Adding multiple wells#
This notebook shows how a WellModel can be used to fit multiple wells with one response function. The influence of the individual wells is scaled by the distance to the observation point.
Developed by R.C. Caljé, (Artesia Water 2020), D.A. Brakenhoff, (Artesia Water 2019), and R.A. Collenteur, (Artesia Water 2018)
[1]:
import os
import numpy as np
import pandas as pd
import pastas as ps
import matplotlib.pyplot as plt
ps.show_versions()
Python version: 3.10.8
NumPy version: 1.23.5
Pandas version: 2.0.1
SciPy version: 1.10.1
Matplotlib version: 3.7.1
Numba version: 0.57.0
LMfit version: 1.2.1
Latexify version: Not Installed
Pastas version: 1.0.1
Load and set data#
Set the coordinates of the extraction wells and calculate the distances to the observation well.
[2]:
# Specify coordinates observations
xo = 85850
yo = 383362
# Specify coordinates extractions
relevant_extractions = {
"Extraction_2": (83588, 383664),
"Extraction_3": (88439, 382339),
}
# calculate distances
distances = []
for extr, xy in relevant_extractions.items():
xw = xy[0]
yw = xy[1]
distances.append(np.sqrt((xo - xw) ** 2 + (yo - yw) ** 2))
df = pd.DataFrame(
distances,
index=relevant_extractions.keys(),
columns=["Distance to observation well"],
)
df
[2]:
| Distance to observation well | |
|---|---|
| Extraction_2 | 2282.070989 |
| Extraction_3 | 2783.783397 |
Read the stresses from their csv files
[3]:
# read oseries
oseries = pd.read_csv(
"data_notebook_10/Observation_well.csv", index_col=0, parse_dates=[0]
).squeeze()
oseries.name = oseries.name.replace(" ", "_")
# read stresses
stresses = {}
for fname in os.listdir("data_notebook_10"):
series = pd.read_csv(
os.path.join("data_notebook_10", fname), index_col=0, parse_dates=[0]
).squeeze()
stresses[fname.strip(".csv").replace(" ", "_")] = series
Then plot the observations, together with the diferent stresses.
[4]:
# plot timeseries
f1, axarr = plt.subplots(len(stresses.keys()) + 1, sharex=True, figsize=(10, 8))
oseries.plot(ax=axarr[0], color="k")
axarr[0].set_title(oseries.name)
for i, name in enumerate(stresses.keys(), start=1):
stresses[name].plot(ax=axarr[i])
axarr[i].set_title(name)
plt.tight_layout(pad=0)
Create a model with a separate StressModel for each extraction#
First we create a model with a separate StressModel for each groundwater extraction. First we create a model with the heads timeseries and add recharge as a stress.
[5]:
# create model
ml = ps.Model(oseries)
Get the precipitation and evaporation timeseries and round the index to remove the hours from the timestamps.
[6]:
prec = stresses["Precipitation"]
prec.index = prec.index.round("D")
prec.name = "prec"
evap = stresses["Evaporation"]
evap.index = evap.index.round("D")
evap.name = "evap"
Create a recharge stressmodel and add to the model.
[7]:
rm = ps.RechargeModel(prec, evap, ps.Exponential(), "Recharge")
ml.add_stressmodel(rm)
Modify the extraction timeseries.
[8]:
extraction_ts = {}
for name in relevant_extractions.keys():
# get extraction timeseries
s = stresses[name]
# convert index to end-of-month timeseries
s.index = s.index.to_period("M").to_timestamp("M")
# resample to daily values
new_index = pd.date_range(s.index[0], s.index[-1], freq="D")
s_daily = ps.ts.timestep_weighted_resample(s, new_index, fast=True).dropna()
name = name.replace(" ", "_")
s_daily.name = name
# append to stresses list
extraction_ts[name] = s_daily
Add each of the extractions as a separate StressModel.
[9]:
for name, stress in extraction_ts.items():
sm = ps.StressModel(stress, ps.Hantush(), name, up=False, settings="well")
ml.add_stressmodel(sm)
Solve the model.
[10]:
ml.solve()
INFO: Time Series Extraction_3 was extended in the past to 1950-05-01 00:00:00 by adding 0.0 values.
INFO: There are observations between the simulation time steps. Linear interpolation between simulated values is used.
