HydPy-W (base model)¶
Base model wland
is the core of the HydPy implementation of all WALRUS type
models (Brauer et al., 2014), focussing on the interaction between surface water
and near-surface groundwater.
Method Features¶
- class hydpy.models.wland.wland_model.Model[source]¶
Bases:
ELSModel
HydPy-W (base model)
- The following “receiver update methods” are called in the given sequence before performing a simulation step:
Pick_HS_V1
Take the surface water level from a submodel that complies with theWaterLevelModel_V1
interface, if available.
- The following “inlet update methods” are called in the given sequence at the beginning of each simulation step:
Calc_PE_PET_V1
Let a submodel that complies with thePETModel_V1
orPETModel_V2
interface calculate the potential evapotranspiration of the land areas and the potential evaporation of the surface water storage.Calc_FR_V1
Determine the fraction between rainfall and total precipitation.Calc_PM_V1
Calculate the potential snowmelt of the land areas.
- The following methods define the relevant components of a system of ODE equations (e.g. direct runoff):
Calc_FXS_V1
Query the current surface water supply/extraction.Calc_FXG_V1
Query the current seepage/extraction.Calc_PC_V1
Calculate the corrected precipitation.Calc_TF_V1
Calculate the total amount of throughfall of the land areas.Calc_EI_V1
Calculate the interception evaporation of the land areas.Calc_SF_V1
Calculate the frozen amount of throughfall (snowfall) of the land areas.Calc_RF_V1
Calculate the liquid amount of throughfall (rainfall) of the land areas.Calc_AM_V1
Calculate the actual snowmelt of the land areas.Calc_PS_V1
Calculate the precipitation entering the surface water reservoir.Calc_WE_W_V1
Calculate the wetness index for the elevated and the lowland regions.Calc_PVE_PV_V1
Calculate the rainfall (and snowmelt) entering the vadose zone in the elevated and lowland regions.Calc_PQ_V1
Calculate the rainfall (and snowmelt) entering the quickflow reservoir.Calc_BetaE_Beta_V1
Calculate the evapotranspiration reduction factor for the elevated and lowland regions.Calc_ETVE_ETV_V1
Calculate the actual evapotranspiration from the elevated and lowland regions’ vadose zone.Calc_ES_V1
Calculate the actual evaporation from the surface water reservoir.Calc_FQS_V1
Calculate the quickflow.Calc_FGSE_V1
Calculate the groundwater flow between the elevated and the lowland regions.Calc_FGS_V1
Calculate the groundwater drainage or surface water infiltration.Calc_RH_V1
Let a submodel that complies with theDischargeModel_V2
interface calculate the runoff height or, if no such submodel is available, equate it with all other flows in and out of the surface water storage.Calc_DVEq_V1
Calculate the equilibrium storage deficit of the vadose zone.Calc_DVEq_V2
Calculate the equilibrium storage deficit of the vadose zone.Calc_DVEq_V3
Calculate the equilibrium storage deficit of the vadose zone.Calc_DVEq_V4
Calculate the equilibrium storage deficit of the vadose zone.Calc_DGEq_V1
Calculate the equilibrium groundwater depth.Calc_GF_V1
Calculate the gain factor for changes in groundwater depth.Calc_GR_V1
Calculate the elevated region’s groundwater recharge.Calc_CDG_V1
Calculate the change in the groundwater depth due to percolation and capillary rise.Calc_CDG_V2
Calculate the vadose zone’s storage deficit change due to percolation, capillary rise, macropore infiltration, seepage, groundwater flow, and channel water infiltration.
- The following methods define the complete equations of an ODE system (e.g. change in storage of fast water due to effective precipitation and direct runoff):
Update_IC_V1
Update the interception storage.Update_SP_V1
Update the storage deficit.Update_DVE_V1
Update the elevated region’s storage deficit of the vadose zone.Update_DV_V1
Update the lowland region’s storage deficit of the vadose zone.Update_HGE_V1
Update the elevated region’s groundwater level.Update_DG_V1
Update the lowland region’s groundwater depth.Update_HQ_V1
Update the level of the quickflow reservoir.Update_HS_V1
Update the surface water level.
- The following “outlet update methods” are called in the given sequence at the end of each simulation step:
Calc_ET_V1
Calculate the total actual evapotranspiration.Calc_R_V1
Calculate the runoff in m³/s.Pass_R_V1
Update the outlet link sequence.
- The following interface methods are available to main models using the defined model as a submodel:
Get_Temperature_V1
Get the current subbasin-wide air temperature value (that applies to all hydrological response units so that the given index does not matter).Get_MeanTemperature_V1
Get the current subbasin-wide air temperature value.Get_Precipitation_V1
Get the current subbasin-wide precipitation value (that applies to all hydrological response units so that the given index does not matter).Get_SnowCover_V1
Get the selected response unit’s current snow cover degree.
- 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_PE_PET_PETModel_V1
Let a submodel that complies with thePETModel_V1
interface calculate the potential evapotranspiration of the land areas and the potential evaporation of the surface water storage.Calc_PE_PET_PETModel_V2
Let a submodel that complies with thePETModel_V2
interface calculate the potential interception evaporation and potential vadose zone evapotranspiration of the land areas and the potential evaporation of the surface water storage.Return_ErrorDV_V1
Calculate the difference between the equilibrium and the actual storage deficit of the vadose zone.Return_DVH_V1
Return the storage deficit of the vadose zone at a specific height above the groundwater table.Return_DVH_V2
Return the storage deficit of the vadose zone at a specific height above the groundwater table.
- Users can hook submodels into the defined main model if they satisfy one of the following interfaces:
PETModel_V1
Simple interface for calculating all potential evapotranspiration values in one step.PETModel_V2
Interface for calculating separate potential interception, soil, and water evapotranspiration values.DischargeModel_V2
Simple interface for calculating discharge in mm/T based on the current water depth.WaterLevelModel_V1
Pure getter interface for querying the current water level.
- The following “submodels” might be called by one or more of the implemented methods or are meant to be directly called by the user:
PegasusDGEq
Pegasus iterator for finding the equilibrium groundwater depth.QuadDVEq_V1
Adaptive quadrature method for integrating the equilibrium storage deficit of the vadose zone.QuadDVEq_V2
Adaptive quadrature method for integrating the equilibrium storage deficit of the vadose zone.
- petmodel¶
Required submodel that complies with one of the following interfaces: PETModel_V1 or PETModel_V2.
- petmodel_is_mainmodel¶
- petmodel_typeid¶
- dischargemodel¶
Required submodel that complies with the following interface: DischargeModel_V2.
- dischargemodel_is_mainmodel¶
- dischargemodel_typeid¶
- waterlevelmodel¶
Required submodel that complies with the following interface: WaterLevelModel_V1.
- waterlevelmodel_is_mainmodel¶
- waterlevelmodel_typeid¶
- REUSABLE_METHODS: ClassVar[tuple[type[ReusableMethod], ...]] = ()¶
- class hydpy.models.wland.wland_model.Pick_HS_V1[source]¶
Bases:
Method
Take the surface water level from a submodel that complies with the
WaterLevelModel_V1
interface, if available.- Requires the control parameter:
- Updates the state sequence:
- Calculates the factor sequence:
- Basic equation:
\(HS = 1000 \cdot (WaterLevel - BL)\)
Examples:
>>> from hydpy.models.wland_wag import * >>> parameterstep()
Without an available submodel,
Pick_HS_V1
does not change the current value of sequenceHS
and setsDHS
(the change ofHS
) accordingly to zero:>>> states.hs(3000.0) >>> model.pick_hs_v1() >>> states.hs hs(3000.0) >>> factors.dhs dhs(0.0)
We take
exch_waterlevel
as an example to demonstrate thatPick_HS_V1
correctly uses submodels that follow theWaterLevelModel_V1
interface and the defined channel bottom level (BL
) for updatingHS
and logs such changes via sequenceDHS
:>>> bl(3.0) >>> with model.add_waterlevelmodel_v1("exch_waterlevel"): ... pass >>> from hydpy import Element, Node >>> wl = Node("wl", variable="WaterLevel") >>> wl.sequences.sim = 5.0 >>> e = Element("e", receivers=wl, outlets="q") >>> e.model = model >>> model.pick_hs_v1() >>> states.hs hs(2000.0) >>> factors.dhs dhs(-1000.0)
- class hydpy.models.wland.wland_model.Calc_FXS_V1[source]¶
Bases:
Method
Query the current surface water supply/extraction.
- Requires the control parameter:
- Requires the derived parameter:
- Requires the input sequence:
- Calculates the flux sequence:
- Basic equation:
- \[\begin{split}FXS_{fluxes} = \begin{cases} 0 &|\ FXS_{inputs} = 0 \\ \frac{FXS_{inputs}}{ASR} &|\ FXS_{inputs} \neq 0 \land NU > 1 \\ inf &|\ FXS_{inputs} \neq 0 \land NU = 1 \end{cases}\end{split}\]
Examples:
>>> from hydpy.models.wland import * >>> parameterstep() >>> nu(2) >>> derived.asr(0.5) >>> inputs.fxs = 2.0 >>> model.calc_fxs_v1() >>> fluxes.fxs fxs(4.0) >>> nu(1) >>> derived.asr(0.0) >>> model.calc_fxs_v1() >>> fluxes.fxs fxs(inf) >>> inputs.fxs = 0.0 >>> model.calc_fxs_v1() >>> fluxes.fxs fxs(0.0)
- class hydpy.models.wland.wland_model.Calc_FXG_V1[source]¶
Bases:
Method
Query the current seepage/extraction.
- Requires the derived parameter:
- Requires the input sequence:
- Calculates the flux sequence:
- Basic equation:
- \[\begin{split}FXG_{fluxes} = \begin{cases} 0 &|\ FXG_{inputs} = 0 \\ \frac{FXG_{inputs}}{AGR} &|\ FXG_{inputs} \neq 0 \land AGR > 0 \\ inf &|\ FXG_{inputs} \neq 0 \land AGR = 0 \end{cases}\end{split}\]
Examples:
>>> from hydpy.models.wland import * >>> parameterstep() >>> derived.agr(0.4) >>> inputs.fxg = 2.0 >>> model.calc_fxg_v1() >>> fluxes.fxg fxg(5.0) >>> derived.agr(0.0) >>> model.calc_fxg_v1() >>> fluxes.fxg fxg(inf) >>> inputs.fxg = 0.0 >>> model.calc_fxg_v1() >>> fluxes.fxg fxg(0.0)
- class hydpy.models.wland.wland_model.Calc_PC_V1[source]¶
Bases:
Method
Calculate the corrected precipitation.
- Requires the control parameter:
- Requires the input sequence:
- Calculates the flux sequence:
- Basic equation:
\(PC = CP \cdot P\)
Examples:
>>> from hydpy.models.wland import * >>> parameterstep() >>> cp(1.2) >>> inputs.p = 2.0 >>> model.calc_pc_v1() >>> fluxes.pc pc(2.4)
- class hydpy.models.wland.wland_model.Calc_PE_PET_PETModel_V1[source]¶
Bases:
Method
Let a submodel that complies with the
PETModel_V1
interface calculate the potential evapotranspiration of the land areas and the potential evaporation of the surface water storage.- Required by the method:
- Requires the derived parameter:
- Calculates the flux sequences:
Example:
We use
evap_ret_tw2002
as an example:>>> from hydpy.models.wland_wag import * >>> parameterstep() >>> nu(4) >>> at(1.0) >>> aur(0.25, 0.15, 0.1, 0.5) >>> lt(FIELD, FIELD, FIELD, WATER) >>> derived.nul.update() >>> from hydpy import prepare_model >>> with model.add_petmodel_v1("evap_ret_tw2002"): ... hrualtitude(200.0, 600.0, 1000.0, 100.0) ... coastfactor(0.6) ... evapotranspirationfactor(1.1) ... with model.add_radiationmodel_v2("meteo_glob_io"): ... inputs.globalradiation = 200.0 ... with model.add_tempmodel_v2("meteo_temp_io"): ... temperatureaddend(1.0) ... inputs.temperature = 14.0 >>> model.calc_pe_pet_v1() >>> fluxes.pe pe(3.07171, 2.86215, 2.86215, 3.128984) >>> fluxes.pet pet(3.07171, 2.86215, 2.86215, 0.0)
- class hydpy.models.wland.wland_model.Calc_PE_PET_PETModel_V2[source]¶
Bases:
Method
Let a submodel that complies with the
PETModel_V2
interface calculate the potential interception evaporation and potential vadose zone evapotranspiration of the land areas and the potential evaporation of the surface water storage.- Required by the method:
- Requires the derived parameter:
- Calculates the flux sequences:
Examples:
We use
evap_pet_ambav1
as an example. All data stems from the integration tests vegetation, snow, and water area:>>> from hydpy import pub >>> pub.timegrids = "2000-08-01", "2000-08-02", "1d" >>> from hydpy.models.wland_wag import * >>> parameterstep("1h") >>> nu(3) >>> at(1.0) >>> aur(0.5, 0.3, 0.2) >>> lt(FIELD, DECIDIOUS, WATER) >>> lai(5.0) >>> derived.nul.update() >>> inputs.t = 15.0 >>> inputs.p = 0.0 >>> states.sp = 0.0, 1.0, 0.0 >>> from hydpy import prepare_model >>> with model.add_petmodel_v2("evap_pet_ambav1") as ambav: ... measuringheightwindspeed(10.0) ... leafalbedo(0.2) ... leafalbedosnow(0.8) ... groundalbedo(0.2) ... groundalbedosnow(0.8) ... cropheight.field = 10.0 ... cropheight.decidious = 10.0 ... cropheight.water = 0.0 ... leafresistance(40.0) ... wetsoilresistance(100.0) ... soilresistanceincrease(1.0) ... wetnessthreshold(0.5) ... cloudtypefactor(0.2) ... nightcloudfactor(1.0) ... inputs.windspeed = 2.0 ... inputs.relativehumidity = 80.0 ... inputs.atmosphericpressure = 1000.0 ... states.soilresistance = 100.0 ... with model.add_radiationmodel_v4("meteo_psun_sun_glob_io"): ... inputs.sunshineduration = 6.0 ... inputs.possiblesunshineduration = 16.0 ... inputs.globalradiation = 190.0
The first example reproduces the results for the first simulated day of the integration tests vegetation (first response unit) and water area (third response unit):
>>> ambav.sequences.logs.loggedprecipitation = [0.0] >>> ambav.sequences.logs.loggedpotentialsoilevapotranspiration = [1.0] >>> model.calc_pe_pet_v1() >>> fluxes.pe pe(3.301949, 1.146759, 1.890672) >>> fluxes.pet pet(2.292368, 0.796134, 0.0)
The second example reproduces the results for the third simulated day of the snow integration test (second response unit):
>>> ambav.sequences.logs.loggedprecipitation = [10.0] >>> ambav.sequences.logs.loggedpotentialsoilevapotranspiration = [2.282495] >>> model.calc_pe_pet_v1() >>> fluxes.pe pe(3.301949, 1.146759, 1.890672) >>> fluxes.pet pet(2.324763, 0.807385, 0.0)
- class hydpy.models.wland.wland_model.Calc_PE_PET_V1[source]¶
Bases:
Method
Let a submodel that complies with the
PETModel_V1
orPETModel_V2
interface calculate the potential evapotranspiration of the land areas and the potential evaporation of the surface water storage.- Required submethods:
- Requires the derived parameter:
- Calculates the flux sequences:
- class hydpy.models.wland.wland_model.Calc_TF_V1[source]¶
Bases:
Method
Calculate the total amount of throughfall of the land areas.
- Requires the control parameters:
- Requires the derived parameters:
- Requires the flux sequence:
- Requires the state sequence:
- Calculates the flux sequence:
- Basic equation (discontinuous):
- \[\begin{split}TF = \begin{cases} P &|\ IC > IT \\ 0 &|\ IC < IT \end{cases}\end{split}\]
Examples:
>>> from hydpy import pub, UnitTest >>> pub.timegrids = '2000-03-30', '2000-04-03', '1d' >>> from hydpy.models.wland import * >>> parameterstep() >>> nu(2) >>> lt(FIELD, WATER) >>> ih(0.2) >>> lai.field_mar = 5.0 >>> lai.field_apr = 10.0 >>> derived.moy.update() >>> derived.nul.update() >>> fluxes.pc = 5.0 >>> test = UnitTest( ... model=model, ... method=model.calc_tf_v1, ... last_example=6, ... parseqs=(states.ic, fluxes.tf), ... ) >>> test.nexts.ic = -4.0, 0.0, 1.0, 2.0, 3.0, 7.0
Without smoothing:
>>> sh(0.0) >>> derived.rh1.update() >>> model.idx_sim = pub.timegrids.init['2000-03-31'] >>> test() | ex. | ic | tf | ------------------------------- | 1 | -4.0 -4.0 | 0.0 0.0 | | 2 | 0.0 0.0 | 0.0 0.0 | | 3 | 1.0 1.0 | 2.5 0.0 | | 4 | 2.0 2.0 | 5.0 0.0 | | 5 | 3.0 3.0 | 5.0 0.0 | | 6 | 7.0 7.0 | 5.0 0.0 |
With smoothing:
>>> sh(1.0) >>> derived.rh1.update() >>> model.idx_sim = pub.timegrids.init['2000-04-01'] >>> test() | ex. | ic | tf | ----------------------------------- | 1 | -4.0 -4.0 | 0.0 0.0 | | 2 | 0.0 0.0 | 0.00051 0.0 | | 3 | 1.0 1.0 | 0.05 0.0 | | 4 | 2.0 2.0 | 2.5 0.0 | | 5 | 3.0 3.0 | 4.95 0.0 | | 6 | 7.0 7.0 | 5.0 0.0 |
- class hydpy.models.wland.wland_model.Calc_EI_V1[source]¶
Bases:
Method
Calculate the interception evaporation of the land areas.
- Requires the derived parameters:
- Requires the flux sequence:
- Requires the state sequence:
- Calculates the flux sequence:
- Basic equation (discontinuous):
- \[\begin{split}EI = \begin{cases} PE &|\ IC > 0 \\ 0 &|\ IC < 0 \end{cases}\end{split}\]
Examples:
>>> from hydpy.models.wland import * >>> parameterstep() >>> nu(2) >>> derived.nul.update() >>> fluxes.pe = 5.0 >>> from hydpy import UnitTest >>> test = UnitTest( ... model=model, ... method=model.calc_ei_v1, ... last_example=9, ... parseqs=(states.ic, fluxes.ei) ... ) >>> test.nexts.ic = -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0
Without smoothing:
>>> sh(0.0) >>> derived.rh1.update() >>> test() | ex. | ic | ei | ------------------------------- | 1 | -4.0 -4.0 | 0.0 0.0 | | 2 | -3.0 -3.0 | 0.0 0.0 | | 3 | -2.0 -2.0 | 0.0 0.0 | | 4 | -1.0 -1.0 | 0.0 0.0 | | 5 | 0.0 0.0 | 2.5 0.0 | | 6 | 1.0 1.0 | 5.0 0.0 | | 7 | 2.0 2.0 | 5.0 0.0 | | 8 | 3.0 3.0 | 5.0 0.0 | | 9 | 4.0 4.0 | 5.0 0.0 |
With smoothing:
>>> sh(1.0) >>> derived.rh1.update() >>> test() | ex. | ic | ei | ------------------------------------ | 1 | -4.0 -4.0 | 0.0 0.0 | | 2 | -3.0 -3.0 | 0.000005 0.0 | | 3 | -2.0 -2.0 | 0.00051 0.0 | | 4 | -1.0 -1.0 | 0.05 0.0 | | 5 | 0.0 0.0 | 2.5 0.0 | | 6 | 1.0 1.0 | 4.95 0.0 | | 7 | 2.0 2.0 | 4.99949 0.0 | | 8 | 3.0 3.0 | 4.999995 0.0 | | 9 | 4.0 4.0 | 5.0 0.0 |
- class hydpy.models.wland.wland_model.Calc_FR_V1[source]¶
Bases:
Method
Determine the fraction between rainfall and total precipitation.
