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MT3D-USGS: Transport with the SFR/LAK/UZF Packages (SFT/LKT/UZT), and chemical reactions (RCT)
A more comprehensive demonstration of setting up an MT3D-USGS model that uses all of the new packages included in the first release of MT3D-USGS. Also includes RCT.
Problem Description:
300 row x 300 col x 3 layer x 2 stress period model
Flow model uses SFR, LAK, and UZF with connections between all three
Transport model simulates streamflow transport (SFT), with connection to a single lake (LKT)
Transport model simulates overland runoff and spring discharge (UZT) to surface water network
Start by importing some libraries:
[1]:
import os
import sys
from tempfile import TemporaryDirectory
import matplotlib as mpl
import numpy as np
import pandas as pd
import flopy
print(sys.version)
print(f"numpy version: {np.__version__}")
print(f"matplotlib version: {mpl.__version__}")
print(f"flopy version: {flopy.__version__}")
3.9.19 (main, Mar 20 2024, 16:40:02)
[GCC 11.4.0]
numpy version: 1.26.4
matplotlib version: 3.8.4
flopy version: 3.7.0.dev0
Create a MODFLOW model and store it, in this case in the variable ‘mf’. The modelname will be the name given to all MODFLOW files. The exe_name should be the name of the MODFLOW executable. In this case, we want to use version: ‘mfnwt’ for MODFLOW-NWT
[2]:
# temporary directory
temp_dir = TemporaryDirectory()
model_ws = temp_dir.name
modelpth = os.path.join(model_ws, "no3")
modelname = "no3"
mfexe = "mfnwt"
mtexe = "mt3dusgs"
# Make sure modelpth directory exists
if not os.path.isdir(modelpth):
os.makedirs(modelpth, exist_ok=True)
# Instantiate MODFLOW object in flopy
mf = flopy.modflow.Modflow(
modelname=modelname, exe_name=mfexe, model_ws=modelpth, version="mfnwt"
)
Set up model discretization
[3]:
Lx = 90000.0
Ly = 90000.0
nrow = 300
ncol = 300
nlay = 3
delr = Lx / ncol
delc = Ly / nrow
xmax = ncol * delr
ymax = nrow * delc
X, Y = np.meshgrid(
np.linspace(delr / 2, xmax - delr / 2, ncol),
np.linspace(ymax - delc / 2, 0 + delc / 2, nrow),
)
Instantiate output control (oc) package for MODFLOW-NWT
[4]:
oc = flopy.modflow.ModflowOc(mf)
Instantiate solver package for MODFLOW-NWT
[5]:
# Newton-Raphson Solver: Create a flopy nwt package object
headtol = 1.0e-4
fluxtol = 5
maxiterout = 5000
thickfact = 1e-06
linmeth = 2
iprnwt = 1
ibotav = 1
nwt = flopy.modflow.ModflowNwt(
mf,
headtol=headtol,
fluxtol=fluxtol,
maxiterout=maxiterout,
thickfact=thickfact,
linmeth=linmeth,
iprnwt=iprnwt,
ibotav=ibotav,
options="SIMPLE",
)
Instantiate discretization (DIS) package for MODFLOW-NWT
[6]:
elv_pth = os.path.join(
"..",
"..",
"examples",
"data",
"mt3d_example_sft_lkt_uzt",
"dis_arrays",
"grnd_elv.txt",
)
# Top of Layer 1 elevation determined using GW Vistas and stored locally
grndElv = np.loadtxt(elv_pth)
# Bottom of layer 1 elevation also determined from use of GUI and stored locally
bt1_pth = os.path.join(
"..",
"..",
"examples",
"data",
"mt3d_example_sft_lkt_uzt",
"dis_arrays",
"bot1.txt",
)
bot1Elv = np.loadtxt(bt1_pth)
bot2Elv = np.ones(bot1Elv.shape) * 100
bot3Elv = np.zeros(bot2Elv.shape)
botm = [bot1Elv, bot2Elv, bot3Elv]
botm = np.array(botm)
Steady = [False, False]
nstp = [1, 1]
tsmult = [1.0, 1.0]
# Stress periods
perlen = [9131.25, 9131.25]
# Create the discretization object
# itmuni = 4 (days); lenuni = 1 (feet)
dis = flopy.modflow.ModflowDis(
mf,
nlay,
nrow,
ncol,
nper=2,
delr=delr,
delc=delc,
top=grndElv,
botm=botm,
laycbd=0,
itmuni=4,
lenuni=1,
steady=Steady,
nstp=nstp,
tsmult=tsmult,
perlen=perlen,
)
Instantiate upstream weighting (UPW) flow package for MODFLOW-NWT
[7]:
