Type: Package
Title: Lumped/Semi-Distributed Hydrological Model for Education Purposes
Version: 1.1-1
Date: 2020-02-25
Depends: R (≥ 3.0.0), stats
Description: The model, developed at the Vienna University of Technology, is a lumped conceptual rainfall-runoff model, following the structure of the HBV model. The model can also be run in a semi-distributed fashion and with dual representation of soil layer. The model runs on a daily or shorter time step and consists of a snow routine, a soil moisture routine and a flow routing routine. See Parajka, J., R. Merz, G. Bloeschl (2007) <doi:10.1002/hyp.6253> Uncertainty and multiple objective calibration in regional water balance modelling: case study in 320 Austrian catchments, Hydrological Processes, 21, 435-446.
NeedsCompilation: yes
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Packaged: 2020-02-25 14:09:34 UTC; alviglio
Author: Alberto Viglione [aut, cre], Juraj Parajka [aut]
Maintainer: Alberto Viglione <alberto.viglione@polito.it>
Repository: CRAN
Date/Publication: 2020-02-26 12:00:10 UTC

Lumped hydrological model developed at the Vienna University of Technology for education purposes

Description

TUWmodel is a lumped conceptual rainfall-runoff model, following the structure of the HBV model. The model runs on a daily or shorter timestep and consists of a snow routine, a soil moisture routine and a flow routing routine. See Parajka, J., R. Merz, G. Bloeschl (2007) Uncertainty and multiple objective calibration in regional water balance modelling: case study in 320 Austrian catchments, Hydrological Processes, 21, 435-446.

Usage

 TUWmodel (prec, airt, ep, area=1, 
           param=c(1.2,1.2,2,-2,0,0.9,100,3.3,0.5,9,105,50,2,10,26.5), 
           incon=c(50,0,2.5,2.5), itsteps=NULL)

Arguments

prec

vector/matrix of precipitation input [mm/timestep] (ncol = number of zones)

airt

vector/matrix of air temperatures [degC]

ep

vector/matrix of potential evapotranspiration [mm/timestep]

area

if more zones, vector of the percentage of area for each zone (or proportional to it, i.e., if the sum is different from 1, it will be rescaled to be 1)

param

vector/matrix of parameters (ncol = number of zones):

  1. SCF snow correction factor [-] (e.g., 0.9-1.5);

  2. DDF degree day factor [mm/degC/timestep] (e.g., 0.0-5.0 mm/degC/day);

  3. Tr threshold temperature above which precipitation is rain [degC] (e.g., 1.0-3.0 degC);

  4. Ts threshold temperature below which precipitation is snow [degC] (e.g., -3.0-1.0 degC);

  5. Tm threshold temperature above which melt starts [degC] (e.g., -2.0-2.0 degC);

  6. LPrat parameter related to the limit for potential evaporation [-] (e.g., 0.0-1.0);

  7. FC field capacity, i.e., max soil moisture storage [mm] (e.g., 0-600 mm);

  8. BETA the non linear parameter for runoff production [-] (e.g., 0.0-20.0);

  9. k0 storage coefficient for very fast response [timestep] (e.g., 0.0-2.0 days);

  10. k1 storage coefficient for fast response [timestep] (e.g., 2.0-30.0 days);

  11. k2 storage coefficient for slow response [timestep] (e.g., 30.0-250.0 days);

  12. lsuz threshold storage state, i.e., the very fast response start if exceeded [mm] (e.g., 1.0-100.0 mm);

  13. cperc constant percolation rate [mm/timestep] (e.g., 0.0-8.0 mm/day);

  14. bmax maximum base at low flows [timestep] (e.g., 0.0-30.0 days);

  15. croute free scaling parameter [timestep^2/mm] (e.g., 0.0-50.0 days^2/mm);

incon

vector/matrix of initial conditions for the model (ncol = number of zones): SSM0 soil moisture [mm]; SWE0 snow water equivalent [mm]; SUZ0 initial value for fast (upper zone) response storage [mm]; SLZ0 initial value for slow (lower zone) response storage [mm]

itsteps

length of the output (if NULL all the time series are used)

Details

More details about the model structure are given in the Appendix of

Parajka, J., R. Merz, G. Bloeschl (2007) Uncertainty and multiple objective calibration in regional water balance modelling: case study in 320 Austrian catchments, Hydrological Processes, 21, 435-446, doi:10.1002/hyp.6253.