- Requires the control parameters:
- Requires the input sequence:
- Calculates the aide sequence:
- Basic equation:
\(FR = \frac{T- \left( TT - TI / 2 \right)}{TI}\)
- Restriction:
\(0 \leq FR \leq 1\)
Examples:
>>> from hydpy.models.wland import * >>> parameterstep() >>> tt(1.0) >>> ti(4.0) >>> from hydpy import UnitTest >>> test = UnitTest( ... model=model, ... method=model.calc_fr_v1, ... last_example=9, ... parseqs=(inputs.t, aides.fr) ... ) >>> test.nexts.t = -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0 >>> test() | ex. | t | fr | --------------------- | 1 | -3.0 | 0.0 | | 2 | -2.0 | 0.0 | | 3 | -1.0 | 0.0 | | 4 | 0.0 | 0.25 | | 5 | 1.0 | 0.5 | | 6 | 2.0 | 0.75 | | 7 | 3.0 | 1.0 | | 8 | 4.0 | 1.0 | | 9 | 5.0 | 1.0 |
- class hydpy.models.wland.wland_model.Calc_RF_V1[source]¶
Bases:
Method
Calculate the liquid amount of throughfall (rainfall) of the land areas.
- Requires the derived parameter:
- Requires the flux sequence:
- Requires the aide sequence:
- Calculates the flux sequence:
- Basic equation:
\(RF = FR \cdot TF\)
Example:
>>> from hydpy.models.wland import * >>> parameterstep() >>> nu(2) >>> derived.nul.update() >>> fluxes.tf = 2.0 >>> aides.fr = 0.8 >>> model.calc_rf_v1() >>> fluxes.rf rf(1.6, 0.0)
- class hydpy.models.wland.wland_model.Calc_SF_V1[source]¶
Bases:
Method
Calculate the frozen amount of throughfall (snowfall) of the land areas.
- Requires the derived parameter:
- Requires the flux sequence:
- Requires the aide sequence:
- Calculates the flux sequence:
- Basic equation:
\(SF = (1-FR) \cdot TF\)
Example:
>>> from hydpy.models.wland import * >>> parameterstep() >>> nu(2) >>> derived.nul.update() >>> fluxes.tf = 2.0 >>> aides.fr = 0.8 >>> model.calc_sf_v1() >>> fluxes.sf sf(0.4, 0.0)
- class hydpy.models.wland.wland_model.Calc_PM_V1[source]¶
Bases:
Method
Calculate the potential snowmelt of the land areas.
- Requires the control parameters:
- Requires the derived parameters:
- Requires the input sequence:
- Calculates the flux sequence:
- Basic equation (discontinous):
\(PM = max \left( DDF \cdot (T - DDT), 0 \right)\)
Examples:
>>> from hydpy.models.wland import * >>> simulationstep("12h") >>> parameterstep("1d") >>> nu(2) >>> derived.nul.update() >>> ddf(4.0) >>> ddt(1.0) >>> from hydpy import UnitTest >>> test = UnitTest( ... model=model, ... method=model.calc_pm_v1, ... last_example=11, ... parseqs=(inputs.t, fluxes.pm) ... ) >>> test.nexts.t = -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0
Without smoothing:
>>> st(0.0) >>> derived.rt2.update() >>> test() | ex. | t | pm | -------------------------- | 1 | -4.0 | 0.0 0.0 | | 2 | -3.0 | 0.0 0.0 | | 3 | -2.0 | 0.0 0.0 | | 4 | -1.0 | 0.0 0.0 | | 5 | 0.0 | 0.0 0.0 | | 6 | 1.0 | 0.0 0.0 | | 7 | 2.0 | 2.0 0.0 | | 8 | 3.0 | 4.0 0.0 | | 9 | 4.0 | 6.0 0.0 | | 10 | 5.0 | 8.0 0.0 | | 11 | 6.0 | 10.0 0.0 |
With smoothing:
>>> st(1.0) >>> derived.rt2.update() >>> test() | ex. | t | pm | ------------------------------ | 1 | -4.0 | 0.0 0.0 | | 2 | -3.0 | 0.000001 0.0 | | 3 | -2.0 | 0.000024 0.0 | | 4 | -1.0 | 0.000697 0.0 | | 5 | 0.0 | 0.02 0.0 | | 6 | 1.0 | 0.411048 0.0 | | 7 | 2.0 | 2.02 0.0 | | 8 | 3.0 | 4.000697 0.0 | | 9 | 4.0 | 6.000024 0.0 | | 10 | 5.0 | 8.000001 0.0 | | 11 | 6.0 | 10.0 0.0 |
- class hydpy.models.wland.wland_model.Calc_AM_V1[source]¶
Bases:
Method
Calculate the actual snowmelt of the land areas.
- Requires the derived parameters:
- Requires the flux sequence:
- Requires the state sequence:
- Calculates the flux sequence:
- Basic equation (discontinous):
- \[\begin{split}AM = \begin{cases} PM &|\ SP > 0 \\ 0 &|\ SP < 0 \end{cases}\end{split}\]
Examples:
>>> from hydpy.models.wland import * >>> parameterstep() >>> nu(2) >>> derived.nul.update() >>> fluxes.pm = 2.0 >>> from hydpy import UnitTest >>> test = UnitTest( ... model=model, ... method=model.calc_am_v1, ... last_example=9, ... parseqs=(states.sp, fluxes.am) ... ) >>> test.nexts.sp = -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0
Without smoothing:
>>> sh(0.0) >>> derived.rh1.update() >>> test() | ex. | sp | am | ------------------------------- | 1 | -4.0 -4.0 | 0.0 0.0 | | 2 | -3.0 -3.0 | 0.0 0.0 | | 3 | -2.0 -2.0 | 0.0 0.0 | | 4 | -1.0 -1.0 | 0.0 0.0 | | 5 | 0.0 0.0 | 1.0 0.0 | | 6 | 1.0 1.0 | 2.0 0.0 | | 7 | 2.0 2.0 | 2.0 0.0 | | 8 | 3.0 3.0 | 2.0 0.0 | | 9 | 4.0 4.0 | 2.0 0.0 |
With smoothing:
>>> sh(1.0) >>> derived.rh1.update() >>> test() | ex. | sp | am | ------------------------------------ | 1 | -4.0 -4.0 | 0.0 0.0 | | 2 | -3.0 -3.0 | 0.000002 0.0 | | 3 | -2.0 -2.0 | 0.000204 0.0 | | 4 | -1.0 -1.0 | 0.02 0.0 | | 5 | 0.0 0.0 | 1.0 0.0 | | 6 | 1.0 1.0 | 1.98 0.0 | | 7 | 2.0 2.0 | 1.999796 0.0 | | 8 | 3.0 3.0 | 1.999998 0.0 | | 9 | 4.0 4.0 | 2.0 0.0 |
- class hydpy.models.wland.wland_model.Calc_PS_V1[source]¶
Bases:
Method
Calculate the precipitation entering the surface water reservoir.
Example:
>>> from hydpy.models.wland import * >>> parameterstep() >>> fluxes.pc = 3.0 >>> model.calc_ps_v1() >>> fluxes.ps ps(3.0)
- class hydpy.models.wland.wland_model.Calc_WE_W_V1[source]¶
Bases:
Method
Calculate the wetness index for the elevated and the lowland regions.
- Requires the control parameters:
- Requires the derived parameters:
- Requires the fixed parameter:
- Requires the state sequences:
- Calculates the aide sequences:
- Basic equation for the lowland region (the elevated region is handled analogous):
\(W = cos \left( \frac{min \big( max(DV, \, 0), \, CW \big) \cdot Pi}{CW} \right) \cdot \frac{1}{2} + \frac{1}{2}\)
Examples:
>>> from hydpy.models.wland import * >>> parameterstep() >>> cwe(200.0) >>> cw(400.0)
>>> derived.nuge(1) >>> derived.nug(1) >>> from hydpy import UnitTest >>> test = UnitTest( ... model=model, ... method=model.calc_we_w_v1, ... last_example=11, ... parseqs=(states.dve, states.dv, aides.we, aides.w) ... ) >>> test.nexts.dve = ( ... -50.0, -5.0, 0.0, 5.0, 50.0, 100.0, 150.0, 195.0, 200.0, 205.0, 250.0) >>> test.nexts.dv = tuple(dv + 100.0 for dv in test.nexts.dve) >>> test() | ex. | dve | dv | we | w | --------------------------------------------- | 1 | -50.0 | 50.0 | 1.0 | 0.96194 | | 2 | -5.0 | 95.0 | 1.0 | 0.867161 | | 3 | 0.0 | 100.0 | 1.0 | 0.853553 | | 4 | 5.0 | 105.0 | 0.998459 | 0.8394 | | 5 | 50.0 | 150.0 | 0.853553 | 0.691342 | | 6 | 100.0 | 200.0 | 0.5 | 0.5 | | 7 | 150.0 | 250.0 | 0.146447 | 0.308658 | | 8 | 195.0 | 295.0 | 0.001541 | 0.1606 | | 9 | 200.0 | 300.0 | 0.0 | 0.146447 | | 10 | 205.0 | 305.0 | 0.0 | 0.132839 | | 11 | 250.0 | 350.0 | 0.0 | 0.03806 |
>>> derived.nuge(0) >>> derived.nug(0) >>> model.calc_we_w_v1() >>> aides.we we(nan) >>> aides.w w(nan)
- class hydpy.models.wland.wland_model.Calc_PVE_PV_V1[source]¶
Bases:
Method
Calculate the rainfall (and snowmelt) entering the vadose zone in the elevated and lowland regions.
- Requires the control parameters:
- Requires the derived parameters:
- Requires the flux sequences:
- Requires the aide sequences:
- Calculates the flux sequences:
- Basic equation for the lowland region (the elevated region is handled analogous):
- \[\begin{split}PV = \sum_{i=1}^{NUL} \left ( \frac{AUR_i}{AGR} \cdot (RF_i + AM_i) \cdot \begin{cases} 0 &|\ LT_i = SEALED \\ 1-W &|\ LT_i \neq SEALED \end{cases} \right )\end{split}\]
Example:
>>> from hydpy.models.wland import * >>> parameterstep() >>> nu(4) >>> derived.nul.update() >>> lt(FIELD, SOIL, SEALED, WATER) >>> aur(0.35, 0.1, 0.05, 0.5) >>> fluxes.rf = 3.0, 2.0, 1.0, 0.0 >>> fluxes.am = 1.0, 2.0, 3.0, 0.0 >>> aides.we = 0.75 >>> aides.w = 0.25
>>> er(True) >>> derived.agre.update() >>> derived.agr.update() >>> model.calc_pve_pv_v1() >>> fluxes.pve pve(1.0) >>> fluxes.pv pv(0.0)
>>> er(False) >>> derived.agre.update() >>> derived.agr.update() >>> model.calc_pve_pv_v1() >>> fluxes.pve pve(0.0) >>> fluxes.pv pv(3.0)
- class hydpy.models.wland.wland_model.Calc_PQ_V1[source]¶
Bases:
Method
Calculate the rainfall (and snowmelt) entering the quickflow reservoir.
- Requires the control parameters:
- Requires the derived parameters:
- Requires the flux sequences:
- Requires the aide sequences:
- Calculates the flux sequence:
- Basic equation:
- \[\begin{split}PQ = \sum_{i=1}^{NUL} \frac{AUR_i}{ALR} \cdot (RF_i + AM_i) \cdot \begin{cases} 1 &|\ LT_i = SEALED \\ WE &|\ LT_i \neq SEALED \ \land \ ER_i \\ W &|\ LT_i \neq SEALED \ \land \ \overline{ER_i} \end{cases}\end{split}\]
Example:
>>> from hydpy.models.wland import * >>> parameterstep() >>> nu(4) >>> lt(FIELD, SOIL, SEALED, WATER) >>> aur(0.3, 0.15, 0.05, 0.5) >>> derived.nul.update() >>> derived.alr.update() >>> fluxes.rf = 3.0, 2.0, 1.0, 0.0 >>> fluxes.am = 1.0, 2.0, 2.0, 0.0 >>> aides.w = 0.75 >>> model.calc_pq_v1() >>> fluxes.pq pq(3.0)
- class hydpy.models.wland.wland_model.Calc_BetaE_Beta_V1[source]¶
Bases:
Method
Calculate the evapotranspiration reduction factor for the elevated and lowland regions.
- Requires the control parameters:
- Requires the derived parameters:
- Requires the state sequences:
- Calculates the aide sequences:
- Basic equation for the lowland region (the elevated region is handled analogous):
\(Beta = \frac{1 - x}{1 + x} \cdot \frac{1}{2} + \frac{1}{2}\)
\(x = exp \left( Zeta1 \cdot (DV - Zeta2) \right)\)
Examples:
>>> from hydpy.models.wland import * >>> parameterstep() >>> zeta1(0.02) >>> zeta2(400.0)
>>> derived.nuge(1) >>> derived.nug(1) >>> from hydpy import UnitTest >>> test = UnitTest( ... model=model, ... method=model.calc_betae_beta_v1, ... last_example=12, ... parseqs=(states.dve, states.dv, aides.betae, aides.beta) ... ) >>> test.nexts.dve = ( ... -100.0, 0.0, 100.0, 200.0, 300.0, 400.0, ... 500.0, 600.0, 700.0, 800.0, 900.0, 100000.0 ... ) >>> test.nexts.dv = tuple(reversed(test.nexts.dve)) >>> test() | ex. | dve | dv | betae | beta | --------------------------------------------------- | 1 | -100.0 | 100000.0 | 0.999955 | 0.0 | | 2 | 0.0 | 900.0 | 0.999665 | 0.000045 | | 3 | 100.0 | 800.0 | 0.997527 | 0.000335 | | 4 | 200.0 | 700.0 | 0.982014 | 0.002473 | | 5 | 300.0 | 600.0 | 0.880797 | 0.017986 | | 6 | 400.0 | 500.0 | 0.5 | 0.119203 | | 7 | 500.0 | 400.0 | 0.119203 | 0.5 | | 8 | 600.0 | 300.0 | 0.017986 | 0.880797 | | 9 | 700.0 | 200.0 | 0.002473 | 0.982014 | | 10 | 800.0 | 100.0 | 0.000335 | 0.997527 | | 11 | 900.0 | 0.0 | 0.000045 | 0.999665 | | 12 | 100000.0 | -100.0 | 0.0 | 0.999955 |
>>> derived.nuge(0) >>> derived.nug(0) >>> model.calc_betae_beta_v1() >>> aides.betae betae(nan) >>> aides.beta beta(nan)
- class hydpy.models.wland.wland_model.Calc_ETVE_ETV_V1[source]¶
Bases:
Method
Calculate the actual evapotranspiration from the elevated and lowland regions’ vadose zone.
- Requires the control parameters:
- Requires the derived parameters:
- Requires the flux sequences:
- Requires the aide sequences:
- Calculates the flux sequences:
The following equation uses the Wigmosta et al. (1994) approach to extend the original WALRUS equation to cope with different potential values for
PE
andPET
. (See the documentation on methodUpdate_SoilEvapotranspiration_V3
, which covers the corner cases of this approach in more detail.)- Basic equation for the lowland region (the elevated region is handled analogous):
- \[\begin{split}ETV = \sum_{i=1}^{NUL} \frac{AUR_i}{AGR} \cdot \frac{PE_i - EI_i}{PE_i} \cdot PET_i \cdot \begin{cases} 0 &|\ LT_i = SEALED \\ Beta &|\ LT_i \neq SEALED \end{cases}\end{split}\]
Example:
>>> from hydpy.models.wland import * >>> parameterstep() >>> nu(4) >>> lt(FIELD, SOIL, SEALED, WATER) >>> aur(0.2, 0.2, 0.1, 0.5) >>> fluxes.pe = 5.0 >>> fluxes.pet = 4.0 >>> fluxes.ei = 1.0, 3.0, 2.0, 0.0 >>> aides.betae = 0.75 >>> aides.beta = 0.25 >>> derived.nul.update()
>>> er(True) >>> derived.agr.update() >>> derived.agre.update() >>> model.calc_etve_etv_v1() >>> fluxes.etve etve(1.8)
>>> er(False) >>> derived.agr.update() >>> derived.agre.update() >>> model.calc_etve_etv_v1() >>> fluxes.etv etv(0.6)
- class hydpy.models.wland.wland_model.Calc_ES_V1[source]¶
Bases:
Method
Calculate the actual evaporation from the surface water reservoir.
- Requires the derived parameters:
- Requires the flux sequence:
- Requires the state sequence:
- Calculates the flux sequence:
- Basic equation (discontinous):
- \[\begin{split}ES = \begin{cases} PE &|\ HS > 0 \\ 0 &|\ HS \leq 0 \end{cases}\end{split}\]
Examples:
>>> from hydpy.models.wland import * >>> parameterstep() >>> nu(2) >>> derived.nul.update() >>> fluxes.pe = 3.0, 5.0 >>> from hydpy import UnitTest >>> test = UnitTest( ... model=model, ... method=model.calc_es_v1, ... last_example=9, ... parseqs=(states.hs, fluxes.es) ... ) >>> test.nexts.hs = -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0
Without smoothing:
>>> sh(0.0) >>> derived.rh1.update() >>> test() | ex. | hs | es | -------------------- | 1 | -4.0 | 0.0 | | 2 | -3.0 | 0.0 | | 3 | -2.0 | 0.0 | | 4 | -1.0 | 0.0 | | 5 | 0.0 | 2.5 | | 6 | 1.0 | 5.0 | | 7 | 2.0 | 5.0 | | 8 | 3.0 | 5.0 | | 9 | 4.0 | 5.0 |
With smoothing:
>>> sh(1.0) >>> derived.rh1.update() >>> test() | ex. | hs | es | ------------------------- | 1 | -4.0 | 0.0 | | 2 | -3.0 | 0.000005 | | 3 | -2.0 | 0.00051 | | 4 | -1.0 | 0.05 | | 5 | 0.0 | 2.5 | | 6 | 1.0 | 4.95 | | 7 | 2.0 | 4.99949 | | 8 | 3.0 | 4.999995 | | 9 | 4.0 | 5.0 |
- class hydpy.models.wland.wland_model.Calc_ET_V1[source]¶
Bases:
Method
Calculate the total actual evapotranspiration.
- Requires the control parameter:
- Requires the derived parameters:
- Requires the flux sequences:
- Calculates the flux sequence:
- Basic equation:
\(ET = ASR \cdot ES + AGRE \cdot ETV\!E + AGR \cdot ETV + \sum_{i=1}^{NUL} AUR_i \cdot EI_i\)
Example:
>>> from hydpy.models.wland import * >>> parameterstep() >>> nu(3) >>> lt(FIELD, SEALED, WATER) >>> aur(0.5, 0.3, 0.2) >>> er(False) >>> derived.nul.update() >>> derived.asr.update() >>> derived.agr.update() >>> derived.agre.update() >>> fluxes.ei = 1.0, 2.0, 0.0 >>> fluxes.etv = 3.0 >>> fluxes.etve = 5.0 >>> fluxes.es = 4.0 >>> model.calc_et_v1() >>> fluxes.et et(3.4)
- class hydpy.models.wland.wland_model.Calc_DVEq_V1[source]¶
Bases:
Method
Calculate the equilibrium storage deficit of the vadose zone.
