1. Influence of raingauge network characteristics on hydrological response at catchment scale 4 th International Workshop on Hydrological Extremes AMHY-FRIEND group 15-17 September 2011, Cosenza, Italy Domenico CARACCIOLO, Elisa ARNONE, Leonardo Valerio NOTO Dipartimento di Ingegneria Civile, Ambientale ed Aerospaziale (DICA), Università di Palermo, 90128 Palermo, ITALY

2. spatial scalenetwork density location of raingaugesresolution time flood phenomenon The uniformity of precipitation monitoring network, in terms of spatial scale (network density and location of raingauges) and resolution time, allows the reproduction, with acceptable accuracy, of the characteristics of the flood phenomenon. Precipitation data is one of the most important inputs required in hydrological modeling and forecasting. In an hydrological model, accurate knowledge of precipitation is essential for an acceptable estimation of hydrograph flood

3. Previous studies In this context, over the last thirty years, several studies concerning the influence of rainfall point measurement for the estimation of total runoff volume have been carried out influence of the spatial distribution of raingaugesinfluence of the number of raingauges neveranalyzed simultaneously In particular, some studies have been focused on the analysis of the influence of the spatial distribution of raingauges, others on the influence of the number of raingauges; however the two issues have never been analyzed simultaneously

4. Wilson et al. (1979): Use 1 or 20 fictitious raingauges to record rainfall concerning to 15 events Use 1 or 20 fictitious raingauges to record rainfall concerning to 15 events The spatial distribution of rainfall has a strong influence on the runoff. The number of raingauges has an important role for the correct estimation of the hydrograph peak The spatial distribution of rainfall has a strong influence on the runoff. The number of raingauges has an important role for the correct estimation of the hydrograph peak The work is based on the determination of the appropriate raingauges network for the estimation of flood hydrograph, using a physically based distributed- parameter hydrologicmodel The work is based on the determination of the appropriate raingauges network for the estimation of flood hydrograph, using a physically based distributed- parameter hydrologic model Krajewski et al. (1991): The cases considered were: case 1: 87 raingauges, temporal interval: 5 minutes ("real") case 1: 87 raingauges, temporal interval: 5 minutes ("real") case 2: 1 raingauge, temporal interval: 1 hour case 2: 1 raingauge, temporal interval: 1 hour case 3: 5 raingauges, temporal interval: 1 hour case 3: 5 raingauges, temporal interval: 1 hour case 4: 87 raingauges, temporal interval: 1 hour case 4: 87 raingauges, temporal interval: 1 hour case 5: use of the lumped model case 5: use of the lumped model Higher sensitivity of basin response with respect to the temporal resolution than to the spatial resolution of the rainfall data

5. Obled et al. (1994) The use of 21 instead of 5 raingauges is irrelevant to the estimation of the precipitation The small differences that we have in terms of estimation of the precipitation become important when the precipitation is transformed to runoff TOPMODEL Goodrich et al. (1995) Uncertainty of measuring rainfall due to the number and location of gauges Uncertainty of measuring rainfall due to the number and location of gauges Existence of sufficient spatial and temporal variability in rainfall Existence of sufficient spatial and temporal variability in rainfall In this paper they show the influence of the different positions of the raingauges for the estimation of the runoff

6. The aim of this work is to use a physically based distributed-parameter hydrologic model (tRIBS) to investigate the influence of the raingauges network configuration in terms of number and spatial distribution, on the estimation of : discharge hydrograph discharge hydrograph hydrograph peak hydrograph peak time-to-peak time-to-peak total runoff volume total runoff volume This has been done considering the spatial distribution of soil types in the basin as well Purpose

7. Physically based distributed-parameter hydrologic model Developed at MIT (2003) Representation of the surface with TIN (Triangular Irregular Network) Hydrologic model tRIBS (TIN Real-Time Integrated Basin Simulator) Triangle Voronoi Cells

8. The hydrologic model has been applied to the Baron Fork at Eldon watershed, a catchment of Oklahoma (800 km 2 )

9. Experimental part Assumptions : The radar measurements, available in the area (NEXRAD), have been assumed as representative of the "real" distribution of precipitation The radar measurements, available in the area (NEXRAD), have been assumed as representative of the "real" distribution of precipitation The "real" hydrological response of the catchment was considered as obtained from the model tRIBS using as meteoric input the real precipitation (NEXRAD) The "real" hydrological response of the catchment was considered as obtained from the model tRIBS using as meteoric input the real precipitation (NEXRAD) The position of 8 raingauges was generated randomly. Precipitation value is set equal to the corresponding NEXRAD raster cell value

