DISTRIBUTED RAINFALL RUNOFF MODELS APPLIED TO THE DARGLE
DISTRIBUTED RAINFALL RUNOFF MODELS APPLIED TO THE DARGLE Prof. Eng. Ezio TODINI e-mail : todini@geomin. unibo. it
DISTRIBUTED RAINFALL-RUNOFF MODELLING Rainfall Runoff Models Black Box M. Semi Distributed M. Advantages of Distributed Models - Physical meaning of model parameters Limited calibration requirements - Distributed representation of phenomena Possibility of internal analysis
Model 1: AFFDEF Mass Balance in each cell Main model characteristics: - Modified CN for estimating infiltration - Radiation method for evapotranspiration - Muskingum-Cunge for ovrland channel flow
Model 2: TOPKAPI Main model characteristics Model for the single cell - Vertical lumping of hydraulic conductivity - Dunne infiltration - Soil horizontal flow, overland channel flows represented using a kinematic equation - Horizontal lumping of kinematic equations
TOPKAPI Distributed approach The model for the single cell SOIL COMPONENT mass conservation moment conservation ODE
TOPKAPI Distributed approach The model for the single cell SURFACE COMPONENT mass conservation moment conservation … ODE
TOPKAPI Distributed approach The model for the single cell CHANNEL COMPONENT mass conservation moment conservation … ODE
TOPKAPI Distributed approach Parameters
Model 3: MIKE SHE Main model characteristics: - 1 D Richards equations for unsaturated zone - 3 D Boussinesq equation for greoundwater - Parabolic approximation for overland flow
Case study The Dargle Republic of Eireland County of Wicklow
Case study - Surface Area circa 122 km 2 - Elevation from 20 m to 713 m a. s. l. - Sandy and sandy loam for about 1. 5 m
Saturation mechanism Dunne Horton The “unrealistic” profile used in MIKE SHE to meet the observations
Results: AFFDEF Efficiency Coefficients Variance of obs. = 17. 85 Variance of errors= 6. 97 Nash Sutcliffe= 0. 59 Explained Variance= 0. 61 Coefficient of correlation =0. 91 Volume Control = 0. 74 Willmott= 0. 93
Risults: AFFDEF Areal threshold Saturated Hydraulic Conductivity 5 [Km 2 ] Average computer time = 5 min 0. 01 [ms-1] Efficiency Coefficients Infiltration Res. Const 4320000[s] Infiltration constant 0. 7 Infiltration Capacity 0. 1 Uniform value for curve number: 20 Variance of obs. = 17. 85 Variance of errors= 6. 97 Nash Sutcliffe= 0. 59 Explained Variance= 0. 61 Coefficient of correlation =0. 91 Volume Control = 0. 74 Willmott= 0. 93
Results: TOPKAPI Efficiency Coefficients Variance of obs. = 17. 85 Variance of errors= 4. 01 Nash Sutcliffe= 0. 77 Explained Variance= 0. 77 Coefficient of correlation =0. 91 Volume Control = 0. 90 Willmott= 0. 95
Results: TOPKAPI Permeability [m 3 s-1] θS θR α L [m] Soil Type 9. 1 E-04 0. 453 0. 041 2. 5 0. 9 Sandy Loam 9. 1 E-04 0. 453 0. 041 2. 5 0. 7 Sandy Loam 3. 1 E-05 0. 453 0. 041 2. 5 1. 5 Sandy Loam 9. 1 E-04 0. 463 0. 020 2. 5 1. 0 Loam 4. 1 E-05 0. 453 0. 041 2. 5 1. 5 Sandy Loam 9. 1 E-04 0. 463 0. 020 2. 5 0. 7 Loam Average comp. time = 5 min Efficiency Coefficients Variance of obs. = 17. 85 Variance of errors= 4. 01 Nash Sutcliffe= 0. 77 Explained Variance= 0. 77 Coefficient of correlation =0. 91 Volume Control = 0. 90 Willmott= 0. 95
Results: MIKE SHE Efficiency Coefficients Variance of obs. = 17. 85 Variance of errors= 8. 32 Nash Sutcliffe= 0. 52 Explained Variance= 0. 54 Coefficient of correlation =0. 85 Volume Control = 0. 80 Willmott= 0. 90
Results: MIKE SHE Average computer time = 2. 5 h Efficiency Coefficients Thickness of soil layer -1. 3 [m] Horizontal hydraulic conductivity 5*10 -4 Vertical hydraulic conductivity 1*10 -5 [m s-1] Storativity coefficient 0. 2 [m-1] [m s-1] Variance of obs. = 17. 85 Variance of errors= 8. 32 Nash Sutcliffe= 0. 52 Explained Variance= 0. 54 Coefficient of correlation =0. 85 Volume Control = 0. 80 Willmott= 0. 90
Distributed soil moisture Saturation percentage
TOPKAPI CALIBRATION TOOL
Example of link ECMWF -TOPKAPI on the Po Basin The basin closed at Ponte Spessa (Surface area 36, 900 km 2 ) Ponte Spessa
The DEM The Soil Types The Land Uses
Reproduction of the 1994 event in the Po river
ECMWF: deterministic run
ECMWF: deterministic run
ECMWF: deterministic run
ECMWF: deterministic run
ECMWF: deterministic run
- Slides: 28