- Requires the control parameters:
- Requires the derived parameter:
- Requires the state sequence:
- Calculates the aide sequence:
- Basic equation (discontinuous):
- \[\begin{split}DV\!Eq = \begin{cases} 0 &|\ DG \leq PsiAE \\ ThetaS \cdot \left( DG - \frac{DG^{1-1/b}}{(1-1/b) \cdot PsiAE^{-1/B}} - \frac{PsiAE}{1-B} \right) &|\ PsiAE < DG \end{cases}\end{split}\]
Examples:
>>> from hydpy.models.wland import * >>> parameterstep() >>> thetas(0.4) >>> psiae(300.0) >>> b(5.0) >>> from hydpy import UnitTest >>> test = UnitTest( ... model=model, ... method=model.calc_dveq_v1, ... last_example=6, ... parseqs=(states.dg, aides.dveq) ... ) >>> test.nexts.dg = 200.0, 300.0, 400.0, 800.0, 1600.0, 3200.0
Without smoothing:
>>> test() | ex. | dg | dveq | ----------------------------- | 1 | 200.0 | 0.0 | | 2 | 300.0 | 0.0 | | 3 | 400.0 | 1.182498 | | 4 | 800.0 | 21.249634 | | 5 | 1600.0 | 97.612368 | | 6 | 3200.0 | 313.415248 |
- class hydpy.models.wland.wland_model.Return_DVH_V1[source]¶
Bases:
Method
Return the storage deficit of the vadose zone at a specific height above the groundwater table.
- Required by the method:
- Requires the control parameters:
- Requires the derived parameter:
- Basic equation (discontinous):
- \[\begin{split}DVH = \begin{cases} 0 &|\ DG \leq PsiAE \\ ThetaS \cdot \left(1 - \left( \frac{h}{PsiAE} \right)^{-1/b} \right) &|\ PsiAE < DG \end{cases}\end{split}\]
This power law is the differential of the equation underlying method
Calc_DVEq_V1
with respect to height. Brauer et al. (2014) also cites it (equation 6) but does not use it directly.Examples:
>>> from hydpy.models.wland import * >>> parameterstep() >>> thetas(0.4) >>> psiae(300.0) >>> b(5.0)
With smoothing:
>>> from hydpy import repr_ >>> sh(0.0) >>> derived.rh1.update() >>> for h in [200.0, 299.0, 300.0, 301.0, 400.0, 500.0, 600.0]: ... print(repr_(h), repr_(model.return_dvh_v1(h))) 200.0 0.0 299.0 0.0 300.0 0.0 301.0 0.000266 400.0 0.022365 500.0 0.038848 600.0 0.05178
Without smoothing:
>>> sh(1.0) >>> derived.rh1.update() >>> for h in [200.0, 299.0, 300.0, 301.0, 400.0, 500.0, 600.0]: ... print(repr_(h), repr_(model.return_dvh_v1(h))) 200.0 0.0 299.0 0.000001 300.0 0.00004 301.0 0.000267 400.0 0.022365 500.0 0.038848 600.0 0.05178
- class hydpy.models.wland.wland_model.Calc_DVEq_V2[source]¶
Bases:
Method
Calculate the equilibrium storage deficit of the vadose zone.
- Required submethod:
- Requires the control parameters:
- Requires the derived parameters:
- Requires the state sequence:
- Calculates the aide sequence:
- Basic equation:
\(DHEq = \int_{0}^{DG} Return\_DVH\_V1(h) \ \ dh\)
Method
Calc_DVEq_V2
integratesReturn_DVH_V1
numerically, based on the Lobatto-Gauß quadrature. Hence, it should give nearly identical results as methodCalc_DVEq_V1
, which provides the analytical solution to the underlying power law. The benefit of methodCalc_DVEq_V2
is that it supports the regularisation ofReturn_DVH_V1
, whichCalc_DVEq_V1
does not. In our experience, this benefit does not justify the additional numerical cost. However, we keep it for educational purposes, mainly as a starting point to implement alternative relationships between the soil water deficit and the groundwater table that we cannot solve analytically.Examples:
>>> from hydpy.models.wland import * >>> parameterstep() >>> derived.nug(0) >>> model.calc_dveq_v2() >>> aides.dveq dveq(nan)
>>> derived.nug(1) >>> thetas(0.4) >>> psiae(300.0) >>> b(5.0) >>> from hydpy import UnitTest >>> test = UnitTest( ... model=model, ... method=model.calc_dveq_v2, ... last_example=8, ... parseqs=(states.dg, aides.dveq) ... ) >>> test.nexts.dg = 200.0, 299.0, 300.0, 301.0, 400.0, 800.0, 1600.0, 3200.0
Without smoothing:
>>> sh(0.0) >>> derived.rh1.update() >>> test() | ex. | dg | dveq | ----------------------------- | 1 | 200.0 | 0.0 | | 2 | 299.0 | 0.0 | | 3 | 300.0 | 0.0 | | 4 | 301.0 | 0.000133 | | 5 | 400.0 | 1.182498 | | 6 | 800.0 | 21.249634 | | 7 | 1600.0 | 97.612368 | | 8 | 3200.0 | 313.415248 |
With smoothing:
>>> sh(1.0) >>> derived.rh1.update() >>> test() | ex. | dg | dveq | ----------------------------- | 1 | 200.0 | 0.0 | | 2 | 299.0 | 0.0 | | 3 | 300.0 | 0.000033 | | 4 | 301.0 | 0.000176 | | 5 | 400.0 | 1.182542 | | 6 | 800.0 | 21.24972 | | 7 | 1600.0 | 97.612538 | | 8 | 3200.0 | 313.415588 |
- class hydpy.models.wland.wland_model.Calc_DVEq_V3[source]¶
Bases:
Method
Calculate the equilibrium storage deficit of the vadose zone.
- Required by the method:
- Requires the control parameters:
- Requires the derived parameter:
- Requires the state sequence:
- Calculates the aide sequence:
- Basic equation (discontinuous):
- \[\begin{split}DHEq = ThetaR \cdot DG + \begin{cases} 0 &|\ DG \leq PsiAE \\ ThetaS \cdot \left( DG - \frac{DG^{1-1/b}}{(1-1/b) \cdot PsiAE^{-1/B}} - \frac{PsiAE}{1-B} \right) &|\ PsiAE < DG \end{cases}\end{split}\]
Method
Calc_DVEq_V3
extends the original WALRUS relationship between the groundwater depth and the equilibrium water deficit of the vadose zone defined by equation 5 of Brauer et al. (2014) and implemented into application modelwland
by methodCalc_DVEq_V1
. ParameterThetaR
introduces a (small) amount of water to fill the tension-saturated area directly above the groundwater table. This “residual saturation” allows the direct injection of water into groundwater without risking infinitely fast groundwater depth changes.Examples:
>>> from hydpy.models.wland import * >>> parameterstep() >>> thetas(0.4) >>> thetar(0.01) >>> psiae(300.0) >>> b(5.0) >>> from hydpy import UnitTest >>> test = UnitTest( ... model=model, ... method=model.calc_dveq_v3, ... last_example=8, ... parseqs=(states.dg, aides.dveq) ... ) >>> test.nexts.dg = 200.0, 299.0, 300.0, 301.0, 400.0, 800.0, 1600.0, 3200.0
Without smoothing:
>>> test() | ex. | dg | dveq | ----------------------------- | 1 | 200.0 | 2.0 | | 2 | 299.0 | 2.99 | | 3 | 300.0 | 3.0 | | 4 | 301.0 | 3.01013 | | 5 | 400.0 | 5.152935 | | 6 | 800.0 | 28.718393 | | 7 | 1600.0 | 111.172058 | | 8 | 3200.0 | 337.579867 |
- class hydpy.models.wland.wland_model.Return_DVH_V2[source]¶
Bases:
Method
Return the storage deficit of the vadose zone at a specific height above the groundwater table.
- Required by the methods:
- Requires the control parameters:
- Requires the derived parameter:
- Basic equation (discontinous):
- \[\begin{split}DVH = ThetaR + \begin{cases} 0 &|\ DG \leq PsiAE \\ (ThetaS-ThetaR) \cdot \left(1 - \left( \frac{h}{PsiAE} \right)^{-1/b} \right) &|\ PsiAE < DG \end{cases}\end{split}\]
The given equation is the differential of the equation underlying method
Calc_DVEq_V3
with respect to height.Examples:
>>> from hydpy.models.wland import * >>> parameterstep() >>> thetas(0.4) >>> thetar(0.01) >>> psiae(300.0) >>> b(5.0)
With smoothing:
>>> from hydpy import repr_ >>> sh(0.0) >>> derived.rh1.update() >>> for h in [200.0, 299.0, 300.0, 301.0, 400.0, 500.0, 600.0]: ... print(repr_(h), repr_(model.return_dvh_v2(h))) 200.0 0.01 299.0 0.01 300.0 0.01 301.0 0.010259 400.0 0.031806 500.0 0.047877 600.0 0.060485
Without smoothing:
>>> sh(1.0) >>> derived.rh1.update() >>> for h in [200.0, 299.0, 300.0, 301.0, 400.0, 500.0, 600.0]: ... print(repr_(h), repr_(model.return_dvh_v2(h))) 200.0 0.01 299.0 0.010001 300.0 0.010039 301.0 0.01026 400.0 0.031806 500.0 0.047877 600.0 0.060485
- class hydpy.models.wland.wland_model.Calc_DVEq_V4[source]¶
Bases:
Method
Calculate the equilibrium storage deficit of the vadose zone.
- Required submethod:
- Requires the control parameters:
- Requires the derived parameters:
- Requires the state sequence:
- Calculates the aide sequence:
- Basic equation:
\(DHEq = \int_{0}^{DG} Return\_DVH\_V2(h) \ \ dh\)
Method
Calc_DVEq_V4
integratesReturn_DVH_V2
numerically based on the Lobatto-Gauß quadrature. The short discussion in the documentation onCalc_DVEq_V2
(which integratesReturn_DVH_V1
) also applies toCalc_DVEq_V4
.Examples:
>>> from hydpy.models.wland import * >>> parameterstep() >>> derived.nug(0) >>> model.calc_dveq_v4() >>> aides.dveq dveq(nan)
>>> derived.nug(1) >>> thetas(0.4) >>> thetar(0.01) >>> psiae(300.0) >>> b(5.0) >>> from hydpy import UnitTest >>> test = UnitTest( ... model=model, ... method=model.calc_dveq_v4, ... last_example=8, ... parseqs=(states.dg, aides.dveq) ... ) >>> test.nexts.dg = 200.0, 299.0, 300.0, 301.0, 400.0, 800.0, 1600.0, 3200.0
Without smoothing:
>>> sh(0.0) >>> derived.rh1.update() >>> test() | ex. | dg | dveq | ----------------------------- | 1 | 200.0 | 2.0 | | 2 | 299.0 | 2.99 | | 3 | 300.0 | 3.0 | | 4 | 301.0 | 3.01013 | | 5 | 400.0 | 5.152935 | | 6 | 800.0 | 28.718393 | | 7 | 1600.0 | 111.172058 | | 8 | 3200.0 | 337.579867 |
With smoothing:
>>> sh(1.0) >>> derived.rh1.update() >>> test() | ex. | dg | dveq | ----------------------------- | 1 | 200.0 | 2.1 | | 2 | 299.0 | 3.09 | | 3 | 300.0 | 3.100032 | | 4 | 301.0 | 3.110172 | | 5 | 400.0 | 5.252979 | | 6 | 800.0 | 28.818477 | | 7 | 1600.0 | 111.272224 | | 8 | 3200.0 | 337.680198 |
- class hydpy.models.wland.wland_model.Return_ErrorDV_V1[source]¶
Bases:
Method
Calculate the difference between the equilibrium and the actual storage deficit of the vadose zone.
- Required by the method:
- Required submethod:
- Requires the control parameters:
- Requires the derived parameter:
- Requires the state sequence:
- Basic equation:
\(DV\!Eq_{Calc\_DV\!Eq\_V3} - DV\)
Method
Return_ErrorDV_V1
usesCalc_DVEq_V3
to calculate the equilibrium deficit corresponding to the current groundwater depth. The following example shows that it resets the valuesDG
andDVEq
, which it needs to change temporarily, to their original states.Example:
>>> from hydpy.models.wland import * >>> parameterstep() >>> thetas(0.4) >>> thetar(0.01) >>> psiae(300.0) >>> b(5.0) >>> states.dg = -9.0 >>> aides.dveq = -99.0 >>> states.dv = 3.152935 >>> from hydpy import round_ >>> round_(model.return_errordv_v1(400.0)) 2.0 >>> states.dg dg(-9.0) >>> aides.dveq dveq(-99.0)
Technical checks:
As mentioned above, method
Return_ErrorDV_V1
changes the values of the sequencesDG
andDVEq
, but only temporarily. Hence, we do not include them in the method specifications, even if the following check considers this erroneous:>>> from hydpy.core.testtools import check_selectedvariables >>> from hydpy.models.wland.wland_model import Return_ErrorDV_V1 >>> print(check_selectedvariables(Return_ErrorDV_V1)) Definitely missing: dg and dveq Possibly missing (REQUIREDSEQUENCES): Calc_DVEq_V3: DG Possibly missing (RESULTSEQUENCES): Calc_DVEq_V3: DVEq
- class hydpy.models.wland.wland_model.Calc_DGEq_V1[source]¶
Bases:
Method
Calculate the equilibrium groundwater depth.
- Required submethod:
- Requires the control parameters:
- Requires the derived parameter:
- Requires the state sequence:
- Calculates the aide sequence:
Method
Calc_DGEq_V1
calculates the equilibrium groundwater depth for the current water deficit of the vadose zone, following methodsReturn_DVH_V2
andCalc_DVEq_V3
. As we are not aware of an analytical solution, we solve it numerically via classPegasusDGEq
, which performs an iterative root-search based on the Pegasus method.Examples:
>>> from hydpy.models.wland import * >>> parameterstep() >>> thetas(0.4) >>> thetar(0.01) >>> psiae(300.0) >>> b(5.0) >>> from hydpy import UnitTest >>> test = UnitTest( ... model=model, ... method=model.calc_dgeq_v1, ... last_example=13, ... parseqs=(states.dv, aides.dgeq) ... ) >>> test.nexts.dv = ( ... -1.0, -0.01, 0.0, 0.01, 1.0, 2.0, 2.99, 3.0, ... 3.01012983, 5.1529353, 28.71839324, 111.1720584, 337.5798671) >>> test() | ex. | dv | dgeq | ----------------------------- | 1 | -1.0 | 0.0 | | 2 | -0.01 | 0.0 | | 3 | 0.0 | 0.0 | | 4 | 0.01 | 1.0 | | 5 | 1.0 | 100.0 | | 6 | 2.0 | 200.0 | | 7 | 2.99 | 299.0 | | 8 | 3.0 | 300.0 | | 9 | 3.01013 | 301.0 | | 10 | 5.152935 | 400.0 | | 11 | 28.718393 | 800.0 | | 12 | 111.172058 | 1600.0 | | 13 | 337.579867 | 3200.0 |
- class hydpy.models.wland.wland_model.Calc_GF_V1[source]¶
Bases:
Method
Calculate the gain factor for changes in groundwater depth.
- Required submethod:
- Requires the control parameters:
- Requires the derived parameter:
- Requires the state sequence:
- Requires the aide sequence:
- Calculates the aide sequence:
- Basic equation (discontinuous):
- \[\begin{split}GF = \begin{cases} 0 &|\ DG \leq 0 \\ Return\_DVH\_V2(DGEq - DG)^{-1} &|\ 0 < DG \end{cases}\end{split}\]
The original WALRUS model attributes a passive role to groundwater dynamics. All water entering or leaving the underground is added to or subtracted from the vadose zone, and the groundwater table only reacts to such changes until it is in equilibrium with the updated water deficit in the vadose zone. Hence, the movement of the groundwater table is generally slow. However, in catchments with near-surface water tables, we often observe fast responses of groundwater to input forcings, maybe due to rapid infiltration along macropores or the re-infiltration of channel water. In such situations, where the input water somehow bypasses the vadose zone, the speed of the rise of the groundwater table depends not only on the effective pore size of the soil material but also on the soil’s degree of saturation directly above the groundwater table. The smaller the remaining pore size, the larger the fraction between the water table’s rise and the actual groundwater recharge. We call this fraction the “gain factor” (
GF
).The WALRUS model does not explicitly account for the soil moisture in different depths above the groundwater table. To keep the vertically lumped approach, we use the difference between the actual (
DG
) and the equilibrium groundwater depth (DGEq
) as an indicator for the wetness above the groundwater table. WhenDG
is identical toDGEq
, soil moisture and groundwater are in equilibrium. Then, the tension-saturated area is fully developed, and the groundwater table moves quickly (depending onThetaR
). The opposite case is whenDG
is much smaller thanDGEq
. Such a situation occurs after a fast rise of the groundwater table when the soil water still needs much redistribution before it can be in equilibrium with groundwater. In the most extreme case, the gain factor is just as large as indicated by the effective pore size alone (depending onThetaS
).The above discussion only applies as long as the groundwater table is below the soil surface. For large-scale ponding (see Brauer et al. (2014), section 5.11), we set
GF
to zero. See the documentation on the methodsCalc_CDG_V1
andCalc_FGS_V1
for related discussions.Examples:
>>> from hydpy.models.wland import * >>> parameterstep() >>> thetas(0.4) >>> thetar(0.01) >>> psiae(300.0) >>> b(5.0) >>> sh(0.0) >>> aides.dgeq = 5000.0 >>> derived.rh1.update() >>> from hydpy import UnitTest >>> test = UnitTest( ... model=model, ... method=model.calc_gf_v1, ... last_example=16, ... parseqs=(states.dg, aides.gf) ... ) >>> test.nexts.dg = ( ... -10.0, -1.0, 0.0, 1.0, 10.0, ... 1000.0, 2000.0, 3000.0, 4000.0, 4500.0, 4600.0, ... 4690.0, 4699.0, 4700.0, 4701.0, 4710.0) >>> test() | ex. | dg | gf | ---------------------------- | 1 | -10.0 | 0.0 | | 2 | -1.0 | 0.0 | | 3 | 0.0 | 2.81175 | | 4 | 1.0 | 5.623782 | | 5 | 10.0 | 5.626316 | | 6 | 1000.0 | 5.963555 | | 7 | 2000.0 | 6.496601 | | 8 | 3000.0 | 7.510869 | | 9 | 4000.0 | 10.699902 | | 10 | 4500.0 | 20.88702 | | 11 | 4600.0 | 31.440737 | | 12 | 4690.0 | 79.686112 | | 13 | 4699.0 | 97.470815 | | 14 | 4700.0 | 100.0 | | 15 | 4701.0 | 100.0 | | 16 | 4710.0 | 100.0 |
>>> sh(1.0) >>> derived.rh1.update() >>> test() | ex. | dg | gf | ---------------------------- | 1 | -10.0 | 0.0 | | 2 | -1.0 | 0.056232 | | 3 | 0.0 | 2.81175 | | 4 | 1.0 | 5.567544 | | 5 | 10.0 | 5.626316 | | 6 | 1000.0 | 5.963555 | | 7 | 2000.0 | 6.496601 | | 8 | 3000.0 | 7.510869 | | 9 | 4000.0 | 10.699902 | | 10 | 4500.0 | 20.88702 | | 11 | 4600.0 | 31.440737 | | 12 | 4690.0 | 79.686112 | | 13 | 4699.0 | 97.465434 | | 14 | 4700.0 | 99.609455 | | 15 | 4701.0 | 99.994314 | | 16 | 4710.0 | 100.0 |
- class hydpy.models.wland.wland_model.Calc_GR_V1[source]¶
Bases:
Method
Calculate the elevated region’s groundwater recharge.
