Developed by Stijn Klop and Mark Bakker
The purpose of this notebook is to compare several of the response functions available in Pastas. The Gamma and Hantush response function are compared with the Four Parameter function.
The Gamma step response function is defined as: $$ s(t) = A \dfrac{1}{\Gamma(n)} \int_{0}^{t} \tau^{n-1} \cdot e^{-\tau/a} d\tau $$
The Hantush step response function is defined as: $$ s(t) = A \dfrac{1}{\int_{0}^{\infty} \tau^{-1} \cdot e^{-\tau/a - b/\tau} d\tau} \int_{0}^{t} \tau^{-1} \cdot e^{-\tau/a - b/\tau} d\tau $$
Both these response functions are compared with the Four Parameter response function. The Four Parameter response function is defined as: $$ s(t) = A \dfrac{1}{\int_{0}^{\infty} \tau^{n-1} \cdot e^{-\tau/a - b/\tau} d\tau} \int_{0}^{t} \tau^{n-1} \cdot e^{-\tau/a - b/\tau} d\tau $$
The Hantush and Gamma response function are special cases of the Four Parameter function. In this notebook these response functions are compared. This is done using syntheticly generated groundwater observations.
First the required packages are imported and the data is loaded. The rainfall and evaporation data are imported from KNMI station De Bilt using Pastas. An extraction time series is imported using the Pandas package. For this example the time series are selected from the year 1980 until 2000.
In [1]:
import numpy as np
import matplotlib.pyplot as plt
from scipy.special import gammainc, gammaincinv
from scipy.integrate import quad
import pandas as pd
import pastas as ps
%matplotlib inline
In [2]:
rain = ps.read.read_knmi('data_notebook_5/etmgeg_260.txt', variables='RH').series
evap = ps.read.read_knmi('data_notebook_5/etmgeg_260.txt', variables='EV24').series
extraction = pd.read_csv('data_notebook_5/extraction.csv',
index_col=0, parse_dates=True, dayfirst=True)['Extraction']
rain = rain['1980':'1999']
evap = evap['1980':'1999']
extraction = extraction['1980':'2000']
In [3]:
def gamma_tmax(A, n, a, cutoff=0.99):
return gammaincinv(n, cutoff) * a
def gamma_step(A, n, a, cutoff=0.99):
tmax = gamma_tmax(A, n, a, cutoff)
t = np.arange(0, tmax, 1)
s = A * gammainc(n, t / a)
return s
def gamma_block(A, n, a, cutoff=0.99):
# returns the gamma block response starting at t=0 with intervals of delt = 1
s = gamma_step(A, n, a, cutoff)
return np.append(s[0], s[1:] - s[:-1])
def hantush_func(t, a, b):
return (t ** -1) * np.exp(-(a / t) - (t / b))
def hantush_step(A, a, b, tmax=1000, cutoff=0.99):
t = np.arange(0, tmax)
f = np.zeros(tmax)
for i in range(1,tmax):
f[i] = quad(hantush_func, i-1, i, args=(a, b))[0]
F = np.cumsum(f)
return (A / quad(hantush_func, 0, np.inf, args=(a, b))[0]) * F
def hantush_block(A, a, b, tmax=1000, cutoff=0.99):
s = hantush_step(A, a, b, tmax=tmax, cutoff=cutoff)
return s[1:] - s[:-1]
The first test is to compare the Gamma with the Four Parameter response function. Using the function defined above the Gamma block response function can be generated. The parameters for the block response Atrue, ntrue and atrue are defined together with the dtrue parameter. A synthetic groundwater head series is generated using the block response function and the rainfall data series.
In [4]:
Atrue = 800
ntrue = 1.1
atrue = 200
dtrue = 20
h = gamma_block(Atrue, ntrue, atrue) * 0.001
tmax = gamma_tmax(Atrue, ntrue, atrue)
plt.plot(h)
plt.xlabel('Time (days)')
plt.ylabel('Head response (m) due to 1 mm of rain in day 1')
plt.title('Gamma block response with tmax=' + str(int(tmax)));
In [5]:
step = gamma_block(Atrue, ntrue, atrue)[1:]
lenstep = len(step)
h = dtrue * np.ones(len(rain) + lenstep)
for i in range(len(rain)):
h[i:i + lenstep] += rain[i] * step
head = pd.DataFrame(index=rain.index, data=h[:len(rain)],)
head = head['1990':'1999']
plt.figure(figsize=(12,5))
plt.plot(head,'k.', label='head')
plt.legend(loc=0)
plt.ylabel('Head (m)')
plt.xlabel('Time (years)')
Out[5]:
In [6]:
ml = ps.Model(head)
sm = ps.StressModel(rain, ps.Gamma, name='recharge', settings='prec')
ml.add_stressmodel(sm)
ml.solve(noise=False)
ml.plots.results()
Out[6]:
The results of the Pastas simulation show that Pastas is able to simulate the synthetic groundwater head. The parameters calculated with Pastas are equal to the parameters used to generate the synthetic groundwater series; Atrue, ntrue, atrue and dtrue.