# UPW must be instantiated after DIS. Otherwise, during the mf.write_input() procedures,
# flopy will crash.
# First line of UPW input is: IUPWCB HDRY NPUPW IPHDRY
hdry = -1.00e30
iphdry = 0
# Next variables are: LAYTYP, LAYAVG, CHANI, LAYVKA, LAYWET
laytyp = [1, 3, 3] # >0: convertible
layavg = 0 # 0: harmonic mean
chani = 1.0 # >0: CHANI is the horizontal anisotropy for the entire layer
layvka = 0 # =0: indicates VKA is vertical hydraulic conductivity
laywet = 0 # Always set equal to zero in UPW package
hk = 20
# hani = 1 # Not needed because CHANI > 1
vka = 0.5 # Is equal to vert. K b/c LAYVKA = 0
ss = 0.00001
sy = 0.20
upw = flopy.modflow.ModflowUpw(
mf,
laytyp=laytyp,
layavg=layavg,
chani=chani,
layvka=layvka,
laywet=laywet,
ipakcb=53,
hdry=hdry,
iphdry=iphdry,
hk=hk,
vka=vka,
ss=ss,
sy=sy,
)
Instantiate basic (BAS or BA6) package for MODFLOW-NWT
[8]:
ibnd1_pth = os.path.join(
"..",
"..",
"examples",
"data",
"mt3d_example_sft_lkt_uzt",
"bas_arrays",
"ibnd_lay1.txt",
)
ibnd1 = np.loadtxt(ibnd1_pth)
ibnd2 = np.ones(ibnd1.shape)
ibnd3 = np.ones(ibnd2.shape)
ibnd = [ibnd1, ibnd2, ibnd3]
ibnd = np.array(ibnd)
StHd1_pth = os.path.join(
"..",
"..",
"examples",
"data",
"mt3d_example_sft_lkt_uzt",
"bas_arrays",
"strthd1.txt",
)
StHd1 = np.loadtxt(StHd1_pth)
StHd2_pth = os.path.join(
"..",
"..",
"examples",
"data",
"mt3d_example_sft_lkt_uzt",
"bas_arrays",
"strthd2.txt",
)
StHd2 = np.loadtxt(StHd2_pth)
StHd3_pth = os.path.join(
"..",
"..",
"examples",
"data",
"mt3d_example_sft_lkt_uzt",
"bas_arrays",
"strthd3.txt",
)
StHd3 = np.loadtxt(StHd3_pth)
strtElev = [StHd1, StHd2, StHd3]
strtElev = np.array(strtElev)
hdry = 999.0
bas = flopy.modflow.ModflowBas(mf, ibound=ibnd, hnoflo=hdry, strt=strtElev)
Instantiate general head boundary (GHB) package for MODFLOW-NWT
[9]:
