The MATILDA framework combines a simple positive degree-day routine (DDM) to compute glacial melt with the hydrological bucket model HBV (Bergström, 1986). The aim is to provide an easy-access open-source tool to assess the characteristics of small and medium-sized glacierized catchments and enable users to estimate future water resources for different climate change scenarios. MATILDA is an ongoing project and therefore a work in progress.
In the basic setup, MATILDA uses a modified version of the pypdd tool to calculate glacial melt based on a positive degree-day approach and a modified version of HBV from the Lumped Hydrological Models Playground (LHMP). The output contains the modeled time series for various components of the water balance, basic statistics for these variables, a choice of model effieciency coefficients (e.g. NSE, KGE...), and several plots of in- and output data.
The tool should run with every Python3 version on most computer operating systems. It was developed on Python 3.6.9 on Ubuntu 20.04. It requires the following Python3 libraries:
- xarray
- numpy
- pandas
- matplotlib
- scipy
- datetime
- hydroeval
- HydroErr
- Plotly
The MATILDA package and dependencies can be installed to your local machine by using pip or a comparable package manager. You can either install the package by using the link to this repository:
pip install git+https://github.com/cryotools/matilda.git
...or clone this repository to you local machine, navigate to top directory and use:
pip install .
The minimum input is a CSV-file containing timeseries of air temperature (°C), total precipitation (mm) and (if available) evapotranspiration data (mm) in the format shown below. If evapotranspiration is not provided it is estimated from air temperature following Oudin et.al. 2010. A series of runoff observations (mm) is used to calibrate/validate the model. All data sets need at least daily resolution.
| TIMESTAMP | T2 | RRR | PE |
|---|---|---|---|
| 2011-01-01 00:00:00 | -18.2 | 0.00 | 0.00 |
| 2011-01-01 01:00:00 | -18.3 | 0.1 | 0.00 |
| 2011-01-01 02:00:00 | -18.2 | 0.1 | 0.00 |
| ... | ... | ... | ... |
| Date | Qobs |
|---|---|
| 2011-01-01 | 0.17 |
| 2011-01-01 | 0.19 |
| ... | ... |
The forcing data is scaled to the mean glacier elevation and the mean catchment elevation respectively using linear lapse rates. Reference altitudes for the input data, the whole catchment, and the glacierized fraction need to be provided. Automated routines for catchment delineation and the download of public glacier data will be added to MATILDA in future versions.
To include the deltaH parameterization from Huss and Hock 2015 within the DDM routine to calculate glacier area evolution in the study period, information on the glaciers is necessary. The routine requires an initial glacier profile containing the spatial distribution of ice over elevation bands at the beginning of the study period in form of a dataframe:
| Elevation | Area | WE | EleZone |
|---|---|---|---|
| 3720 | 0.005 | 10786.061 | 3700 |
| 3730 | 0.001 | 13687.801 | 3700 |
| 3740 | 0.001 | 12571.253 | 3700 |
| 3750 | 0.002 | 12357.987 | 3800 |
| .. | ... | ... | ... |
- Elevation - elevation of each band (10 m intervals recommended)
- Area - glacierized area in each band as fraction of the total catchment area (column sum is the glacierized fraction of the whole catchment)
- WE - ice thickness in mm w.e.
- EleZone - combined bands over 100-200 m.
The deltaH parametrization routine is based on the workflow outlined by Seibert et.al. (2018).
The MATILDA package consists of four different modules: parameter setup, data preprocessing, core simulation, and postprocessing. All modules can be used individually or via the superior MATILDA_simulation function. To use the full workflow the following steps are recommended:
- Load your data.
- Define the spin-up and simulation periods. At least one year of spin-up is recommended.
- Specify your catchment properties (catchment area, glacierized area, average elevation, average glacier elevation).
- Define the output frequency (daily, weekly, monthly or yearly).
- Specify parameters as you please using the MATILDA_parameter function. If no parameters are specified, default values are applied.
- Run the data preprocessing with the MATILDA_preproc function.
- Run the actual simulation with the MATILDA_submodules function.
- The simulation will give you a quick overview of your output and (if you provide observations) model efficiency coefficients are calculated.
- Plot runoff, meteorological parameters, and HBV output variables using MATILDA_plots function.
- All the output including the plots and parameters can be saved to your local disk with the MATILDA_save_output function.
An example script and 3y of sample data can be found here.
- pypdd - Python positive degree day model for glacier surface mass balance
- LHMP - Lumped Hydrological Models Playgroud - HBV Model
- Phillip Schuster - Initial work - (https://github.com/phiscu)
- Ana-Lena Tappe - Initial work - (https://github.com/anatappe)
See also the list of contributors who participated in this project.
This project is licensed under the MIT License.
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For PyPDD:
- Seguinot, J. (2019). PyPDD: a positive degree day model for glacier surface mass balance (Version v0.3.1). Zenodo. http://doi.org/10.5281/zenodo.3467639
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For LHMP and HBV:
- Ayzel, G. (2016). Lumped Hydrological Models Playground. github.com/hydrogo/LHMP, doi:10.5281/zenodo.59680.
- Ayzel G. (2016). LHMP: lumped hydrological modelling playground. Zenodo. doi:10.5281/zenodo.59501.
- Bergström, S. (1992). The HBV model: Its structure and applications. Swedish Meteorological and Hydrological Institute. PDF
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For the deltaH parametrization:
- Seibert et.al. (2018). Representing glacier geometry changes in a semi-distributed hydrological model. https://doi.org/10.5194/hess-22-2211-2018