The next step is to simulate the synthetic head series using Pastas with the Four Parameter response function. A new pastas model is created using the same head series as input. A stressmodel is created with the rainfall and the Four Parameter response function. The model is solved and the results are plotted.
In [7]:
ml2 = ps.Model(head)
sm2 = ps.StressModel(rain, ps.FourParam, name='recharge', settings='prec')
ml2.add_stressmodel(sm2)
ml2.solve(noise=False)
ml2.plots.results()
Out[7]:
The results of the Pastas simulation show that the groundwater head series can be simulated using the Four Parameter resposne function. The parameters calculated using Pastas only slightly deviate from the parameters Atrue, ntrue, atrue and dtrue defined above. The parameter recharge_b is almost equal to 0 (meaning that the Four Parameter responce function is almost equal to the Gamma response function, as can be seen above).
In the second example of this notebook the Four Parameter response function is compared to the Hantush response function. A Hantush block response is plotted using the parameters; Atrue_hantush, atrue_hantush, btrue_hantush. The parameter btrue_hantush is calculated using rho and atrue_hantush according to Veling & Maas (2010).
The Hantush block response is used together with the extraction data series to simulate the synthetic groundwater head.
In [8]:
Atrue_hantush = -0.01 # Atrue is negative since a positive extraction results in a drop in groundwater head.
atrue_hantush = 100 # the parameter a is equal to cS in the hantush equation.
rho = 2
btrue_hantush = atrue_hantush * rho ** 2 / 4
dtrue_hantush = 20
h_hantush = hantush_block(Atrue_hantush, atrue_hantush, btrue_hantush)
plt.plot(h_hantush)
plt.xlabel('Time (days)')
plt.ylabel('Head response (m) due to 1 m3 of extraction in day 1')
plt.title('Hantush block response with tmax=' + str(1000));
In [9]:
step_hantush = hantush_block(Atrue_hantush, atrue_hantush, btrue_hantush)[1:]
lenstep = len(step_hantush)
h_hantush = dtrue * np.ones(len(extraction) + lenstep)
for i in range(len(extraction)):
h_hantush[i:i + lenstep] += extraction[i] * step_hantush
head_hantush = pd.DataFrame(index=extraction.index, data=h_hantush[:len(extraction)],)
head_hantush = head_hantush['1990':'1999']
plt.figure(figsize=(12,5))
plt.plot(head_hantush,'k.', label='head')
plt.legend(loc=0)
plt.ylabel('Head (m)')
plt.xlabel('Time (years)')
Out[9]:
In [10]:
ml3 = ps.Model(head_hantush)
sm3 = ps.StressModel(extraction, ps.Hantush, name='extraction', settings='well', up=False)
ml3.add_stressmodel(sm3)
ml3.solve(noise=False)
ml3.plots.results()
Out[10]:
The results of the Pastas simulation show that the observed head can be simulated using the Hantush response function. The parameters calibrated with Pastas are very close to the true parameters.
In [11]:
ml4 = ps.Model(head_hantush)
sm4 = ps.StressModel(extraction, ps.FourParam, name='extraction', settings='well', up=False)
ml4.add_stressmodel(sm4)
ml4.solve(noise=False)
ml4.plots.results()
Out[11]:
The Pastas model is able to simulate the synthetic groundwater head. The parameters calibrated with Pastas with the Four Parameter function are close to the true parameters used to generate the groundwater head series.
The deviations between the true and calibrated parameters are caused by the numerical aproximation of the Four Parameter response function used in Pastas.
Hantush, M. S., & Jacob, C. E. (1955). Non‐steady radial flow in an infinite leaky aquifer. Eos, Transactions American Geophysical Union, 36(1), 95-100.
Veling, E. J. M., & Maas, C. (2010). Hantush well function revisited. Journal of hydrology, 393(3), 381-388.
Von Asmuth, J. R., Maas, K., Bakker, M., & Petersen, J. (2008). Modeling time series of ground water head fluctuations subjected to multiple stresses. Ground Water, 46(1), 30-40.