Lumped Model Run #

Or drive it from a YAML file

Everything this page assembles in Python can live in a run configuration instead -- one file holding the paths, dates and settings, read by Catchment.from_yaml. See Run configuration.

To run the HBV lumped model inside Hapi you need to prepare the meteorological inputs (rainfall, temperature and potential evapotranspiration), HBV parameters, and the HBV model (you can load Bergström, 1992 version of HBV from Hapi )

First load the prepared lumped version of the HBV module inside Hapi, the triangular routing function and the wrapper function that runs the lumped model RUN.

from hapi.rrm.hbv_bergestrom92 import HBVBergestrom92 as HBVLumped
from hapi.run import Run
from hapi.catchment import Catchment
from hapi.routing import Routing

- read the meteorological data, data has be in the form of numpy array with the following order [rainfall, ET, Temp, Tm], ET is the potential evapotranspiration, Temp is the temperature (C), and Tm is the long term monthly average temperature.

Parameterpath = Comp + "/data/lumped/Coello_Lumped2021-03-08_muskingum.txt"
MeteoDataPath = Comp + "/data/lumped/meteo_data-MSWEP.csv"

### meteorological data
start = "2009-01-01"
end = "2011-12-31"
name = "Coello"
Coello = Catchment(name, start, end)
Coello.read_lumped_inputs(MeteoDataPath)

- Meteorological data

start = "2009-01-01"
end = "2011-12-31"
name = "Coello"
Coello = Catchment(name, start, end)
Coello.read_lumped_inputs(MeteoDataPath)

- Lumped model prepare the initial conditions, cathcment area and the lumped model.

# catchment area
AreaCoeff = 1530
# [Snow pack, Soil moisture, Upper zone, Lower Zone, Water content]
InitialCond = [0,10,10,10,0]

Coello.read_lumped_model(HBVLumped, AreaCoeff, InitialCond)

- Load the pre-estimated parameters snow option (if you want to simulate snow accumulation and snow melt or not)

Snow = 0 # no snow subroutine
# if routing using Maxbas True, if Muskingum False
Coello.read_parameters(Parameterpath, Snow)

- Prepare the routing options.

# RoutingFn = Routing.triangular_routing_2
RoutingFn = Routing.muskingum_v
Route = 1

- now all the data required for the model are prepared in the right form, now you can call the run_lumped wrapper to initiate the calculation

Run.run_lumped(Coello, Route, RoutingFn)

to calculate some metrics for the quality assessment of the calculate discharge the statista.descriptors contains some metrics like rmse, nse, kge and wb , you need to load it, a measured time series of doscharge for the same period of the simulation is also needed for the comparison.

all methods in statista.descriptors takes two numpy arrays of the same length and return real number.

import statista.descriptors as metrics

# The observed record has to be read before it can be scored against; the page never
# loaded it, so `QGauges` was `None`.
Coello.read_discharge_gauges(Path + "Qout_c.csv", fmt="%Y-%m-%d")

Metrics = dict()
Qobs = Coello.QGauges['q']

Metrics['RMSE'] = metrics.rmse(Qobs, Coello.Qsim['q'])
Metrics['NSE'] = metrics.nse(Qobs, Coello.Qsim['q'])
Metrics['NSEhf'] = metrics.nse_hf(Qobs, Coello.Qsim['q'])
Metrics['KGE'] = metrics.kge(Qobs, Coello.Qsim['q'])
Metrics['WB'] = metrics.wb(Qobs, Coello.Qsim['q'])

print("RMSE= " + str(round(Metrics['RMSE'],2)))
print("NSE= " + str(round(Metrics['NSE'],2)))
print("NSEhf= " + str(round(Metrics['NSEhf'],2)))
print("KGE= " + str(round(Metrics['KGE'],2)))
print("WB= " + str(round(Metrics['WB'],2)))

To plot the calculated and measured discharge import matplotlib

gaugei = 0
plotstart = "2009-01-01"
plotend = "2011-12-31"
Coello.plot_hydrograph(plotstart, plotend, gaugei, title="Lumped Model")

To save the results

start = "2009-01-01"
end = "2010-04-20"

Path = SaveTo + "Results-Lumped-Model" + str(dt.datetime.now())[0:10] + ".txt"
Coello.results.save(result=5, start=start, end=end, path=Path)

Lumped Model Run#

Lumped Model Run #