1. WEEK 1 E. FACHE, A. GANGOTRA, K. MAHFOUD, A. MARTYSZUNIS, I. MIRALLES, G. ROSAT, S. SCHROERS, A. TILLOY 19. February 2016

2. OUR WEEKLY QUESTION Influence of rainfall spatial distribution on the simulated hydrograph and best strategy for hydrological modelling.

3. THE WORK AXIS The theoretical methods (and implementation with ArcGIS) Models Return period and answer to the question Influence of rainfall spatial distribution on the simulated hydrograph and best strategy for hydrological modelling.

4. THE METHODS Mean Gage attribution method Thiessen polygon Inverse distance method Kriegen method Influence of rainfall spatial distribution on the simulated hydrograph and best strategy for hydrological modelling.

5. THIESSEN POLYGON The weighted mean method The rainfall recorded by each rain gauge station should be weighted according to the area it represents Suitable for: Areas of moderate size When rainfall stations are few compared to the size of the basin Influence of rainfall spatial distribution on the simulated hydrograph and best strategy for hydrological modelling. Gauge stations areas

6. INVERSE DISTANCE METHOD Is a deterministic method which produce smooth surfaces Results depend on weighting parameters and search window Quick interpolation from sparse data on irregularity spaced samples Dual approach 1.Extracting distances from ArcGIS 2.Direct implementation of method in Hec HMS Influence of rainfall spatial distribution on the simulated hydrograph and best strategy for hydrological modelling.

7. KRIEGEN METHOD Advanced geostatitiscal method – generates estimated surface with scattered observation points Assumption – Constant mean is unknown Influence of rainfall spatial distribution on the simulated hydrograph and best strategy for hydrological modelling.

8. KRIEGEN METHOD Implementation in ArcGIS Influence of rainfall spatial distribution on the simulated hydrograph and best strategy for hydrological modelling. Input data: Rainfall time series at 5 different gauging stations Kriging using Spatial Analyst (Interpolation) Parameter specifications: Ordinary Kriging, Spherical Semi Variogram Result: 61 maps generated (for each time step) Results extracted for centroid of each sub catchment and implementation in Hec HMS

9. RESULTS COMPARISON Influence of rainfall spatial distribution on the simulated hydrograph and best strategy for hydrological modelling. Interpolated Hyetographs

10. RESULTS COMPARISON Influence of rainfall spatial distribution on the simulated hydrograph and best strategy for hydrological modelling. Comparison of the different methods

11. Between the three methods (Interpolation results = time series of rainfall) HEC HMS MODEL Influence of rainfall spatial distribution on the simulated hydrograph and best strategy for hydrological modelling. Calibrated model

12. HEC HMS MODEL Influence of rainfall spatial distribution on the simulated hydrograph and best strategy for hydrological modelling.

13. THE RETURN PERIOD Influence of rainfall spatial distribution on the simulated hydrograph and best strategy for hydrological modelling.

14. THE RETURN PERIOD Influence of rainfall spatial distribution on the simulated hydrograph and best strategy for hydrological modelling.

15. Two different methods to estimate the return period of the biggest discharge Hydro bank data: Instantaneous discharge: s=449, x0=652 THE RETURN PERIOD Influence of rainfall spatial distribution on the simulated hydrograph and best strategy for hydrological modelling.

16. REMARKS Krieging method should have more sample points in order to estimate a better variogram Better results in a very mountanious catchment could be achieved with a method which includes secondary information, e.g. DEM which accounts for higher rainfall in higher elevation also distance to the sea or a rainfall radar images could improve the interpolation Influence of rainfall spatial distribution on the simulated hydrograph and best strategy for hydrological modelling. => Multivariate methods vs. univariate methods: Reduction parameters Meteo France method: AURELHY: based on a kriging method, takes in account topographic parameters PLUVIA ethod from CEREG: parameters are directly taken from the DEM

17. CONCLUSION There is not much difference between the IDW and Kriging methods due to the few gauging stations (variogram = poor quality) As the centroids only represent one point in the basin, results might be very similar in HecHMS and we should use an other software to input the real map of rainfall spatial distribution Exact calibration of model seems more influential than spatial variation of rainfall

18. THE BEST STRATEGY FOR HYDROLOGICAL MODELLING Comparison for one flash flood: no difference between the used methods Maybe, not similar for a different flood event, e.g. snow melting, summer flood... Tendance to the Kriging method, the more sophistical one