hland_v4

Version 4 of H-Land combines HBV96’s (Lindström et al., 1997) and COSERO’s (Kling, 2006, Kling et al., 2005) process equations. We implemented it on behalf of the German Federal Institute of Hydrology (BfG) as an alternative to hland_v1 for modelling large river basins in central Europe. All processes “above the soil” (input data correction, interception, snowmelt) and “inside the soil” (evaporation, generation of effective precipitation), as well as the handling of water areas, are identical with hland_v1 (and so with HBV96). Most processes “below the soil” agree with COSERO (runoff generation and runoff concentration).

The following figure shows the general structure of hland_v4:

_images/HydPy-H-Land_Version-4.png

Comparing the above figure with the analogue figure of hland_v1 reveals that hland_v4 models three instead of two runoff components, requiring a larger number of vertically arranged storages. The two upper storages (BW1 and BW2) are zone-specific. In comparison, the upper zone layer (UZ) of hland_v1 is subbasin-specific. For the subbasin-wide lower zone storage (LZ), there is no difference to hland_v1. hland_v4 allows for additional runoff concentration via a linear storage cascade (SC), like implemented in hland_v2, which is supposed to improve flexibility when modelling the effects of sealed areas on runoff generation. Setting the number of storages to one agrees with COSERO’s approach to model runoff concentration with a single bucket. In agreement with COSERO but in contrast to hland_v2 and hland_v3, hland_v4 does not send its base flow through the storage cascade.

Integration tests

Note

When new to HydPy, consider reading section How to understand integration tests? first.

We define the settings of the following test calculations as similar to the ones of application model hland_v1 as possible. Hence, comparing the test results gives a good impression of the functional differences of both models.

>>> from hydpy import pub
>>> pub.timegrids = "01.01.2000", "05.01.2000", "1h"

>>> from hydpy.models.hland_v4 import *
>>> parameterstep("1h")
>>> from hydpy import Node, Element
>>> outlet = Node("outlet")
>>> land = Element("land", outlets=outlet)
>>> land.model = model

>>> nmbzones(1)
>>> sclass(1)
>>> area(1.0)
>>> zonearea(1.0)
>>> zonez(1.0)

>>> zrelp(2.0)
>>> zrelt(2.0)
>>> zrele(2.0)

>>> psi(1.0)

>>> from hydpy import IntegrationTest
>>> IntegrationTest.plotting_options.axis1 = (
...     inputs.p, fluxes.qab1, fluxes.qab2, fluxes.q1, fluxes.rt)
>>> IntegrationTest.plotting_options.axis2 = inputs.t
>>> test = IntegrationTest(land)
>>> test.dateformat = "%d/%m %H:00"

field

We assign identical values to all parameters, besides those that are specific to hland_v4 (H1, TAb1, TVs1, H1, TAb1, TVs1, and NmbStorages):

>>> zonetype(FIELD)

>>> pcorr(1.2)
>>> pcalt(0.1)
>>> rfcf(1.1)
>>> sfcf(1.3)
>>> tcalt(0.6)
>>> ecorr(0.8)
>>> ecalt(-0.1)
>>> epf(0.1)
>>> etf(0.1)
>>> ered(0.5)
>>> icmax(2.0)
>>> sfdist(1.0)
>>> smax(inf)
>>> sred(0.0)
>>> tt(0.0)
>>> ttint(2.0)
>>> dttm(1.0)
>>> cfmax(0.5)
>>> cfvar(0.1)
>>> gmelt(1.0)
>>> gvar(0.2)
>>> cfr(0.1)
>>> whc(0.4)
>>> fc(200.0)
>>> lp(0.8)
>>> beta(2.0)
>>> h1(10.0)
>>> tab1(2.0)
>>> tvs1(2.0)
>>> h2(10.0)
>>> tab2(10.0)
>>> tvs2(10.0)
>>> k4(0.005)
>>> gamma(0.0)
>>> maxbaz(3.0)
>>> nmbstorages(5)
>>> recstep(100)

>>> test.inits = ((states.ic, 0.0),
...               (states.sp, 0.0),
...               (states.wc, 0.0),
...               (states.sm, 100.0),
...               (states.bw1, 0.0),
...               (states.bw2, 0.0),
...               (states.lz, 10.0),
...               (states.sc, 0.05))