An example of using TUWmodel, including R scripts for automatic calibration, can be found in the Supplement of

Ceola, S., et al. (2015) Virtual laboratories: new opportunities for collaborative water science, Hydrology and Earth System Sciences, 19(4), 2101-2117, doi:10.5194/hess-19-2101-2015.

Value

TUWmodel gives a vector of simulated runoff as q [mm/timestep], and the following vector/matrices:

  1. qzones simulated runoff for each zone [mm/timestep];

  2. q0 surface runoff [mm/timestep];

  3. q1 subsurface runoff [mm/timestep];

  4. q2 baseflow [mm/timestep];

  5. rain liquid precipitation [mm/timestep];

  6. snow solid precipitation [mm/timestep];

  7. melt snowmelt [mm/timestep];

  8. moist soil moisture [mm];

  9. swe snow water equivalent [mm];

  10. eta actual evapo-transpiration [mm/timestep];

  11. suz upper storage zone [mm];

  12. slz lower storage zone [mm];

Examples

## Load the data
data(example_TUWmodel)


## Simulate runoff and plot observed vs simulated series
## Lumped case (weighted means of the inputs)
simLump <- TUWmodel(prec=apply(P_Vils, 1, weighted.mean, w=areas_Vils), 
                    airt=apply(T_Vils, 1, weighted.mean, w=areas_Vils),
                    ep=apply(PET_Vils, 1, weighted.mean, w=areas_Vils), 
                    area=sum(areas_Vils),
             param=c(1.02,1.70,2,0,-0.336,
                     0.934,121,2.52,
                     0.473,9.06,142,
                     50.1,2.38,10,25))

plot(as.Date(names(Q_Vils)), Q_Vils, type="l", xlab="", ylab="Discharges [mm/day]")
 lines(as.Date(rownames(T_Vils)), simLump$q, col=2)
legend("topleft", legend=c("Observations","Simulations"), col=c(1,2), lty=1, bty="n")

plot(as.Date(rownames(SWE_Vils)), apply(SWE_Vils, 1, weighted.mean, w=areas_Vils), 
     type="l", xlab="", ylab="Snow Water Equivalent [mm]")
 lines(as.Date(rownames(T_Vils)), simLump$swe, col=2)

## Distribute input case (6 zones)
simDist <- TUWmodel(prec=P_Vils, airt=T_Vils, ep=PET_Vils, area=areas_Vils/sum(areas_Vils),
             param=c(1.02,1.70,2,0,-0.336,
                     0.934,121,2.52, 
                     0.473,9.06,142,
                     50.1,2.38,10,25))

plot(as.Date(names(Q_Vils)), Q_Vils, type="l", xlab="", ylab="Discharges [mm/day]")
 lines(as.Date(rownames(T_Vils)), simDist$q, col=2)
legend("topleft", legend=c("Observations","Simulations"), col=c(1,2), lty=1, bty="n")

plot(as.Date(rownames(SWE_Vils)), apply(SWE_Vils, 1, weighted.mean, w=areas_Vils),
     type="l", xlab="", ylab="Snow Water Equivalent [mm]")
 lines(as.Date(rownames(T_Vils)), apply(simDist$swe, 1, weighted.mean, w=areas_Vils), col=2)

## Distributed input and parameters case
parametri <- matrix(rep(c(1.02,1.70,2,0,-0.336,
                          0.934,121,2.52,
                          0.473,9.06,142,
                          50.1,2.38,10,25), 6), ncol=6)
parametri[2,] <- c(1.4, 1.7, 1.9, 2.2, 2.4, 3.0)
simDist2 <- TUWmodel(prec=P_Vils,
                    airt=T_Vils, 
                    ep=PET_Vils, 
                    area=areas_Vils/sum(areas_Vils),
             param=parametri)

plot(as.Date(names(Q_Vils)), Q_Vils, type="l", xlab="", ylab="Discharges [mm/day]")
 lines(as.Date(rownames(T_Vils)), simDist2$q, col=2)
legend("topleft", legend=c("Observations","Simulations"), col=c(1,2), lty=1, bty="n")

Lumped dual layer hydrological model developed at the Vienna University of Technology for education purposes

Description

TUWmodel_dual is a lumped conceptual rainfall-runoff model with dual representation of soil layer developed at the TUW, following the structure of the HBV model. The dual soil layer consists of a skin soil layer which represents the layer observed by satellite soil moisture sensors and a root zone soil storage which is identical with the original TUWmodel concept. The model runs on a daily or shorter timestep and has similar structure as the original TUWmodel function. More information about the dual layer concept and its implementation can be found in Parajka, J., V. Naeimi, G. Bloeschl and J. Komma (2009) Matching ERS scatterometer based soil moisture patterns with simulations of a conceptual dual layer hydrologic model over Austria, Hydrol. Earth Syst. Sci., 13, 259-271.

