def Dist_HBV2(ConceptualModel,
lakecell,
q_lake,
DEM,
flow_acc,
flow_acc_plan,
sp_prec,
sp_et,
sp_temp,
sp_pars,
p2,
init_st=None,
ll_temp=None,
q_0=None):
"""
original function
"""
n_steps = sp_prec.shape[
2] + 1 # no of time steps =length of time series +1
# intiialise vector of nans to fill states
dummy_states = np.empty([n_steps, 5]) # [sp,sm,uz,lz,wc]
dummy_states[:] = np.nan
# Get the mask
mask, no_val = raster.get_mask(DEM)
# shape of the fpl raster (rows, columns)-------------- rows are x and columns are y
x_ext, y_ext = mask.shape
# y_ext, x_ext = mask.shape # shape of the fpl raster (rows, columns)------------ should change rows are y and columns are x
# Get deltas of pixel
geo_trans = DEM.GetGeoTransform(
) # get the coordinates of the top left corner and cell size [x,dx,y,dy]
dx = np.abs(geo_trans[1]) / 1000.0 # dx in Km
dy = np.abs(geo_trans[-1]) / 1000.0 # dy in Km
px_area = dx * dy # area of the cell
# Enumerate the total number of pixels in the catchment
tot_elem = np.sum(
np.sum([
[1 for elem in mask_i if elem != no_val] for mask_i in mask
])) # get row by row and search [mask_i for mask_i in mask]
# total pixel area
px_tot_area = tot_elem * px_area # total area of pixels
# Get number of non-value data
st = [] # Spatially distributed states
qlz = []
quz = []
#------------------------------------------------------------------------------
for x in range(x_ext): # no of rows
st_i = []
q_lzi = []
q_uzi = []
# q_out_i = []
# run all cells in one row ----------------------------------------------------
for y in range(y_ext): # no of columns
if mask[x, y] != no_val: # only for cells in the domain
# Calculate the states per cell
# TODO optimise for multiprocessing these loops
# _, _st, _uzg, _lzg = ConceptualModel.simulate_new_model(avg_prec = sp_prec[x, y,:],
_, _st, _uzg, _lzg = ConceptualModel.Simulate(
prec=sp_prec[x, y, :],
temp=sp_temp[x, y, :],
et=sp_et[x, y, :],
par=sp_pars[x, y, :],
p2=p2,
init_st=init_st,
ll_temp=None,
q_0=q_0,
snow=0) #extra_out = True
# append column after column in the same row -----------------
st_i.append(np.array(_st))
#calculate upper zone Q = K1*(LZ_int_1)
q_lz_temp = np.array(sp_pars[x, y, 6]) * _lzg
q_lzi.append(q_lz_temp)
# calculate lower zone Q = k*(UZ_int_3)**(1+alpha)
q_uz_temp = np.array(sp_pars[x, y, 5]) * (np.power(
_uzg, (1.0 + sp_pars[x, y, 7])))
q_uzi.append(q_uz_temp)
#print("total = "+str(fff)+"/"+str(tot_elem)+" cell, row= "+str(x+1)+" column= "+str(y+1) )
else: # if the cell is novalue-------------------------------------
# Fill the empty cells with a nan vector
st_i.append(
dummy_states
) # fill all states(5 states) for all time steps = nan
q_lzi.append(
dummy_states[:, 0]
) # q lower zone =nan for all time steps = nan
q_uzi.append(
dummy_states[:, 0]
) # q upper zone =nan for all time steps = nan
# store row by row-------- ----------------------------------------------------
#st.append(st_i) # state variables
st.append(st_i) # state variables
qlz.append(np.array(q_lzi)) # lower zone discharge mm/timestep
quz.append(
np.array(q_uzi)) # upper zone routed discharge mm/timestep
#------------------------------------------------------------------------------
# convert to arrays
st = np.array(st)
qlz = np.array(qlz)
quz = np.array(quz)
#%% convert quz from mm/time step to m3/sec
area_coef = p2[1] / px_tot_area
quz = quz * px_area * area_coef / (p2[0] * 3.6)
no_cells = list(
set([
flow_acc_plan[i, j] for i in range(x_ext) for j in range(y_ext)
if not np.isnan(flow_acc_plan[i, j])
]))
# no_cells=list(set([int(flow_acc_plan[i,j]) for i in range(x_ext) for j in range(y_ext) if flow_acc_plan[i,j] != no_val]))
no_cells.sort()
#%% routing lake discharge with DS cell k & x and adding to cell Q
q_lake = routing.Muskingum_V(q_lake, q_lake[0],
sp_pars[lakecell[0], lakecell[1], 10],
sp_pars[lakecell[0], lakecell[1],
11], p2[0])
q_lake = np.append(q_lake, q_lake[-1])
# both lake & Quz are in m3/s
#new
quz[lakecell[0],
lakecell[1], :] = quz[lakecell[0], lakecell[1], :] + q_lake
#%% cells at the divider
quz_routed = np.zeros_like(quz) * np.nan
# for all cell with 0 flow acc put the quz
for x in range(x_ext): # no of rows
for y in range(y_ext): # no of columns
if mask[x, y] != no_val and flow_acc_plan[x, y] == 0:
quz_routed[x, y, :] = quz[x, y, :]
#%% new
for j in range(1, len(no_cells)): #2):#
for x in range(x_ext): # no of rows
for y in range(y_ext): # no of columns
# check from total flow accumulation
if mask[x,
y] != no_val and flow_acc_plan[x,
y] == no_cells[j]:
# print(no_cells[j])
q_r = np.zeros(n_steps)
for i in range(len(flow_acc[str(x) + "," +
str(y)])): # no_cells[j]
# bring the indexes of the us cell
x_ind = flow_acc[str(x) + "," + str(y)][i][0]
y_ind = flow_acc[str(x) + "," + str(y)][i][1]
