Open, Quick and Reproducible Hydrological Model Deployment Cloud Platform
- 1Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China
- 2Department of Land, Air and Water Resources, University of California, Davis, Davis, California 95616, USA
- 3Department of Civil and Environmental Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
- 4Chengdu University of Information Technology, School of Atmospheric Sciences, Chengdu, Sichuan 610103, China
Model and data are essential for current geoscientific research. Too many hydrological models are available for potential modelers, plus too much spatial terrestrial data related to modeling is accessible to users. More importantly, reproducibility is one of the key features in science, which is barely discussed in hydrological models. Two significant reasons are that (1) the various hydrological models are incompatible since they require different variables, even if some of them share the same terminology, and (2) the complexity of model structure makes it impossible to deploy a model swiftly in any new research area.
Our project is to establish a Global Hydrological Data Cloud (GHDC, https://shuddata.com) providing essential terrestrial variables for generic hydrological modeling, as modelers provide the watershed boundary and model requests. The data retrieved from the GHDC covers terrain, topology, soil/geology, landuse, hydraulic parameters and meteorological time-series data. The demonstration of three watershed examples with the Simulator of Hydrologic Unstructured Domains (SHUD), can be a standard paradigm for physically-based hydrological modeling and instructive for other modeling processes, as the procedures are transferable to other hydrological models and regions.
How to cite: Shu, L., Chang, Y., Meng, X., Ullrich, P., Duffy, C., Chen, H., Lyu, S., Zhang, Y., and Li, Z.: Open, Quick and Reproducible Hydrological Model Deployment Cloud Platform, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-16869, https://doi.org/10.5194/egusphere-egu23-16869, 2023.