hydromt_wflow.workflows.rootzoneclim

hydromt_wflow.workflows.rootzoneclim(dsrun: ~xarray.core.dataset.Dataset, ds_obs: ~xarray.core.dataset.Dataset, ds_like: ~xarray.core.dataset.Dataset, flwdir: ~xarray.core.dataarray.DataArray, ds_cc_hist: ~xarray.core.dataset.Dataset | None = None, ds_cc_fut: ~xarray.core.dataset.Dataset | None = None, return_period: list = [2, 3, 5, 10, 15, 20, 25, 50, 60, 100], Imax: float = 2.0, start_hydro_year: str = 'Sep', start_field_capacity: str = 'Apr', LAI: bool = False, rootzone_storage: bool = False, correct_cc_deficit: bool = False, chunksize: int = 100, missing_days_threshold: int = 330, logger=<Logger hydromt_wflow.workflows.rootzoneclim (WARNING)>)

Estimates the root zone storage parameter.

for current observed and (optionally) for future climate-based streamflow data.

The root zone storage capacity parameter is calculated per subcatchment and is converted to a gridded map at model resolution. Optionally, this function can return the wflow_sbm parameter RootingDepth by dividing the root zone storage parameter by (theta_s - theta_r).

The method is based on the estimation of maximum annual storage deficits based on precipitation and estimated actual evaporation time series, which in turn are estimated from observed streamflow data and long-term precipitation and potential evap. data, as explained in Bouaziz et al. (2022). The main assumption is that vegetation adapts its rootzone storage capacity to overcome dry spells with a certain return period (typically 20 years for forest ecosystems). In response to a changing climtate, it is likely that vegetation also adapts its rootzone storage capacity, thereby changing model parameters for future conditions. This method also allows to estimate the change in rootzone storage capacity in response to a changing climate.

Parameters:

• dsrun (xr.Dataset) – Geodataset with streamflow locations and timeseries, named “discharge” (m3/s). The geodataset expects the coordinate names “index” (for each station id).

• ds_obs (xr.Dataset) – Dataset with the observed forcing data (precip and pet) [mm/timestep].

• ds_like (xr.Dataset) – Dataset with staticmaps at model resolution.

• flwdir (FlwDirRaster) – flwdir object

• ds_cc_hist (xr.Dataset) – Dataset with the simulated historical forcing data (precip and pet) [mm/timestep], based on a climate model. The default is None.

• ds_cc_fut (xr.Dataset) – Dataset with the simulated future climate forcing data (precip and pet) [mm/timestep], based on a climate model. The default is None.

• return_period (list) – List with one or more values indiciating the return period(s) (in years) for wich the rootzone storage depth should be calculated. The default is [2,3,5,10,15,20,25,50,60,100]

• Imax (float) – The maximum interception storage capacity [mm]. The default is 2 mm.

• start_hydro_year (str) – The start month (abreviated to the first three letters of the month, starting with a capital letter) of the hydrological year. The default is “Sep”.

• start_field_capacity (str) – The end of the wet season / commencement of dry season. This is the moment when the soil is at field capacity, i.e. there is no storage deficit yet. The default is “Apr”.

• rootzone_storage (bool) – Boolean to indicate whether the rootzone storage maps should be stored in the staticmaps or not. The default is False.

• LAI (bool) – Determine whether the LAI will be used to determine Imax. Requires to have run setup_laimaps. The default is False.

• chunksize (int) – Chunksize on time dimension for processing data (not for saving to disk!). A default value of 100 is used on the time dimension.

• correct_cc_deficit (bool) – Determines whether a bias-correction of the future deficit should be applied. If the climate change scenario and hist period are bias-corrected, this should probably be set to False.

• missing_days_threshold (int, optional) – Minimum number of days within a year for that year to be counted in the long-term Budyko analysis.

Returns:

• ds_out (xr.Dataset) – Dataset containing root zone storage capacity (optional) and RootingDepth for several forcing and return periods.

• gdf_basins_all (GeoDataFrame) – Geodataframe containing the root zone storage capacity values for each basin before filling NaN.

References

Bouaziz, L. J. E., Aalbers, E. E., Weerts, A. H., Hegnauer, M., Buiteveld, H., Lammersen, R., Stam, J., Sprokkereef, E., Savenije, H. H. G. and Hrachowitz, M. (2022). Ecosystem adaptation to climate change: the sensitivity of hydrological predictions to time-dynamic model parameters, Hydrology and Earth System Sciences, 26(5), 1295-1318. DOI: 10.5194/hess-26-1295-2022.