Large difference in flux in transient time serie between recharge array and recharge dictionnay assignation

[valDallalba]

Hello,

I'm having issues here on a large-scale model when I assign the recharge in a transient simulation.

I want to assign a recharge array on top of my model using the recharge from time series array module in flopy. I have a domain that specifies which cells are to take into account for the simulation and I would like to apply the recharge to all the top cells of my model (not only on the first layer with idomain=1, but on the first top layer where idomain=1).

Here is a capture of the three first layers of my idomain.

I tried to use the fp.mf6.modflow.mfgwfrcha.ModflowGwfrcha() with a time series array assign to each time step in my simulation, (which is really useful to work with since my simulation is large and runs over a large number of steps) but when I'm analyzing the budgets output of my model I realized that the flux corresponding to my recharge array package is "under assigned"/too low compare to the corresponding flux of my input arrays or what I should normally expect from my study case.

I then tried to assign directly the recharge flux, cell by cell using the classical "dictionary type" approach and fp.mf6.ModflowGwfrch() (which required much more effort and simulation time due to the large number of files needed to be created in the transient approach, one for each point with a unique time series). I create these time series with the same array as before but now I check and assign cell by cell the recharge value. Now, with this approach the budgets make sense and are not underestimated.

I compared both the sum of the flux of the input data which are similar since created from the same input. However once assign with modflow the two approaches produce really different results in the recharge budget.

If anybody has already encountered this kind of issue/behavior it would be really helpful to have your insight.

Here is the syntax I used in both cases :

For the recharge array time series, which is not working :

#recharge precip
rch_prcp = fp.mf6.modflow.mfgwfrcha.ModflowGwfrcha(gwf, fixed_cell=False, pname='rch_precip', recharge='TIMEARRAYSERIES rcharray_precip')

#initialize the time array series
rch_prcp.tas.initialize(
    filename = 'recharge/rcharray_precip.tas',
    tas_array=stress_data_recharge_precip_simu,
    time_series_namerecord="rcharray_precip",
    interpolation_methodrecord="LINEAR") 

For the classic time series, which is working but is too time-consuming to work with (my recharge varies for every cell in my model and thus I need to create a time series for each of these cells):

rch_prcp = fp.mf6.ModflowGwfrch(gwf, pname=name_base_condition, stress_period_data=ts_rch_precip)

#add the first time serie
rch_prcp.ts.initialize(
    filename   = name_base_condition+"/"+name_base_condition+"_0.ts",
    timeseries = ts_data_all[0],
    time_series_namerecord     = name_base_condition+'_0',
    interpolation_methodrecord = "linear")

for ts in range(1, len(ts_data_all)):
    rch_prcp.ts.append_package(
    filename   = name_base_condition+'/'+name_base_condition+'_{}.ts'.format(ts),
    timeseries = ts_data_all[ts],
    time_series_namerecord     = name_base_condition+"_{}".format(ts),
    interpolation_methodrecord = "linear")

I'm happy to give more info if needed!!

Thanks to all,

Valentin