>>> inputs.p.series = (
...     0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
...     0.2, 0.0, 0.0, 1.3, 5.6, 2.9, 4.9, 10.6, 0.1, 0.7, 3.0, 2.1, 10.4, 3.5, 3.4,
...     1.2, 0.1, 0.0, 0.0, 0.4, 0.1, 3.6, 5.9, 1.1, 20.7, 37.9, 8.2, 3.6, 7.5, 18.5,
...     15.4, 6.3, 1.9, 4.9, 2.7, 0.5, 0.2, 0.5, 2.4, 0.4, 0.2, 0.0, 0.0, 0.3, 2.6,
...     0.7, 0.3, 0.3, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
...     1.3, 0.0, 0.0, 0.0, 0.7, 0.4, 0.1, 0.4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
...     0.0, 0.0, 0.0, 0.0, 0.0, 0.0)
>>> inputs.t.series = (
...     21.2, 19.4, 18.9, 18.3, 18.9, 22.5, 25.1, 28.3, 27.8, 31.4, 32.2, 35.2, 37.1,
...     31.2, 24.3, 25.4, 25.9, 23.7, 21.6, 21.2, 20.4, 19.8, 19.6, 19.2, 19.2, 19.2,
...     18.9, 18.7, 18.5, 18.3, 18.5, 18.8, 18.8, 19.0, 19.2, 19.3, 19.0, 18.8, 18.7,
...     17.8, 17.4, 17.3, 16.8, 16.5, 16.3, 16.2, 15.5, 14.6, 14.7, 14.6, 14.1, 14.3,
...     14.9, 15.7, 16.0, 16.7, 17.1, 16.2, 15.9, 16.3, 16.3, 16.4, 16.5, 18.4, 18.3,
...     18.1, 16.7, 15.2, 13.4, 12.4, 11.6, 11.0, 10.5, 11.7, 11.9, 11.2, 11.1, 11.9,
...     12.2, 11.8, 11.4, 11.6, 13.0, 17.1, 18.2, 22.4, 21.4, 21.8, 22.2, 20.1, 17.8,
...     15.2, 14.5, 12.4, 11.7, 11.9)
>>> inputs.tn.series = inputs.t.series-1.0
>>> inputs.epn.series = (
...     0.100707, 0.097801, 0.096981, 0.09599, 0.096981, 0.102761, 0.291908, 1.932875,
...     4.369536, 7.317556, 8.264362, 9.369867, 5.126178, 6.62503, 7.397619, 2.39151,
...     1.829834, 1.136569, 0.750986, 0.223895, 0.099425, 0.098454, 0.098128, 0.097474,
...     0.097474, 0.097474, 0.096981, 0.096652, 0.096321, 0.09599, 0.187298, 1.264612,
...     3.045538, 1.930758, 2.461001, 6.215945, 3.374783, 8.821555, 4.046025, 2.110757,
...     2.239257, 2.877848, 1.591452, 0.291604, 0.092622, 0.092451, 0.091248, 0.089683,
...     0.089858, 0.089683, 0.088805, 0.089157, 0.090207, 0.091593, 0.154861, 0.470369,
...     1.173726, 4.202296, 4.359715, 5.305753, 5.376027, 4.658915, 7.789594, 4.851567,
...     5.30692, 3.286036, 1.506216, 0.274762, 0.087565, 0.085771, 0.084317, 0.083215,
...     0.082289, 0.0845, 0.084864, 0.083584, 0.0834, 0.084864, 0.310229, 1.391958,
...     3.195876, 5.191651, 7.155036, 8.391432, 8.391286, 10.715238, 9.383394, 7.861915,
...     6.298329, 2.948416, 1.309232, 0.32955, 0.089508, 0.085771, 0.0845, 0.084864)

>>> test.reset_inits()
>>> conditions = sequences.conditions

hland_v4 neither implements an approach analogue to HBV96’s contributing area concept nor a substep mechanism controlling the numerical accuracy of the runoff generation module. Hence, we provide only a single “field” example, that is comparable both with the first and the second example of hland_v1:

>>> test("hland_v4_field")
Click to see the table
Click to see the graph

There is no indication of an error in the water balance:

>>> from hydpy import round_
>>> round_(model.check_waterbalance(conditions))
0.0

Regarding numerical accuracy of the linear storage cascade, please see the low accuracy example of hland_v2, which also applies the storage cascade instead of the triangular Unit Hydrograph.

