Lumped Model Calibration

To calibrate the HBV lumped model inside Hapi you need to follow the same steps of running the lumped model with few extra steps to define the requirement of the calibration algorithm.

    import pandas as pd
    import datetime as dt
    from hapi.rrm.hbv_bergestrom92 import HBVBergestrom92 as HBVLumped
    from hapi.calibration import Calibration
    from hapi.catchment import Catchment
    from hapi.conceptual import ParameterSet
    from hapi.routing import Routing
    from hapi.run import Run
    import statista.descriptors as metrics

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

    start = "2009-01-01"
    end = "2011-12-31"
    name = "Coello"

    # `Calibration` holds a catchment rather than being one, so the model is built first
    # and the readers are called on `Coello.model`.
    Coello = Calibration(Catchment(name, start, end))
    Coello.model.read_lumped_inputs(MeteoDataPath)


    # catchment area
    AreaCoeff = 1530
    # temporal resolution
    # [Snow pack, Soil moisture, Upper zone, Lower Zone, Water content]
    InitialCond = [0,10,10,10,0]
    # no snow subroutine
    Snow = False
    Coello.model.read_lumped_model(HBVLumped, AreaCoeff, InitialCond)

    # Calibration boundaries
    UB = pd.read_csv(Path + "/lumped/UB-3.txt", index_col = 0, header = None)
    parnames = UB.index
    UB = UB[1].tolist()
    LB = pd.read_csv(Path + "/lumped/LB-3.txt", index_col = 0, header = None)
    LB = LB[1].tolist()

    Maxbas = True
    # `maxbas` is lower case, and `snow` has to be a bool -- `0` is refused by name.
    Coello.read_parameters_bound(UB, LB, Snow, maxbas=Maxbas)

    parameters = []
    # Routing
    Route = 1
    RoutingFn = Routing.triangular_routing_1

    Basic_inputs = dict(Route=Route, RoutingFn=RoutingFn, InitialValues = parameters)

    ### Objective function
    # outlet discharge
    Coello.model.read_discharge_gauges(Path+"Qout_c.csv", fmt="%Y-%m-%d")

    OF_args=[]
    OF=metrics.rmse

    Coello.read_objective_function(metrics.rmse, OF_args)

- after defining all the components of the lumped model, we have to define the calibration arguments

    ApiObjArgs = dict(hms=100, hmcr=0.95, par=0.65, dbw=2000, fileout=1, xinit =0,
                          filename=Path + "/Lumped_History"+str(dt.datetime.now())[0:10]+".txt")

    for i in range(len(ApiObjArgs)):
        print(list(ApiObjArgs.keys())[i], str(ApiObjArgs[list(ApiObjArgs.keys())[i]]))

    # pll_type = 'POA'
    pll_type = None

    ApiSolveArgs = dict(store_sol=True, display_opts=True, store_hst=True, hot_start=False)

    OptimizationArgs=[ApiObjArgs, pll_type, ApiSolveArgs]

• Run Calibration

    cal_parameters = Coello.calibrate_lumped(Basic_inputs, OptimizationArgs, print_error=None)

    print("Objective Function = " + str(round(cal_parameters[0],2)))
    print("Parameters are " + str(cal_parameters[1]))
    print("Time = " + str(round(cal_parameters[2]['time']/60,2)) + " min")