- Requires the control parameter:
- Requires the derived parameters:
- Requires the state sequence:
- Calculates the flux sequence:
Basic equations (discontinous):
\[\begin{split}GR = \begin{cases} 0 &|\ AC \leq DV\!E \\ AC - DV\!E &|\ AC > DV\!E \end{cases}\end{split}\]Examples:
>>> from hydpy.models.wland import * >>> parameterstep() >>> ac(200.0) >>> rg(False) >>> from hydpy import UnitTest >>> test = UnitTest(model=model, ... method=model.calc_gr_v1, ... last_example=7, ... parseqs=(states.dve, fluxes.gr)) >>> test.nexts.dve = (100.0, 190.0, 199.0, 200.0, 201.0, 210.0, 300.0)
Discontinuous example:
>>> sh(0.0) >>> derived.rh2.update() >>> test() | ex. | dve | gr | ----------------------- | 1 | 100.0 | 100.0 | | 2 | 190.0 | 10.0 | | 3 | 199.0 | 1.0 | | 4 | 200.0 | 0.0 | | 5 | 201.0 | 0.0 | | 6 | 210.0 | 0.0 | | 7 | 300.0 | 0.0 |
Continuous example:
>>> sh(10.0) >>> derived.rh2.update() >>> test() | ex. | dve | gr | -------------------------- | 1 | 100.0 | 100.0 | | 2 | 190.0 | 10.01 | | 3 | 199.0 | 1.885788 | | 4 | 200.0 | 1.320935 | | 5 | 201.0 | 0.885788 | | 6 | 210.0 | 0.01 | | 7 | 300.0 | 0.0 |
- class hydpy.models.wland.wland_model.Calc_CDG_V1[source]¶
Bases:
Method
Calculate the change in the groundwater depth due to percolation and capillary rise.
- Requires the control parameters:
- Requires the derived parameters:
- Requires the flux sequences:
- Requires the state sequences:
- Requires the aide sequence:
- Calculates the flux sequence:
- Basic equation (discontinuous):
- \[\begin{split}CDG = \frac{DV - min(DV\!Eq, \ DG)}{CV} + \begin{cases} \frac{FGS - FGSE - FXG}{ThetaS} &|\ DGC \\ 0 &|\ \overline{DGC} \end{cases}\end{split}\]
Note that this equation differs in two respects from equation 6 of Brauer et al. (2014).
First, in the case of large-scale ponding,
DVEq
always stays at zero, and we letDG
take control of the speed of the water table movement. See the documentation on methodCalc_FGS_V1
for additional information on the differences betweenwland
and WALRUS for this rare situation.Second, one can set
DGC
toTrue
to enforce a more direct connection between the groundwater and surface water storage level. According to Brauer et al. (2014), groundwater drainage and extraction increase and surface water infiltration and seepage decrease the water deficit in the vadose zone but do not change groundwater depth directly. This abstraction is as if, for example, groundwater drainage would take water from the soil’s top. The groundwater level only reacts with some delay due to its tendency to advance towards the increased equilibrium depth. WithDGC
enabled, the mentioned fluxes change the vadose zone’s deficit and the groundwater depth simultaneously.Examples:
Without large-scale ponding and a direct groundwater connection:
>>> from hydpy.models.wland import * >>> simulationstep("12h") >>> parameterstep("1d") >>> dgc(False) >>> cv(10.0) >>> thetas(0.5) >>> sh(0.0) >>> derived.rh1.update() >>> states.dv = 100.0 >>> states.dg = 1000.0 >>> fluxes.fgs = 2.0 >>> fluxes.fgse = 0.5 >>> fluxes.fxg = 2.5 >>> aides.dveq = 80.0 >>> model.calc_cdg_v1() >>> fluxes.cdg cdg(1.0)
Without large-scale ponding and with a direct groundwater connection:
>>> dgc(True) >>> model.calc_cdg_v1() >>> fluxes.cdg cdg(-1.0)
With large-scale ponding and without smoothing:
>>> from hydpy import UnitTest >>> test = UnitTest( ... model=model, ... method=model.calc_cdg_v1, ... last_example=5, ... parseqs=(states.dg, fluxes.cdg) ... ) >>> dgc(False) >>> states.dv = -10.0 >>> aides.dveq = 0.0 >>> test.nexts.dg = 10.0, 1.0, 0.0, -1.0, -10.0 >>> test() | ex. | dg | cdg | ----------------------- | 1 | 10.0 | -0.5 | | 2 | 1.0 | -0.5 | | 3 | 0.0 | -0.5 | | 4 | -1.0 | -0.45 | | 5 | -10.0 | 0.0 |
With large-scale ponding and smoothing:
>>> sh(1.0) >>> derived.rh1.update() >>> test() | ex. | dg | cdg | --------------------------- | 1 | 10.0 | -0.5 | | 2 | 1.0 | -0.499891 | | 3 | 0.0 | -0.492458 | | 4 | -1.0 | -0.449891 | | 5 | -10.0 | 0.0 |
- class hydpy.models.wland.wland_model.Calc_CDG_V2[source]¶
Bases:
Method
Calculate the vadose zone’s storage deficit change due to percolation, capillary rise, macropore infiltration, seepage, groundwater flow, and channel water infiltration.
- Requires the control parameter:
- Requires the derived parameters:
- Requires the flux sequences:
- Requires the state sequences:
- Requires the aide sequences:
- Calculates the flux sequence:
- Basic equation:
\(CDG = \frac{DV-min(DV\!Eq, \ DG)}{CV} + GF \cdot \big( FGS - FGSE - PV - FXG \big)\)
Method
Calc_CDG_V2
extendsCalc_CDG_V1
, which implements the (nearly) original WALRUS relationship defined by equation 6 of Brauer et al. (2014)). See the documentation on methodCalc_GF_V1
for a comprehensive explanation of the reason for this extension.Examples:
Without large-scale ponding:
>>> from hydpy.models.wland import * >>> simulationstep("12h") >>> parameterstep("1d") >>> cv(10.0) >>> sh(0.0) >>> derived.rh1.update() >>> states.dv = 100.0 >>> states.dg = 1000.0 >>> fluxes.pv = 1.0 >>> fluxes.fxg = 0.5 >>> fluxes.fgse = 1.5 >>> fluxes.fgs = 4.0 >>> aides.dveq = 80.0 >>> aides.gf = 2.0 >>> model.calc_cdg_v2() >>> fluxes.cdg cdg(3.0)
With large-scale ponding and without smoothing:
>>> from hydpy import UnitTest >>> test = UnitTest( ... model=model, ... method=model.calc_cdg_v2, ... last_example=5, ... parseqs=(states.dg, fluxes.cdg) ... ) >>> aides.gf = 0.0 >>> states.dv = -10.0 >>> aides.dveq = 0.0 >>> test.nexts.dg = 10.0, 1.0, 0.0, -1.0, -10.0 >>> test() | ex. | dg | cdg | ----------------------- | 1 | 10.0 | -0.5 | | 2 | 1.0 | -0.5 | | 3 | 0.0 | -0.5 | | 4 | -1.0 | -0.45 | | 5 | -10.0 | 0.0 |
With large-scale ponding and with smoothing:
>>> sh(1.0) >>> derived.rh1.update() >>> test() | ex. | dg | cdg | --------------------------- | 1 | 10.0 | -0.5 | | 2 | 1.0 | -0.499891 | | 3 | 0.0 | -0.492458 | | 4 | -1.0 | -0.449891 | | 5 | -10.0 | 0.0 |
- class hydpy.models.wland.wland_model.Calc_FGSE_V1[source]¶
Bases:
Method
Calculate the groundwater flow between the elevated and the lowland regions.
- Requires the control parameters:
- Requires the derived parameter:
- Requires the state sequences:
- Calculates the flux sequence:
The basic equation of method
Calc_FGSE_V1
relies on the one ofCalc_FGS_V1
introduced by Brauer et al. (2014). We decided so to calculateFGSE
in a WALRUS-like manner.Basic equation:
\[\begin{split}FGSE = \frac{\Delta \cdot |\Delta|}{CGE} \\\\ \Delta = HGE - (1000 \cdot GL - DG)\end{split}\]Examples:
>>> from hydpy.models.wland import * >>> simulationstep("12h") >>> parameterstep("1d") >>> gl(5.0) >>> cge(1000000.0) >>> states.dg = 3000.0
Normal flow:
>>> states.hge = 4000.0 >>> model.calc_fgse_v1() >>> fluxes.fgse fgse(2.0)
No flow:
>>> states.hge = 2000.0 >>> model.calc_fgse_v1() >>> fluxes.fgse fgse(0.0)
Reversed flow:
>>> states.hge = 0.0 >>> model.calc_fgse_v1() >>> fluxes.fgse fgse(-2.0)
- class hydpy.models.wland.wland_model.Calc_FGS_V1[source]¶
Bases:
Method
Calculate the groundwater drainage or surface water infiltration.
- Requires the control parameters:
- Requires the derived parameters:
- Requires the state sequences:
- Calculates the flux sequence:
For large-scale ponding,
wland
and WALRUS calculateFGS
differently (even for discontinuous parameterisations). In such cases, he WALRUS model redistributes water instantaneously (see Brauer et al. (2014), section 5.11), which relates to infinitely high flow velocities and cannot be handled by the numerical integration algorithm underlyingwland
. Hence, we introduce the parameterCGF
instead. Setting it to a value larger than zero increases the flow velocity with increasing large-scale ponding. The larger the value ofCGF
, the stronger the functional similarity of both approaches. But note that very high values can result in increased computation times.Basic equations (discontinous):
\[\begin{split}FGS = Gradient \cdot ContactSurface \cdot Conductivity \\ \\ HG^* = max(CD - DG, \ 0) \\ HS^* = max(HS, \ 0) \\ Gradient = \begin{cases} HG^* - HS^* &|\ RG \\ CD - DG - HS^* &|\ \overline{RG} \\ \end{cases} \\ ContactSurface = \begin{cases} |HG^* - HS^*| &|\ RG \\ max(HG^*, \ HS^*) &|\ \overline{RG} \\ \end{cases} \\ Excess = max(-DG, HS - CD, \ 0) \\ Conductivity = (1 + CGF \cdot Excess) / CG\end{split}\]Examples:
>>> from hydpy.models.wland import * >>> simulationstep("12h") >>> parameterstep("1d") >>> cg(10000.0) >>> rg(False) >>> derived.cd(600.0) >>> states.hs = 300.0 >>> from hydpy import UnitTest >>> test = UnitTest(model=model, ... method=model.calc_fgs_v1, ... last_example=15, ... parseqs=(states.dg, states.hs, fluxes.fgs)) >>> test.nexts.dg = ( ... -100.0, -1.0, 0.0, 1.0, 100.0, 200.0, 290.0, 299.0, ... 300.0, 301.0, 310.0, 400.0, 500.0, 600.0, 700.0)
Without smoothing and without increased conductivity for large-scale ponding:
>>> cgf(0.0) >>> sh(0.0) >>> derived.rh2.update() >>> test() | ex. | dg | hs | fgs | ---------------------------------- | 1 | -100.0 | 300.0 | 14.0 | | 2 | -1.0 | 300.0 | 9.04505 | | 3 | 0.0 | 300.0 | 9.0 | | 4 | 1.0 | 300.0 | 8.95505 | | 5 | 100.0 | 300.0 | 5.0 | | 6 | 200.0 | 300.0 | 2.0 | | 7 | 290.0 | 300.0 | 0.155 | | 8 | 299.0 | 300.0 | 0.01505 | | 9 | 300.0 | 300.0 | 0.0 | | 10 | 301.0 | 300.0 | -0.015 | | 11 | 310.0 | 300.0 | -0.15 | | 12 | 400.0 | 300.0 | -1.5 | | 13 | 500.0 | 300.0 | -3.0 | | 14 | 600.0 | 300.0 | -4.5 | | 15 | 700.0 | 300.0 | -6.0 |
Without smoothing but with increased conductivity for large-scale ponding:
>>> cgf(0.1) >>> test() | ex. | dg | hs | fgs | ----------------------------------- | 1 | -100.0 | 300.0 | 294.0 | | 2 | -1.0 | 300.0 | 10.85406 | | 3 | 0.0 | 300.0 | 9.0 | | 4 | 1.0 | 300.0 | 8.95505 | | 5 | 100.0 | 300.0 | 5.0 | | 6 | 200.0 | 300.0 | 2.0 | | 7 | 290.0 | 300.0 | 0.155 | | 8 | 299.0 | 300.0 | 0.01505 | | 9 | 300.0 | 300.0 | 0.0 | | 10 | 301.0 | 300.0 | -0.015 | | 11 | 310.0 | 300.0 | -0.15 | | 12 | 400.0 | 300.0 | -1.5 | | 13 | 500.0 | 300.0 | -3.0 | | 14 | 600.0 | 300.0 | -4.5 | | 15 | 700.0 | 300.0 | -6.0 |
With smoothing and with increased conductivity for large-scale ponding:
>>> sh(1.0) >>> derived.rh2.update() >>> test() | ex. | dg | hs | fgs | ----------------------------------- | 1 | -100.0 | 300.0 | 294.0 | | 2 | -1.0 | 300.0 | 10.87215 | | 3 | 0.0 | 300.0 | 9.369944 | | 4 | 1.0 | 300.0 | 8.97296 | | 5 | 100.0 | 300.0 | 5.0 | | 6 | 200.0 | 300.0 | 2.0 | | 7 | 290.0 | 300.0 | 0.155 | | 8 | 299.0 | 300.0 | 0.01505 | | 9 | 300.0 | 300.0 | 0.0 | | 10 | 301.0 | 300.0 | -0.015 | | 11 | 310.0 | 300.0 | -0.15 | | 12 | 400.0 | 300.0 | -1.5 | | 13 | 500.0 | 300.0 | -3.0 | | 14 | 600.0 | 300.0 | -4.5 | | 15 | 700.0 | 300.0 | -6.0 |
Another difference to the original WALRUS model is optional. Enabling the
RG
option restricts theFGS
in two ways. First, the groundwater depth used for calculating the gradient between the surface and the groundwater level is restricted to the channel depth so that extremely low groundwater levels do not result in extremely high surface water infiltration. Second, the contact surface is restricted to the difference between the surface and the (restricted) groundwater level so that uncertainties in channel depth estimates have a less severe effect both on groundwater drainage and surface water infiltration:>>> rg(True) >>> test() | ex. | dg | hs | fgs | ------------------------------------ | 1 | -100.0 | 300.0 | 168.0 | | 2 | -1.0 | 300.0 | 5.44512 | | 3 | 0.0 | 300.0 | 4.684972 | | 4 | 1.0 | 300.0 | 4.47899 | | 5 | 100.0 | 300.0 | 2.0 | | 6 | 200.0 | 300.0 | 0.5 | | 7 | 290.0 | 300.0 | 0.005 | | 8 | 299.0 | 300.0 | 0.00005 | | 9 | 300.0 | 300.0 | 0.0 | | 10 | 301.0 | 300.0 | -0.00005 | | 11 | 310.0 | 300.0 | -0.005 | | 12 | 400.0 | 300.0 | -0.5 | | 13 | 500.0 | 300.0 | -2.0 | | 14 | 600.0 | 300.0 | -4.493836 | | 15 | 700.0 | 300.0 | -4.5 |
- class hydpy.models.wland.wland_model.Calc_FQS_V1[source]¶
Bases:
Method
Calculate the quickflow.
- Requires the control parameters:
- Requires the state sequence:
- Calculates the flux sequence:
- Basic equation:
\(FQS = \frac{HQ}{CQ}\)
Examples:
>>> from hydpy.models.wland import * >>> simulationstep("12h") >>> parameterstep("1d") >>> nu(2) >>> cq(10.0) >>> states.hq = 100.0 >>> model.calc_fqs_v1() >>> fluxes.fqs fqs(5.0)
Without land areas, quickflow is zero:
>>> nu(1) >>> model.calc_fqs_v1() >>> fluxes.fqs fqs(0.0)
- class hydpy.models.wland.wland_model.Calc_RH_V1[source]¶
Bases:
Method
Let a submodel that complies with the
DischargeModel_V2
interface calculate the runoff height or, if no such submodel is available, equate it with all other flows in and out of the surface water storage.- Requires the derived parameters:
- Requires the flux sequences:
- Requires the state sequence:
- Calculates the flux sequence:
- Basic equation (without submodel):
\(RH = ASR \cdot (PS - ES + FXS) + ALR \cdot FQS + AGR \cdot FGS\)
Examples:
>>> from hydpy.models.wland_wag import * >>> simulationstep("12h") >>> parameterstep("1d")
Without an available submodel,
Calc_RH_V1
applies the given basic equation:>>> derived.asr(0.2) >>> derived.alr(0.8) >>> derived.agr(0.6) >>> fluxes.ps = 3.0 >>> fluxes.fxs = 1.0 >>> fluxes.es = 2.0 >>> fluxes.fqs(0.5) >>> fluxes.fgs(0.2) >>> model.calc_rh_v1() >>> fluxes.rh rh(0.92)
We use
wq_walrus
, which implements WALRUS’ standard approach for calculatingRH
, to demonstrate thatCalc_RH_V1
correctly uses submodels that follow theDischargeModel_V2
interface:>>> sh(0.1) >>> states.hs(3000.0) >>> derived.cd(5000.0) >>> with model.add_dischargemodel_v2("wq_walrus"): ... crestheight(2.0) ... bankfulldischarge(2.0) ... dischargeexponent(2.0) >>> model.calc_rh_v1() >>> fluxes.rh rh(0.111111)
- class hydpy.models.wland.wland_model.Update_IC_V1[source]¶
Bases:
Method
Update the interception storage.
- Requires the derived parameter:
- Requires the flux sequences:
- Updates the state sequence:
- Basic equation:
\(\frac{dIC}{dt} = PC - TF - EI\)
Example:
>>> from hydpy.models.wland import * >>> parameterstep() >>> nu(2) >>> derived.nul.update() >>> fluxes.pc = 2.0 >>> fluxes.tf = 1.0 >>> fluxes.ei = 3.0 >>> states.ic.old = 4.0 >>> model.update_ic_v1() >>> states.ic ic(2.0, 0.0)
- class hydpy.models.wland.wland_model.Update_SP_V1[source]¶
Bases:
Method
Update the storage deficit.
- Requires the derived parameter:
- Requires the flux sequences:
- Updates the state sequence:
- Basic equation:
\(\frac{dSP}{dt} = SF - AM\)
Example:
>>> from hydpy.models.wland import * >>> parameterstep() >>> nu(2) >>> derived.nul.update() >>> fluxes.sf = 1.0 >>> fluxes.am = 2.0 >>> states.sp.old = 3.0 >>> model.update_sp_v1() >>> states.sp sp(2.0, 0.0)
- class hydpy.models.wland.wland_model.Update_DVE_V1[source]¶
Bases:
Method
Update the elevated region’s storage deficit of the vadose zone.
- Requires the derived parameter:
- Requires the flux sequences:
- Updates the state sequence:
- Basic equation:
\(\frac{dDV\!E}{dt} = -(PV\!E - ETV\!E - GR)\)
Examples:
>>> from hydpy.models.wland import * >>> parameterstep() >>> derived.nuge(1) >>> fluxes.pve = 1.0 >>> fluxes.etve = 2.0 >>> fluxes.gr = 3.0 >>> states.dve.old = 5.0 >>> model.update_dve_v1() >>> states.dve dve(9.0)
>>> derived.nuge(0) >>> model.update_dve_v1() >>> states.dve dve(nan)
- class hydpy.models.wland.wland_model.Update_DV_V1[source]¶
Bases:
Method
Update the lowland region’s storage deficit of the vadose zone.
