Benefits and drawbacks of using data assimilation for hydrological modelling in karstic regions.

1. Benefits and drawbacks of using data assimilation for hydrological modelling in karstic regions. Recent work on the Lez catchment in Southern France Mathieu COUSTAU, Elizabeth HARADER, Valérie BORRELL ESTUPINA, Sophie RICCI, Olivier THUAL, Christophe BOUVIER, Andrea PIACENTINI Made possible by a collaboration Montpellier, September 2005 Session : Testing simulation and forecasting models in non-stationary conditions IAHS General Assembly in Göteborg - July 2013 1

2. General Context & Issues Environmental context The Mediterranean region is subject to violent rainfall producing devastating flash floods. These flash floods are responsible for numerous deaths and expensive material damages. How to improve forecast of these floods ? Scientific issue Hydrological models are helpful to understand and to forecast these floods. However The physical processes involved are not well represented.The precipitation used to force models is not accurately measured, nor is it stationary, and it should be be corrected in order to reduce model uncertainty.Model parameters require some level of adaptation to better represent catchment behaviour. How to deal with the limited performance of the hydrological models linked to the heterogeneity or non stationary of the observed data between the calibration and validation steps ? Methodological difficulty Data assimilation is an innovative method that allows to correct the model parameters as well as the forcing input to the model (precipitation) when discharge observations are available. As a result, the capacity of the model to cope with non stationary conditions is improved. Which data assimilation strategy should be implemented in order to improve flash flood forecatsing ? 2 Conclusion Intro Study Case Model Correction Results

3. Project Work Packages : The observation tasks The model work package The vulnerability study The climate changes and pumping scenarii impacts Research labs A 4-year project focused on the karstic water resource and active management Private company Regional water agency Departmental administration Context & Issues : The Lez GMU* project *Gestion Multi Usages : Multiple-Use Management Main objective discussed in this presentation : How to improve flash flood forecasting using data assimilation? Sources: Dörflinger and al., 2008 3 Conclusion Intro Study Case Model Correction Results

4. The Lez catchment • Small catchment • 114 km2 • Short lag times, 2-5h • Vineyards, scrubland, forest (poor urban cover) • From 34 m asl to 700 m • Karstic formations • spring = resurgence of a 380 km2 karstic aquifer • karst covered with soil or karst outcrops • Extreme rainfall events (mesoscale convective systems ) • The spring has been exploited for more than 100 years for water needs of the population • The Mediterranean region is prone to flash flooding events due to intense rainfalls. Sources: Nuissier and al., 2008; Dörflinger and al., 2008; Kong A Siou and al., 2011; Coustau, 2008 4 Conclusion Intro Study Case Model Correction Results

5. Rain and Floods studied after 1994 4 Rain Gauges within the surface catchment, measurements at an hourly time step Weather radar over the region with different correcting algorithms (Hydram, Calamar) with a temporal resolution of 5 minutes Around 20 raingauges available in the region with a daily time step 1 streamflow gauge at the outlet (Peak discharge : 40 to 467 m3/s) 21 flood events Small floods and false alarms are not considered. The data Base 5 5 Conclusion Intro Study Case Model Correction Results

6. Weather Radar : benefits & challenges • Nîmes radar • S band • indirect measure • Resolution • 1 km • 5 minutes • Indirect measurements are subject to large errors depending on the reflectivity. The difference between corrected radar rainfall and rain from gauges can be high. Corrections : • Mean Field Bias (MFB) to correct radar rainfall with discharge data. • Data Assimilation to correct radar rainfall with discharge data. 6 6 Conclusion Intro Study Case Model Correction Results

7. p(t) r(t) = C(t) * p(t) min(1,w/S) * ds * S(t) p(t) – r(t) S S(t) S0 ds * S(t) (1-min(1,w/S)) * ds * S(t) The Hydrological Model Loss Function : modified SCS Conceptual reservoir model = SCS loss function adaptated for karstic watershed Sources: Borrell and al. 2008, Coustau and al. 2010 sol + karst • 2 parameters : w and ds • S (initial condition) : the potential storage depth must be calibrated IRD, HSM 7 7 Conclusion Intro Study Case Model Correction Results

