TUWmodel: Lumped hydrological model developed at the Vienna University...

In TUWmodel: Lumped/Semi-Distributed Hydrological Model 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): SCF snow correction factor [-] (e.g., 0.9-1.5); DDF degree day factor [mm/degC/timestep] (e.g., 0.0-5.0 mm/degC/day); Tr threshold temperature above which precipitation is rain [degC] (e.g., 1.0-3.0 degC); Ts threshold temperature below which precipitation is snow [degC] (e.g., -3.0-1.0 degC); Tm threshold temperature above which melt starts [degC] (e.g., -2.0-2.0 degC); LPrat parameter related to the limit for potential evaporation [-] (e.g., 0.0-1.0); FC field capacity, i.e., max soil moisture storage [mm] (e.g., 0-600 mm); BETA the non linear parameter for runoff production [-] (e.g., 0.0-20.0); k0 storage coefficient for very fast response [timestep] (e.g., 0.0-2.0 days); k1 storage coefficient for fast response [timestep] (e.g., 2.0-30.0 days); k2 storage coefficient for slow response [timestep] (e.g., 30.0-250.0 days); lsuz threshold storage state, i.e., the very fast response start if exceeded [mm] (e.g., 1.0-100.0 mm); cperc constant percolation rate [mm/timestep] (e.g., 0.0-8.0 mm/day); bmax maximum base at low flows [timestep] (e.g., 0.0-30.0 days); 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")

Example output

TUWmodel documentation built on Feb. 26, 2020, 5:08 p.m.