Accumulation in North America
10.4231/8GEV-2P56
Bailu Zhao
,
Catherine Prigent
,
Filipe Aires
,
Jason K. Keller
,
Qianlai Zhuang
,
Scott Bridgham
,
Sirui Wang
,
Zicheng Yu
06/18/2020
This dataset include the necessary data and codes to reproduce the figures used in the paper.
North American boreal forests are a large carbon source due to wildfires from 1986 to 2016
10.4231/TSK3-1733
Bailu Zhao
,
Jukka Pumpanen
,
Narasinha Shurpali
,
Qianlai Zhuang
06/18/2020
The bundle contains the codes and data used to generate the figures in the paper.
boreal fires burn severity carbon loss EAPS North America TEM
Rising methane emissions from boreal lakes due to increasing ice-free days
10.4231/FYFN-1Z68
Mingyang Guo
,
Narasinha Shurpali
,
Pertti Martikainen
,
Pirkko Kortelainen
,
Qianlai Zhuang
,
Sari Juutinen
,
Zeli Tan
03/30/2020
This publication includes model output of a lake biogeochemistry model ALBM, and data analysis codes which were used for paper 'Rising methane emissions from boreal lakes due to increasing ice-free days' by Guo et al. in Environmental Research...
Quantifying the impacts of wildfires on soil thermal, hydrological and carbon dynamics in northern Eurasia from 2003 to 2016
10.4231/ZJM7-A207
05/02/2024
It contains simulation results of TEM in Eurasia during 2003-2016.
Carbon Dynamics EAPS Terrestrial Ecosystem Model (TEM) Wildfire
A Hybrid Physics-Guided Deep Learning Modeling Framework for Predicting Surface Soil Moisture
10.4231/NR0B-EJ07
Qianlai Zhuang
,
Xinyu Liu
,
Xuan Xi
05/24/2024
This dataset contains the main materials for predicting site-level surface soil moisture based on a developed hybrid physics-guided deep learning modeling framework.
EAPS Long short-term memory (LSTM) Physics-Guided Deep Learning Soil Moisture Terrestrial Ecosystem Model (TEM)
Display #
Results 1 - 10 of 26