# GHB boundaries are located along the top (north) and bottom (south)
# edges of the domain, all 3 layers.
elev_stpt_row1 = 308.82281
elev_stpt_row300 = 239.13811
elev_slp = (308.82281 - 298.83649) / (ncol - 1)
sp = []
for k in [0, 1, 2]: # These indices need to be adjusted for 0-based moronicism
for i in [
0,
299,
]: # These indices need to be adjusted for 0-based silliness
for j in np.arange(
0, 300, 1
): # These indices need to be adjusted for 0-based foolishness
# Skipping cells not satisfying the conditions below
if (i == 1 and (j < 27 or j > 31)) or (
i == 299 and (j < 26 or j > 31)
):
if i % 2 == 0:
sp.append(
[
k,
i,
j,
elev_stpt_row1 - (elev_slp * (j - 1)),
11.3636,
]
)
else:
sp.append(
[
k,
i,
j,
elev_stpt_row300 - (elev_slp * (j - 1)),
11.3636,
]
)
for k in [0, 1, 2]:
for j in np.arange(26, 32, 1):
sp.append([k, 299, j, 238.20, 3409.0801])
ghb = flopy.modflow.ModflowGhb(mf, stress_period_data=sp)
Instantiate streamflow routing (SFR2) package for MODFLOW-NWT
[10]:
# Read pre-prepared reach data into numpy recarrays using numpy.genfromtxt()
# Remember that the cell indices stored in the pre-prepared NO3_ReachInput.csv file are based on 0-based indexing.
# Flopy will convert to 1-based when it writes the files
rpth = os.path.join(
"..",
"..",
"examples",
"data",
"mt3d_example_sft_lkt_uzt",
"sfr_data",
"no3_reachinput.csv",
)
reach_data = np.genfromtxt(rpth, delimiter=",", names=True)
reach_data
# Read pre-prepared segment data into numpy recarrays using numpy.genfromtxt()
spth = os.path.join(
"..",
"..",
"examples",
"data",
"mt3d_example_sft_lkt_uzt",
"sfr_data",
"no3_segmentdata.csv",
)
ss_segment_data = np.genfromtxt(spth, delimiter=",", names=True)
segment_data = {0: ss_segment_data, 1: ss_segment_data}
segment_data[0][0:1]["width1"]
nstrm = len(reach_data)
nss = len(segment_data[0])
nsfrpar = 0
const = 128390.4 # constant for manning's equation, units of cfs
dleak = 0.0001
ipakcb = 53 # flag for writing SFR output to cell-by-cell budget (on unit 53)
istcb2 = 37 # flag for writing SFR output to text file
isfropt = 1
dataset_5 = {
0: [nss, 0, 0],
1: [-1, 0, 0],
} # dataset 5 (see online guide) (ITMP, IRDFLG, IPTFLG)
# Input arguments generally follow the variable names defined in the Online Guide to MODFLOW
sfr = flopy.modflow.ModflowSfr2(
mf,
nstrm=nstrm,
nss=nss,
const=const,
dleak=dleak,
ipakcb=ipakcb,
istcb2=istcb2,
isfropt=isfropt,
reachinput=True,
reach_data=reach_data,
segment_data=segment_data,
dataset_5=dataset_5,
unit_number=15,
)
Instantiate Lake (LAK) package for MODFLOW-NWT
[11]:
# Read pre-prepared lake arrays
LakArr_pth = os.path.join(
"..",
"..",
"examples",
"data",
"mt3d_example_sft_lkt_uzt",
"lak_arrays",
"lakarr1.txt",
)
LakArr_lyr1 = np.loadtxt(LakArr_pth)
LakArr_lyr2 = np.zeros(LakArr_lyr1.shape)