10. events of precipitation occurred during 1998 were taken into account. The nine events were chosen according to the average intensity of precipitation (I) classified as high (I> 2.5 mm/h), medium (1.5 mm/h 0.6), medium (0.25 2.5 mm/h), medium (1.5 mm/h 0.6), medium (0.25 <CV <0.6) and low (CV <0.25). CV, for each event, is calculated from the raster obtained by adding the hourly precipitation raster (NEXRAD)

11. The analysis has been carried out assuming five different soil spatial distributions: ssc cscr silty-claysandy-clay-loam(cs sc) two soil types: silty-clay and sandy-clay-loam (cs and sc) real (r) the real (r) spatial distribution of soil types silty-clay (c)sandy-clay- loam (s) a single soil type: silty-clay (c) (K s =1 mm/h) or sandy-clay- loam (s) (K s =235 mm/h) simplified a simplified fictitious spatial distribution of soil characteristics:

12. Simulations Simulations considering "uniform" precipitation in space and measured by the 8 raingauges (interpolated with the Thiessen polygons). After we have combined the raingauges in pairs, three by three, four by four, five by five, six by six, seven by seven and the complete network Simulations considering "uniform" precipitation in space and measured by the 8 raingauges (interpolated with the Thiessen polygons). After we have combined the raingauges in pairs, three by three, four by four, five by five, six by six, seven by seven and the complete network The hydrographs flood obtained for each combination of raingauges are compared with the "real" hydrological response calculating performance indices The hydrographs flood obtained for each combination of raingauges are compared with the "real" hydrological response calculating performance indices

13. Performance Index statistical correlation index statistical correlation index: RMSE RMSE (Root Mean Squared Error) For each combination of raingauges and for each soil distribution the network of raingauges with the smallest RMSE (RMSE min ) has been chosen RMSE RMSE is calculated for each event Q i,PLUV : flow obtained with the precipitation misured by raingauges Q i,RAD : flow obtained with the precipitation misured by RADAR N: event hours number

14. Event 1: Event 1: 36 houres, CV=medium, I=high For high intensity, the raingauges are placed in the less permeable soil, but also where the precipitation is high Spatial pattern

15. Event 3: Event 3: 7 houres, CV=high, I=low For low intensity, the raingauges are placed in the less permeable soil Spatial pattern

16. Event 4: Event 4: 67 houres, CV=low, I=medium For CV=low, varying the distribution of soil, the raingauges network is almost the same Spatial pattern

17. In order to summarize all results in a single table, the average flow was calculated from the flood hydrograph and each value of RMSE is divided for the corresponding average flow. Normalized values ​​ obtained for each event, were added together and divided by the number of events in order to calculate the average value of RMSE/Q M minimum

18. s sc c csr Using only a raingauge, it is placed in the less permeable soil Using only a raingauge, it is placed in the less permeable soil With a network of two raingauges follows the same pattern With a network of two raingauges follows the same pattern With a network of three, four, …. raingauges there is not a clear criterion for the best position of the i-th gauge With a network of three, four, …. raingauges there is not a clear criterion for the best position of the i-th gauge s

19. … in conclusion...  in case of high average rainfall intensity, the influence of precipitation pattern is greater than that of soil types distribution;  in case of medium or low average rainfall intensitythe effect of precipitation is lower than the effect of soil types distribution;  in case of medium or low average rainfall intensity, the effect of precipitation is lower than the effect of soil types distribution;  if the rainfall spatial variation is medium or low the distribution of raingauges varies little with the change of the distribution of soils. There is not an optimal raingauges network finalized to the estimation of all the considered flood events. There is not an optimal raingauges network finalized to the estimation of all the considered flood events. The network finalized to the best reconstruction of rainfall field does not coincide with the network finalized to the best flood hydrograph estimation. The network finalized to the best reconstruction of rainfall field does not coincide with the network finalized to the best flood hydrograph estimation. For a fixed event, the best raingauges configuration is strongly dependent on the soil types distribution. For a fixed event, the best raingauges configuration is strongly dependent on the soil types distribution. The best raingauges configurationsdepend on theprecipitation events (in terms of intensity and spatial distribution) and on the soil types distribution (general trend to locate the raingauges where the soil is less permeable): The best raingauges configurations depend on the precipitation events (in terms of intensity and spatial distribution) and on the soil types distribution (general trend to locate the raingauges where the soil is less permeable):

20. Thank you for your attention