internal lake

Regarding the simulation of internal lakes, the functionalities of hland_v4 are identical to hland_v1. Hence, the simulated baseflow shown in the following table agrees with the internal lake example of hland_v1. Nevertheless, the resulting subbasin’s outflow appears less smoothed due to hland_v4 not sending its baseflow through the linear storage cascade:

>>> recstep(100)
>>> zonetype(ILAKE)
>>> ttice(13.0)
>>> test.inits.sc = 0.0
>>> test.reset_inits()
>>> conditions = sequences.conditions
>>> test("hland_v4_ilake")
Click to see the table
Click to see the graph

There is no indication of an error in the water balance:

>>> round_(model.check_waterbalance(conditions))
0.0

snow classes

Regarding snow simulation, all functionalities of hland_v4 are identical with the ones of hland_v1. Hence, all snow data in the following table agrees with the snow classes example of hland_v1:

>>> sclass(2)
>>> sfdist(linear=0.2)
>>> zonetype(FIELD)
>>> t_series = inputs.t.series.copy()
>>> tn_series = inputs.tn.series.copy()
>>> inputs.t.series[:48] = -20.0
>>> inputs.t.series[48:] = 20.0
>>> inputs.tn.series = inputs.t.series
>>> test("hland_v4_snow_classes")
Click to see the table
Click to see the graph

There is no indication of an error in the water balance:

>>> round_(model.check_waterbalance(conditions))
0.0

glacier

Regarding glacier simulation, all functionalities of hland_v4 are identical with the ones of hland_v1. Hence, all snow data in the following table agrees with the glacier example of hland_v1:

>>> zonetype(GLACIER)
>>> test.inits.sc = 0.05
>>> test.reset_inits()
>>> conditions = sequences.conditions
>>> test("hland_v4_glacier")
Click to see the table
Click to see the graph

There is no indication of an error in the water balance:

>>> round_(model.check_waterbalance(conditions))
0.0

multiple zones

This example focuses on the spatial configuration of hland_v4. The most marked difference to hland_v1 is the separate modelling of the faster runoff components (QAb1, QAb2) for each zone. Hence, there are five distinct values for each runoff component in the following table. In contrast, the table of the corresponding multiple zones example of hland_v1 contains only a single value for the only fast runoff component Q0 of hland_v1:

>>> ttice(0.0)
>>> name2value = {par.name: par.value for par in control}
>>> nmbzones(5)
>>> sclass(1)
>>> area(15.0)
>>> zonearea(5.0, 4.0, 3.0, 2.0, 1.0)
>>> zonetype(FIELD, FOREST, GLACIER, ILAKE, SEALED)
>>> sfdist(1.0)
>>> for name, value in name2value.items():
...     if name not in ("nmbzones", "sclass", "area", "zonearea", "zonetype", "sfdist"):
...         control[name].value = value
>>> inputs.t.series = t_series
>>> inputs.tn.series = tn_series
>>> test("hland_v4_multiple_zones",
...      axis1=(inputs.p, fluxes.rt, fluxes.qt),
...      axis2=inputs.t)
Click to see the table
Click to see the graph

There is no indication of an error in the water balance:

>>> round_(model.check_waterbalance(conditions))
0.0

snow redistribution

Regarding snow redistribution, all functionalities of hland_v4 are identical with the ones of hland_v1. Hence, all snow data in the following table agrees with the snow redistribution 1 example of hland_v1:

>>> zonez(0.0, 10.0, 30.0, 0.0, 20.0)
>>> smax(200.0)
>>> sred(n_zones=1)
>>> inputs.t.series = 5.0
>>> test("hland_v4_snow_redistribution",
...      axis1=(states.sp, states.wc), axis2=(factors.tc, fluxes.pc))
Click to see the table
Click to see the graph

There is no indication of an error in the water balance:

>>> round_(model.check_waterbalance(conditions))
0.0
class hydpy.models.hland_v4.Model[source]

Bases: AdHocModel

HBV96-SC/COSERO version of HydPy-H-Land (hland_v4).

The following “run methods” are called in the given sequence during each simulation step:

  • Calc_TC_V1 Adjust the measured air temperature to the altitude of the individual zones.

  • Calc_TMean_V1 Calculate the areal mean temperature of the subbasin.

  • Calc_FracRain_V1 Determine the temperature-dependent fraction of (liquid) rainfall and (total) precipitation.

