Hybrid hydrological modeling for large alpine  basins: a semi-distributed approach

Li, Bu; Sun, Ting; Tian, Fuqiang; Tudaji, Mahmut; Qin, Li; Ni, Guangheng

doi:https://doi.org/10.5194/hess-28-4521-2024

Articles | Volume 28, issue 20

https://doi.org/10.5194/hess-28-4521-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-28-4521-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 28, issue 20

Research article

|

16 Oct 2024

Research article |

| 16 Oct 2024

Hybrid hydrological modeling for large alpine basins: a semi-distributed approach

Bu Li, Ting Sun, Fuqiang Tian, Mahmut Tudaji, Li Qin, and Guangheng Ni

Data sets

ASTER GDEM 30M Geospatial Data Cloud Site http://www.gscloud.cn/sources/details/310?pid=302

MOD15A2H v006 MODIS/Terra Leaf Area Index/FPAR 8-Day L4 Global 500 m SIN Grid USGS https://doi.org/10.5067/MODIS/MOD15A2H.006

China meteorological forcing dataset (1979-2018) TPDC https://doi.org/10.11888/AtmosphericPhysics.tpe.249369.file

MOD13A3 MODIS/Terra vegetation Indices Monthly L3 Global 1 km SIN Grid V006 K. Didan https://doi.org/10.5067/MODIS/MOD13A3.006 2022-05-16

Model code and software

The code of hybrid hydrological models Bu Li https://cloud.tsinghua.edu.cn/d/1bb19608a7024abfaa3e/

Short summary

This paper developed hybrid semi-distributed hydrological models by employing a process-based model as the backbone and utilizing deep learning to parameterize and replace internal modules. The main contribution is to provide a high-performance tool enriched with explicit hydrological knowledge for hydrological prediction and to improve understanding about the hydrological sensitivities to climate change in large alpine basins.