CCAM: China Catchment Attributes and Meteorology dataset

Hao, Zhen; Jin, Jin; Xia, Runliang; Tian, Shimin; Yang, Wushuang; Liu, Qixing; Zhu, Min; Ma, Tao; Jing, Chengran; Zhang, Yanning

doi:https://doi.org/10.5194/essd-13-5591-2021

Articles | Volume 13, issue 12

https://doi.org/10.5194/essd-13-5591-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/essd-13-5591-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 13, issue 12

Data description paper

|

03 Dec 2021

Data description paper |

| 03 Dec 2021

CCAM: China Catchment Attributes and Meteorology dataset

Zhen Hao, Jin Jin, Runliang Xia, Shimin Tian, Wushuang Yang, Qixing Liu, Min Zhu, Tao Ma, Chengran Jing, and Yanning Zhang

Data sets

CCAM: China Catchment Attributes and Meteorology dataset Zhen Hao, Jin Jin, Runliang Xia, Shimin Tian, Wushuang Yang, Qixing Liu, Min Zhu, Tao Ma, and Chengran Jing https://doi.org/10.5281/zenodo.5729444

Model code and software

CCAM: China Catchment Attributes and Meteorology dataset Zhen Hao https://doi.org/10.5281/zenodo.5749718

Short summary

CCAM is proposed to promote large-sample hydrological research in China. The first catchment attribute dataset and catchment-scale meteorological time series dataset in China are built. We also built HydroMLYR, a hydrological dataset with standardized streamflow observations supporting machine learning modeling. The open-source code producing CCAM supports the calculation of custom watersheds.