Usage

 TUWmodel_dual (prec, airt, ep, area=1, 
            param=c(1.2,1.2,2,-2,0,0.9,100,3.3,0.5,9,105,50,2,10,26.5,1,0.8,10), 
            incon=c(50,0,2.5,2.5,0), itsteps=NULL)

Arguments

prec

vector/matrix of precipitation input [mm/timestep] (ncol = number of zones)

airt

vector/matrix of air temperatures [degC]

ep

vector/matrix of potential evapotranspiration [mm/timestep]

area

if more zones, vector of the percentage of area for each zone (or proportional to it, i.e., if the sum is different from 1, it will be rescaled to be 1)

param

vector/matrix of parameters (ncol = number of zones):

  1. SCF snow correction factor [-] (e.g., 0.9-1.5);

  2. DDF degree day factor [mm/degC/timestep] (e.g., 0.0-5.0 mm/degC/day);

  3. Tr threshold temperature above which precipitation is rain [degC] (e.g., 1.0-3.0 degC);

  4. Ts threshold temperature below which precipitation is snow [degC] (e.g., -3.0-1.0 degC);

  5. Tm threshold temperature above which melt starts [degC] (e.g., -2.0-2.0 degC);

  6. LPrat parameter related to the limit for potential evaporation [-] (e.g., 0.0-1.0);

  7. FC field capacity, i.e., max soil moisture storage [mm] (e.g., 0-600 mm);

  8. BETA the non linear parameter for runoff production [-] (e.g., 0.0-20.0);

  9. k0 storage coefficient for very fast response [timestep] (e.g., 0.0-2.0 days);

  10. k1 storage coefficient for fast response [timestep] (e.g., 2.0-30.0 days);

  11. k2 storage coefficient for slow response [timestep] (e.g., 30.0-250.0 days);

  12. lsuz threshold storage state, i.e., the very fast response start if exceeded [mm] (e.g., 1.0-100.0 mm);

  13. cperc constant percolation rate [mm/timestep] (e.g., 0.0-8.0 mm/day);

  14. bmax maximum base at low flows [timestep] (e.g., 0.0-30.0 days);

  15. croute free scaling parameter [timestep^2/mm] (e.g., 0.0-50.0 days^2/mm);

  16. fc_skin field capacity, i.e., max soil moisture storage, of the top soil skin layer [mm] (e.g., 0.1-10 mm);

  17. f_eta ???? [??] (e.g., 0.7-0.95 ??);

  18. k_moist ???? [??] (e.g., 5-15 ??);

incon

vector/matrix of initial conditions for the model (ncol = number of zones): SSM0 initial value of soil moisture [mm]; SWE0 initial value of snow water equivalent [mm]; SUZ0 initial value for fast (upper zone) response storage [mm]; SLZ0 initial value for slow (lower zone) response storage [mm]; SSL0 initial value of top soil skin layer storage [mm]

itsteps

length of the output (if NULL all the time series are used)

Details

More details about the model structure are given in

Parajka, J., V. Naeimi, G. Bloeschl and J. Komma (2009) Matching ERS scatterometer based soil moisture patterns with simulations of a conceptual dual layer hydrologic model over Austria, Hydrol. Earth Syst. Sci., 13, 259-271.

Value

TUWmodel_dual gives a vector of simulated runoff as q [mm/timestep], and the following vector/matrices:

  1. qzones simulated runoff for each zone [mm/timestep];

  2. q0 surface runoff [mm/timestep];

  3. q1 subsurface runoff [mm/timestep];

  4. q2 baseflow [mm/timestep];

  5. rain liquid precipitation [mm/timestep];

  6. snow solid precipitation [mm/timestep];

  7. melt snowmelt [mm/timestep];

  8. moist soil moisture [mm];

  9. swe snow water equivalent [mm];

  10. eta actual evapo-transpiration [mm/timestep];

  11. suz upper storage zone [mm];

  12. slz lower storage zone [mm];

  13. ssl top soil skin layer storage [mm];

