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Modelling the rainfall-runoff process Available Rainfall Runoff (RR) models: UHM NAM SMAP URBAN.

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Presentation on theme: "Modelling the rainfall-runoff process Available Rainfall Runoff (RR) models: UHM NAM SMAP URBAN."— Presentation transcript:

1 Modelling the rainfall-runoff process Available Rainfall Runoff (RR) models: UHM NAM SMAP URBAN

2 Modelling the rainfall-runoff process (NAM) RAINFALL POTENTIAL EVAPORATION MODELPARAMETERS RUNOFF COMPONENTS EVAPORATION RECHARGE

3 NAM : “nedbør-afstrømnings model” Describes the land phase of the hydrological cycle The NAM is a lumped, conceptual model: lumpedcatchment regarded as one unit. parameters are average values conceptualbased on considerations of the physical processes Similar models: Stanford, SSARR, HBV, SMAR,..

4 Types of Application General hydrological analysis - runoff distribution - estimates of infiltration / evaporation Flood Forecasting - subcatchment inflow to river model - links to meteorological models Extension of streamflow records - advanced gap-filling - improved basis for extreme value analysis etc. Prediction of low flow - for irrigation management - for water quality control

5 equations The NAM Model

6 NAM, Initial conditions Initial Water Content of Surface and Root zone storages Initial values for Overland flow and Interflow Initial Groundwater Depth Data to be specified: Recommended to disregard the first half year or so of the results to eliminate erroneous Initial Conditions!

7 NAM, Model Calibration Water balance in system Runoff hydrographs, peak and shape Comparison of Runoff results with observations Most NAM Parameters of empirical nature => values must be determined by Calibration: Generally recommended to change only one parameter between each run !

8 NAM, Model Calibration 1. Manual Step-by-step procedure (changing one variable at a time) 2. Autocalibration Automatic optimisation routine using multi-objective optimisation strategy. 4 objectives: 1) Overall Volume error (= water balance) 2) Overall root mean square error (= hydrograph shape) 3) average root mean square error of peak flow events 4) average root mean square error of low flow events Easy to use - BUT EVALUATION OF VARIABLE VALUES REQUIRED TO JUDGE HYDROLOGICAL SENSIBILITY

9 NAM simulation, Liver creek

10 RR Parameter editor Editor-file: *.rr11 Editing of Model-specific parameters for Rural Catchments : NAM Model-specific parameters comprise: Surface, Root-zone and Snow melt data Ground water data Initial Conditions Irrigated Area

11 RR Parameter editor (NAM) Example: Surface-Rootzone variables

12 RR Input to HD Simulation Inclusion of Runoff results in River model: 1)RR-simulation (produce RR Result-file) 2)Specify Catchment definitions in Network Editor (input to single points or distributed along reach) 3)Specify RR Result-filename in Simulation Editor


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