Published October 19, 2021 | Version 1.01

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)

Name Size
md5:b5e36c7f0a795faf298b3781251dc80c
838.9 MB Download
md5:959df53f6a6d5325463b6eb2722fd51c
838.9 MB Download
md5:0b70ac1251caeab78a6a8beb84a1142e
838.9 MB Download
md5:7d4f10222fb349fc5bd47cd96d746e56
838.9 MB Download
md5:69abbe91530f1501f5c8db81ac939abd
268.7 MB Download
md5:71f6b8437758743ed1309246bb4da53b
734.0 MB Download
md5:f9852377c9b3cddc42bc9fa6ef5c0152
734.0 MB Download
md5:40c6abc593b345e6b2cdb5a4151056a7
734.0 MB Download
md5:6a597bd6b2d765e78cac4861437de729
734.0 MB Download
md5:02ac52c23fd01ae534fff21162c23ed5
734.0 MB Download
md5:228ee51f48eef7823706e2f67c9ecad9
550.0 MB Download
md5:b08b98aa44d68b2fa673b6a9d4b0c72c
734.0 MB Download
md5:007f09e57b6d3ba2c8a0550b66ed4a2d
734.0 MB Download
md5:71f6b8437758743ed1309246bb4da53b
734.0 MB Download
md5:f9852377c9b3cddc42bc9fa6ef5c0152
734.0 MB Download
md5:40c6abc593b345e6b2cdb5a4151056a7
734.0 MB Download
md5:6a597bd6b2d765e78cac4861437de729
734.0 MB Download
md5:02ac52c23fd01ae534fff21162c23ed5
734.0 MB Download
md5:228ee51f48eef7823706e2f67c9ecad9
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.