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| Created: | Jan 15, 2025 at 1:25 a.m. | |
| Last updated: | Jan 15, 2025 at 10:02 p.m. | |
| Citation: | See how to cite this resource |
| Sharing Status: | Public |
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Abstract
This HydroShare Resorce provides the scripts for data retrievel and processing, model running and postanalysis, and figure creation for the manuscript under review by JOH. The abstract of the manuscript is as follows: Seasonal soil freezing and thawing processes significantly influence runoff generation dynamics during cold periods, affecting various hydrological and agricultural systems, including flood generation, soil erosion, and plant health. Representing frozen soil conditions in land surface or hydrological models is therefore crucial. While fully distributed models implement the process by solving energy-mass balance equations to obtain soil temperature profiles, parsimonious models using “snow tanks” or frozen ground states can provide suitable modeling solutions with reduced computational demands. However, even these parsimonious approaches to representing frozen ground typically require some additional complexity through additional inputs or surface energy balance calculations. This study evaluates the applicability of a simplified soil temperature prediction model that determines frozen/unfrozen ground states using only air temperature and snow cover data, reducing model complexity. We first validate the model performance using AmeriFlux network in-situ measurements across the United States and Canada. Furthermore, we provide a comprehensive assessment at the global scale with ERA5-LAND reanalysis data (1980-2020). The model demonstrates robust performance globally, achieving an average true frozen rate of 0.90 and false frozen rate of 0.06. We also investigate the model performance by month, and, while monthly analyses show drops in model performance for certain months, these lower scores are primarily due to the limited number of freeze-thaw events during these periods, which makes the model appear less accurate than it actually is. In terms of spatial performance, the model shows reduced accuracy in mountainous regions, including the Tibetan Plateau, Rocky Mountains, and Andes, suggesting the need for region-specific parameter calibration in orographic settings. Nevertheless, this parsimonious soil temperature model demonstrates significant potential as a computationally efficient solution for incorporating frozen ground effects in distributed hydrological models with simple conceptual runoff generation schemes.
Subject Keywords
Coverage
Spatial
Content
readme.md
Introduction
This HydroShare Resource provides the codes, processed data, and results for the following study under review at JOH: "Assessing the Global Performance of a Parsimonious Soil Temperature Model for Frozen Ground Prediction" by Donghui Li, Alexander Michalek, and Gabriele Villarini.
The files are organized as below:
- readme.md: this markdown file.
- code: the folder of codes for raw data retrieval, process, and analysis.
- data: the folder of processed data.
- results: the folder of results.
code
There are four Jupyter Notebook files provided here:
get_era5.ipynb: this notebook contains the scripts to automatically retrieve global hourly data of ERA5-LAND reanalysis from the Climate Data Store by ECMWF.cal_soil_temp.ipynb: this notebook contains the scripts to implement the parsimonious soil temperature model used in this paper and the parallel computing at the global scale.post_analysis.ipynb: this notebook contains the scripts to post analyze the global simulation of frozen ground using the simple soil temperature model.validate_with_observation.ipynb: this notebook contains the scripts to validate the model with observed data obtained from AmeriFlux observation network, including the data preprocess and post analysis.
data
There are two kinds of data provided here: AmeriFlux in-situ observation data for North and South america, and ERA5-LAND reanalysis data for the entire globe.
AmeriFlux: the folder of AmeriFlux data.site_time_series.zip: the folder of processed time series data for each selected sites.site_lonlat_all_sites.json: the metedata of all AmeriFlux sites.ERA5-LAND: the folder ERA5-LAND data.daily_data.zip: the processed daily data, organized by the IPCC AR5 regions.- Note: due to the size limitation of HydroShare, the data may not be uploaded completely. If there is any issue with accessing the data, please use this link to access the data on DropBox.
- Note: within the
daily_data.zip, there are two regions not defined in IPCC AR5 regions:ALA_cornerandCA_corner. The two regions are created by me to represent the grids in the west corner of Alaska and the northwest corner of the Canada west coast, which are not covered in the IPCC AR5 region splits.
results
There are two results provided here: confusion matrix calculated with ERA5-LAND reanalysis data covering the entire globe, and the confusion matrix calculated with AmeriFlux in-situ measurement data covering sites in the United States and Canada.
confusion_matrix_by_ameriflux.zip: the confusion matrix calculated for AmeriFlux sites.confusion_matrix_by_ar5_region.zip: the confusion matrix calculated for ERA5-LAND reanalysis data.
Credits
Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
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| Princeton University |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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