- Requires the derived parameters:
- Requires the flux sequences:
- Updates the state sequence:
- Basic equation:
\(\frac{dDV}{dt} = - \left( FXG + PV - ETV - FGS + FGSE \cdot \frac{AGRE}{AGR} \right)\)
Example:
>>> from hydpy.models.wland import * >>> parameterstep() >>> derived.nug(1) >>> derived.agre(0.2) >>> derived.agr(0.4) >>> fluxes.fxg = 1.0 >>> fluxes.pv = 2.0 >>> fluxes.etv = 3.0 >>> fluxes.fgs = 4.0 >>> fluxes.fgse = 5.0 >>> states.dv.old = 6.0 >>> model.update_dv_v1() >>> states.dv dv(7.5)
>>> derived.nug(0) >>> model.update_dv_v1() >>> states.dv dv(nan)
- class hydpy.models.wland.wland_model.Update_HGE_V1[source]¶
Bases:
Method
Update the elevated region’s groundwater level.
- Requires the control parameter:
- Requires the derived parameter:
- Requires the flux sequences:
- Updates the state sequence:
- Basic equation:
\(\frac{dDGE}{dt} = CDG\)
Example:
>>> from hydpy.models.wland import * >>> parameterstep() >>> thetas(0.5) >>> derived.nuge(1) >>> fluxes.gr = 1.0 >>> fluxes.fgse = 2.0 >>> states.hge.old = 3.0 >>> model.update_hge_v1() >>> states.hge hge(1.0)
>>> derived.nuge(0) >>> model.update_hge_v1() >>> states.hge hge(nan)
- class hydpy.models.wland.wland_model.Update_DG_V1[source]¶
Bases:
Method
Update the lowland region’s groundwater depth.
- Requires the derived parameter:
- Requires the flux sequence:
- Updates the state sequence:
- Basic equation:
\(\frac{dDG}{dt} = CDG\)
Example:
>>> from hydpy.models.wland import * >>> parameterstep() >>> derived.nug(1) >>> states.dg.old = 2.0 >>> fluxes.cdg = 3.0 >>> model.update_dg_v1() >>> states.dg dg(5.0)
>>> derived.nug(0) >>> model.update_dg_v1() >>> states.dg dg(nan)
- class hydpy.models.wland.wland_model.Update_HQ_V1[source]¶
Bases:
Method
Update the level of the quickflow reservoir.
- Requires the flux sequences:
- Updates the state sequence:
- Basic equation:
\(\frac{dHQ}{dt} = PQ - FQS\)
Example:
>>> from hydpy.models.wland import * >>> parameterstep() >>> states.hq.old = 2.0 >>> fluxes.pq = 3.0 >>> fluxes.fqs = 4.0 >>> model.update_hq_v1() >>> states.hq hq(1.0)
- class hydpy.models.wland.wland_model.Update_HS_V1[source]¶
Bases:
Method
Update the surface water level.
- Requires the derived parameters:
- Requires the flux sequences:
- Updates the state sequence:
- Basic equation:
\(\frac{dHS}{dt} = PS - ES + FXS + \frac{ALR \cdot FQS + AGR \cdot FGS - RH}{ASR}\)
Example:
>>> from hydpy.models.wland import * >>> parameterstep() >>> derived.alr(0.8) >>> derived.asr(0.2) >>> derived.agr(0.8) >>> states.hs.old = 2.0 >>> fluxes.fxs = 3.0 >>> fluxes.ps = 4.0 >>> fluxes.es = 5.0 >>> fluxes.fgs = 6.0 >>> fluxes.fqs = 7.0 >>> fluxes.rh = 8.0 >>> model.update_hs_v1() >>> states.hs hs(16.0)
- class hydpy.models.wland.wland_model.Calc_R_V1[source]¶
Bases:
Method
Calculate the runoff in m³/s.
- Requires the derived parameter:
- Requires the flux sequence:
- Calculates the flux sequence:
- Basic equation:
\(R = QF \cdot RH\)
Example:
>>> from hydpy.models.wland import * >>> parameterstep() >>> derived.qf(2.0) >>> fluxes.rh = 3.0 >>> model.calc_r_v1() >>> fluxes.r r(6.0)
- class hydpy.models.wland.wland_model.Pass_R_V1[source]¶
Bases:
Method
Update the outlet link sequence.
- class hydpy.models.wland.wland_model.Get_Temperature_V1[source]¶
Bases:
Method
Get the current subbasin-wide air temperature value (that applies to all hydrological response units so that the given index does not matter).
- Requires the input sequence:
Example:
>>> from hydpy.models.wland import * >>> parameterstep() >>> inputs.t = 2.0 >>> model.get_temperature_v1(0) 2.0 >>> model.get_temperature_v1(1) 2.0
- class hydpy.models.wland.wland_model.Get_MeanTemperature_V1[source]¶
Bases:
Method
Get the current subbasin-wide air temperature value.
- Requires the input sequence:
Example:
>>> from hydpy.models.wland import * >>> parameterstep() >>> inputs.t = 2.0 >>> from hydpy import round_ >>> round_(model.get_meantemperature_v1()) 2.0
- class hydpy.models.wland.wland_model.Get_Precipitation_V1[source]¶
Bases:
Method
Get the current subbasin-wide precipitation value (that applies to all hydrological response units so that the given index does not matter).
- Requires the flux sequence:
Example:
>>> from hydpy.models.wland import * >>> parameterstep() >>> nu(2) >>> fluxes.pc = 2.0 >>> model.get_precipitation_v1(0) 2.0 >>> model.get_precipitation_v1(1) 2.0
- class hydpy.models.wland.wland_model.Get_SnowCover_V1[source]¶
Bases:
Method
Get the selected response unit’s current snow cover degree.
- Requires the state sequence:
Example:
Each response unit with a non-zero amount of snow counts as completely covered:
>>> from hydpy.models.wland import * >>> parameterstep() >>> nu(2) >>> states.sp = 0.0, 2.0 >>> model.get_snowcover_v1(0) 0.0 >>> model.get_snowcover_v1(1) 1.0
- class hydpy.models.wland.wland_model.PegasusDGEq(model: Model)[source]¶
Bases:
Pegasus
Pegasus iterator for finding the equilibrium groundwater depth.
- class hydpy.models.wland.wland_model.QuadDVEq_V1(model: Model)[source]¶
Bases:
Quad
Adaptive quadrature method for integrating the equilibrium storage deficit of the vadose zone.
- class hydpy.models.wland.wland_model.QuadDVEq_V2(model: Model)[source]¶
Bases:
Quad
Adaptive quadrature method for integrating the equilibrium storage deficit of the vadose zone.
- class hydpy.models.wland.wland_model.BaseModel[source]¶
Bases:
ELSModel
Base model for
wland_wag
andwland_gd
.- check_waterbalance(initial_conditions: dict[str, dict[str, dict[str, float | ndarray[Any, dtype[float64]]]]]) float [source]¶
Determine the water balance error of the previous simulation run in mm.
Method
check_waterbalance()
calculates the balance error as follows:\[\begin{split}Error = \Sigma In - \Sigma Out + \Sigma \Delta H - \Delta Vol_{basin} - \Delta Vol_{hru} \\ \\ \Sigma In = \sum_{t=t_0}^{t_1} PC_t + FXG_t + FXS_t \\ \Sigma Out = \sum_{t=t_0}^{t_1} ET_t + RH_t \\ \Sigma \Delta H= ASR \cdot \sum_{t=t_0}^{t_1} DHS_t \\ \Delta Vol_{basin} = ALR \cdot f_{\Delta}(HQ) + AGRE \cdot \big( ThetaS \cdot f_{\Delta}(HGE) - f_{\Delta}(DV\!E) \big) - AGR \cdot f_{\Delta}(DV) + ASR \cdot f_{\Delta}(HS) \\ \Delta Vol_{hru} = \sum_{k=1}^{NUL} AUR^k \cdot \big( f_{\Delta}(IC^k) + f_{\Delta}(SP) \big) \\ \\ f_{\Delta}(x) = x_{t1} - x_{t0}\end{split}\]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 via property
conditions
. See the integration tests of the application modelwland_wag
for some examples.
- REUSABLE_METHODS: ClassVar[tuple[type[ReusableMethod], ...]] = ()¶
- class hydpy.models.wland.wland_model.Main_PETModel_V1[source]¶
Bases:
ELSModel
Base class for HydPy-W models that use submodels that comply with the
PETModel_V1
interface.- petmodel: SubmodelProperty¶
- petmodel_is_mainmodel¶
- petmodel_typeid¶
- add_petmodel_v1¶
Initialise the given petmodel that follows the
PETModel_V1
interface.>>> from hydpy.models.wland_wag import * >>> parameterstep() >>> nu(3) >>> at(10.0) >>> aur(0.5, 0.3, 0.2) >>> lt(FIELD, TREES, WATER) >>> with model.add_petmodel_v1("evap_ret_tw2002"): ... nmbhru ... hruarea ... evapotranspirationfactor(field=1.0, trees=2.0, water=1.5) nmbhru(3) hruarea(5.0, 3.0, 2.0)
>>> etf = model.petmodel.parameters.control.evapotranspirationfactor >>> etf evapotranspirationfactor(field=1.0, trees=2.0, water=1.5) >>> lt(TREES, FIELD, WATER) >>> etf evapotranspirationfactor(field=2.0, trees=1.0, water=1.5) >>> from hydpy import round_ >>> round_(etf.average_values()) 1.4
- REUSABLE_METHODS: ClassVar[tuple[type[ReusableMethod], ...]] = ()¶
- class hydpy.models.wland.wland_model.Main_PETModel_V2[source]¶
Bases:
ELSModel
Base class for HydPy-W models that use submodels that comply with the
PETModel_V2
interface.- petmodel: SubmodelProperty¶
- petmodel_is_mainmodel¶
- petmodel_typeid¶
- add_petmodel_v2¶
Initialise the given petmodel that follows the
PETModel_V2
interface.>>> from hydpy import pub >>> pub.timegrids = "2000-01-01", "2001-01-01", "1d" >>> from hydpy.models.wland_wag import * >>> parameterstep() >>> nu(12) >>> at(10.0) >>> aur(0.006, 0.02, 0.034, 0.048, 0.062, 0.076, ... 0.09, 0.104, 0.118, 0.132, 0.146, 0.164) >>> lt(SEALED, FIELD, WINE, ORCHARD, SOIL, PASTURE, ... WETLAND, TREES, CONIFER, DECIDIOUS, MIXED, WATER) >>> lai(1.0) >>> lai.conifer_jan = 2.0 >>> lai.pasture_dec = 3.0 >>> with model.add_petmodel_v2("evap_pet_ambav1") as petmodel: ... nmbhru ... hrutype ... water ... interception ... soil ... plant ... my_lai = leafareaindex ... "my_lai", my_lai.field_jun, my_lai.conifer_jan, my_lai.pasture_dec ... petmodel.preparemethod2arguments["prepare_nmbzones"] ... petmodel.preparemethod2arguments["prepare_subareas"] ... groundalbedo(conifer=0.05, decidious=0.1, field=0.15, mixed=0.2, ... orchard=0.25, pasture=0.3, sealed=0.35, soil=0.4, ... trees=0.45, water=0.5, wetland=0.55, wine=0.6) nmbhru(12) hrutype(SEALED, FIELD, WINE, ORCHARD, SOIL, PASTURE, WETLAND, TREES, CONIFER, DECIDIOUS, MIXED, WATER) water(conifer=False, decidious=False, field=False, mixed=False, orchard=False, pasture=False, sealed=False, soil=False, trees=False, water=True, wetland=False, wine=False) interception(conifer=True, decidious=True, field=True, mixed=True, orchard=True, pasture=True, sealed=True, soil=True, trees=True, water=False, wetland=True, wine=True) soil(conifer=True, decidious=True, field=True, mixed=True, orchard=True, pasture=True, sealed=False, soil=True, trees=True, water=False, wetland=True, wine=True) plant(conifer=True, decidious=True, field=True, mixed=True, orchard=True, pasture=True, sealed=False, soil=False, trees=True, water=False, wetland=True, wine=True) ('my_lai', 1.0, 2.0, 3.0) ((12,), {}) ((array([0.06, 0.2 , 0.34, 0.48, 0.62, 0.76, 0.9 , 1.04, 1.18, 1.32, 1.46, 1.64]),), {})
>>> assert model is model.petmodel.tempmodel >>> assert model is model.petmodel.precipmodel >>> assert model is model.petmodel.snowcovermodel
>>> ga = model.petmodel.parameters.control.groundalbedo >>> ga groundalbedo(conifer=0.05, decidious=0.1, field=0.15, mixed=0.2, orchard=0.25, pasture=0.3, sealed=0.35, soil=0.4, trees=0.45, water=0.5, wetland=0.55, wine=0.6) >>> lt(FIELD, SEALED, WINE, ORCHARD, SOIL, PASTURE, ... WETLAND, TREES, CONIFER, DECIDIOUS, MIXED, WATER) >>> ga groundalbedo(conifer=0.05, decidious=0.1, field=0.35, mixed=0.2, orchard=0.25, pasture=0.3, sealed=0.15, soil=0.4, trees=0.45, water=0.5, wetland=0.55, wine=0.6) >>> from hydpy import round_ >>> round_(ga.average_values()) 0.3117
- REUSABLE_METHODS: ClassVar[tuple[type[ReusableMethod], ...]] = ()¶
- class hydpy.models.wland.wland_model.Main_DischargeModel_V2[source]¶
Bases:
ELSModel
Base class for HydPy-W models that use submodels that comply with the
DischargeModel_V2
interface.- dischargemodel: SubmodelProperty¶
- dischargemodel_is_mainmodel¶
- dischargemodel_typeid¶
- add_dischargemodel_v2¶
Initialise the given dischargemodel that follows the
DischargeModel_V2
interface.Note the dependency on the derived parameter
CD
:>>> from hydpy.models.wland_wag import * >>> parameterstep() >>> sh(10.0) >>> with model.add_dischargemodel_v2("wq_walrus", update=False): ... pass Traceback (most recent call last): ... hydpy.core.exceptiontools.AttributeNotReady: While trying to add a submodel to the main model `wland_wag`, the following error occurred: While trying to determine the missing value of the derived parameter `cd` of element `?`, the following error occurred: While trying to subtract variable `gl` and `BL` instance `bl(?)`, the following error occurred: For variable `bl`, no value has been defined so far.
You can define its value manually for testing:
>>> derived.cd(2000.0) >>> with model.add_dischargemodel_v2("wq_walrus", update=False): ... channeldepth ... crestheighttolerance channeldepth(2.0) crestheighttolerance(0.01)
>>> model.dischargemodel.parameters.control.channeldepth channeldepth(2.0) >>> model.dischargemodel.parameters.control.crestheighttolerance crestheighttolerance(0.01)
However,
add_dischargemodel_v2()
updates the channel depth whenever possible to make the added submodel consistent with the main model’s current control parameter values:>>> gl(4.0) >>> bl(3.0) >>> with model.add_dischargemodel_v2("wq_walrus", update=False): ... channeldepth ... crestheighttolerance channeldepth(1.0) crestheighttolerance(0.01)
>>> derived.cd cd(1000.0) >>> model.dischargemodel.parameters.control.channeldepth channeldepth(1.0) >>> model.dischargemodel.parameters.control.crestheighttolerance crestheighttolerance(0.01)
- REUSABLE_METHODS: ClassVar[tuple[type[ReusableMethod], ...]] = ()¶
- class hydpy.models.wland.wland_model.Main_WaterLevelModel_V1[source]¶
Bases:
ELSModel
Base class for HydPy-W models that use submodels that comply with the
WaterLevelModel_V1
interface.- waterlevelmodel: SubmodelProperty¶
- waterlevelmodel_is_mainmodel¶
- waterlevelmodel_typeid¶
- add_waterlevelmodel_v1¶
Initialise the given waterlevelmodel that follows the
WaterLevelModel_V1
interface.>>> from hydpy.models.wland_wag import * >>> parameterstep() >>> from hydpy.models import exch_waterlevel >>> with model.add_waterlevelmodel_v1(exch_waterlevel): ... pass >>> assert isinstance(model.waterlevelmodel, exch_waterlevel.Model)
- REUSABLE_METHODS: ClassVar[tuple[type[ReusableMethod], ...]] = ()¶
- class hydpy.models.wland.wland_model.Sub_TempModel_V1[source]¶
Bases:
ELSModel
,TempModel_V1
Base class for HydPy-W models that comply with the
TempModel_V1
submodel interface.- REUSABLE_METHODS: ClassVar[tuple[type[ReusableMethod], ...]] = ()¶
- numconsts: NumConstsELS¶
- numvars: NumVarsELS¶
- cymodel: CyModelProtocol | None¶
- parameters: parametertools.Parameters¶
- sequences: sequencetools.Sequences¶
- masks: masktools.Masks¶
- class hydpy.models.wland.wland_model.Sub_PrecipModel_V1[source]¶
Bases:
ELSModel
,PrecipModel_V1
Base class for HydPy-W models that comply with the
PrecipModel_V1
submodel interface.- REUSABLE_METHODS: ClassVar[tuple[type[ReusableMethod], ...]] = ()¶
- numconsts: NumConstsELS¶
- numvars: NumVarsELS¶
- cymodel: CyModelProtocol | None¶
- parameters: parametertools.Parameters¶
- sequences: sequencetools.Sequences¶
- masks: masktools.Masks¶
- class hydpy.models.wland.wland_model.Sub_SnowCoverModel_V1[source]¶
Bases:
ELSModel
,SnowCoverModel_V1
Base class for HydPy-W models that comply with the
SnowCoverModel_V1
submodel interface.- REUSABLE_METHODS: ClassVar[tuple[type[ReusableMethod], ...]] = ()¶
- numconsts: NumConstsELS¶
- numvars: NumVarsELS¶
- cymodel: CyModelProtocol | None¶
- parameters: parametertools.Parameters¶
- sequences: sequencetools.Sequences¶
- masks: masktools.Masks¶
Parameter Features¶
Parameter tools¶
- class hydpy.models.wland.wland_parameters.SoilParameter(subvars: SubParameters)[source]¶
Bases:
Parameter
Base class for parameters related to the soil character.
Some parameters of HydPy-W are strongly related to the soil character and come with default values. To apply these default values, use the soil keyword in combination with one of the available soil constants.
We take parameter
B
and the soil characterSAND
as an example, which has the default value 4.05:>>> from hydpy.models.wland import * >>> parameterstep() >>> b(soil=SAND) >>> b b(soil=SAND) >>> from hydpy import round_ >>> round_(b.value) 4.05
You are free to ignore the default values and to set anything you like:
>>> b.value = 3.0 >>> b b(3.0)
The string representation relies on the soil keyword only when used to define the value directly beforehand:
>>> b(4.05) >>> b b(4.05)
For a list of the available defaults, see the respective parameter’s documentation or the error message that class
SoilParameter
raises if one passes a wrong value:>>> b(soil=0) Traceback (most recent call last): ... ValueError: While trying the set the value of parameter `b` of element `?`, the following error occurred: The given soil constant `0` is not among the available ones. Please use one of the following constants: SAND (1), LOAMY_SAND (2), SANDY_LOAM (3), SILT_LOAM (4), LOAM (5), SANDY_CLAY_LOAM (6), SILT_CLAY_LOAM (7), CLAY_LOAM (8), SANDY_CLAY (9), SILTY_CLAY (10), and CLAY (11).
Combining the soil keyword with other keywords is not allowed:
>>> b(soil=SAND, landuse='acre') Traceback (most recent call last): ... TypeError: While trying the set the value of parameter `b` of element `?`, the following error occurred: It is not allowed to combine keyword `soil` with other keywords, but the following ones are also used: landuse.