  • Calc_RFC_SFC_V1 Calculate the corrected fractions of rainfall/snowfall and total precipitation.

  • Calc_PC_V1 Apply the precipitation correction factors and adjust precipitation to the altitude of the individual zones.

  • Calc_EP_V1 Adjust the potential norm evaporation to the actual temperature.

  • Calc_EPC_V1 Apply the evaporation correction factors and adjust evaporation to the altitude of the individual zones.

  • Calc_TF_Ic_V1 Calculate throughfall and update the interception storage accordingly.

  • Calc_EI_Ic_V1 Calculate interception evaporation and update the interception storage accordingly.

  • Calc_SP_WC_V1 Add throughfall to the snow layer.

  • Calc_SPL_WCL_SP_WC_V1 Calculate the subbasin-internal redistribution losses of the snow layer.

  • Calc_SPG_WCG_SP_WC_V1 Calculate the subbasin-internal redistribution gains of the snow layer.

  • Calc_CFAct_V1 Adjust the day degree factor for snow to the current day of the year.

  • Calc_Melt_SP_WC_V1 Calculate the melting of the ice content within the snow layer and update both the snow layers’ ice and the water content.

  • Calc_Refr_SP_WC_V1 Calculate refreezing of the water content within the snow layer and update both the snow layers’ ice and the water content.

  • Calc_In_WC_V1 Calculate the actual water release from the snow layer due to the exceedance of the snow layers’ capacity for (liquid) water.

  • Calc_SWE_V1 Calculate the total snow water equivalent.

  • Calc_SR_V1 Calculate the sealed surface runoff.

  • Calc_GAct_V1 Adjust the day degree factor for glacier ice to the current day of the year.

  • Calc_GlMelt_In_V1 Calculate the melting of non-snow-covered glaciers and add it to the water release of the snow module.

  • Calc_R_SM_V1 Calculate effective precipitation and update the soil moisture.

  • Calc_EA_SM_V1 Calculate soil evaporation and update the soil moisture.

  • Calc_QAb1_QVs1_BW1_V1 Calculate the flow and the percolation from the surface flow reservoir and update it.

  • Calc_QAb2_QVs2_BW2_V1 Calculate the flow and the percolation from the interflow reservoir and update it.

  • Calc_InUH_V3 Calculate linear storage cascade input.

  • Calc_OutUH_SC_V1 Calculate the linear storage cascade output (state-space approach).

  • Calc_LZ_V2 Add percolation from the interflow reservoir and lake precipitation to the lower zone storage.

  • Calc_EL_LZ_V1 Calculate lake evaporation.

  • Calc_Q1_LZ_V1 Calculate the slow response of the lower zone layer.

  • Calc_RT_V2 Calculate the total discharge in mm.

  • Calc_QT_V1 Calculate the total discharge in m³/s.

The following “outlet update methods” are called in the given sequence at the end of 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:

  • Calc_QAb_QVs_BW_V1 Calculate the flow and the percolation from a two-outlet reservoir and update it.

check_waterbalance(initial_conditions: Dict[str, Dict[str, ArrayFloat]]) → float[source]

Determine the water balance error of the previous simulation run in mm.

Method check_waterbalance() calculates the balance error as follows:

\[\sum_{k=1}^{NmbZones} RelZoneAreas^k \cdot \left( \sum_{t=t0}^{t1} \big( PC_t^k + GLMelt_t^k - EI_t^k - EA_t^k - EL_t^k \big) + \big( IC_{t0}^k - IC_{t1}^k \big) + \frac{1}{SClass} \cdot \sum_{c=1}^{SClass} \Big( \big( SP_{t0}^{c,k} - SP_{t1}^{c,k} \big) + \big( WC_{t0}^{c,k} - WC_{t1}^{c,k} \big) \Big) + \big( SM_{t0}^k - SM_{t1}^k \big) + \big( BW1_{t0}^k - BW2_{t1}^k \big) + \big( BW2_{t0}^k - BW2_{t1}^k \big) \right) - \sum_{t=t0}^{t1} RT_t + RelLowerZoneArea \cdot \big( LZ_{t0} - LZ_{t1} \big) + RelLandArea \cdot \sum_{i=1}^{NmbStorages} \big( SC_{t0}^i - SC_{t1}^i \big)\]

The returned error should always be in scale with numerical precision so that it does not affect the simulation results in any relevant manner.