LakArr_lyr3 = np.zeros(LakArr_lyr2.shape)
LakArr = [LakArr_lyr1, LakArr_lyr2, LakArr_lyr3]
LakArr = np.array(LakArr)
nlakes = int(np.max(LakArr))
ipakcb = ipakcb # From above
theta = -1.0 # Implicit
nssitr = 10 # Maximum number of iterations for Newton’s method
sscncr = 1.000e-03 # Convergence criterion for equilibrium lake stage solution
surfdep = 2.000e00 # Height of small topological variations in lake-bottom
stages = 268.00 # Initial stage of each lake at the beginning of the run
# ITMP > 0, read lake definition data
# ITMP1 ≥ 0, read new recharge, evaporation, runoff, and withdrawal data for each lake
# LWRT > 0, suppresses printout from the lake package
bdlknc_lyr1 = LakArr_lyr1.copy()
bdlknc_lyr2 = LakArr_lyr1.copy()
bdlknc_lyr3 = np.zeros(LakArr_lyr1.shape)
# Need to expand bdlknc_lyr1 non-zero values by 1 in either direction
# (left/right and up/down)
for i in np.arange(0, LakArr_lyr1.shape[0]):
for j in np.arange(0, LakArr_lyr1.shape[1]):
im1 = i - 1
ip1 = i + 1
jm1 = j - 1
jp1 = j + 1
if im1 >= 0:
if LakArr_lyr1[i, j] == 1 and LakArr_lyr1[im1, j] == 0:
bdlknc_lyr1[im1, j] = 1
if ip1 < LakArr_lyr1.shape[0]:
if LakArr_lyr1[i, j] == 1 and LakArr_lyr1[ip1, j] == 0:
bdlknc_lyr1[ip1, j] = 1
if jm1 >= 0:
if LakArr_lyr1[i, j] == 1 and LakArr_lyr1[i, jm1] == 0:
bdlknc_lyr1[i, jm1] = 1
if jp1 < LakArr_lyr1.shape[1]:
if LakArr_lyr1[i, j] == 1 and LakArr_lyr1[i, jp1] == 0:
bdlknc_lyr1[i, jp1] = 1
bdlknc = [bdlknc_lyr1, bdlknc_lyr2, bdlknc_lyr3]
bdlknc = np.array(bdlknc)
flux_data = {0: [[0.0073, 0.0073, 0.0, 0.0]], 1: [[0.0073, 0.0073, 0.0, 0.0]]}
lak = flopy.modflow.ModflowLak(
mf,
nlakes=nlakes,
ipakcb=ipakcb,
theta=theta,
nssitr=nssitr,
sscncr=sscncr,
surfdep=surfdep,
stages=stages,
lakarr=LakArr,
bdlknc=bdlknc,
flux_data=flux_data,
unit_number=16,
)
Instantiate gage package for use with MODFLOW-NWT package
[12]:
gages = [
[1, 225, 90, 3],
[2, 68, 91, 3],
[3, 33, 92, 3],
[4, 165, 93, 3],
[5, 123, 94, 3],
[6, 77, 95, 3],
[7, 173, 96, 3],
[8, 328, 97, 3],
[9, 115, 98, 3],
[-1, -101, 1],
]
# gages = [[1,38,61,1],[2,67,62,1], [3,176,63,1], [4,152,64,1], [5,186,65,1], [6,31,66,1]]
files = [
"no3.gag",
"seg1_gag.out",
"seg2_gag.out",
"seg3_gag.out",
"seg4_gag.out",
"seg5_gag.out",
"seg6_gag.out",
"seg7_gag.out",
"seg8_gag.out",
"seg9_gag.out",
"lak1_gag.out",
]
numgage = len(gages)
gage = flopy.modflow.ModflowGage(
mf, numgage=numgage, gage_data=gages, filenames=files
)
Instantiate Unsaturated-Zone Flow (UZF) package for MODFLOW-NWT
[13]:
nuztop = 2
iuzfopt = 2
irunflg = 1
ietflg = 0
iuzfcb = 52
iuzfcb2 = 0
ntrail2 = 20
nsets2 = 20
nuzgag = 2
surfdep = 2.0
eps = 3.0
thts = 0.30
thti = 0.13079
fname_uzbnd = os.path.join(
"..",
"..",
"examples",
"data",
"mt3d_example_sft_lkt_uzt",
"uzf_arrays",
"iuzbnd.txt",
)
fname_runbnd = os.path.join(
"..",
"..",
