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Hapi is an open-source Python Framework for building raster-based conceptual distributed hydrological models using HBV96 lumped model & Muskingum routing method at a catchment scale (Farrag & Corzo, 2021), Hapi gives a high degree of flexibility to all components of the model (spatial discretization - cell size, temporal resolution, parameterization approaches and calibration (Farrag et al., 2021)).
Hapi
- Modified version of HBV96 hydrological model (Bergström, 1992) with 15 parameters in case of considering snow processes, and 10 parameters without snow, in addition to 2 parameters of Muskingum routing method
- Remote sensing module to download the meteorological inputs required for the hydrologic model simulation (ECMWF)
- GIS modules to enable the modeler to fully prepare the meteorological inputs and do all the preprocessing needed to build the model (align rasters with the DEM), in addition to various methods to manipulate and convert different forms of distributed data (rasters, NetCDF, shapefiles)
- Sensitivity analysis module based on the concept of one-at-a-time OAT and analysis of the interaction among model parameters using the Sobol concept ((Rusli et al., 2015)) and a visualization
- Statistical module containing interpolation methods for generating distributed data from gauge data, some distribution for frequency analysis and Maximum likelihood method for distribution parameter estimation.
- Visualization module for animating the results of the distributed model, and the meteorological inputs
- Optimization module, for calibrating the model based on the Harmony search method
The recent version of Hapi (Hapi 1.0.1) integrates the global hydrological parameters obtained by Beck et al., (2016), to reduce model complexity and uncertainty of parameters.
- Developing a regionalization method for connection model parameters with some catchment characteristics for better model calibration.
- Developing and integrate river routing method (kinematic and diffusive wave approximation)
- Apply the model for large scale (regional/continental) cases
- Developing a DEM processing module for generating the river network at different DEM spatial resolutions.
For using Hapi please cite Farrag et al. (2021) and Farrag & Corzo (2021)
- In April 14-15 we had a two days session for Masters and PhD student in IHE-Delft to explain the different modules and the distributed hydrological model in Hapi Day 1 , Day 2
Farrag, M. & Corzo, G. (2021) MAfarrag/Hapi: Hapi. doi:10.5281/ZENODO.4662170
Farrag, M., Perez, G. C. & Solomatine, D. (2021) Spatio-Temporal Hydrological Model Structure and Parametrization Analysis. J. Mar. Sci. Eng. 9(5), 467. doi:10.3390/jmse9050467 Link
Beck, H. E., Dijk, A. I. J. M. van, Ad de Roo, Diego G. Miralles, T. R. M. & Jaap Schellekens, and L. A. B. (2016) Global-scale regionalization of hydrologic model parameters-Supporting materials 3599–3622. doi:10.1002/2015WR018247.Received
Bergström, S. (1992) The HBV model - its structure and applications. Smhi Rh 4(4), 35.
Rusli, S. R., Yudianto, D. & Liu, J. tao. (2015) Effects of temporal variability on HBV model calibration. Water Sci. Eng. 8(4), 291–300. Elsevier Ltd. doi:10.1016/j.wse.2015.12.002
Installing hapi from the conda-forge channel can be achieved by:
conda install -c conda-forge hapi
It is possible to list all of the versions of hapi available on your platform with:
conda search hapi --channel conda-forge
to install the last development to time you can install the library from github
pip install git+https://github.com/MAfarrag/HAPI
to install the last release you can easly use pip
pip install HAPI-Nile==1.6.0
>>> import Hapi
- module names: lower case word, preferably one word if not, separate words with underscores (module.py, my_module.py).
- class names: PascalCase (Model, MyClass).
- class method/function: CamelCase(getFile, readConfig).should have a verb one them, because they perform some action