dS2 is a distributed hydrological model, based on the model defined by Kirchner (2009). The model assumes that the simulated discharge is solely dependent on the storage of that particular area. This model allows the user to apply this concept in a distributed fashion, where the spatial resolution is determined by the input files. The temporal resolution can be modified by the user as well.
- Python 3.6, including basic libraries, such as: datetime, glob, os, pickle
- NumPy (1.16.2)
- pandas (0.24.1)
- xarray (0.11.3)
- tqdm (4.31.1)
All forcing input files should be entered as mm h-1, even if a coarser temporal resolution is used.
Both input and output directories can be relative to the current model path, or direct to another directory using the full path name.
| Name | Description | Unit | Notes |
|---|---|---|---|
| Precipitation | Precipitation values per pixel per time step | mm h-1 | - |
| Evaporation | Evaporation values per pixel per time step | mm h-1 | - |
| Temperature | Temperature per pixel per time step | mm h-1 | Only for snow |
| Catchment | Map indicating the location of each (sub)basin, indices corresponding to outlet, main outlet must be "1.0" | - | 2D ASCII file, where pixels outside the catchment are indicated with a NaN |
| Distance | Map with the distance of each pixel to the main outlet, via the stream network | m | 2D ASCII files, pixels outside the catchment are indicated with a NaN |
| routingInfo.pkl | Pickle object with 3 dictionaries: outletLoc, catchLoc, outletInfo | - | See below |
routingInfo.pkl:
- outletLoc: dict with outletID as keys, and the 1D cell number of the outlet location
- catchLoc: dict with outletID as keys, and the 1D cell numbers of all pixels within that subcatchment
- outletInfo: dict with outletID as keys, and a list as value with two items:
- Downstream outletID
- Distance to downstream outletID [m]
| Name | Description | Example value | Unit | Notes |
|---|---|---|---|---|
| alpha | gQ parameter - intersect | -1.171 | - | - |
| beta | gQ parameter - slope | 0.57 | - | - |
| gamma | gQ parameter - curvature | -0.03 | - | If set to zero, gQ function is equal to original power-law a*Q^b |
| eps | ET - Reduction factor for ET | 0.89 | - | - |
| tau | Routing - Average velocity of water | 2 | m s-1 | Average velocity of water to travel from a point to the outlet via the river network |
| Qt | ET - Minimum discharge value for ET | 1e-4 | mm h-1 | If Qt+1 is expected to cross this threshold, ET is set to zero for these pixels |
| T0 | Snow - Critical temperature for snowfall and melt | 0 | deg C | - |
| ddf | Snow - Degree day factor for snowmelt | 2.00 | mm day-1 degC-1 | Value is corrected for dt |
| rdf | Snow - Radiation day factor for snowmelt | 0.26 | mm day-1 (W/m2)-1 | Value is corrected for dt |
| dt | Stability - Size of timestep in hours | 1 | h | If timestep coarser than 1 hour, specify here |
| max_RAM | Stability - Maximum RAM the model can use | 1024 | MB | Chunksize per file is determined by the model options |
| size_one_value | Stability - Size of a single value | 4 | bytes | float32: 4 bytes, float64: 8 bytes |
| LB | Stability - Lower boundary for solver | 1e-3 | - | Factor determining the minimum value for Qt+1 (Qt * LB) |
| max_gQ_difference | Stability - Maximum allowed difference in gQ values | 2 | - | Difference is calculated based on abs(g(Q)_t2 - g(Q)_t1) / min(g(Q)_t2 - g(Q)_t1) |
| dt_reduction | Stability - Factor controlling the reduction in dt | 0.15 | - | The new timestep is based on the order of magnitude difference between gQ, to the power of this value |
| min_extra_dt | Stability - Number of minimum extra dt per dt | 5 | - | Parameter controls the minimum of internal timestep per dt |
| max_extra_dt | Stability - Number of maximum extra dt per dt | 50 | - | Parameter controls the maximum of internal timestep per dt |
dS2_model.py: Main model codedS2_settings_and_run.py: File with model settings and example code to run the modelfuncFluxes.py: Functions for snowmelt and evaporationfuncParams.py: Function to change parameters, with correction for unitsfuncRouting.py: Functions required for routingfuncSolver.py: Functions for both the RK4-approach with flexible dt, and the textbook Cash-Karp approachreadInput.py: Functions to read settings class, and other file formatsreadOutput.py: Functions to write memmap files and translate memmap to NetCDF file
Buitink, J., Melsen, L. A., Kirchner, J. W., and Teuling, A. J. (2020), A distributed simple dynamical systems approach (dS2 v1.0) for computationally efficient hydrological modelling at high spatio-temporal resolution, Geoscientific Model Development, 13, 6093–6110, https://doi.org/10.5194/gmd-13-6093-2020.
Kirchner, J. W. (2009), Catchments as simple dynamical systems: Catchment characterization, rainfall-runoff modeling, and doing hydrology backward, Water Resources Research, 45, W02429, https://doi.org/10.1029/2008WR006912.