Pick the required initial conditions before starting the simulation run via property conditions. See the integration tests of the application model hland_v4 for some examples.

class hydpy.models.hland_v4.Masks[source]

Bases: Masks

Masks applicable to hland_v4.

The following classes are selected:
class hydpy.models.hland_v4.AideSequences(master: Sequences, cls_fastaccess: Type[TypeFastAccess_co] | None = None, cymodel: CyModelProtocol | None = None)

Bases: AideSequences

Aide sequences of model hland_v4.

The following classes are selected:

  • SPE() Subbasin-internal redistribution excess of the snow’s ice content [mm/T].

  • WCE() Subbasin-internal redistribution excess of the snow’s water content [mm/T].

class hydpy.models.hland_v4.ControlParameters(master: Parameters, cls_fastaccess: Type[FastAccessParameter] | None = None, cymodel: CyModelProtocol | None = None)

Bases: SubParameters

Control parameters of model hland_v4.

The following classes are selected:

  • Area() Subbasin area [km²].

  • NmbZones() Number of zones (hydrological response units) in a subbasin [-].

  • SClass() Number of snow classes in each zone [-].

  • ZoneType() Type of each zone [-].

  • ZoneArea() Zone area [km²].

  • Psi() Fraction of the actual sealing of zones classified as SEALED [-].

  • ZoneZ() Zone elevation [100m].

  • ZRelP() Subbasin-wide reference elevation level for precipitation [100m].

  • ZRelT() Subbasin-wide reference elevation level for temperature [100m].

  • ZRelE() Subbasin-wide reference elevation level for evaporation [100m].

  • PCorr() General precipitation correction factor [-].

  • PCAlt() Elevation correction factor for precipitation [1/100m].

  • RfCF() Rainfall correction factor [-].

  • SfCF() Snowfall correction factor [-].

  • TCAlt() Elevation correction factor for temperature [-1°C/100m].

  • ECorr() General evaporation correction factor [-].

  • ECAlt() Elevation correction factor for evaporation [-1/100m].

  • EPF() Decrease in potential evaporation due to precipitation [T/mm].

  • ETF() Temperature factor for evaporation [1/°C].

  • ERed() Factor for restricting actual to potential evaporation [-].

  • TTIce() Temperature threshold for lake evaporation [°C].

  • IcMax() Maximum interception storage [mm].

  • SFDist() Distribution of snowfall [-].

  • SMax() Maximum snow water equivalent [mm].

  • SRed() Snow redistribution paths [-].

  • TT() Temperature threshold for snow/rain [°C].

  • TTInt() Temperature interval with a mixture of snow and rain [°C].

  • DTTM() Difference between TTM and TT [°C].

  • CFMax() Average degree day factor for snow (on glaciers or not) [mm/°C/T].

  • CFVar() Annual variability of CFMax [mm/°C/T].

  • GMelt() Degree day factor for glacial ice [mm/°C/T].

  • GVar() Annual variability of GMelt [mm/°C/T].

  • CFR() Refreezing factor for water stored within the snow layer [-].

  • WHC() Relative water holding capacity of the snow layer [-].

  • FC() Maximum soil moisture content (field capacity) [mm].

  • LP() Relative limit for potential evaporation [-].

  • Beta() Nonlinearity parameter of the soil routine [-].

  • RecStep() Number of internal computation steps per simulation time step [-].

  • H1() Outlet level of the reservoir for simulating surface flow [mm].

  • TAb1() Recession coefficient for simulating surface flow [T].

  • TVs1() Recession coefficient for simulating percolation from the surface flow module [T].

  • H2() Outlet level of the reservoir for simulating interflow [mm].

  • TAb2() Recession coefficient for simulating interflow [T].

  • TVs2() Recession coefficient for simulating percolation from the interflow module [T].

  • K4() Recession coefficient of the lower zone layer [1/T].

  • Gamma() Nonlinearity parameter of the lower zone layer [-].

  • MaxBaz() Base length of the triangle unit hydrograph [T].

  • NmbStorages() Number of storages of the linear storage cascade [-].

class hydpy.models.hland_v4.DerivedParameters(master: Parameters, cls_fastaccess: Type[FastAccessParameter] | None = None, cymodel: CyModelProtocol | None = None)

Bases: SubParameters

Derived parameters of model hland_v4.