>>> b(landuse='acre') Traceback (most recent call last): ... NotImplementedError: While trying the set the value of parameter `b` of element `?`, the following error occurred: The value(s) of parameter `b` of element `?` could not be set based on the given keyword arguments.
- class hydpy.models.wland.wland_parameters.LanduseParameterLand(subvars: SubParameters)[source]¶
Bases:
ZipParameter
Base class for 1-dimensional parameters relevant for all land-related units.
We take the parameter
DDT
as an example. You can define its values by using the names of all land use-related constants in lower-case as keywords:>>> from hydpy import print_vector, round_ >>> from hydpy.models.wland import * >>> simulationstep("1d") >>> parameterstep("1d") >>> nu(13) >>> lt(SEALED, FIELD, WINE, ORCHARD, SOIL, PASTURE, WETLAND, ... TREES, CONIFER, DECIDIOUS, MIXED, SEALED, WATER) >>> ddf(sealed=0.0, field=1.0, wine=2.0, orchard=3.0, soil=4.0, pasture=5.0, ... wetland=6.0, trees=7.0, conifer=8.0, decidious=9.0, mixed=10.0) >>> ddf ddf(conifer=8.0, decidious=9.0, field=1.0, mixed=10.0, orchard=3.0, pasture=5.0, sealed=0.0, soil=4.0, trees=7.0, wetland=6.0, wine=2.0) >>> print_vector(ddf.values) 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 0.0, nan
You can average the current values with regard to the hydrological response area fractions, defined via parameter
AUR
:>>> aur(0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.11, 0.12, 0.22) >>> round_(ddf.average_values()) 5.641026
You can query or change the values related to specific land use types via attribute access:
>>> print_vector(ddf.sealed) 0.0, 0.0 >>> ddf.sealed = 11.0, 12.0 >>> ddf ddf(11.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 12.0, nan) >>> ddf.sealed = 12.0 >>> ddf ddf(conifer=8.0, decidious=9.0, field=1.0, mixed=10.0, orchard=3.0, pasture=5.0, sealed=12.0, soil=4.0, trees=7.0, wetland=6.0, wine=2.0)
- constants: dict[str, int] = {'CONIFER': 20, 'DECIDIOUS': 21, 'FIELD': 13, 'MIXED': 22, 'ORCHARD': 15, 'PASTURE': 17, 'SEALED': 12, 'SOIL': 16, 'TREES': 19, 'WATER': 23, 'WETLAND': 18, 'WINE': 14}¶
Mapping of the constants’ names and values.
- mask: masktools.IndexMask¶
- class hydpy.models.wland.wland_parameters.LanduseMonthParameter(subvars: SubParameters)[source]¶
Bases:
KeywordParameter2D
Base class for parameters which values depend on the actual month and land-use type.
- columnnames: tuple[str, ...] = ('jan', 'feb', 'mar', 'apr', 'may', 'jun', 'jul', 'aug', 'sep', 'oct', 'nov', 'dec')¶
Constants¶
HydPy-W provides two types of constants: those associated with the average soil character of a sub-catchment and those associated with the land-use type of the different hydrological response units of a sub-catchment. They are all available via wildcard-imports:
>>> from hydpy.models.wland import *
>>> (SAND, LOAMY_SAND, SANDY_LOAM, SILT_LOAM, LOAM, SANDY_CLAY_LOAM,
... SILT_CLAY_LOAM, CLAY_LOAM, SANDY_CLAY, SILTY_CLAY, CLAY)
(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
>>> (SEALED, FIELD, WINE, ORCHARD, SOIL, PASTURE, WETLAND, TREES,
... CONIFER, DECIDIOUS, MIXED)
(12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22)
- hydpy.models.wland.wland_constants.SAND = 1¶
Soil character constant for sand.
- hydpy.models.wland.wland_constants.LOAMY_SAND = 2¶
Soil character constant for loamy sand.
- hydpy.models.wland.wland_constants.SANDY_LOAM = 3¶
Soil character constant for sandy loam.
- hydpy.models.wland.wland_constants.SILT_LOAM = 4¶
Soil character constant for silt loam.
- hydpy.models.wland.wland_constants.LOAM = 5¶
Soil character constant for loam.
- hydpy.models.wland.wland_constants.SANDY_CLAY_LOAM = 6¶
Soil character constant for sandy clay loam.
- hydpy.models.wland.wland_constants.SILT_CLAY_LOAM = 7¶
Soil character constant for silt clay loam.
- hydpy.models.wland.wland_constants.CLAY_LOAM = 8¶
Soil character constant for clay loam.
- hydpy.models.wland.wland_constants.SANDY_CLAY = 9¶
Soil character constant for sandy clay.
- hydpy.models.wland.wland_constants.SILTY_CLAY = 10¶
Soil character constant for silty clay.
- hydpy.models.wland.wland_constants.CLAY = 11¶
Soil character constant for clay.
- hydpy.models.wland.wland_constants.SEALED = 12¶
Land type constant for sealed surface.
- hydpy.models.wland.wland_constants.FIELD = 13¶
Land type constant for fields.
- hydpy.models.wland.wland_constants.WINE = 14¶
Land type constant for viticulture.
- hydpy.models.wland.wland_constants.ORCHARD = 15¶
Land type constant for orchards.
- hydpy.models.wland.wland_constants.SOIL = 16¶
Land type constant for bare, unsealed soils.
- hydpy.models.wland.wland_constants.PASTURE = 17¶
Land type constant for pasture.
- hydpy.models.wland.wland_constants.WETLAND = 18¶
Land type constant for wetlands.
- hydpy.models.wland.wland_constants.TREES = 19¶
Land type constant for loose tree populations.
- hydpy.models.wland.wland_constants.CONIFER = 20¶
Land type constant for coniferous forests.
- hydpy.models.wland.wland_constants.DECIDIOUS = 21¶
Land type constant for deciduous forests.
- hydpy.models.wland.wland_constants.MIXED = 22¶
Land type constant for mixed forests.
- hydpy.models.wland.wland_constants.WATER = 23¶
Land type constant for the surface water storage.
Control parameters¶
- class hydpy.models.wland.ControlParameters(master: Parameters, cls_fastaccess: type[FastAccessParameter] | None = None, cymodel: CyModelProtocol | None = None)
Bases:
SubParameters
Control parameters of model wland.
- The following classes are selected:
AT()
Total area [km²].NU()
Number of hydrological response units [-].LT()
Landuse type [-].ER()
Elevated region [-].AUR()
Relative area of each hydrological response unit [-].GL()
The lowland region’s average ground level [m].BL()
Channel bottom level [m].CP()
Factor for correcting precipitation [-].LAI()
Leaf area index [-].IH()
Interception capacity with respect to the leaf surface area [mm].TT()
Threshold temperature for snow/rain [°C].TI()
Temperature interval with a mixture of snow and rain [°C].DDF()
Day degree factor [mm/°C/T].DDT()
Day degree threshold temperature [°C].CWE()
Wetness index parameter for the elevated region [mm].CW()
Wetness index parameter for the lowland region [mm].CV()
Vadose zone relaxation time constant for the lowland region [T].CGE()
Groundwater reservoir constant for the elevated region [mm T].CG()
Groundwater reservoir constant for the lowland region [mm T].RG()
Groundwater reservoir restriction [-].CGF()
Groundwater reservoir flood factor [1/mm].DGC()
Direct groundwater connect [-].CQ()
Quickflow reservoir relaxation time [T].B()
Pore size distribution parameter [-].PsiAE()
Air entry pressure [mm].ThetaS()
Soil moisture content at saturation [-].ThetaR()
Residual soil moisture deficit at tension saturation [-].AC()
Air capacity for the elevated region [mm].Zeta1()
Curvature parameter of the evapotranspiration reduction function [-].Zeta2()
Inflection point of the evapotranspiration reduction function [mm].SH()
General smoothing parameter related to the height of water columns [mm].ST()
General smoothing parameter related to temperature [°C].
- class hydpy.models.wland.wland_control.AT(subvars: SubParameters)[source]¶
Bases:
Parameter
Total area [km²].
- class hydpy.models.wland.wland_control.NU(subvars: SubParameters)[source]¶
Bases:
Parameter
Number of hydrological response units [-].
- Required by the methods:
Parameter
NU
automatically sets the length of most 1-dimensional parameters and sequences of HydPy-W:>>> from hydpy.models.wland import * >>> parameterstep() >>> nu(3) >>> lt.shape (3,) >>> states.ic.shape (3,)
Changing the value of parameter
NU
reshapes the related parameters and sequences and eventually deletes predefined values:>>> states.ic = 1.0 >>> states.ic ic(1.0, 1.0, 1.0) >>> nu(2) >>> states.ic ic(nan, nan)
Redefining the same value for parameter
NU
does not affect any related parameter and sequence object:>>> states.ic = 1.0 >>> states.ic ic(1.0, 1.0) >>> nu(2) >>> states.ic ic(1.0, 1.0)
- class hydpy.models.wland.wland_control.LT(subvars: SubParameters)[source]¶
Bases:
NameParameter
Landuse type [-].
- Required by the methods:
For better readability, use the land-use-related constants defined in module
wland_constants
to set the individual hydrological response units’ land-use types:>>> from hydpy.models.wland import * >>> parameterstep() >>> nu(12) >>> lt(SEALED, FIELD, WINE, ORCHARD, SOIL, PASTURE, ... WETLAND, TREES, CONIFER, DECIDIOUS, MIXED, WATER) >>> lt lt(SEALED, FIELD, WINE, ORCHARD, SOIL, PASTURE, WETLAND, TREES, CONIFER, DECIDIOUS, MIXED, WATER)
Note that
wland
generally requires a single surface water storage unit, which must be placed at the last position. Trying to set another land type causes the following error:>>> lt(SEALED, FIELD, WINE, ORCHARD, SOIL, PASTURE, ... WETLAND, TREES, CONIFER, DECIDIOUS, MIXED, MIXED) Traceback (most recent call last): ... ValueError: While trying to set the land use types via parameter `lt` of element `?`, the following error occurred: The last land use type must be `WATER`, but `MIXED` is given.
Trying to define multiple such units results in the following error:
>>> lt(SEALED, FIELD, WINE, ORCHARD, SOIL, PASTURE, ... WETLAND, TREES, CONIFER, DECIDIOUS, WATER, WATER) Traceback (most recent call last): ... ValueError: While trying to set the land use types via parameter `lt` of element `?`, the following error occurred: W-Land requires a single surface water storage unit, but 2 units are defined as such.
- class hydpy.models.wland.wland_control.ER(subvars: SubParameters)[source]¶
Bases:
LanduseParameterLand
Elevated region [-].
- Required by the methods:
- class hydpy.models.wland.wland_control.AUR(subvars: SubParameters)[source]¶
Bases:
Parameter
Relative area of each hydrological response unit [-].
- Required by the methods:
- class hydpy.models.wland.wland_control.GL(subvars: SubParameters)[source]¶
Bases:
Parameter
The lowland region’s average ground level [m].
- Required by the method:
- class hydpy.models.wland.wland_control.BL(subvars: SubParameters)[source]¶
Bases:
Parameter
Channel bottom level [m].
- Required by the method:
- class hydpy.models.wland.wland_control.CP(subvars: SubParameters)[source]¶
Bases:
Parameter
Factor for correcting precipitation [-].
- Required by the method:
- class hydpy.models.wland.wland_control.LAI(subvars: SubParameters)[source]¶
Bases:
LanduseMonthParameter
Leaf area index [-].
- Required by the method:
- class hydpy.models.wland.wland_control.IH(subvars: SubParameters)[source]¶
Bases:
Parameter
Interception capacity with respect to the leaf surface area [mm].
- Required by the method:
- class hydpy.models.wland.wland_control.TT(subvars: SubParameters)[source]¶
Bases:
Parameter
Threshold temperature for snow/rain [°C].
- Required by the method:
- class hydpy.models.wland.wland_control.TI(subvars: SubParameters)[source]¶
Bases:
Parameter
Temperature interval with a mixture of snow and rain [°C].
- Required by the method:
- class hydpy.models.wland.wland_control.DDF(subvars: SubParameters)[source]¶
Bases:
LanduseParameterLand
Day degree factor [mm/°C/T].
- Required by the method:
- class hydpy.models.wland.wland_control.DDT(subvars: SubParameters)[source]¶
Bases:
Parameter
Day degree threshold temperature [°C].
- Required by the method:
- class hydpy.models.wland.wland_control.CWE(subvars: SubParameters)[source]¶
Bases:
Parameter
Wetness index parameter for the elevated region [mm].
- Required by the method:
- class hydpy.models.wland.wland_control.CW(subvars: SubParameters)[source]¶
Bases:
Parameter
Wetness index parameter for the lowland region [mm].
- Required by the method:
- class hydpy.models.wland.wland_control.CV(subvars: SubParameters)[source]¶
Bases:
Parameter
Vadose zone relaxation time constant for the lowland region [T].
- Required by the methods:
- class hydpy.models.wland.wland_control.CGE(subvars: SubParameters)[source]¶
Bases:
Parameter
Groundwater reservoir constant for the elevated region [mm T].
- Required by the method:
- class hydpy.models.wland.wland_control.CG(subvars: SubParameters)[source]¶
Bases:
Parameter
Groundwater reservoir constant for the lowland region [mm T].
- Required by the method:
- class hydpy.models.wland.wland_control.RG(subvars: SubParameters)[source]¶
Bases:
Parameter
Groundwater reservoir restriction [-].
- Required by the method:
- class hydpy.models.wland.wland_control.CGF(subvars: SubParameters)[source]¶
Bases:
Parameter
Groundwater reservoir flood factor [1/mm].
- Required by the method:
- class hydpy.models.wland.wland_control.DGC(subvars: SubParameters)[source]¶
Bases:
Parameter
Direct groundwater connect [-].
- Required by the method:
- class hydpy.models.wland.wland_control.CQ(subvars: SubParameters)[source]¶
Bases:
Parameter
Quickflow reservoir relaxation time [T].
- Required by the method:
- class hydpy.models.wland.wland_control.B(subvars: SubParameters)[source]¶
Bases:
SoilParameter
Pore size distribution parameter [-].
- Required by the methods:
Calc_DGEq_V1
Calc_DVEq_V1
Calc_DVEq_V2
Calc_DVEq_V3
Calc_DVEq_V4
Calc_GF_V1
Return_DVH_V1
Return_DVH_V2
Return_ErrorDV_V1
Parameter
B
comes with the following default values:>>> from hydpy.models.wland import * >>> parameterstep() >>> b.print_defaults() SAND: 4.05 LOAMY_SAND: 4.38 SANDY_LOAM: 4.9 SILT_LOAM: 5.3 LOAM: 5.39 SANDY_CLAY_LOAM: 7.12 SILT_CLAY_LOAM: 7.75 CLAY_LOAM: 8.52 SANDY_CLAY: 10.4 SILTY_CLAY: 10.4 CLAY: 11.4
See the documentation on class
SoilParameter
for further information.
- class hydpy.models.wland.wland_control.PsiAE(subvars: SubParameters)[source]¶
Bases:
SoilParameter
Air entry pressure [mm].
- Required by the methods:
Calc_DGEq_V1
Calc_DVEq_V1
Calc_DVEq_V2
Calc_DVEq_V3
Calc_DVEq_V4
Calc_GF_V1
Return_DVH_V1
Return_DVH_V2
Return_ErrorDV_V1
Parameter
PsiAE
comes with the following default values:>>> from hydpy.models.wland import * >>> parameterstep() >>> psiae.print_defaults() SAND: 121.0 LOAMY_SAND: 90.0 SANDY_LOAM: 218.0 SILT_LOAM: 786.0 LOAM: 478.0 SANDY_CLAY_LOAM: 299.0 SILT_CLAY_LOAM: 356.0 CLAY_LOAM: 630.0 SANDY_CLAY: 153.0 SILTY_CLAY: 490.0 CLAY: 405.0
See the documentation on class
SoilParameter
for further information.
- class hydpy.models.wland.wland_control.ThetaS(subvars: SubParameters)[source]¶
Bases:
SoilParameter
Soil moisture content at saturation [-].
- Required by the methods:
Calc_CDG_V1
Calc_DGEq_V1
Calc_DVEq_V1
Calc_DVEq_V2
Calc_DVEq_V3
Calc_DVEq_V4
Calc_GF_V1
Return_DVH_V1
Return_DVH_V2
Return_ErrorDV_V1
Update_HGE_V1
Parameter
ThetaS
comes with the following default values:>>> from hydpy.models.wland import * >>> parameterstep() >>> thetas.print_defaults() SAND: 0.395 LOAMY_SAND: 0.41 SANDY_LOAM: 0.435 SILT_LOAM: 0.485 LOAM: 0.451 SANDY_CLAY_LOAM: 0.42 SILT_CLAY_LOAM: 0.477 CLAY_LOAM: 0.476 SANDY_CLAY: 0.426 SILTY_CLAY: 0.492 CLAY: 0.482
See the documentation on class
SoilParameter
for further information.- trim(lower=None, upper=None) bool [source]¶
Trim
ThetaS
following \(1e^{-6} \leq ThetaS \leq 1.0\) and, ifThetaR
exists for the relevant application model, also following \(ThetaR \leq ThetaS\).>>> from hydpy.models.wland import * >>> parameterstep()
>>> thetas(0.0) >>> thetas thetas(0.000001)
>>> thetar.value = 0.5 >>> thetas(0.4) >>> thetas thetas(0.5)
>>> thetas(soil=SANDY_LOAM) >>> thetas thetas(0.5)
>>> thetas(1.01) >>> thetas thetas(1.0)
- class hydpy.models.wland.wland_control.ThetaR(subvars: SubParameters)[source]¶
Bases:
Parameter
Residual soil moisture deficit at tension saturation [-].
- Required by the methods:
Calc_DGEq_V1
Calc_DVEq_V3
Calc_DVEq_V4
Calc_GF_V1
Return_DVH_V2
Return_ErrorDV_V1
- class hydpy.models.wland.wland_control.AC(subvars: SubParameters)[source]¶
Bases:
Parameter
Air capacity for the elevated region [mm].
- Required by the method:
- ToDo: We should principally derive
AC
fromSoilParameter
, but Brauer et al. (2014) provides no soil-specific default values for it because it is not part of the original WALRUS model. Do we want to determine consistent ones by ourselves?
- class hydpy.models.wland.wland_control.Zeta1(subvars: SubParameters)[source]¶
Bases:
Parameter
Curvature parameter of the evapotranspiration reduction function [-].
- Required by the method:
- class hydpy.models.wland.wland_control.Zeta2(subvars: SubParameters)[source]¶
Bases:
Parameter
Inflection point of the evapotranspiration reduction function [mm].
- Required by the method:
- class hydpy.models.wland.wland_control.SH(subvars: SubParameters)[source]¶
Bases:
Parameter
General smoothing parameter related to the height of water columns [mm].
- Required by the methods:
- class hydpy.models.wland.wland_control.ST(subvars: SubParameters)[source]¶
Bases:
Parameter
General smoothing parameter related to temperature [°C].
Derived parameters¶
- class hydpy.models.wland.DerivedParameters(master: Parameters, cls_fastaccess: type[FastAccessParameter] | None = None, cymodel: CyModelProtocol | None = None)
Bases:
SubParameters
Derived parameters of model wland.
