HydPy-Snow-CemaNeige-T-MinMax (CemaNeige model with additional minimum and maximum air temperature as input)

The CemaNeige model snow_cn_minmax is a small extension of snow_cn. In contrast to snow_cn, it usually relies on daily minimum and maximum air temperatures to differentiate between liquid and frozen precipitation. Hence, even more than snow_cn, snow_cn_minmax is primarily designed for simulating with daily time steps.

Integration tests

Note

When new to HydPy, consider reading section Integration Tests first.

Example 1

The following integration test is equivalent to Example 1 of the application model snow_cn except for TMin and TMax, of course. They are made up freely because airGR provides no fitting data.

The following configurations, with the exception of the control parameters GradTMin and GradTMax and the input sequences TMin and TMax, are identical to those of the corresponding integration test for snow_cn and thus do not require further explanation:

>>> from hydpy import pub
>>> pub.timegrids = "1990-04-01", "1990-06-01", "1d"

>>> from hydpy.models.snow_cn_minmax import *
>>> parameterstep("1d")
>>> from hydpy import Element
>>> land = Element("land")
>>> land.model = model

>>> from hydpy import IntegrationTest
>>> IntegrationTest.plotting_options.axis1 = inputs.p, fluxes.pnet
>>> IntegrationTest.plotting_options.axis2 = inputs.tmin, inputs.tmax, states.g
>>> test = IntegrationTest(land)
>>> test.dateformat = "%d.%m."

>>> hysteresis(False)

>>> nlayers(5)
>>> hypsodata = [
...     286.0, 309.0, 320.0, 327.0, 333.0, 338.0, 342.0, 347.0, 351.0, 356.0, 360.0,
...     365.0, 369.0, 373.0, 378.0, 382.0, 387.0, 393.0, 399.0, 405.0, 411.0, 417.0,
...     423.0, 428.0, 434.0, 439.0, 443.0, 448.0, 453.0, 458.0, 463.0, 469.0, 474.0,
...     480.0, 485.0, 491.0, 496.0, 501.0, 507.0, 513.0, 519.0, 524.0, 530.0, 536.0,
...     542.0, 548.0, 554.0, 560.0, 566.0, 571.0, 577.0, 583.0, 590.0, 596.0, 603.0,
...     609.0, 615.0, 622.0, 629.0, 636.0, 642.0, 649.0, 656.0, 663.0, 669.0, 677.0,
...     684.0, 691.0, 698.0, 706.0, 714.0, 722.0, 730.0, 738.0, 746.0, 754.0, 762.0,
...     770.0, 777.0, 786.0, 797.0, 808.0, 819.0, 829.0, 841.0, 852.0, 863.0, 875.0,
...     887.0, 901.0, 916.0, 934.0, 952.0, 972.0, 994.0, 1012.0, 1029.0, 10540.0,
...     10800.0, 11250.0, 12780.0
... ]
>>> model.prepare_layers(hypsodata=hypsodata)

>>> cn1(0.962)
>>> cn2(2.249)

>>> cn4(0.9)

>>> meanansolidprecip(83.0)

>>> gradp(0.00041)