# sum the Q of the US cells (already routed for its cell)
# route first with there own k & xthen sum
q_r = q_r + routing.Muskingum_V(
quz_routed[x_ind, y_ind, :],
quz_routed[x_ind, y_ind,
0], sp_pars[x_ind, y_ind, 10],
sp_pars[x_ind, y_ind, 11], p2[0])
# q=q_r
# add the routed upstream flows to the current Quz in the cell
quz_routed[x, y, :] = quz[x, y, :] + q_r
#%% check if the max flow _acc is at the outlet
# if tot_elem != np.nanmax(flow_acc_plan):
# raise ("flow accumulation plan is not correct")
# outlet is the cell that has the max flow_acc
outlet = np.where(flow_acc_plan == np.nanmax(
flow_acc_plan)) #np.nanmax(flow_acc_plan)
outletx = outlet[0][0]
outlety = outlet[1][0]
#%%
qlz = np.array([np.nanmean(qlz[:, :, i]) for i in range(n_steps)
]) # average of all cells (not routed mm/timestep)
# convert Qlz to m3/sec
qlz = qlz * p2[1] / (p2[0] * 3.6) # generation
qout = qlz + quz_routed[outletx, outlety, :]
return qout, st, quz_routed, qlz, quz
def HapiWithlake(Model, Lake, ll_temp=None, q_0=None):
"""
============================================================
HapiWithlake(Model, Lake,ll_temp=None, q_0=None)
============================================================
HapiWithlake 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)
Model.qout = Model.qout[:-1]
def SpatialRouting(Model):
"""
====================================================================
SpatialRouting(qlz,quz,flow_acc,flow_direct,sp_pars,p2)
====================================================================
SpatialRouting method routes the discharge from cell to another following
the flow direction input raster
Inputs:
----------
1-qlz:
[numpy ndarray] 3D array of the lower zone discharge
2-quz:
[numpy ndarray] 3D array of the upper zone discharge
3-flow_acc:
[gdal.dataset] flow accumulation raster file of the catchment (clipped to the catchment only)
4-flow_direct:
[gdal.dataset] flow Direction raster file of the catchment (clipped to the catchment only)
5-sp_pars:
[numpy ndarray] 3D array of the parameters
6-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
Outputs:
----------
1-qout:
[numpy array] 1D timeseries of discharge at the outlet of the catchment
of unit m3/sec
2-quz_routed:
[numpy ndarray] 3D array of the upper zone discharge accumulated and
routed at each time step
3-qlz_translated:
[numpy ndarray] 3D array of the lower zone discharge translated at each time step
"""
# # routing lake discharge with DS cell k & x and adding to cell Q
# q_lake=Routing.Muskingum_V(q_lake,q_lake[0],sp_pars[lakecell[0],lakecell[1],10],sp_pars[lakecell[0],lakecell[1],11],p2[0])
# q_lake=np.append(q_lake,q_lake[-1])
# # both lake & Quz are in m3/s
# #new
# quz[lakecell[0],lakecell[1],:]=quz[lakecell[0],lakecell[1],:]+q_lake
### cells at the divider
Model.quz_routed = np.zeros_like(Model.quz)
"""
lower zone discharge is going to be just translated without any attenuation
in order to be able to calculate total discharge (uz+lz) at internal points
in the catchment
"""
Model.qlz_translated = np.zeros_like(Model.quz) #*np.nan
Model.Qtot = np.zeros_like(Model.quz)
# for all cell with 0 flow acc put the quz
for x in range(Model.rows): # no of rows
for y in range(Model.cols): # no of columns
if Model.FlowAccArr[
x,
y] != Model.NoDataValue and Model.FlowAccArr[x,
y] == 0:
Model.quz_routed[x, y, :] = Model.quz[x, y, :]
Model.qlz_translated[x, y, :] = Model.qlz[x, y, :]
### remaining cells
for j in range(1, len(Model.acc_val)):
#TODO parallelize
# all cells with the same acc_val can run at the same time
for x in range(Model.rows): # no of rows
for y in range(Model.cols): # no of columns
# check from total flow accumulation
if Model.FlowAccArr[
x, y] != Model.NoDataValue and Model.FlowAccArr[
x, y] == Model.acc_val[j]:
# for UZ
q_uzi = np.zeros(Model.TS)
# for lz
qlzi = np.zeros(Model.TS)
# iterate to route uz and translate lz
for i in range(len(
Model.FDT[str(x) + "," +
str(y)])): # Model.acc_val[j]
# bring the indexes of the us cell
x_ind = Model.FDT[str(x) + "," + str(y)][i][0]
y_ind = Model.FDT[str(x) + "," + str(y)][i][1]
# sum the Q of the US cells (already routed for its cell)
# route first with there own k & xthen sum
q_uzi = q_uzi + routing.Muskingum_V(
Model.quz_routed[x_ind, y_ind, :],
Model.quz_routed[x_ind, y_ind, 0],
Model.Parameters[x_ind, y_ind, 10],
Model.Parameters[x_ind, y_ind,
11], Model.Timef)
qlzi = qlzi + Model.qlz_translated[x_ind, y_ind, :]
# add the routed upstream flows to the current Quz in the cell
Model.quz_routed[x, y, :] = Model.quz[x, y, :] + q_uzi
Model.qlz_translated[x,
y, :] = Model.qlz[x, y, :] + qlzi
outletx = Model.Outlet[0][0]
outlety = Model.Outlet[1][0]
Model.qout = Model.qlz_translated[
outletx, outlety, :] + Model.quz_routed[outletx, outlety, :]
Model.Qtot = Model.qlz_translated + Model.quz_routed