- The following classes are selected:
MOY()
References the “global” month of the year index array [-].NUL()
Number of land-related hydrological response units [-].NUGE()
Number of groundwater-affected hydrological response units in the elevated region [-].NUG()
Number of groundwater-affected hydrological response units in the lowland region [-].ALR()
Relative land area [-].ASR()
Relative surface water area fraction [-].AGRE()
Relative groundwater area in the elevated region [-].AGR()
Relative groundwater area in the lowland region [-].QF()
Factor for converting mm/T to m³/s [T m³ / mm s].CD()
Channel depth [mm].RH1()
Regularisation parameter related to the height of water columns used when applying regularisation functionsmooth_logistic1()
[mm].RH2()
Regularisation parameter related to the height of water columns used when applying regularisation functionsmooth_logistic2()
[mm].RT2()
Regularisation parameter related to temperature for applying regularisation functionsmooth_logistic2()
) [°C].
- class hydpy.models.wland.wland_derived.MOY(subvars: SubParameters)[source]¶
Bases:
MOYParameter
References the “global” month of the year index array [-].
- Required by the method:
- class hydpy.models.wland.wland_derived.NUL(subvars: SubParameters)[source]¶
Bases:
Parameter
Number of land-related hydrological response units [-].
- Required by the methods:
Calc_AM_V1
Calc_EI_V1
Calc_ES_V1
Calc_ETVE_ETV_V1
Calc_ET_V1
Calc_PE_PET_PETModel_V1
Calc_PE_PET_PETModel_V2
Calc_PE_PET_V1
Calc_PM_V1
Calc_PQ_V1
Calc_PVE_PV_V1
Calc_RF_V1
Calc_SF_V1
Calc_TF_V1
Update_IC_V1
Update_SP_V1
- class hydpy.models.wland.wland_derived.NUGE(subvars: SubParameters)[source]¶
Bases:
Parameter
Number of groundwater-affected hydrological response units in the elevated region [-].
- Required by the methods:
Calc_BetaE_Beta_V1
Calc_FGSE_V1
Calc_GR_V1
Calc_WE_W_V1
Update_DVE_V1
Update_HGE_V1
- update()[source]¶
Update
NUG
based on \(NUGE = \sum (ER \ \land \ LT \neq WATER \ \land \ LT \neq SEALED)\).>>> from hydpy.models.wland import * >>> parameterstep() >>> nu(10) >>> lt(SEALED, FIELD, SEALED, CONIFER, SEALED, ... SEALED, FIELD, SEALED, SEALED, WATER) >>> er(True, True, True, True, True, ... False, False, False, False, False) >>> derived.nuge.update() >>> derived.nuge nuge(2)
- class hydpy.models.wland.wland_derived.NUG(subvars: SubParameters)[source]¶
Bases:
Parameter
Number of groundwater-affected hydrological response units in the lowland region [-].
- Required by the methods:
Calc_BetaE_Beta_V1
Calc_CDG_V1
Calc_CDG_V2
Calc_DGEq_V1
Calc_DVEq_V1
Calc_DVEq_V2
Calc_DVEq_V3
Calc_DVEq_V4
Calc_FGS_V1
Calc_WE_W_V1
Return_ErrorDV_V1
Update_DG_V1
Update_DV_V1
- update()[source]¶
Update
NUG
based on \(NUG = \sum (\overline{ER} \ \land \ LT \neq WATER \ \land \ LT \neq SEALED)\).>>> from hydpy.models.wland import * >>> parameterstep() >>> nu(10) >>> lt(SEALED, FIELD, SEALED, CONIFER, SEALED, ... SEALED, FIELD, SEALED, SEALED, WATER) >>> er(False, False, False, False, False, ... True, True, True, True, False) >>> derived.nug.update() >>> derived.nug nug(2)
- class hydpy.models.wland.wland_derived.ALR(subvars: SubParameters)[source]¶
Bases:
Parameter
Relative land area [-].
- Required by the methods:
- class hydpy.models.wland.wland_derived.ASR(subvars: SubParameters)[source]¶
Bases:
Parameter
Relative surface water area fraction [-].
- Required by the methods:
- class hydpy.models.wland.wland_derived.AGRE(subvars: SubParameters)[source]¶
Bases:
Parameter
Relative groundwater area in the elevated region [-].
- Required by the methods:
- update()[source]¶
Update
AGR
based on \(AGRE = \sum_{i=1}^{NU} AUR_i \cdot (ER_i \ \land \ LT_i \neq WATER \ \land \ LT_i \neq SEALED)\).>>> from hydpy.models.wland import * >>> parameterstep() >>> nu(6) >>> lt(SEALED, SOIL, SEALED, FIELD, FIELD, WATER) >>> er(True, True, True, True, False, False) >>> aur(0.02, 0.06, 0.1, 0.14, 0.18, 0.5) >>> derived.agre.update() >>> derived.agre agre(0.2)
- class hydpy.models.wland.wland_derived.AGR(subvars: SubParameters)[source]¶
Bases:
Parameter
Relative groundwater area in the lowland region [-].
- Required by the methods:
Calc_ETVE_ETV_V1
Calc_ET_V1
Calc_FXG_V1
Calc_PVE_PV_V1
Calc_RH_V1
Update_DV_V1
Update_HS_V1
- update()[source]¶
Update
AGR
based on \(AGR = \sum_{i=1}^{NU} AUR_i \cdot (\overline{ER_i} \ \land \ LT_i \neq WATER \ \land \ LT_i \neq SEALED)\).>>> from hydpy.models.wland import * >>> parameterstep() >>> nu(6) >>> lt(SEALED, SOIL, SEALED, FIELD, FIELD, WATER) >>> er(False, False, False, False, True, False) >>> aur(0.02, 0.06, 0.1, 0.14, 0.18, 0.5) >>> derived.agr.update() >>> derived.agr agr(0.2)
- class hydpy.models.wland.wland_derived.QF(subvars: SubParameters)[source]¶
Bases:
Parameter
Factor for converting mm/T to m³/s [T m³ / mm s].
- Required by the method:
- class hydpy.models.wland.wland_derived.CD(subvars: SubParameters)[source]¶
Bases:
Parameter
Channel depth [mm].
- Required by the method:
- class hydpy.models.wland.wland_derived.RH1(subvars: SubParameters)[source]¶
Bases:
Parameter
Regularisation parameter related to the height of water columns used when applying regularisation function
smooth_logistic1()
[mm].- Required by the methods:
Calc_AM_V1
Calc_CDG_V1
Calc_CDG_V2
Calc_DVEq_V2
Calc_DVEq_V4
Calc_EI_V1
Calc_ES_V1
Calc_GF_V1
Calc_TF_V1
Return_DVH_V1
Return_DVH_V2
- update()[source]¶
Calculate the smoothing parameter value.
The documentation on module
smoothtools
explains the following example in some detail:>>> from hydpy.models.wland import * >>> parameterstep() >>> sh(0.0) >>> derived.rh1.update() >>> from hydpy.cythons.smoothutils import smooth_logistic1 >>> from hydpy import round_ >>> round_(smooth_logistic1(0.1, derived.rh1)) 1.0 >>> sh(2.5) >>> derived.rh1.update() >>> round_(smooth_logistic1(2.5, derived.rh1)) 0.99
- class hydpy.models.wland.wland_derived.RH2(subvars: SubParameters)[source]¶
Bases:
Parameter
Regularisation parameter related to the height of water columns used when applying regularisation function
smooth_logistic2()
[mm].- Required by the methods:
- update()[source]¶
Calculate the smoothing parameter value.
The documentation on module
smoothtools
explains the following example in some detail:>>> from hydpy.models.wland import * >>> from hydpy.cythons.smoothutils import smooth_logistic2 >>> from hydpy import round_ >>> parameterstep() >>> sh(0.0) >>> derived.rh2.update() >>> round_(smooth_logistic2(0.0, derived.rh2)) 0.0 >>> sh(2.5) >>> derived.rh2.update() >>> round_(smooth_logistic2(2.5, derived.rh2)) 2.51
- class hydpy.models.wland.wland_derived.RT2(subvars: SubParameters)[source]¶
Bases:
Parameter
Regularisation parameter related to temperature for applying regularisation function
smooth_logistic2()
) [°C].- Required by the method:
- update()[source]¶
Calculate the smoothing parameter value.
The documentation on module
smoothtools
explains the following example in some detail:>>> from hydpy.models.wland import * >>> from hydpy.cythons.smoothutils import smooth_logistic2 >>> from hydpy import round_ >>> parameterstep() >>> st(0.0) >>> derived.rt2.update() >>> round_(smooth_logistic2(0.0, derived.rt2)) 0.0 >>> st(2.5) >>> derived.rt2.update() >>> round_(smooth_logistic2(2.5, derived.rt2)) 2.51
Fixed parameters¶
- class hydpy.models.wland.FixedParameters(master: Parameters, cls_fastaccess: type[FastAccessParameter] | None = None, cymodel: CyModelProtocol | None = None)
Bases:
SubParameters
Fixed parameters of model wland.
- The following classes are selected:
Pi()
π [-].
- class hydpy.models.wland.wland_fixed.Pi(subvars: SubParameters)[source]¶
Bases:
FixedParameter
π [-].
- Required by the method:
Solver parameters¶
- class hydpy.models.wland.SolverParameters(master: Parameters, cls_fastaccess: type[FastAccessParameter] | None = None, cymodel: CyModelProtocol | None = None)
Bases:
SubParameters
Solver parameters of model wland.
- The following classes are selected:
AbsErrorMax()
Absolute numerical error tolerance [mm/T].RelErrorMax()
Relative numerical error tolerance [-].RelDTMin()
Smallest relative integration time step size allowed [-].RelDTMax()
Largest relative integration time step size allowed [-].
- class hydpy.models.wland.wland_solver.AbsErrorMax(subvars)[source]¶
Bases:
SolverParameter
Absolute numerical error tolerance [mm/T].
- class hydpy.models.wland.wland_solver.RelErrorMax(subvars)[source]¶
Bases:
SolverParameter
Relative numerical error tolerance [-].
- class hydpy.models.wland.wland_solver.RelDTMin(subvars)[source]¶
Bases:
SolverParameter
Smallest relative integration time step size allowed [-].
- class hydpy.models.wland.wland_solver.RelDTMax(subvars)[source]¶
Bases:
SolverParameter
Largest relative integration time step size allowed [-].
Sequence Features¶
Sequence tools¶
- class hydpy.models.wland.wland_sequences.BaseFluxSequence1D(subvars: ModelSequences[ModelSequence, FastAccess])[source]¶
Bases:
FluxSequence
Base class for
FluxSequence1DComplete
andFluxSequence1DLand
that supports aggregation with respect toAUR
.
- class hydpy.models.wland.wland_sequences.FluxSequence1DComplete(subvars: ModelSequences[ModelSequence, FastAccess])[source]¶
Bases:
BaseFluxSequence1D
Base class for 1-dimensional flux sequences that contain values for all hydrological response units.
The following example shows how subclass
PET
works:>>> from hydpy.models.wland import * >>> parameterstep() >>> nu(4) >>> lt(FIELD, CONIFER, SEALED, WATER) >>> aur(0.1, 0.2, 0.3, 0.4) >>> fluxes.pe = 5.0, 2.0, 4.0, 1.0 >>> from hydpy import round_ >>> round_(fluxes.pe.average_values()) 2.5
- mask¶
- class hydpy.models.wland.wland_sequences.FluxSequence1DLand(subvars: ModelSequences[ModelSequence, FastAccess])[source]¶
Bases:
BaseFluxSequence1D
Base class for 1-dimensional flux sequences that contain values for the land-related hydrological response units.
The following example shows how subclass
EI
works:>>> from hydpy.models.wland import * >>> parameterstep() >>> nu(5) >>> lt(FIELD, CONIFER, SEALED, FIELD, WATER) >>> aur(0.05, 0.1, 0.15, 0.2, 0.5) >>> fluxes.ei = 5.0, 2.0, 4.0, 1.0, nan >>> from hydpy import round_ >>> round_(fluxes.ei.average_values()) 2.5
- mask¶
- class hydpy.models.wland.wland_sequences.FluxSequence1DSoil(subvars: ModelSequences[ModelSequence, FastAccess])[source]¶
Bases:
BaseFluxSequence1D
Base class for 1-dimensional flux sequences that contain values for the soil-related hydrological response units.
The following example shows how subclass
PET
works:>>> from hydpy.models.wland import * >>> parameterstep() >>> nu(5) >>> lt(FIELD, CONIFER, SEALED, FIELD, WATER) >>> aur(0.1, 0.2, 0.12, 0.3, 0.2) >>> fluxes.pet = 5.0, 2.0, 4.0, 1.0, nan >>> from hydpy import round_ >>> round_(fluxes.pet.average_values()) 2.0
- mask¶
- class hydpy.models.wland.wland_sequences.StateSequence1DLand(subvars: ModelSequences[ModelSequence, FastAccess])[source]¶
Bases:
StateSequence
Base class for 1-dimensional state sequences that support aggregation with respect to
AUR
for all land-related hydrological response units.The following example shows how subclass
IC
works:>>> from hydpy.models.wland import * >>> parameterstep() >>> nu(5) >>> lt(FIELD, CONIFER, SEALED, FIELD, WATER) >>> aur(0.05, 0.1, 0.15, 0.2, 0.5) >>> states.ic = 5.0, 2.0, 4.0, 1.0, nan >>> from hydpy import round_ >>> round_(states.ic.average_values()) 2.5
- mask¶
Input sequences¶
- class hydpy.models.wland.InputSequences(master: Sequences, cls_fastaccess: type[TypeFastAccess_co] | None = None, cymodel: CyModelProtocol | None = None)
Bases:
InputSequences
Input sequences of model wland.
- class hydpy.models.wland.wland_inputs.T(subvars: ModelSequences[ModelSequence, FastAccess])[source]¶
Bases:
InputSequence
Air temperature [°C].
- Required by the methods:
Calc_FR_V1
Calc_PM_V1
Get_MeanTemperature_V1
Get_Temperature_V1
- STANDARD_NAME: ClassVar[StandardInputNames] = 'air_temperature'¶
- class hydpy.models.wland.wland_inputs.P(subvars: ModelSequences[ModelSequence, FastAccess])[source]¶
Bases:
InputSequence
Precipitation [mm/T].
- Required by the method:
- STANDARD_NAME: ClassVar[StandardInputNames] = 'precipitation'¶
- class hydpy.models.wland.wland_inputs.FXG(subvars: ModelSequences[ModelSequence, FastAccess])[source]¶
Bases:
InputSequence
Seepage/extraction (normalised to
AT
) [mm/T].- Required by the method:
- STANDARD_NAME: ClassVar[StandardInputNames] = 'artificial_groundwater_recharge'¶
- class hydpy.models.wland.wland_inputs.FXS(subvars: ModelSequences[ModelSequence, FastAccess])[source]¶
Bases:
InputSequence
Surface water supply/extraction (normalised to
AT
) [mm/T].- Required by the method:
- STANDARD_NAME: ClassVar[StandardInputNames] = 'artificial_surface_water_supply'¶
Factor sequences¶
- class hydpy.models.wland.FactorSequences(master: Sequences, cls_fastaccess: type[TypeFastAccess_co] | None = None, cymodel: CyModelProtocol | None = None)
Bases:
FactorSequences
Factor sequences of model wland.
- The following classes are selected:
DHS()
External change of the surface water depth [mm/T].
- class hydpy.models.wland.wland_factors.DHS(subvars: ModelSequences[ModelSequence, FastAccess])[source]¶
Bases:
FactorSequence
External change of the surface water depth [mm/T].
- Calculated by the method:
Flux sequences¶
- class hydpy.models.wland.FluxSequences(master: Sequences, cls_fastaccess: type[TypeFastAccess_co] | None = None, cymodel: CyModelProtocol | None = None)
Bases:
FluxSequences
Flux sequences of model wland.
- The following classes are selected:
PC()
Corrected precipitation [mm/T].PE()
Potential evaporation from the interception and the surface water storage [mm/T].PET()
Potential evapotranspiration from the vadose zone [mm/T].TF()
Total amount of throughfall [mm/T].EI()
Interception evaporation [mm/T].RF()
Rainfall (or, more concrete, the liquid amount of throughfall) [mm/T].SF()
Snowfall (or, more concrete, the frozen amount of throughfall) [mm/T].PM()
Potential snowmelt [mm/T].AM()
Actual snowmelt [mm/T].PS()
Precipitation that enters the surface water reservoir [mm/T].PVE()
Rainfall (and snowmelt) entering the vadose zone in the elevated region [mm/T].PV()
Rainfall (and snowmelt) entering the vadose zone in the lowland region [mm/T].PQ()
Rainfall (and snowmelt) entering the quickflow reservoir [mm/T].ETVE()
Actual evapotranspiration from the vadose zone in the elevated region [mm/T].ETV()
Actual evapotranspiration from the vadose zone in the lowland region [mm/T].ES()
Actual evaporation from the surface water [mm/T].ET()
Total actual evapotranspiration [mm/T].GR()
Groundwater recharge in the elevated region [mm/T].FXS()
Surface water supply/extraction (normalised toASR
) [mm/T].CDG()
Change in the groundwater depth due to percolation and capillary rise [mm/T].FGSE()
Groundwater flow between the elevated and the lowland region [mm/T].FGS()
Groundwater drainage/surface water infiltration [mm/T].FQS()
Quickflow [mm/T].RH()
Runoff height [mm/T].R()
Runoff [m³/s].
- class hydpy.models.wland.wland_fluxes.PC(subvars: ModelSequences[ModelSequence, FastAccess])[source]¶
Bases:
FluxSequence
Corrected precipitation [mm/T].
- Calculated by the method:
- Required by the methods:
- class hydpy.models.wland.wland_fluxes.PE(subvars: ModelSequences[ModelSequence, FastAccess])[source]¶
Bases:
FluxSequence1DComplete
Potential evaporation from the interception and the surface water storage [mm/T].
- Calculated by the methods:
Calc_PE_PET_PETModel_V1
Calc_PE_PET_PETModel_V2
Calc_PE_PET_V1
- Required by the methods:
- class hydpy.models.wland.wland_fluxes.PET(subvars: ModelSequences[ModelSequence, FastAccess])[source]¶
Bases:
FluxSequence1DSoil
Potential evapotranspiration from the vadose zone [mm/T].
- Calculated by the methods:
Calc_PE_PET_PETModel_V1
Calc_PE_PET_PETModel_V2
Calc_PE_PET_V1
- Required by the method:
- class hydpy.models.wland.wland_fluxes.TF(subvars: ModelSequences[ModelSequence, FastAccess])[source]¶
Bases:
FluxSequence1DLand
Total amount of throughfall [mm/T].
- Calculated by the method:
- Required by the methods:
- class hydpy.models.wland.wland_fluxes.EI(subvars: ModelSequences[ModelSequence, FastAccess])[source]¶
Bases:
FluxSequence1DLand
Interception evaporation [mm/T].
- Calculated by the method:
- Required by the methods:
- class hydpy.models.wland.wland_fluxes.RF(subvars: ModelSequences[ModelSequence, FastAccess])[source]¶
Bases:
FluxSequence1DLand
Rainfall (or, more concrete, the liquid amount of throughfall) [mm/T].
- Calculated by the method:
- Required by the methods:
- class hydpy.models.wland.wland_fluxes.SF(subvars: ModelSequences[ModelSequence, FastAccess])[source]¶
Bases:
FluxSequence1DLand
Snowfall (or, more concrete, the frozen amount of throughfall) [mm/T].
- Calculated by the method:
- Required by the method:
- class hydpy.models.wland.wland_fluxes.PM(subvars: ModelSequences[ModelSequence, FastAccess])[source]¶
Bases:
FluxSequence1DLand
Potential snowmelt [mm/T].
- Calculated by the method:
- Required by the method:
- class hydpy.models.wland.wland_fluxes.AM(subvars: ModelSequences[ModelSequence, FastAccess])[source]¶
Bases:
FluxSequence1DLand
Actual snowmelt [mm/T].
