Datasets for Ecohydrological Model for Grassland Lacking Historical Measurements
Authors/Creators
Description
We proposed the distributed dynamic process model (DDPM) and here are the example datasets of our model.
Datasets I : Eva_module_datasets.zip: Here are the datasets for running the DDPM Eva module.
Datasets II: RP&FLC_module_datasets.zip: Here are the datasets for running the DDPM RP & FLC module.
Also, you can find our model at https://github/myli1993/DDPM_ver1.0.
You can watch my report on MODSIM 2021 at https://www.bilibili.com/video/BV1vR4y1s7vc.
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When using this dataset, please cite:
[1] Li, MY.; Liu, TX.; Duan, LM.; et al. Confluence simulations based on dynamic channel parameters in the grasslands lacking historical measurements. Journal of Hydrology, 2023, Volume 627, 130425. https://doi.org/10.1016/j.jhydrol.2023.130425
[2] Li, MY.; Liu, TX.; Duan, LM.; et al. A novel evapotranspiration downscaling approach based on dynamic sensitive parameters and deep learning in the grassland lacking historical measurements. Ecological Indicators, 2025, Volume 178, 113839. https://doi.org/10.1016/j.ecolind.2025.113839
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What's new:
Version 1.01:
Here, we added the datasets for Eva module, and renamed the datasets for RP & FLC module. If you have downloaded the datasets in Version 1.0, you don't have to download the RP&FLC_module_datasets.zip for another time.
Files
Files
(13.5 GB)
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md5:b5e36c7f0a795faf298b3781251dc80c
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550.0 MB | Download |
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md5:71f6b8437758743ed1309246bb4da53b
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734.0 MB | Download |
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md5:f9852377c9b3cddc42bc9fa6ef5c0152
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734.0 MB | Download |
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md5:40c6abc593b345e6b2cdb5a4151056a7
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734.0 MB | Download |
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md5:6a597bd6b2d765e78cac4861437de729
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734.0 MB | Download |
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md5:02ac52c23fd01ae534fff21162c23ed5
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734.0 MB | Download |
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md5:228ee51f48eef7823706e2f67c9ecad9
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550.0 MB | Download |
Additional details
References
- Li, MY.; Liu, TX.; Duan, LM.; et al. Confluence simulations based on dynamic channel parameters in the grasslands lacking historical measurements. Journal of Hydrology, 2023, Volume 627, 130425.
- Li, MY.; Liu, TX.; Duan, LM.; et al. A novel evapotranspiration downscaling approach based on dynamic sensitive parameters and deep learning in the grassland lacking historical measurements. Ecological Indicators, 2025, Volume 178, 113839.