Datasets

Creator
Type
Date

subject: EAPS date: 2024

Total is 5 Results
Water vapor stable isotope memory effects of common tubing materials

10.4231/Y13T-6775

Alexandra Meyer ORCID logo , Lisa Welp-Smith ORCID logo

06/27/2024

A laboratory experiment testing the water vapor isotopic memory of a few common air intake tubing materials (PFA, FEP, PTFE, HDPE, and copper, plus Dekabon and Bev-A-Line XX). Memory metrics for common materials are quantified at 2 different temperatures.

carryover deuterium excess EAPS isotopic Los Gatos memory effect water isotopes Water Vapor

Quantifying the impacts of wildfires on soil thermal, hydrological and carbon dynamics in northern Eurasia from 2003 to 2016

10.4231/ZJM7-A207

Qianlai Zhuang ORCID logo , Yiming Xu

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 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)

West African Heat Low Climatology and Comparison with Dry Tropical Cyclones

10.4231/GMQS-DJ89

Aaron Paul Kruskie ORCID logo , Daniel Robert Chavas ORCID logo

07/29/2024

This repository contains the data files and scripts required to recreate the figures for Kruskie et al. 2024 (in review), which examines the climatology of heat lows in West Africa and their relationship to tropical cyclones using potential...

Climatology EAPS sensible heat flux tropical cyclone West Africa

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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