"examples",
"data",
"mt3d_example_sft_lkt_uzt",
"uzf_arrays",
"irunbnd.txt",
)
iuzfbnd = np.loadtxt(fname_uzbnd)
irunbnd = np.loadtxt(fname_runbnd)
uzgag = [[106, 160, 121, 3], [1, 1, -122, 1]]
finf = {0: 1.8250e-03, 1: 1.8250e-03}
uzf = flopy.modflow.ModflowUzf1(
mf,
nuztop=nuztop,
iuzfopt=iuzfopt,
irunflg=irunflg,
ietflg=ietflg,
ipakcb=iuzfcb,
iuzfcb2=iuzfcb2,
ntrail2=ntrail2,
nsets=nsets2,
surfdep=surfdep,
uzgag=uzgag,
iuzfbnd=1,
irunbnd=0,
vks=1.0e-6,
eps=3.5,
thts=0.35,
thtr=0.15,
thti=0.20,
)
Instantiate Drain (DRN) package for MODFLOW-NWT
[14]:
fname_drnElv = os.path.join(
"..",
"..",
"examples",
"data",
"mt3d_example_sft_lkt_uzt",
"drn_arrays",
"elv.txt",
)
fname_drnCond = os.path.join(
"..",
"..",
"examples",
"data",
"mt3d_example_sft_lkt_uzt",
"drn_arrays",
"cond.txt",
)
drnElv = np.loadtxt(fname_drnElv)
drnCond = np.loadtxt(fname_drnCond)
drnElv_lst = pd.DataFrame(
{
"lay": 1,
"row": np.nonzero(drnElv)[0] + 1,
"col": np.nonzero(drnElv)[1] + 1,
"elv": drnElv[np.nonzero(drnElv)],
"cond": drnCond[np.nonzero(drnCond)],
},
columns=["lay", "row", "col", "elv", "cond"],
)
# Convert the DataFrame into a list of lists for the drn constructor
stress_period_data = drnElv_lst.values.tolist()
# Create a dictionary, 1 entry for each of the two stress periods.
stress_period_data = {0: stress_period_data, 1: stress_period_data}
drn = flopy.modflow.ModflowDrn(
mf, ipakcb=ipakcb, stress_period_data=stress_period_data
)
Instantiate linkage with mass transport routing (LMT) package for MODFLOW-NWT (generates linker file)
[15]:
lmt = flopy.modflow.ModflowLmt(
mf,
output_file_name="NO3.ftl",
output_file_header="extended",
output_file_format="formatted",
package_flows=["all"],
)
Now work on MT3D-USGS file creation
[16]:
# Start by setting up MT3D-USGS class and pass in MODFLOW-NWT object for setting up a number of the BTN arrays
mt = flopy.mt3d.Mt3dms(
modflowmodel=mf,
modelname=modelname,
model_ws=modelpth,
version="mt3d-usgs",
namefile_ext="mtnam",
exe_name=mtexe,
ftlfilename="no3.ftl",
ftlfree=True,
)
Instantiate basic transport (BTN) package for MT3D-USGS
[17]:
ncomp = 1
lunit = "FT"
sconc = 0.0
prsity = 0.3
cinact = -1.0
thkmin = 0.000001
nprs = -2
nprobs = 10
nprmas = 10
dt0 = 0.1
nstp = 1
mxstrn = 500
ttsmult = 1.2
ttsmax = 100
# These observations need to be entered with 0-based indexing
obs = [[0, 104, 158], [1, 104, 158], [2, 104, 158]]
btn = flopy.mt3d.Mt3dBtn(
mt,
MFStyleArr=True,
DRYCell=True,
lunit=lunit,
sconc=sconc,
ncomp=ncomp,
prsity=prsity,
cinact=cinact,
obs=obs,
thkmin=thkmin,
nprs=nprs,
nprobs=nprobs,
chkmas=True,
nprmas=nprmas,
dt0=dt0,
nstp=nstp,
mxstrn=mxstrn,
ttsmult=ttsmult,
ttsmax=ttsmax,
)
Instantiate advection (ADV) package for MT3D-USGS
[18]:
mixelm = 0
percel = 1.0000
mxpart = 5000
nadvfd = 1 # (1 = Upstream weighting)