Examples

## Load the data
data(example_TUWmodel)


## Simulate runoff and plot observed vs simulated series
## Lumped case (weighted means of the inputs)
simLump <- TUWmodel_dual(prec=apply(P_Vils, 1, weighted.mean, w=areas_Vils), 
                     airt=apply(T_Vils, 1, weighted.mean, w=areas_Vils),
                     ep=apply(PET_Vils, 1, weighted.mean, w=areas_Vils), 
                     area=sum(areas_Vils),
              param=c(1.02,1.70,2,0,-0.336,
                      0.934,121,2.52,
                      0.473,9.06,142,
                      50.1,2.38,10,25,1,0.8,10))

plot(as.Date(names(Q_Vils)), Q_Vils, type="l", xlab="", ylab="Discharges [mm/day]")
 lines(as.Date(rownames(T_Vils)), simLump$q, col=2)
legend("topleft", legend=c("Observations","Simulations"), col=c(1,2), lty=1, bty="n")

plot(as.Date(rownames(SWE_Vils)), apply(SWE_Vils, 1, weighted.mean, w=areas_Vils), 
     type="l", xlab="", ylab="Snow Water Equivalent [mm]")
 lines(as.Date(rownames(T_Vils)), simLump$swe, col=2)

## Distribute input case (6 zones)
simDist <- TUWmodel_dual(prec=P_Vils, airt=T_Vils, ep=PET_Vils, area=areas_Vils/sum(areas_Vils),
             param=c(1.02,1.70,2,0,-0.336,
                     0.934,121,2.52, 
                     0.473,9.06,142,
                     50.1,2.38,10,25,1,0.8,10))

plot(as.Date(names(Q_Vils)), Q_Vils, type="l", xlab="", ylab="Discharges [mm/day]")
 lines(as.Date(rownames(T_Vils)), simDist$q, col=2)
legend("topleft", legend=c("Observations","Simulations"), col=c(1,2), lty=1, bty="n")

plot(as.Date(rownames(SWE_Vils)), apply(SWE_Vils, 1, weighted.mean, w=areas_Vils),
     type="l", xlab="", ylab="Snow Water Equivalent [mm]")
 lines(as.Date(rownames(T_Vils)), apply(simDist$swe, 1, weighted.mean, w=areas_Vils), col=2)

## Distributed input and parameters case
parametri <- matrix(rep(c(1.02,1.70,2,0,-0.336,
                          0.934,121,2.52,
                          0.473,9.06,142,
                          50.1,2.38,10,25,1,0.8,10), 6), ncol=6)
parametri[2,] <- c(1.4, 1.7, 1.9, 2.2, 2.4, 3.0)
simDist2 <- TUWmodel_dual(prec=P_Vils,
                      airt=T_Vils, 
                      ep=PET_Vils, 
                      area=areas_Vils/sum(areas_Vils),
                param=parametri)

plot(as.Date(names(Q_Vils)), Q_Vils, type="l", xlab="", ylab="Discharges [mm/day]")
 lines(as.Date(rownames(T_Vils)), simDist2$q, col=2)
legend("topleft", legend=c("Observations","Simulations"), col=c(1,2), lty=1, bty="n")

Data-sample

Description

Data for examples in the package

Usage

 data(example_TUWmodel)

Format

The data are time series for the river Vils at Vils (Laende):

  1. Q_Vils vector of observed daily discharges [mm/day];

  2. P_Vils matrix of observed daily precipitation [mm/day] for 6 zones;

  3. T_Vils matrix of observed daily temperatures [degC] for 6 zones;

  4. PET_Vils matrix of potential evapotranspiration [mm/day] for 6 zones;

  5. SWE_Vils matrix of observed snow water equivalent [mm] for 6 zones;

  6. areas_Vils areas of the 6 zones.

Examples

data(example_TUWmodel)

ls()

t <- as.Date(rownames(P_Vils))
plot(t, apply(P_Vils, 1, weighted.mean, w=areas_Vils), 
     type="S", xlab="", ylab="Precipitation [mm/day]")

plot(t, apply(PET_Vils, 1, weighted.mean, w=areas_Vils), 
     type="l", xlab="", ylab="Potential evapotranspiration [mm/day]")

plot(t, T_Vils[,1], type="l", xlab="", ylab="Temperature [degC]")
 lines(t, T_Vils[,6], col=3)

plot(t, SWE_Vils[,6], col=3, type="l", xlab="", ylab="Snow water equivalent [mm]")
 lines(t, SWE_Vils[,1], col=1)

plot(as.Date(names(Q_Vils)), Q_Vils, type="l", xlab="", ylab="Runoff [mm/day]")