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Modelling the rainfall-runoff process

Modelling the rainfall-runoff process. Available Rainfall Runoff (RR) models: UHM NAM SMAP URBAN. RAINFALL POTENTIAL EVAPORATION. MODEL. PARAMETERS. RUNOFF COMPONENTS EVAPORATION RECHARGE. Modelling the rainfall-runoff process (NAM). NAM : “nedbør-afstrømnings model”.

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Modelling the rainfall-runoff process

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

  2. RAINFALL POTENTIALEVAPORATION MODEL PARAMETERS RUNOFF COMPONENTS EVAPORATION RECHARGE Modelling the rainfall-runoff process (NAM)

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

  4. 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 Types of Application

  5. The NAM Model equations

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

  7. NAM, Model Calibration Most NAM Parameters of empirical nature=> values must be determined by Calibration: • Water balance in system • Runoff hydrographs, peak and shape • Comparison of Runoff results with observations 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 • 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 Editor-file: *.rr11

  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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