adv = flopy.mt3d.Mt3dAdv(
mt, mixelm=mixelm, percel=percel, mxpart=mxpart, nadvfd=nadvfd
)
Instantiate generalized conjugate gradient solver (GCG) package for MT3D-USGS
[19]:
mxiter = 1
iter1 = 50
isolve = 3
ncrs = 0
accl = 1.000000
cclose = 1.00e-06
iprgcg = 5
gcg = flopy.mt3d.Mt3dGcg(
mt,
mxiter=mxiter,
iter1=iter1,
isolve=isolve,
ncrs=ncrs,
accl=accl,
cclose=cclose,
iprgcg=iprgcg,
)
Instantiate dispersion (DSP) package for MT3D-USGS
[20]:
al = 0.1 # longitudinal dispersivity
trpt = 0.1 # ratio of the horizontal transverse dispersivity to 'AL'
trpv = 0.1 # ratio of the vertical transverse dispersitvity to 'AL'
dmcoef = 1.0000e-10
dsp = flopy.mt3d.Mt3dDsp(
mt, al=al, trpt=trpt, trpv=trpv, dmcoef=dmcoef, multiDiff=True
)
Instantiate source-sink mixing (SSM) package for MT3D-USGS
[21]:
# no user-specified concentrations associated with boundary conditions
mxss = 11199
ssm = flopy.mt3d.Mt3dSsm(mt, mxss=mxss)
Instantiate reaction (RCT) package for MT3D-USGS
[22]:
isothm = 0
ireact = 1
irctop = 2
igetsc = 0
ireaction = 0
rc1 = 6.3258e-04 # first-order reaction rate for the dissolved phase
rc2 = 0.0 # Decay on Soil Layer
rct = flopy.mt3d.Mt3dRct(
mt, isothm=isothm, ireact=ireact, igetsc=igetsc, rc1=rc1, rc2=rc2
)
Instantiate streamflow transport (SFT) package for MT3D-USGS
[23]:
nsfinit = len(reach_data)
mxsfbc = len(reach_data)
icbcsf = 0
ioutobs = 92
isfsolv = 1
wimp = 0.5
wups = 1.0
cclosesf = 1.0e-6
mxitersf = 10
crntsf = 1.0
iprtxmd = 0
coldsf = 0
dispsf = 0
obs_sf = [225, 293, 326, 491, 614, 691, 864, 1192, 1307]
sf_stress_period_data = {0: [0, 0, 0], 1: [0, 0, 0], 2: [0, 0, 0]}
gage_output = [None, None, "no3.sftobs"]
sft = flopy.mt3d.Mt3dSft(
mt,
nsfinit=nsfinit,
mxsfbc=mxsfbc,
icbcsf=icbcsf,
ioutobs=ioutobs,
isfsolv=isfsolv,
wimp=wimp,
wups=wups,
cclosesf=cclosesf,
mxitersf=mxitersf,
crntsf=crntsf,
iprtxmd=iprtxmd,
coldsf=coldsf,
dispsf=dispsf,
nobssf=len(obs_sf),
obs_sf=obs_sf,
sf_stress_period_data=sf_stress_period_data,
filenames=gage_output,
)
Instantiate unsaturated-zone transport (UZT) package for MT3D-USGS
[24]:
mxuzcon = np.count_nonzero(irunbnd)
icbcuz = 45
iet = 0
wc = np.ones((nlay, nrow, ncol)) * 0.29
sdh = np.ones((nlay, nrow, ncol))
uzt = flopy.mt3d.Mt3dUzt(
mt,
mxuzcon=mxuzcon,
icbcuz=icbcuz,
iet=iet,
iuzfbnd=iuzfbnd,
sdh=sdh,
cuzinf=1.4158e-03,
filenames="no3",
)
Instantiate lake transport (LKT) package for MT3D-USGS
[25]:
nlkinit = 1
mxlkbc = 720
icbclk = 81
ietlak = 1
coldlak = 1
lkt_flux_data = {0: [[0, 1, 0.01667]], 1: [[0, 1, 0.02667]]}
lkt = flopy.mt3d.Mt3dLkt(
mt,
nlkinit=nlkinit,
mxlkbc=mxlkbc,
icbclk=icbclk,
ietlak=ietlak,
coldlak=coldlak,
lk_stress_period_data=lkt_flux_data,
)
Write the MT3D-USGS input files for inspecting and running
[26]:
mf.write_input()
mt.write_input()
# mf.run_model()
# mt.run_model()