>>> gradtmean(nan)
>>> gradtmean.values[90:152] = [
...     0.59, 0.591, 0.591, 0.592, 0.593, 0.593, 0.594, 0.595, 0.595, 0.596, 0.596,
...     0.597, 0.597, 0.597, 0.598, 0.598, 0.598, 0.599, 0.599, 0.599, 0.599, 0.6, 0.6,
...     0.6, 0.6, 0.6, 0.601, 0.601, 0.601, 0.601, 0.601, 0.601, 0.601, 0.601, 0.601,
...     0.601, 0.601, 0.601, 0.601, 0.601, 0.602, 0.602, 0.602, 0.602, 0.602, 0.602,
...     0.602, 0.602, 0.602, 0.602, 0.602, 0.602, 0.602, 0.602, 0.602, 0.602, 0.602,
...     0.602, 0.602, 0.602, 0.602, 0.602]
>>> gradtmin(nan)
>>> gradtmin.values[90:152] = [
...     0.425, 0.426, 0.427, 0.427, 0.428, 0.429, 0.429, 0.43, 0.431, 0.431, 0.432,
...     0.432, 0.433, 0.433, 0.434, 0.434, 0.435, 0.435, 0.436, 0.436, 0.436, 0.437,
...     0.437, 0.437, 0.438, 0.438, 0.438, 0.438, 0.439, 0.439, 0.439, 0.439, 0.439,
...     0.439, 0.44, 0.44, 0.44, 0.44, 0.44, 0.44, 0.44, 0.44, 0.44, 0.44, 0.44, 0.44,
...     0.44, 0.44, 0.44, 0.44, 0.44, 0.44, 0.44, 0.44, 0.44, 0.44, 0.44, 0.439, 0.439,
...     0.439, 0.439, 0.439]
>>> gradtmax(nan)
>>> gradtmax.values[90:152] = [
...     0.783, 0.784, 0.785, 0.785, 0.786, 0.787, 0.787, 0.787, 0.788, 0.788, 0.788,
...     0.788, 0.788, 0.788, 0.788, 0.788, 0.787, 0.787, 0.787, 0.786, 0.786, 0.785,
...     0.785, 0.784, 0.784, 0.783, 0.783, 0.782, 0.781, 0.781, 0.78, 0.779, 0.778,
...     0.778, 0.777, 0.776, 0.775, 0.775, 0.774, 0.773, 0.772, 0.772, 0.771, 0.77,
...     0.77, 0.769, 0.768, 0.768, 0.767, 0.767, 0.766, 0.766, 0.765, 0.765, 0.764,
...     0.764, 0.764, 0.763, 0.763, 0.763, 0.762, 0.762]

>>> parameters.update()
>>> derived.zmean(577)

>>> test.inits = (
...     (states.g, [0.0, 0.0, 0.0, 0.0, 0.0]),
...     (states.etg, [0.0, 0.0, 0.0, 0.0, 0.0]),
...     (states.gratio, [0.0, 0.0, 0.0, 0.0, 0.0]),
... )

>>> inputs.p.series = (
...     0.4, 0.0, 1.0, 1.6, 3.1, 0.0, 0.0, 0.0, 0.0, 2.4, 2.1, 0.8, 8.4, 16.1, 2.9,
...     0.0, 0.4, 12.2, 8.8, 6.3, 2.5, 0.0, 0.0, 1.7, 2.5, 9.9, 7.5, 0.3, 6.3, 4.6,
...     1.1, 0.0, 0.0, 0.0, 0.4, 9.1, 0.0, 13.4, 4.0, 0.0, 0.0, 8.6, 5.3, 2.5, 1.8,
...     0.0, 2.4, 13.3, 5.8, 4.3, 20.0, 9.8, 2.1, 1.6, 3.7, 0.2, 0.0, 0.0, 0.1, 0.7,
...     2.2)
>>> inputs.t.series = (
...     7.2, 7.1, 6.4, 7.4, 7.5, 4.3, 3.5, 3.5, 4.5, 5.0, 1.9, 1.8, 3.0, 1.3, 0.2, 1.8,
...     1.5, 3.7, 7.4, 7.6, 5.8, 6.0, 8.1, 8.1, 8.6, 8.7, 6.6, 8.8, 9.6, 9.2, 7.8, 8.0,
...     10.6, 13.8, 12.1, 7.7, 7.0, 10.0, 9.1, 11.3, 6.6, 7.4, 5.9, 7.6, 9.2, 11.8,
...     13.8, 12.1, 10.8, 11.7, 11.1, 7.3, 8.0, 10.3, 8.0, 10.4, 14.0, 14.2, 14.9,
...     15.6, 16.3)
>>> inputs.tmin.series = (
...     4.5, 4.0, 3.9, 5.8, 7.4, 3.4, 2.5, 0.1, 2.3, 4.9, 1.3, -1.5, 2.0, -0.5, -3.7,
...     0.4, -1.6, 3.2, 6.2, 6.4, 3.4, 5.8, 4.6, 5.0, 7.7, 4.8, 3.5, 5.4, 8.6, 6.1,
...     5.1, 7.1, 8.9, 10.8, 9.2, 5.7, 5.8, 6.5, 9.0, 10.9, 4.1, 4.5, 2.5, 3.7, 8.0,
...     10.0, 11.3, 11.9, 8.8, 9.9, 10.0, 6.5, 6.6, 6.4, 7.7, 6.5, 13.0, 13.9, 12.9,
...     12.7, 16.0)
>>> inputs.tmax.series = (
...     10.2, 10.0, 10.0, 10.9, 7.6, 4.9, 7.5, 4.3, 7.8, 6.5, 4.1, 3.2, 4.8, 1.8, 3.8,
...     3.0, 4.8, 5.9, 7.6, 9.0, 9.2, 8.3, 11.8, 8.8, 10.9, 12.1, 8.7, 10.0, 11.1,
...     11.4, 9.6, 10.7, 11.0, 17.0, 14.6, 8.2, 10.5, 10.8, 11.8, 14.8, 9.4, 7.9, 9.6,
...     9.7, 11.7, 15.4, 17.6, 12.3, 14.1, 12.6, 12.2, 10.6, 10.8, 11.6, 11.7, 10.8,
...     15.6, 14.6, 16.3, 19.2, 16.5)

