Influence of raingauge network characteristics on hydrological response at catchment scale

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

2. 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 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.

3. Previousstudies In this context, over the last thirty years, several studies concerning the influenceof rainfall point measurement for the estimation of total runoff volumehave been carried out 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. 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 raingaugeshas an important role for the correct estimation of the hydrograph peak Krajewskiet al. (1991): 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 Wilson et al. (1979): The cases considered were: • case 1: 87 raingauges, temporal interval: 5 minutes ("real") • case 2: 1 raingauge, temporal interval: 1 hour • case 3: 5 raingauges, temporal interval: 1 hour • case 4: 87 raingauges, temporal interval: 1 hour • 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. TOPMODEL 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 precipitationbecome important when the precipitation is transformed to runoff Obledet al. (1994) Goodrichet al. (1995) • Uncertainty of measuring rainfall due to the number and location of gauges • 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. Purpose 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 • hydrograph peak • time-to-peak • total runoff volume This has been done considering the spatial distribution of soil types in the basin as well

7. Hydrologic model tRIBS (TIN Real-Time IntegratedBasin Simulator) • Developedat MIT (2003) • Physically based distributed-parameter hydrologic model • Representation of the surface with TIN (Triangular Irregular Network) VoronoiCells Voronoi Cells Triangle

8. Catchment The hydrologic model has been applied to the Baron Fork at Eldon watershed, a catchment of Oklahoma (800 km2)

9. Experimental part Assumptions : • The radar measurements, available in the area (NEXRAD), have been assumed as representative of the "real" distribution of precipitation • The "real" hydrological responseof the catchmentwas 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. 9 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 <I <2.5 mm/h) and low (I<1.5 mm/h) and the coefficient of variation (CV) of average precipitation classified as high (CV> 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. a simplified fictitious spatial distribution of soil characteristics: The analysis has been carried out assuming five different soil spatial distributions: • a single soil type: silty-clay (c)(Ks=1 mm/h) or sandy-clay-loam(s) (Ks=235 mm/h) • twosoiltypes: silty-clay and sandy-clay-loam(csandsc) • thereal(r) spatial distribution of soil types s sc c cs r

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 • The hydrographs flood obtained for each combination of raingauges are comparedwith the "real"hydrological responsecalculating performance indices

13. Performance Index statistical correlation index: RMSE (RootMeanSquaredError) Qi,PLUV : flow obtained with the precipitation misured by raingauges Qi,RAD : flow obtained with the precipitation misured by RADAR N: event hours number RMSEiscalculatedforeachevent Foreachcombinationofraingauges and foreachsoildistribution the network ofraingaugeswith the smallest RMSE (RMSEmin) hasbeenchosen

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

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

16. Event 4: 67 houres, CV=low, I=medium Spatial pattern ForCV=low, varying the distributionofsoil, the raingauges network isalmost the same

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/QM minimum

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

19. … in conclusion... • 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. • For a fixed event, the best raingauges configurationis strongly dependent on thesoil 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): • in case of highaverage rainfall intensity, the influence of precipitation pattern is greater than that of soil types distribution; • in case of mediumorlowaverage 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.

20. Thank you for your attention