7709518362

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thouska

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Merge branch 'master' of https://github.com/thouska/spotpy

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100.0

/src/spotpy/examples/hymod_python/hymod.py

# -*- coding: utf-8 -*-
"""
Copyright (c) 2015 by Tobias Houska

This file is part of Statistical Parameter Estimation Tool (SPOTPY).

:author: Tobias Houska and Benjamin Manns

:paper: Houska, T., Kraft, P., Chamorro-Chavez, A. and Breuer, L.: 
SPOTting Model Parameters Using a Ready-Made Python Package, 
PLoS ONE, 10(12), e0145180, doi:10.1371/journal.pone.0145180, 2015.
"""

# from numba import jit


def hymod(Precip, PET, cmax, bexp, alpha, Rs, Rq):
    """
    See https://www.proc-iahs.net/368/180/2015/piahs-368-180-2015.pdf for a scientific paper:

    Quan, Z.; Teng, J.; Sun, W.; Cheng, T. & Zhang, J. (2015): Evaluation of the HYMOD model
    for rainfall–runoff simulation using the GLUE method. Remote Sensing and GIS for Hydrology
    and Water Resources, 180 - 185, IAHS Publ. 368. DOI: 10.5194/piahs-368-180-2015.

    :param cmax:
    :param bexp:
    :param alpha:
    :param Rs:
    :param Rq:
    :return: Dataset of water in hymod (has to be calculated in litres)
    :rtype: list
    """

    # HYMOD PROGRAM IS SIMPLE RAINFALL RUNOFF MODEL
    x_loss = 0.0
    # Initialize slow tank state
    x_slow = 2.3503 / (Rs * 22.5)
    x_slow = 0  # --> works ok if calibration data starts with low discharge
    # Initialize state(s) of quick tank(s)
    x_quick = [0, 0, 0]
    t = 0
    output = []
    # START PROGRAMMING LOOP WITH DETERMINING RAINFALL - RUNOFF AMOUNTS

    while t <= len(Precip) - 1:
        Pval = Precip[t]
        PETval = PET[t]
        # Compute excess precipitation and evaporation
        ER1, ER2, x_loss = excess(x_loss, cmax, bexp, Pval, PETval)
        # Calculate total effective rainfall
        ET = ER1 + ER2
        #  Now partition ER between quick and slow flow reservoirs
        UQ = alpha * ET
        US = (1 - alpha) * ET
        # Route slow flow component with single linear reservoir
        x_slow, QS = linres(x_slow, US, Rs)
        # Route quick flow component with linear reservoirs
        inflow = UQ

        for i in range(3):
            # Linear reservoir
            x_quick[i], outflow = linres(x_quick[i], inflow, Rq)
            inflow = outflow

        # Compute total flow for timestep
        output.append(QS + outflow)
        t = t + 1

    return output


# @jit
def power(X, Y):
    X = abs(X)  # Needed to capture invalid overflow with netgative values
    return X**Y


# @jit
def linres(x_slow, inflow, Rs):
    # Linear reservoir
    x_slow = (1 - Rs) * x_slow + (1 - Rs) * inflow
    outflow = (Rs / (1 - Rs)) * x_slow
    return x_slow, outflow


# @jit
def excess(x_loss, cmax, bexp, Pval, PETval):
    # this function calculates excess precipitation and evaporation
    xn_prev = x_loss
    ct_prev = cmax * (
        1 - power((1 - ((bexp + 1) * (xn_prev) / cmax)), (1 / (bexp + 1)))
    )
    # Calculate Effective rainfall 1
    ER1 = max((Pval - cmax + ct_prev), 0.0)
    Pval = Pval - ER1
    dummy = min(((ct_prev + Pval) / cmax), 1)
    xn = (cmax / (bexp + 1)) * (1 - power((1 - dummy), (bexp + 1)))

    # Calculate Effective rainfall 2
    ER2 = max(Pval - (xn - xn_prev), 0)

    # Alternative approach
    evap = (
        1 - (((cmax / (bexp + 1)) - xn) / (cmax / (bexp + 1)))
    ) * PETval  # actual ET is linearly related to the soil moisture state
    xn = max(xn - evap, 0)  # update state