8. Transfered rainfall at the outlet Qm lag (V0) diffusion (K0) Tm t0 t0 + Tm t R t t0 The Hydrological Model • Lag and route transfer function : with 2 parameters (Vo and Ko) Transfer Function: Lag & Route Rainfall on the watershed • Parsimonious Model • Robustness evaluated by cross calibration • Model performance evaluated through the Nash criterion. Sources: Borrell and al. 2008, Coustau and al. 2010 8 8 Conclusion Intro Study Case Model Correction Results

9. Analysis and forecast-like runs 7 different piezometric stations to establish the initial correlation : 0,63 < R2 < 0,81 The SIM parameter to establish the initial correlation : R2 = 0.67 Satisfactory quality for the false forecast-like runs (Nash > 0.7 for 9 events) • The model can be initialized with piezometric levels or the soil humidity. The mix of rain gauges and radar rainfall corrected with MFB considerably improve the performance of the model (in particular the initial estimation). We loose quality in forcast-like mode due to uncertainty in the S estimation. The use of the MFB => no forecast-like runs ! The results in re-analysis mode are improved over the forecast mode. 9 Conclusion Intro Study Case Model Correction Results

10. Observations Qo(t) Assimilation : Extended Kalman Filter Rrain(t) Qb(t) 3 1 2 4 Qo(t) : observations Qb(t) : background run Data Assimilation A sensitivity analysis highligthed the most influence parameters : S and Rainfall HYDROLOGICAL MODEL Qa(t) State Variable Parameters S , Vo correction Q(t) Correction on the observed radar rainfall Qa(t) : analysis run 4 t

11. Data Assimilation to correct S with discharge data A Simplified Extended Kalman Filter (updated BLUE) Kalman gain: K = BHT(HBHT+R)-1 Update: xa = xb-K(yo – H(xb)) Observations Background (S = 160 mm) Analysis : Iteration 1 (S = 140 mm) Analysis : Iteration 5 (S = 131 mm) xcontrol vector H, H observation operator yo observation vector xbbackground xaanalysis B background error cov. matrix R observation error cov. matrix *OpenPALM is a coupling software developped at CERAFCS. 11 11 Conclusion Intro Study Case Model Correction Results

12. Data Assimilation to correct S with discharge data Improvement in the forecast-like mode when correcting initial S value with Data Assimilation : D(ERDP) Calculated for the Background and Analyse Improvement Corrected parameter by DA The correction of S improves the peak flow in re-analysis mode and in forecast-like mode. 12 12 Conclusion Intro Study Case Model Correction Results

13. Data Assimilation to correct radar rain with discharge data • In forecast-like mode : • use the piezometric level to estimate S • and create background hydrograph α =1 (pink) • then assimilate the first observed discharges to find multiplier, α and • calculateanalysis • (green) • In reanalysis mode : • initialise S using known • hydrograph • and create background hydrograph α =1 (pink) • then assimilate to find multiplier, α and • calculateanalysis • (green) Rainfall (mm) Q(m3/s) 13 Conclusion Intro Study Case Model Correction Results

14. Results of the forecast mode Forecats-like runs • Evaluation of the results: • iterate for each episode (1997-2008) • calculate performance criteria: Peak Height (PH) Nash-Sutcliffe criterion (NS) Average NS = 0.50 • Uncertainty in forecast is increased by : • Parameterisation of S with piezometry • Reduced assimilation window,peak not included • Variability of the rainfall error (α constant ) Harader and al. 2012 The correction of precipitation produced by weather radar in re-analysis conditions improved simulation quality. 14 14 Conclusion Intro Study Case Model Correction Results

15. General objective : to propose an appropriate methodology for a flood forecasting model under changing conditions to improve its efficiency Conclusions & Perspectives Data Assimilation of discharge at the beginning of the flood Different rainfall measurements Temporal variability of initial condition Different flood intensities initial condition rainfall The conceptual hydrological model does not guarantee the quality of flood simulations with intensities not observed in the data base used for calibration. The data assimilation loop helps to improve flash flood simulation on re-analysis settings, whatever the flood intensities are. The data assimilation loop opens new perspectives in forecast-like settings. Work in progress : Improving algorithms in order to correct radar rainfall before forcing the model Work on other karstic watersheds Planned work with data assimilation : Multiple windows for correcting rainfall Considering both rain gauges and radar rain in the corrected fields 15 Thank you for your attention.