def FW1(Model,ll_temp=None, q_0=None):
"""
====================================================
FW1(Model,ll_temp=None, q_0=None)
====================================================
FW1 connects two module :
1- The distributed rainfall-runoff module
2- Triangular function-1 routing method
Parameters
----------
Model : TYPE
DESCRIPTION.
ll_temp : TYPE, optional
DESCRIPTION. The default is None.
q_0 : TYPE, optional
DESCRIPTION. The default is None.
Returns
-------
None.
"""
# subcatchment
distrrm.RunLumpedRRM(Model)
distrrm.DistMaxbas1(Model)
qlz1 = np.array([np.nansum(Model.qlz[:,:,i]) for i in range(Model.TS)]) # average of all cells (not routed mm/timestep)
quz1 = np.array([np.nansum(Model.quz[:,:,i]) for i in range(Model.TS)]) # average of all cells (routed mm/timestep)
Model.qout = qlz1 + quz1
Model.qout = Model.qout[:-1]
def FW1Withlake(Model, Lake,ll_temp=None, q_0=None):
"""
==============================================================
FW1Withlake(Model, Lake,ll_temp=None, q_0=None)
==============================================================
FW1 connects two module :
1- The distributed rainfall-runoff module
2- Triangular function-1 routing method
3- Lake module
Parameters
----------
Model : TYPE
DESCRIPTION.
Lake : TYPE
DESCRIPTION.
ll_temp : TYPE, optional
DESCRIPTION. The default is None.
q_0 : TYPE, optional
DESCRIPTION. The default is None.
Returns
-------
None.
"""
plake = Lake.MeteoData[:,0]
et = Lake.MeteoData[:,1]
t = Lake.MeteoData[:,2]
tm = Lake.MeteoData[:,3]
# lake simulation
Lake.Qlake, _ = hbv_lake.simulate(plake, t, et, Lake.Parameters,
[Model.Timef, Lake.CatArea, Lake.LakeArea],
Lake.StageDischargeCurve, 0,
init_st=Lake.InitialCond,
ll_temp=tm, lake_sim=True)
# qlake is in m3/sec
# lake routing
Lake.QlakeR = routing.muskingum(Lake.Qlake, Lake.Qlake[0], Lake.Parameters[11],
Lake.Parameters[12], Model.Timef)
# subcatchment
distrrm.RunLumpedRRM(Model)
distrrm.DistMAXBAS(Model)
qlz1 = np.array([np.nansum(Model.qlz[:,:,i]) for i in range(Model.Parameters.shape[2]+1)]) # average of all cells (not routed mm/timestep)
quz1 = np.array([np.nansum(Model.quz[:,:,i]) for i in range(Model.Parameters.shape[2]+1)]) # average of all cells (routed mm/timestep)
qout = qlz1 + quz1
# qout = (qlz1 + quz1) * Model.CatArea / (Model.Timef* 3.6)
Model.qout = qout[:-1] + Lake.QlakeR
def RRMWithlake(Model, Lake,ll_temp=None, q_0=None):
"""
============================================================
RRMWithlake(Model, Lake,ll_temp=None, q_0=None)
============================================================
RRMWithlake connects three modules the lake, the distributed
ranfall-runoff module and spatial routing module
Parameters
----------
Model : [Catchment object]
DESCRIPTION.
Lake : TYPE
DESCRIPTION.
ll_temp : TYPE, optional
DESCRIPTION. The default is None.
q_0 : TYPE, optional
DESCRIPTION. The default is None.
Returns
-------
None.
"""
plake = Lake.MeteoData[:,0]
et = Lake.MeteoData[:,1]
t = Lake.MeteoData[:,2]
tm = Lake.MeteoData[:,3]
# lake simulation
Lake.Qlake, _ = hbv_lake.simulate(plake, t, et, Lake.Parameters,
[Model.Timef, Lake.CatArea, Lake.LakeArea],
Lake.StageDischargeCurve, 0,
init_st=Lake.InitialCond,
ll_temp=tm, lake_sim=True)
# qlake is in m3/sec
# lake routing
Lake.QlakeR = routing.Muskingum_V(Lake.Qlake, Lake.Qlake[0], Lake.Parameters[11],
Lake.Parameters[12], Model.Timef)
# subcatchment
distrrm.RunLumpedRRM(Model)
# routing lake discharge with DS cell k & x and adding to cell Q
qlake = routing.Muskingum_V(Lake.QlakeR,Lake.QlakeR[0],
Model.Parameters[Lake.OutflowCell[0],Lake.OutflowCell[1],10],
Model.Parameters[Lake.OutflowCell[0],Lake.OutflowCell[1],11],
Model.Timef)
qlake = np.append(qlake,qlake[-1])
# both lake & Quz are in m3/s
Model.quz[Lake.OutflowCell[0],Lake.OutflowCell[1],:] = Model.quz[Lake.OutflowCell[0],Lake.OutflowCell[1],:] + qlake
# run the GIS part to rout from cell to another
distrrm.SpatialRouting(Model)
def RRMModel(Model, ll_temp=None, q_0=None):
"""
=======================================================================
Dist_model(DEM,flow_acc,flow_direct,sp_prec,sp_et,sp_temp,sp_par,p2,kub,klb,init_st,ll_temp,q_0)
=======================================================================
HapiModel connect two modules :
1- The distributed rainfall runoff: model runs separately for each cell
2- The Spatial routing scheme (routing is following river network)
Inputs:
----------
1-DEM:
[gdal.dataset] DEM raster file of the catchment (clipped to the catchment only)
2-flow_acc:
[gdal.dataset] flow accumulation raster file of the catchment (clipped to the catchment only)
3-flow_direct:
[gdal.dataset] flow Direction raster file of the catchment (clipped to the catchment only)
4-sp_prec:
[numpy array] 3d array of the precipitation data, sp_prec should
have the same 2d dimension of raster input
5-sp_et:
[numpy array] 3d array of the evapotranspiration data, sp_et should
have the same 2d dimension of raster input
6-sp_temp:
[numpy array] 3d array of the temperature data, sp_temp should
have the same 2d dimension of raster input
7-sp_par:
[numpy array] number of 2d arrays of the catchment properties spatially
distributed in 2d and the third dimension is the number of parameters,
sp_pars should have the same 2d dimension of raster input
8-p2:
[List] list of unoptimized parameters
p2[0] = tfac, 1 for hourly, 0.25 for 15 min time step and 24 for daily time step
p2[1] = catchment area in km2
9-kub:
[float] upper bound of K value (traveling time in muskingum routing method)
10-klb:
[float] Lower bound of K value (traveling time in muskingum routing method)
11-init_st:
[list] initial state variables values [sp, sm, uz, lz, wc]. default=None
12-ll_temp:
[numpy array] 3d array of the long term average temperature data
13-q_0:
[float] initial discharge m3/s
Outputs:
----------
1-statevariables:
[numpy ndarray] 4D array (rows,cols,time,states) states are [sp,wc,sm,uz,lv]
2-qlz:
[numpy ndarray] 3D array of the lower zone discharge
3-quz:
[numpy ndarray] 3D array of the upper zone discharge
4-qout:
[numpy array] 1D timeseries of discharge at the outlet of the catchment
of unit m3/sec
5-quz_routed:
[numpy ndarray] 3D array of the upper zone discharge accumulated and
routed at each time step
6-qlz_translated:
[numpy ndarray] 3D array of the lower zone discharge translated at each time step
"""
# run the rainfall runoff model separately
distrrm.RunLumpedRRM(Model)
# run the GIS part to rout from cell to another
distrrm.SpatialRouting(Model)