    return ER1, ER2, xn

1	# -- coding: utf-8 --
2	"""	6✔
3	Copyright (c) 2015 by Tobias Houska
4
5	This file is part of Statistical Parameter Estimation Tool (SPOTPY).
6
7	:author: Tobias Houska and Benjamin Manns
8
9	:paper: Houska, T., Kraft, P., Chamorro-Chavez, A. and Breuer, L.:
10	SPOTting Model Parameters Using a Ready-Made Python Package,
11	PLoS ONE, 10(12), e0145180, doi:10.1371/journal.pone.0145180, 2015.
12	"""
13
14	# from numba import jit
15
16
17	def hymod(Precip, PET, cmax, bexp, alpha, Rs, Rq):	6✔
18	"""
19	See https://www.proc-iahs.net/368/180/2015/piahs-368-180-2015.pdf for a scientific paper:
20
21	Quan, Z.; Teng, J.; Sun, W.; Cheng, T. & Zhang, J. (2015): Evaluation of the HYMOD model
22	for rainfall–runoff simulation using the GLUE method. Remote Sensing and GIS for Hydrology
23	and Water Resources, 180 - 185, IAHS Publ. 368. DOI: 10.5194/piahs-368-180-2015.
24
25	:param cmax:
26	:param bexp:
27	:param alpha:
28	:param Rs:
29	:param Rq:
30	:return: Dataset of water in hymod (has to be calculated in litres)
31	:rtype: list
32	"""
33
34	# HYMOD PROGRAM IS SIMPLE RAINFALL RUNOFF MODEL
35	x_loss = 0.0	4✔
36	# Initialize slow tank state
37	x_slow = 2.3503 / (Rs * 22.5)	4✔
38	x_slow = 0 # --> works ok if calibration data starts with low discharge	4✔
39	# Initialize state(s) of quick tank(s)
40	x_quick = [0, 0, 0]	4✔
41	t = 0	4✔
42	output = []	4✔
43	# START PROGRAMMING LOOP WITH DETERMINING RAINFALL - RUNOFF AMOUNTS
44
45	while t <= len(Precip) - 1:	4✔
46	Pval = Precip[t]	4✔
47	PETval = PET[t]	4✔
48	# Compute excess precipitation and evaporation
49	ER1, ER2, x_loss = excess(x_loss, cmax, bexp, Pval, PETval)	4✔
50	# Calculate total effective rainfall
51	ET = ER1 + ER2	4✔
52	# Now partition ER between quick and slow flow reservoirs
53	UQ = alpha * ET	4✔
54	US = (1 - alpha) * ET	4✔
55	# Route slow flow component with single linear reservoir
56	x_slow, QS = linres(x_slow, US, Rs)	4✔
57	# Route quick flow component with linear reservoirs
58	inflow = UQ	4✔
59
60	for i in range(3):	4✔
61	# Linear reservoir
62	x_quick[i], outflow = linres(x_quick[i], inflow, Rq)	4✔
63	inflow = outflow	4✔
64
65	# Compute total flow for timestep
66	output.append(QS + outflow)	4✔
67	t = t + 1	4✔
68
69	return output	4✔
70
71
72	# @jit
73	def power(X, Y):	6✔
74	X = abs(X) # Needed to capture invalid overflow with netgative values	4✔
75	return X**Y	4✔
76
77
78	# @jit
79	def linres(x_slow, inflow, Rs):	6✔
80	# Linear reservoir
81	x_slow = (1 - Rs) * x_slow + (1 - Rs) * inflow	4✔
82	outflow = (Rs / (1 - Rs)) * x_slow	4✔
83	return x_slow, outflow	4✔
84
85
86	# @jit
87	def excess(x_loss, cmax, bexp, Pval, PETval):	6✔
88	# this function calculates excess precipitation and evaporation
89	xn_prev = x_loss	4✔
90	ct_prev = cmax * (	4✔
91	1 - power((1 - ((bexp + 1) * (xn_prev) / cmax)), (1 / (bexp + 1)))
92	)
93	# Calculate Effective rainfall 1
94	ER1 = max((Pval - cmax + ct_prev), 0.0)	4✔
95	Pval = Pval - ER1	4✔
96	dummy = min(((ct_prev + Pval) / cmax), 1)	4✔
97	xn = (cmax / (bexp + 1)) * (1 - power((1 - dummy), (bexp + 1)))	4✔
98
99	# Calculate Effective rainfall 2
100	ER2 = max(Pval - (xn - xn_prev), 0)	4✔
101
102	# Alternative approach
103	evap = (	4✔
104	1 - (((cmax / (bexp + 1)) - xn) / (cmax / (bexp + 1)))
105	) * PETval # actual ET is linearly related to the soil moisture state
106	xn = max(xn - evap, 0) # update state	4✔
107
108	return ER1, ER2, xn	4✔

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