Fit report Observation_well Fit Statistics
=========================================================
nfev 18 EVP 94.41
nobs 2844 R2 0.94
noise True RMSE 0.21
tmin 1960-04-28 12:00:00 AIC -8801.49
tmax 2015-06-29 09:00:00 BIC -8736.01
freq D Obj 63.90
warmup 3650 days 00:00:00 ___
solver LeastSquares Interp. Yes
Parameters (11 optimized)
=========================================================
optimal stderr initial vary
Recharge_A 1518.487738 ±3.93% 210.498526 True
Recharge_a 795.355746 ±5.03% 10.000000 True
Recharge_f -1.265603 ±3.81% -1.000000 True
Extraction_2_A -0.000109 ±1.17% -0.000086 True
Extraction_2_a 1286.799330 ±8.65% 100.000000 True
Extraction_2_b 0.032393 ±16.94% 1.000000 True
Extraction_3_A -0.000043 ±2.91% -0.000171 True
Extraction_3_a 264.109485 ±26.81% 100.000000 True
Extraction_3_b 0.827846 ±54.06% 1.000000 True
constant_d 10.702175 ±1.06% 8.557530 True
noise_alpha 0.005010 ±0.00e+00% 1.000000 True
Visualize the results#
Plot the decomposition to see the individual influence of each of the wells.
[11]:
ml.plots.decomposition();
We can calculate the gain of each extraction (quantified as the effect on the groundwater level of a continuous extraction of ~1 Mm\(^3\)/yr).
[12]:
for name in relevant_extractions.keys():
sm = ml.stressmodels[name]
p = ml.get_parameters(name)
gain = sm.rfunc.gain(p) * 1e6 / 365.25
print(f"{name}: gain = {gain:.3f} m / Mm^3/year")
df.at[name, "gain StressModel"] = gain
Extraction_2: gain = -0.299 m / Mm^3/year
Extraction_3: gain = -0.119 m / Mm^3/year
Create a model with a WellModel#
We can reduce the number of parameters in the model by including the three extractions in a WellModel. This WellModel takes into account the distances from the three extractions to the observation well, and assumes constant geohydrological properties. All of the extractions now share the same response function, scaled by the distance between the extraction well and the observation well.
First we create a new model and add recharge.
[13]:
ml_wm = ps.Model(oseries, oseries.name + "_wm")
rm = ps.RechargeModel(prec, evap, ps.Gamma(), "Recharge")
ml_wm.add_stressmodel(rm)
We have all the information we need to create a WellModel: - timeseries for each of the extractions, these are passed as a list of stresses - distances from each extraction to the observation point, note that the order of these distances must correspond to the order of the stresses.
Note: the WellModel only works with a special version of the Hantush response function called HantushWellModel. This is because the response function must support scaling by a distance \(r\). The HantushWellModel response function has been modified to support this. The Hantush response normally takes three parameters: the gain \(A\), \(a\) and \(b\). This special version accepts 4 parameters: it interprets that fourth parameter as the distance \(r\), and uses it to scale
the parameters accordingly.
Create the WellModel and add to the model.
[14]:
w = ps.WellModel(list(extraction_ts.values()), "Wells", distances)
ml_wm.add_stressmodel(w)
Solve the model.
As we can see, the fit with the measurements (EVP) is similar to the result with the previous model, with each well included separately.
[15]:
ml_wm.solve()
INFO: Time Series Extraction_3 was extended in the past to 1950-05-01 00:00:00 by adding 0.0 values.
INFO: There are observations between the simulation time steps. Linear interpolation between simulated values is used.
INFO: No distance passed to HantushWellModel, assuming r=1.0.
Fit report Observation_well Fit Statistics
===================================================
nfev 30 EVP 93.47
nobs 2844 R2 0.93
noise True RMSE 0.23
tmin 1960-04-28 12:00:00 AIC -13674.57
tmax 2015-06-29 09:00:00 BIC -13621.00
freq D Obj 11.53
warmup 3650 days 00:00:00 ___
solver LeastSquares Interp. Yes
Parameters (9 optimized)
===================================================
optimal stderr initial vary
Recharge_A 1403.730805 ±18.39% 210.498526 True
Recharge_n 1.002361 ±3.50% 1.000000 True
Recharge_a 912.642513 ±28.36% 10.000000 True
Recharge_f -1.999835 ±12.78% -1.000000 True
Wells_A -0.000368 ±57.44% -0.000756 True
Wells_a 493.663491 ±33.44% 100.000000 True
Wells_b -16.163072 ±3.61% -15.674262 True
constant_d 12.095680 ±5.27% 8.557530 True
noise_alpha 56.691823 ±8.43% 1.000000 True
Visualize the results#
Plot the decomposition to see the individual influence of each of the wells
[16]:
ml_wm.plots.decomposition();
Plot the stacked influence of each of the individual extraction wells in the results plot
[17]:
ml_wm.plots.stacked_results(figsize=(10, 8));
INFO: No distance passed to HantushWellModel, assuming r=1.0.