Given that snow_cn_minmax and snow_cn differ only in the calculation of the solid precipitation fraction, it is not surprising that the following test results are quite simple to Example 1 of snow_cn:

>>> test("snow_cn_minmax_example1", update_parameters=False)
Click to see the table

Example 2

This integration test repeats Example 2 of snow_cn with additional data regarding daily minimum and maximum air temperature:

>>> pub.timegrids = "1990-01-01", "1990-01-16", "1d"
>>> hysteresis(True)
>>> cn3(20.0)
>>> cn4(0.4)
>>> gradtmean.values[:16] = [
...     0.434, 0.434, 0.435, 0.436, 0.437, 0.439, 0.44, 0.441, 0.442, 0.444, 0.445,
...     0.446, 0.448, 0.45, 0.451, 0.453]
>>> gradtmin.values[:16] = [
...     0.366, 0.366, 0.367, 0.367, 0.367, 0.367, 0.367, 0.368, 0.368, 0.368, 0.368,
...     0.368, 0.369, 0.369, 0.369, 0.37]
>>> gradtmax.values[:16] = [
...     0.498, 0.5, 0.501, 0.503, 0.504, 0.506, 0.508, 0.51, 0.512, 0.514, 0.517,
...     0.519, 0.522, 0.525, 0.527, 0.53]
>>> parameters.update()
>>> derived.zmean(577)
>>> test.inits = (
...     (states.g, [10.0, 15.0, 20.0, 30.0, 40.0]),
...     (states.etg, [0.0, 0.0, 0.0, 0.0, 0.0]),
...     (states.gratio, [0.8, 0.8, 0.8, 0.8, 0.8]),
...     (logs.glocalmax, derived.gthresh),
... )
>>> inputs.t.series = (
...     5.0, 5.0, 5.0, 5.0, 5.0, 5.0, -1.0, -1.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0
... )
>>> inputs.tmin.series = inputs.t.series - 1.0
>>> inputs.tmax.series = inputs.t.series + 3.0
>>> inputs.p.series = (
...     0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 10.0, 10.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
... )

The defined, constant difference between the minimum and maximum air temperature enforces a behaviour similar to the temperature interval approach of snow_cn defined by the fixed parameters TThreshSnow and TThreshRain. Hence, we observe close agreement with the calculation results of Example 2:

>>> test("snow_cn_minmax_hyst_example2", update_parameters=False)
Click to see the table

Method features

class hydpy.models.snow_cn_minmax.Model[source]

Bases: BaseModel

HydPy-Snow-CemaNeige-T-MinMax (CemaNeige model with additional minimum and maximum air temperature as input).

The following “run methods” are called in the given sequence during each simulation step:
The following “additional methods” might be called by one or more of the other methods or are meant to be directly called by the user:

  • Return_T_V1 Return the altitude-adjusted temperature.

DOCNAME = ('Snow-CemaNeige-T-MinMax', 'CemaNeige model with additional minimum and maximum air temperature as input')
REUSABLE_METHODS = ()

Parameter features

class hydpy.models.snow_cn_minmax.ControlParameters(master: Parameters[TM_co], cls_fastaccess: type[FastAccessParameter] | None = None, cymodel: CyModelProtocol | None = None)

Bases: ControlParameters

Control parameters of model snow_cn_minmax.

The following classes are selected:

  • NLayers Number of snow layers [-].

  • ZLayers Height of each snow layer [m].

  • LayerArea Area of snow layer as a percentage of total area [-].

  • GradP Altitude gradient of precipitation [1/m].

  • GradTMean Altitude gradient of daily mean air temperature for each day of the year [°C/100m].