- Calculated by the method:
- Required by the methods:
- class hydpy.models.wland.wland_fluxes.PS(subvars: ModelSequences[ModelSequence, FastAccess])[source]¶
Bases:
FluxSequence
Precipitation that enters the surface water reservoir [mm/T].
- Calculated by the method:
- Required by the methods:
- class hydpy.models.wland.wland_fluxes.PVE(subvars: ModelSequences[ModelSequence, FastAccess])[source]¶
Bases:
FluxSequence
Rainfall (and snowmelt) entering the vadose zone in the elevated region [mm/T].
- Calculated by the method:
- Required by the method:
- class hydpy.models.wland.wland_fluxes.PV(subvars: ModelSequences[ModelSequence, FastAccess])[source]¶
Bases:
FluxSequence
Rainfall (and snowmelt) entering the vadose zone in the lowland region [mm/T].
- Calculated by the method:
- Required by the methods:
- class hydpy.models.wland.wland_fluxes.PQ(subvars: ModelSequences[ModelSequence, FastAccess])[source]¶
Bases:
FluxSequence
Rainfall (and snowmelt) entering the quickflow reservoir [mm/T].
- Calculated by the method:
- Required by the method:
- class hydpy.models.wland.wland_fluxes.ETVE(subvars: ModelSequences[ModelSequence, FastAccess])[source]¶
Bases:
FluxSequence
Actual evapotranspiration from the vadose zone in the elevated region [mm/T].
- Calculated by the method:
- Required by the methods:
- class hydpy.models.wland.wland_fluxes.ETV(subvars: ModelSequences[ModelSequence, FastAccess])[source]¶
Bases:
FluxSequence
Actual evapotranspiration from the vadose zone in the lowland region [mm/T].
- Calculated by the method:
- Required by the methods:
- class hydpy.models.wland.wland_fluxes.ES(subvars: ModelSequences[ModelSequence, FastAccess])[source]¶
Bases:
FluxSequence
Actual evaporation from the surface water [mm/T].
- Calculated by the method:
- Required by the methods:
- class hydpy.models.wland.wland_fluxes.ET(subvars: ModelSequences[ModelSequence, FastAccess])[source]¶
Bases:
FluxSequence
Total actual evapotranspiration [mm/T].
- Calculated by the method:
- class hydpy.models.wland.wland_fluxes.GR(subvars: ModelSequences[ModelSequence, FastAccess])[source]¶
Bases:
FluxSequence
Groundwater recharge in the elevated region [mm/T].
- Calculated by the method:
- Required by the methods:
- class hydpy.models.wland.wland_fluxes.FXS(subvars: ModelSequences[ModelSequence, FastAccess])[source]¶
Bases:
FluxSequence
Surface water supply/extraction (normalised to
ASR
) [mm/T].- Calculated by the method:
- Required by the methods:
- class hydpy.models.wland.wland_fluxes.FXG(subvars: ModelSequences[ModelSequence, FastAccess])[source]¶
Bases:
FluxSequence
Seepage/extraction (normalised to
ALR
) [mm/T].- Calculated by the method:
- Required by the methods:
- class hydpy.models.wland.wland_fluxes.CDG(subvars: ModelSequences[ModelSequence, FastAccess])[source]¶
Bases:
FluxSequence
Change in the groundwater depth due to percolation and capillary rise [mm/T].
- Calculated by the methods:
- Required by the method:
- class hydpy.models.wland.wland_fluxes.FGSE(subvars: ModelSequences[ModelSequence, FastAccess])[source]¶
Bases:
FluxSequence
Groundwater flow between the elevated and the lowland region [mm/T].
- Calculated by the method:
- Required by the methods:
- class hydpy.models.wland.wland_fluxes.FGS(subvars: ModelSequences[ModelSequence, FastAccess])[source]¶
Bases:
FluxSequence
Groundwater drainage/surface water infiltration [mm/T].
- Calculated by the method:
- Required by the methods:
Calc_CDG_V1
Calc_CDG_V2
Calc_RH_V1
Update_DV_V1
Update_HS_V1
- class hydpy.models.wland.wland_fluxes.FQS(subvars: ModelSequences[ModelSequence, FastAccess])[source]¶
Bases:
FluxSequence
Quickflow [mm/T].
- Calculated by the method:
- Required by the methods:
- class hydpy.models.wland.wland_fluxes.RH(subvars: ModelSequences[ModelSequence, FastAccess])[source]¶
Bases:
FluxSequence
Runoff height [mm/T].
- Calculated by the method:
- Required by the methods:
- class hydpy.models.wland.wland_fluxes.R(subvars: ModelSequences[ModelSequence, FastAccess])[source]¶
Bases:
FluxSequence
Runoff [m³/s].
State sequences¶
- class hydpy.models.wland.StateSequences(master: Sequences, cls_fastaccess: type[TypeFastAccess_co] | None = None, cymodel: CyModelProtocol | None = None)
Bases:
StateSequences
State sequences of model wland.
- The following classes are selected:
IC()
Interception storage [mm].SP()
Snow pack [mm].DVE()
Storage deficit of the vadose zone in the elevated region [mm].DV()
Storage deficit of the vadose zone in the lowland region [mm].HGE()
Groundwater level in the elevated region [mm].DG()
Groundwater depth in the lowland region [mm].HQ()
Level of the quickflow reservoir [mm].HS()
Surface water level [mm].
- class hydpy.models.wland.wland_states.IC(subvars: ModelSequences[ModelSequence, FastAccess])[source]¶
Bases:
StateSequence1DLand
Interception storage [mm].
- Updated by the method:
- Required by the methods:
- class hydpy.models.wland.wland_states.SP(subvars: ModelSequences[ModelSequence, FastAccess])[source]¶
Bases:
StateSequence1DLand
Snow pack [mm].
- Updated by the method:
- Required by the methods:
- class hydpy.models.wland.wland_states.DVE(subvars: ModelSequences[ModelSequence, FastAccess])[source]¶
Bases:
StateSequence
Storage deficit of the vadose zone in the elevated region [mm].
- Updated by the method:
- Required by the methods:
- class hydpy.models.wland.wland_states.DV(subvars: ModelSequences[ModelSequence, FastAccess])[source]¶
Bases:
StateSequence
Storage deficit of the vadose zone in the lowland region [mm].
- Updated by the method:
- Required by the methods:
Calc_BetaE_Beta_V1
Calc_CDG_V1
Calc_CDG_V2
Calc_DGEq_V1
Calc_WE_W_V1
Return_ErrorDV_V1
- class hydpy.models.wland.wland_states.HGE(subvars: ModelSequences[ModelSequence, FastAccess])[source]¶
Bases:
StateSequence
Groundwater level in the elevated region [mm].
- Updated by the method:
- Required by the method:
- class hydpy.models.wland.wland_states.DG(subvars: ModelSequences[ModelSequence, FastAccess])[source]¶
Bases:
StateSequence
Groundwater depth in the lowland region [mm].
- Updated by the method:
- Required by the methods:
Calc_CDG_V1
Calc_CDG_V2
Calc_DVEq_V1
Calc_DVEq_V2
Calc_DVEq_V3
Calc_DVEq_V4
Calc_FGSE_V1
Calc_FGS_V1
Calc_GF_V1
- class hydpy.models.wland.wland_states.HQ(subvars: ModelSequences[ModelSequence, FastAccess])[source]¶
Bases:
StateSequence
Level of the quickflow reservoir [mm].
- Updated by the method:
- Required by the method:
- class hydpy.models.wland.wland_states.HS(subvars: ModelSequences[ModelSequence, FastAccess])[source]¶
Bases:
StateSequence
Surface water level [mm].
- Updated by the methods:
- Required by the methods:
Outlet sequences¶
- class hydpy.models.wland.OutletSequences(master: Sequences, cls_fastaccess: type[TypeFastAccess_co] | None = None, cymodel: CyModelProtocol | None = None)
Bases:
OutletSequences
Outlet sequences of model wland.
- The following classes are selected:
Q()
Discharge [m³/s].
- class hydpy.models.wland.wland_outlets.Q(subvars: ModelSequences[ModelSequence, FastAccess])[source]¶
Bases:
OutletSequence
Discharge [m³/s].
- Calculated by the method:
Aide sequences¶
- class hydpy.models.wland.AideSequences(master: Sequences, cls_fastaccess: type[TypeFastAccess_co] | None = None, cymodel: CyModelProtocol | None = None)
Bases:
AideSequences
Aide sequences of model wland.
- The following classes are selected:
FR()
Fraction rainfall / total precipitation [-].WE()
Wetness index in the elevated region [-].W()
Wetness index in the lowland region [-].BetaE()
Evapotranspiration reduction factor in the elevated region [-].Beta()
Evapotranspiration reduction factor in the lowland region [-].DVEq()
Equilibrium storage deficit of the vadose zone for the actual groundwater depth [mm].DGEq()
Equilibrium groundwater depth for the actual storage deficit of the vadose zone [mm].GF()
Gain factor for changes in groundwater depth [-].
- class hydpy.models.wland.wland_aides.FR(subvars: ModelSequences[ModelSequence, FastAccess])[source]¶
Bases:
AideSequence
Fraction rainfall / total precipitation [-].
- Calculated by the method:
- Required by the methods:
- class hydpy.models.wland.wland_aides.WE(subvars: ModelSequences[ModelSequence, FastAccess])[source]¶
Bases:
AideSequence
Wetness index in the elevated region [-].
- Calculated by the method:
- Required by the methods:
- class hydpy.models.wland.wland_aides.W(subvars: ModelSequences[ModelSequence, FastAccess])[source]¶
Bases:
AideSequence
Wetness index in the lowland region [-].
- Calculated by the method:
- Required by the methods:
- class hydpy.models.wland.wland_aides.BetaE(subvars: ModelSequences[ModelSequence, FastAccess])[source]¶
Bases:
AideSequence
Evapotranspiration reduction factor in the elevated region [-].
- Calculated by the method:
- Required by the method:
- class hydpy.models.wland.wland_aides.Beta(subvars: ModelSequences[ModelSequence, FastAccess])[source]¶
Bases:
AideSequence
Evapotranspiration reduction factor in the lowland region [-].
- Calculated by the method:
- Required by the method:
- class hydpy.models.wland.wland_aides.DVEq(subvars: ModelSequences[ModelSequence, FastAccess])[source]¶
Bases:
AideSequence
Equilibrium storage deficit of the vadose zone for the actual groundwater depth [mm].
- Calculated by the methods:
- Required by the methods:
- class hydpy.models.wland.wland_aides.DGEq(subvars: ModelSequences[ModelSequence, FastAccess])[source]¶
Bases:
AideSequence
Equilibrium groundwater depth for the actual storage deficit of the vadose zone [mm].
- Calculated by the method:
- Required by the method:
- class hydpy.models.wland.wland_aides.GF(subvars: ModelSequences[ModelSequence, FastAccess])[source]¶
Bases:
AideSequence
Gain factor for changes in groundwater depth [-].
- Calculated by the method:
- Required by the method:
Auxiliary Features¶
Masks¶
- class hydpy.models.wland.wland_masks.Complete(variable: variabletools.Variable | None = None, doc: str | None = None, **kwargs)[source]¶
Bases:
IndexMask
Mask including all land use types.
- class hydpy.models.wland.wland_masks.Land(variable: variabletools.Variable | None = None, doc: str | None = None, **kwargs)[source]¶
Bases:
Complete
Mask excluding the land type
WATER
.
- class hydpy.models.wland.wland_masks.Soil(variable: variabletools.Variable | None = None, doc: str | None = None, **kwargs)[source]¶
Bases:
Complete
- class hydpy.models.wland.wland_masks.Sealed(variable: variabletools.Variable | None = None, doc: str | None = None, **kwargs)[source]¶
Bases:
Complete
Mask for the land type
SEALED
.
- class hydpy.models.wland.AideSequences(master: Sequences, cls_fastaccess: type[TypeFastAccess_co] | None = None, cymodel: CyModelProtocol | None = None)¶
Bases:
AideSequences
Aide sequences of model wland.
- The following classes are selected:
FR()
Fraction rainfall / total precipitation [-].WE()
Wetness index in the elevated region [-].W()
Wetness index in the lowland region [-].BetaE()
Evapotranspiration reduction factor in the elevated region [-].Beta()
Evapotranspiration reduction factor in the lowland region [-].DVEq()
Equilibrium storage deficit of the vadose zone for the actual groundwater depth [mm].DGEq()
Equilibrium groundwater depth for the actual storage deficit of the vadose zone [mm].GF()
Gain factor for changes in groundwater depth [-].
- class hydpy.models.wland.ControlParameters(master: Parameters, cls_fastaccess: type[FastAccessParameter] | None = None, cymodel: CyModelProtocol | None = None)¶
Bases:
SubParameters
Control parameters of model wland.
- The following classes are selected:
AT()
Total area [km²].NU()
Number of hydrological response units [-].LT()
Landuse type [-].ER()
Elevated region [-].AUR()
Relative area of each hydrological response unit [-].GL()
The lowland region’s average ground level [m].BL()
Channel bottom level [m].CP()
Factor for correcting precipitation [-].LAI()
Leaf area index [-].IH()
Interception capacity with respect to the leaf surface area [mm].TT()
Threshold temperature for snow/rain [°C].TI()
Temperature interval with a mixture of snow and rain [°C].DDF()
Day degree factor [mm/°C/T].DDT()
Day degree threshold temperature [°C].CWE()
Wetness index parameter for the elevated region [mm].CW()
Wetness index parameter for the lowland region [mm].CV()
Vadose zone relaxation time constant for the lowland region [T].CGE()
Groundwater reservoir constant for the elevated region [mm T].CG()
Groundwater reservoir constant for the lowland region [mm T].RG()
Groundwater reservoir restriction [-].CGF()
Groundwater reservoir flood factor [1/mm].DGC()
Direct groundwater connect [-].CQ()
Quickflow reservoir relaxation time [T].B()
Pore size distribution parameter [-].PsiAE()
Air entry pressure [mm].ThetaS()
Soil moisture content at saturation [-].ThetaR()
Residual soil moisture deficit at tension saturation [-].AC()
Air capacity for the elevated region [mm].Zeta1()
Curvature parameter of the evapotranspiration reduction function [-].Zeta2()
Inflection point of the evapotranspiration reduction function [mm].SH()
General smoothing parameter related to the height of water columns [mm].ST()
General smoothing parameter related to temperature [°C].
- class hydpy.models.wland.DerivedParameters(master: Parameters, cls_fastaccess: type[FastAccessParameter] | None = None, cymodel: CyModelProtocol | None = None)¶
Bases:
SubParameters
Derived parameters of model wland.
- The following classes are selected:
MOY()
References the “global” month of the year index array [-].NUL()
Number of land-related hydrological response units [-].NUGE()
Number of groundwater-affected hydrological response units in the elevated region [-].NUG()
Number of groundwater-affected hydrological response units in the lowland region [-].ALR()
Relative land area [-].ASR()
Relative surface water area fraction [-].AGRE()
Relative groundwater area in the elevated region [-].AGR()
Relative groundwater area in the lowland region [-].QF()
Factor for converting mm/T to m³/s [T m³ / mm s].CD()
Channel depth [mm].RH1()
Regularisation parameter related to the height of water columns used when applying regularisation functionsmooth_logistic1()
[mm].RH2()
Regularisation parameter related to the height of water columns used when applying regularisation functionsmooth_logistic2()
[mm].RT2()
Regularisation parameter related to temperature for applying regularisation functionsmooth_logistic2()
) [°C].
- class hydpy.models.wland.FactorSequences(master: Sequences, cls_fastaccess: type[TypeFastAccess_co] | None = None, cymodel: CyModelProtocol | None = None)¶
Bases:
FactorSequences
Factor sequences of model wland.
- The following classes are selected:
DHS()
External change of the surface water depth [mm/T].
- class hydpy.models.wland.FixedParameters(master: Parameters, cls_fastaccess: type[FastAccessParameter] | None = None, cymodel: CyModelProtocol | None = None)¶
Bases:
SubParameters
Fixed parameters of model wland.
- The following classes are selected:
Pi()
π [-].
- class hydpy.models.wland.FluxSequences(master: Sequences, cls_fastaccess: type[TypeFastAccess_co] | None = None, cymodel: CyModelProtocol | None = None)¶
Bases:
FluxSequences
Flux sequences of model wland.
- The following classes are selected:
PC()
Corrected precipitation [mm/T].PE()
Potential evaporation from the interception and the surface water storage [mm/T].PET()
Potential evapotranspiration from the vadose zone [mm/T].TF()
Total amount of throughfall [mm/T].EI()
Interception evaporation [mm/T].RF()
Rainfall (or, more concrete, the liquid amount of throughfall) [mm/T].SF()
Snowfall (or, more concrete, the frozen amount of throughfall) [mm/T].PM()
Potential snowmelt [mm/T].AM()
Actual snowmelt [mm/T].PS()
Precipitation that enters the surface water reservoir [mm/T].PVE()
Rainfall (and snowmelt) entering the vadose zone in the elevated region [mm/T].PV()
Rainfall (and snowmelt) entering the vadose zone in the lowland region [mm/T].PQ()
Rainfall (and snowmelt) entering the quickflow reservoir [mm/T].ETVE()
Actual evapotranspiration from the vadose zone in the elevated region [mm/T].ETV()
Actual evapotranspiration from the vadose zone in the lowland region [mm/T].ES()
Actual evaporation from the surface water [mm/T].ET()
Total actual evapotranspiration [mm/T].GR()
Groundwater recharge in the elevated region [mm/T].FXS()
Surface water supply/extraction (normalised toASR
) [mm/T].CDG()
Change in the groundwater depth due to percolation and capillary rise [mm/T].FGSE()
Groundwater flow between the elevated and the lowland region [mm/T].FGS()
Groundwater drainage/surface water infiltration [mm/T].FQS()
Quickflow [mm/T].RH()
Runoff height [mm/T].R()
Runoff [m³/s].
- class hydpy.models.wland.InputSequences(master: Sequences, cls_fastaccess: type[TypeFastAccess_co] | None = None, cymodel: CyModelProtocol | None = None)¶
Bases:
InputSequences
Input sequences of model wland.
- class hydpy.models.wland.OutletSequences(master: Sequences, cls_fastaccess: type[TypeFastAccess_co] | None = None, cymodel: CyModelProtocol | None = None)¶
Bases:
OutletSequences
Outlet sequences of model wland.
- The following classes are selected:
Q()
Discharge [m³/s].
- class hydpy.models.wland.SolverParameters(master: Parameters, cls_fastaccess: type[FastAccessParameter] | None = None, cymodel: CyModelProtocol | None = None)¶
Bases:
SubParameters
Solver parameters of model wland.
- The following classes are selected:
AbsErrorMax()
Absolute numerical error tolerance [mm/T].RelErrorMax()
Relative numerical error tolerance [-].RelDTMin()
Smallest relative integration time step size allowed [-].RelDTMax()
Largest relative integration time step size allowed [-].
- class hydpy.models.wland.StateSequences(master: Sequences, cls_fastaccess: type[TypeFastAccess_co] | None = None, cymodel: CyModelProtocol | None = None)¶
Bases:
StateSequences
State sequences of model wland.
- The following classes are selected:
IC()
Interception storage [mm].SP()
Snow pack [mm].DVE()
Storage deficit of the vadose zone in the elevated region [mm].DV()
Storage deficit of the vadose zone in the lowland region [mm].HGE()
Groundwater level in the elevated region [mm].DG()
Groundwater depth in the lowland region [mm].HQ()
Level of the quickflow reservoir [mm].HS()
Surface water level [mm].