INFO: No distance passed to HantushWellModel, assuming r=1.0.
Get parameters for each well (including the distance) and calculate the gain. The WellModel reorders the stresses from closest to the observation well, to furthest from the observation well. We have take this into account during the post-processing.
The gain of extraction 3 is lower than the gain of extraction 2. This will always be the case in a WellModel when the distance from the observation well to extraction 3 is larger than the distance to extraction 2.
[18]:
wm = ml_wm.stressmodels["Wells"]
for i, name in enumerate(relevant_extractions.keys()):
# get parameters
p = wm.get_parameters(model=ml_wm, istress=i)
# calculate gain
gain = wm.rfunc.gain(p) * 1e6 / 365.25
name = wm.stress[i].name
print(f"{name}: gain = {gain:.3f} m / Mm^3/year")
df.at[name, "gain WellModel"] = gain
Extraction_2: gain = -0.242 m / Mm^3/year
Extraction_3: gain = -0.162 m / Mm^3/year
Calculate gain as function of radial distance for and plot the result, including the estimated uncertainty.
[19]:
r = np.logspace(3, 3.6, 101)
# calculate gain and std error vs distance
params = ml_wm.get_parameters(wm.name)
gain_wells = wm.rfunc.gain(params, r=wm.distances.values) * 1e6 / 365.25
gain_vs_dist = wm.rfunc.gain(params, r=r) * 1e6 / 365.25
gain_std_vs_dist = np.sqrt(wm.variance_gain(ml_wm, r=r)) * 1e6 / 365.25
fig, ax = plt.subplots(1, 1, figsize=(10, 3))
ax.plot(r, gain_vs_dist, color="C0", label="gain")
ax.plot(
wm.distances,
gain_wells,
color="C3",
marker="o",
mfc="none",
label="wells",
ls="none",
)
ax.fill_between(
r,
gain_vs_dist - 2 * gain_std_vs_dist,
gain_vs_dist + 2 * gain_std_vs_dist,
alpha=0.35,
label="CI 95%",
)
ax.axhline(0.0, linestyle="dashed", color="k")
ax.legend(loc=(0, 1), frameon=False, ncol=3)
ax.grid(visible=True)
ax.set_xlabel("radial distance [m]")
ax.set_ylabel("gain [m / (Mm$^3$/yr)]");
Compare individual StressModels and WellModel#
Compare the gains that were calculated by the individual StressModels and the WellModel.
[20]:
df.style.format("{:.4f}")
[20]:
| Distance to observation well | gain StressModel | gain WellModel | |
|---|---|---|---|
| Extraction_2 | 2282.0710 | -0.2994 | -0.2420 |
| Extraction_3 | 2783.7834 | -0.1188 | -0.1623 |
Visually compare the two models, including the calculated contribution of the wells:
[21]:
# give models descriptive name
ml.name = "2_wells"
ml_wm.name = "WellModel"
# plot well stresses together on same plot:
smdict = {0: ["Recharge"], 1: ["Extraction_2", "Extraction_3", "Wells"]}
# comparison plot
mc = ps.CompareModels([ml, ml_wm])
mosaic = mc.get_default_mosaic(n_stressmodels=2)
mc.initialize_adjust_height_figure(mosaic=mosaic, smdict=smdict)
mc.plot(smdict=smdict)
sumwells = ml.get_contribution("Extraction_2") + ml.get_contribution("Extraction_3")
mc.axes["con1"].plot(
sumwells.index, sumwells, ls="dashed", color="C0", label="sum 2_wells"
)
mc.axes["con1"].legend(loc=(0, 1), frameon=False, ncol=4);
INFO: No distance passed to HantushWellModel, assuming r=1.0.
INFO: No distance passed to HantushWellModel, assuming r=1.0.