The following classes are selected:
class hydpy.models.hland_v4.FactorSequences(master: Sequences, cls_fastaccess: Type[TypeFastAccess_co] | None = None, cymodel: CyModelProtocol | None = None)

Bases: FactorSequences

Factor sequences of model hland_v4.

The following classes are selected:

  • TMean() Mean subbasin temperature [°C].

  • TC() Corrected temperature [°C].

  • FracRain() Fraction rainfall / total precipitation [-].

  • RfC() Actual precipitation correction related to liquid precipitation [-].

  • SfC() Actual precipitation correction related to frozen precipitation [-].

  • CFAct() Actual degree day factor for snow (on glaciers or not) [mm/°C/T].

  • SWE() Snow water equivalent [mm].

  • GAct() Actual degree day factor for glacier ice [mm/°C/T].

class hydpy.models.hland_v4.FixedParameters(master: Parameters, cls_fastaccess: Type[FastAccessParameter] | None = None, cymodel: CyModelProtocol | None = None)

Bases: SubParameters

Fixed parameters of model hland_v4.

The following classes are selected:
class hydpy.models.hland_v4.FluxSequences(master: Sequences, cls_fastaccess: Type[TypeFastAccess_co] | None = None, cymodel: CyModelProtocol | None = None)

Bases: FluxSequences

Flux sequences of model hland_v4.

The following classes are selected:

  • PC() Corrected precipitation [mm/T].

  • EP() Potential evaporation [mm/T].

  • EPC() Corrected potential evaporation [mm/T].

  • EI() Interception evaporation [mm/T].

  • TF() Throughfall [mm/T].

  • SPL() Subbasin-internal redistribution loss of the snow’s ice content [mm/T].

  • WCL() Subbasin-internal redistribution loss of the snow’s water content [mm/T].

  • SPG() Subbasin-internal redistribution gain of the snow’s ice content [mm/T].

  • WCG() Subbasin-internal redistribution gain of the snow’s water content [mm/T].

  • GlMelt() Glacier melt [mm/T].

  • Melt() Actual melting of frozen water stored in the snow layer [mm/T].

  • Refr() Actual (re)freezing of liquid water stored in the snow layer [mm/T].

  • In_() Snow module release/soil module inflow [mm/T].

  • R() Effective soil response [mm/T].

  • SR() Sealed surface runoff [mm/T].

  • EA() Actual soil evaporation [mm/T].

  • QVs1() Percolation from the surface flow reservoir [mm/T].

  • QAb1() Surface flow [mm/T].

  • QVs2() Percolation from the interflow reservoir [mm/T].

  • QAb2() Interflow [mm/T].

  • EL() Actual lake evaporation [mm/T].

  • Q1() Outflow from the lower zone layer [mm/T].

  • InUH() Input of the triangle unit hydrograph [mm/T].

  • OutUH() Output of the triangle unit hydrograph [mm/T].

  • RT() Total model outflow [mm/T].

  • QT() Total model outflow [m³/s].

class hydpy.models.hland_v4.InputSequences(master: Sequences, cls_fastaccess: Type[TypeFastAccess_co] | None = None, cymodel: CyModelProtocol | None = None)

Bases: InputSequences

Input sequences of model hland_v4.

The following classes are selected:

  • P() Precipitation [mm].

  • T() Temperature [°C].

  • TN() Normal temperature [°C].

  • EPN() Normal potential evaporation [mm].

class hydpy.models.hland_v4.OutletSequences(master: Sequences, cls_fastaccess: Type[TypeFastAccess_co] | None = None, cymodel: CyModelProtocol | None = None)

Bases: OutletSequences

Outlet sequences of model hland_v4.

The following classes are selected:

  • Q() Runoff [m³/s].

class hydpy.models.hland_v4.StateSequences(master: Sequences, cls_fastaccess: Type[TypeFastAccess_co] | None = None, cymodel: CyModelProtocol | None = None)

Bases: StateSequences

State sequences of model hland_v4.

The following classes are selected:

  • Ic() Interception storage [mm].

  • SP() Frozen water stored in the snow layer [mm].

  • WC() Liquid water content of the snow layer [mm].

  • SM() Soil moisture [mm].

  • BW1() Water stored in the surface flow reservoir [mm].

  • BW2() Water stored in the interflow reservoir [mm].

  • LZ() Storage in the lower zone layer [mm].

  • SC() Storage cascade for runoff concentration [mm].