  • GradTMin Altitude gradient of daily minimum air temperature for each day of the year [°C/100m].

  • GradTMax Altitude gradient of daily maximum air temperature for each day of the year [°C/100m].

  • MeanAnSolidPrecip Mean annual solid precipitation [mm/a].

  • CN1 Temporal weighting coefficient for the snow pack’s thermal state [-].

  • CN2 Degree-day melt coefficient [mm/°C/T].

  • CN3 Accumulation threshold [mm].

  • CN4 Fraction of annual snowfall defining the melt threshold [-].

  • Hysteresis Flag that indicates whether hysteresis of build-up and melting of the snow cover should be considered [-].

class hydpy.models.snow_cn_minmax.DerivedParameters(master: Parameters[TM_co], cls_fastaccess: type[FastAccessParameter] | None = None, cymodel: CyModelProtocol | None = None)

Bases: DerivedParameters

Derived parameters of model snow_cn_minmax.

The following classes are selected:

  • DOY References the “global” month of the year index array [-].

  • GThresh Accumulation threshold [mm].

  • ZMean Mean elevation of all layer [m].

class hydpy.models.snow_cn_minmax.FixedParameters(master: Parameters[TM_co], cls_fastaccess: type[FastAccessParameter] | None = None, cymodel: CyModelProtocol | None = None)

Bases: FixedParameters

Fixed parameters of model snow_cn_minmax.

The following classes are selected:

  • ZThreshold Altitude threshold for constant precipitation [m].

  • MinMelt Minimum ratio of actual to potential melt [-].

  • TThreshSnow Temperature below which all precipitation falls as snow [°C].

  • TThreshRain Temperature above which all precipitation falls as rain [°C].

  • MinG Amount of snow below which actual melt can be equal to potential melt [mm].

Sequence features

class hydpy.models.snow_cn_minmax.InputSequences(master: Sequences[TM_co], cls_fastaccess: type[TypeFastAccess_co] | None = None, cymodel: CyModelProtocol | None = None)

Bases: InputSequences

Input sequences of model snow_cn_minmax.

The following classes are selected:

  • P Precipitation [mm/T].

  • T Mean air temperature [°C].

  • TMin Minimum air temperature [°C].

  • TMax Maximum air temperature [°C].

class hydpy.models.snow_cn_minmax.FactorSequences(master: Sequences[TM_co], cls_fastaccess: type[TypeFastAccess_co] | None = None, cymodel: CyModelProtocol | None = None)

Bases: FactorSequences

Factor sequences of model snow_cn_minmax.

The following classes are selected:

  • TLayer Mean air temperature of each snow layer [°C].

  • TMinLayer Minimum air temperature of each snow layer [°C].

  • TMaxLayer Maximum air temperature of each snow layer [°C].

  • SolidFractionPrecipitation Solid fraction of precipitation of each snow layer [-].

class hydpy.models.snow_cn_minmax.FluxSequences(master: Sequences[TM_co], cls_fastaccess: type[TypeFastAccess_co] | None = None, cymodel: CyModelProtocol | None = None)

Bases: FluxSequences

Flux sequences of model snow_cn_minmax.

The following classes are selected:

  • PLayer Precipitation of each snow layer [mm/T].

  • PSnowLayer Snowfall of each snow layer [mm/T].

  • PRainLayer Rainfall of each snow layer [mm/T].

  • PotMelt Potential snow melt of each snow layer [mm/T].

  • Melt Actual snow melt of each snow layer [mm/T].

  • PNetLayer Net precipitation of each snow layer [mm/T].

  • PNet Net precipitation of the complete catchment [mm/T].

class hydpy.models.snow_cn_minmax.StateSequences(master: Sequences[TM_co], cls_fastaccess: type[TypeFastAccess_co] | None = None, cymodel: CyModelProtocol | None = None)

Bases: StateSequences

State sequences of model snow_cn_minmax.

The following classes are selected:

  • G Snow pack [mm].

  • ETG Thermal state of the snow pack [°C].

  • GRatio Ratio of the snow-covered area [-].

class hydpy.models.snow_cn_minmax.LogSequences(master: Sequences[TM_co], cls_fastaccess: type[TypeFastAccess_co] | None = None, cymodel: CyModelProtocol | None = None)

Bases: LogSequences

Log sequences of model snow_cn_minmax.

The following classes are selected: