# Adding groundwater pumping¶

*Developed by R.A. Collenteur & M. Bakker*

In this example notebook it is shown how to simulate the effect of a pumping well on the groundwater levels. We will first create a TFN model with the net recharge as the single stress used to explain the observed heads. Second, this model is extended to include the effect of a pumping well on the heads by adding another stress model. The simulated heads are compared and it can be clearly seen how the addition of the pumping well improves the simulation of the heads.

This example was also shown at the 2018 General Assembly of the European Geophysical Union:

Bakker, M., Collenteur, R., Calje, F. Schaars (2018) Untangling groundwater head series using time series analysis and Pastas. In EGU General Assembly 2018.

```
[1]:
```

```
import pandas as pd
import pastas as ps
import matplotlib.pyplot as plt
ps.show_versions()
```

```
Python version: 3.7.8 | packaged by conda-forge | (default, Jul 31 2020, 02:37:09)
[Clang 10.0.1 ]
Numpy version: 1.18.5
Scipy version: 1.4.0
Pandas version: 1.1.2
Pastas version: 0.16.0b
Matplotlib version: 3.1.3
```

## 1. Read the time series from files¶

All time series for this example have been prepared as csv-files, which are read using the Pandas `read_csv`

- method. The following time series are available:

heads in meters above the Dutch National Datum (NAP), irregular time steps

rain in m/d

Makkink reference evaporation in m/d

Pumping extraction rate in m\(^3\)/d. The pumping well stopped operating after 2012.

```
[2]:
```

```
head = pd.read_csv("data_notebook_5/head_wellex.csv",
index_col="Date", parse_dates=True, squeeze=True)
rain = pd.read_csv("data_notebook_5/prec_wellex.csv",
index_col="Date", parse_dates=True)
evap = pd.read_csv("data_notebook_5/evap_wellex.csv",
index_col="Date", parse_dates=True)
well = pd.read_csv("data_notebook_5/well_wellex.csv",
index_col="Date", parse_dates=True)
# Make a plot of all the time series
fig, ax = plt.subplots(4, 1, sharex=True, figsize=(10,5));
ax[0].plot(head, label=head.name, linestyle=" ", marker=".", markersize=2)
ax[0].legend()
ax[1].plot(rain, label="rain")
ax[1].legend()
ax[2].plot(evap, label="evap")
ax[2].legend()
ax[3].plot(well, label="well")
ax[3].legend()
```

```
[2]:
```

```
<matplotlib.legend.Legend at 0x7fbaf9807d50>
```

## 2. Create a Pastas Model¶

A pastas Model is created. A constant and a noisemodel are automatically added. The effect of the net groundwater recharge \(R(t)\) is simulated using the `ps.RechargeModel`

stress model. Net recharge is calculated as \(R(t) = P(t) - f * E(t)\) where \(f\) is a parameter that is estimated and \(P(t)\) and \(E(t)\) are precipitation and reference evapotranspiration, respectively.

```
[3]:
```

```
# Create the time series model
ml = ps.Model(head, name="groundwater")
# Add the stres model for the net recharge
rm = ps.RechargeModel(rain, evap, name="recharge",
rfunc=ps.Exponential, recharge=ps.rch.Linear())
ml.add_stressmodel(rm)
ml.solve(noise=True)
ml.plot(figsize=(10,4))
# Let's store the simulated values to compare later
sim1 = ml.simulate()
res1 = ml.residuals()
n1 = ml.noise()
```

```
INFO: Cannot determine frequency of series Head: freq=None. The time series is irregular.
INFO: Inferred frequency for time series Prec: freq=D
INFO: Inferred frequency for time series Evap: freq=D
INFO: Time Series Evap was extended to 1985-01-16 00:00:00 with the mean value of the time series.
```

```
Fit report groundwater Fit Statistics
==================================================
nfev 25 EVP 51.79
nobs 3869 R2 0.12
noise True RMSE 0.33
tmin 1995-01-14 00:00:00 AIC -2.10
tmax 2018-01-12 00:00:00 BIC 29.20
freq D Obj 1.95
warmup 3650 days 00:00:00 ___
solver LeastSquares Interpolated No
Parameters (5 were optimized)
==================================================
optimal stderr initial vary
recharge_A 835.822437 ±62.49% 203.104730 True
recharge_a 540.839625 ±62.19% 10.000000 True
recharge_f -1.940472 ±11.00% -1.000000 True
constant_d 16.511745 ±3.23% 15.975755 True
noise_alpha 281.723634 ±27.23% 1.000000 True
Parameter correlations |rho| > 0.5
==================================================
recharge_A recharge_a 1.00
constant_d 0.83
recharge_a constant_d 0.86
```

