Datasets

subject: Terrestrial Ecosystem Model (TEM)

Total is 8 Results
Modeling N2O emission from the pan-Arctic terrestrial ecosystems

10.4231/KZ5W-DC21

Bailu Zhao ORCID logo , Narasinha Shurpali ORCID logo , Qianlai Zhuang ORCID logo , Ye Yuan ORCID logo

05/17/2023

This dataset contains the main codes and data for the N2O emissions quantification from pan-Arctic terrestrial ecosystems on a process-based biogeochemical model.

Arctic Region EAPS N2O emission Permafrost Terrestrial Ecosystem Model (TEM)

A Hybrid Physics-Guided Deep Learning Modeling Framework for Predicting Surface Soil Moisture

10.4231/NR0B-EJ07

Qianlai Zhuang ORCID logo , Xinyu Liu , Xuan Xi ORCID logo

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)

Microbial decomposition processes and vulnerable arctic soil organic carbon in the 21st century

10.4231/ANMV-J384

Junrong Zha , Qianlai Zhuang ORCID logo

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)

A Hybrid Physics-Guided Deep Learning Modeling Framework for Predicting Surface Soil Moisture

10.4231/SBB0-V865

Qianlai Zhuang ORCID logo , Xinyu Liu , Xuan Xi ORCID logo

08/21/2024

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)

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