**Interpreting the results**

As can be seen from the above plot, the observed heads show a clear rise whereas the simulated heads do not show this behaviour. The rise in the heads cannot be explained by an increased precipitation or a decreased evaporation over time, and it is likely another force is driving the heads upwards. Given the location of the well, we can hypothesize that the groundwater pumping caused a lowering of the heads in the beginning of the observations, which decreased when the pumping well was shut down. A next logical step is to add the effect of the pumping well and see if it improves the simulation of the head.

## 3. Add the effect of the pumping well¶

To simulate the effect of the pumping well a new stress model is added. The effect of the well is simulated using the `ps.StressModel`

, which convoluted a stress with a response function. As a response function the `ps.Hantush`

response function is used. The keyword-argument `up=False`

is provided to tell the model this stress is supposed to have a lowering effect on the groundwater levels.

```
[4]:
```

```
# Add the stress model for the pumping well
sm = ps.StressModel(well/1e6, rfunc=ps.Gamma, name="well", settings="well", up=False)
ml.add_stressmodel(sm)
# Solve the model and make a plot
ml.solve()
axes = ml.plots.decomposition(figsize=(10,8))
axes[0].plot(sim1) # Add the previously simulated values to the plot
```

```
INFO: Inferred frequency for time series Well: freq=D
```

```
Fit report groundwater Fit Statistics
==================================================
nfev 24 EVP 74.96
nobs 3869 R2 0.74
noise True RMSE 0.18
tmin 1995-01-14 00:00:00 AIC 6.35
tmax 2018-01-12 00:00:00 BIC 56.44
freq D Obj 1.93
warmup 3650 days 00:00:00 ___
solver LeastSquares Interpolated No
Parameters (8 were optimized)
==================================================
optimal stderr initial vary
recharge_A 718.943898 ±27.99% 203.104730 True
recharge_a 448.480751 ±28.07% 10.000000 True
recharge_f -1.864180 ±9.75% -1.000000 True
well_A -102.841843 ±9.58% -338.167845 True
well_n 1.286498 ±33.95% 1.000000 True
well_a 75.864224 ±57.75% 10.000000 True
constant_d 16.773074 ±1.53% 15.975755 True
noise_alpha 82.906003 ±15.38% 1.000000 True
Parameter correlations |rho| > 0.5
==================================================
recharge_A recharge_a 0.98
constant_d 0.50
recharge_a constant_d 0.61
recharge_f constant_d -0.77
well_n well_a -0.86
```

```
[4]:
```

```
[<matplotlib.lines.Line2D at 0x7fbb007840d0>]
```

**Interpreting the results**

The addition of the pumping well to simulate the heads clearly improved the fit with the observed heads. It can also be seen how the pumping well stops contributing to the lowering of the head after ~2014, indicating the pumping effect of the well has dampened out. The period it takes before the historic pumping has no effect anymore can be approximated by the length of the response function for the well (e.g., `len(ml.get_step_response("well"))`

).

## 4. Analyzing the residuals¶

The difference between the model with and without the pumping becomes even more clear when analyzing the model residuals. The residuals of the model without the well show a clear upward trend, whereas the model with a model does not show this trend anymore.

```
[5]:
```

```
ml.residuals().plot(figsize=(10, 4))
res1.plot()
plt.legend(["Model with well", "Model without well"])
```

```
[5]:
```

```
<matplotlib.legend.Legend at 0x7fbafa225b50>
```