On the use of reservoir management tools increasing the value of historic hydrometeorological data

1. On the use of reservoir management tools increasing the value of historic hydrometeorological data Colloque « Des prévisions hydrologiques opérationnelles vers une optimisation de la gestion des réservoirs », Québec city, 18 sept. 2014 Charles Poirier, ing., M.Sc., responsable des Prévisions Barrages Martin-Pierre Lavigne, Spécialiste en sciences physiques, M.Sc. Service de l’hydrologie et de l’hydraulique Direction de l’Expertise Hydrique Centre d’expertise hydrique du Québec (CEHQ)

2. Plan • Issue addressed • Classic tools • ESP challenges • SWE error of HYDROTEL • ESP – ECoSS as a new tool • Preliminary products • Conclusion

3. Issue addressed Classic tools ESP challenges SWE error of HYDROTEL ESP-ECoSS Products Conclusion From mid-winter to the end of the spring freshet, hard to plan optimal dam releases at the extended time frame horizon (1-8 weeks ahead) 1- Respect of reservoir management plan during the freshet (necessity to fill the reservoir in a safety way) 2- …While minimizing releases’ variation and waste (unturbined) water Main issues related to this complex challenge •  Physical and stochastic processes involved • Runoff still fairly hard to simulate for high streamflows • Great climatic variability over the Quebec’s territory (uncertainty of P and T forecasts) • Great uncertainty of the snowpack’s state • Spatial heterogenity • Curse of dimensionality •  Both state and decision variables • Many possible scenarios…many possible solutions • Computing capability •  Anthropic issues • Human factor (Dam mangers’ trust in data and tools used) • Risk tolerance evolution through time (medias…) • Top-bottom or bottom-up working strategy ?

4. Issue addressedClassic tools ESP challenges SWE error of HYDROTEL ESP-ECoSS Products Conclusion ● Optimisation models Other : ●Neural network ●Decision matrix … Deterministic and / or probabilistic forecasts Clients’ various risk tolerance ● ● State + decision variables Physical and stochastic processes involved ●Hydrological models (± physical, ± distributed) ● Statistical models (hydrologic analogues, post-treatment, etc.)

5. Issue addressedClassic tools ESP challenges SWE error of HYDROTEL ESP-ECoSS Products Conclusion ●Optimisation models (OM) ●Hydrological models(HM) ● Management models (MM) (without optimisation) ●Statistical models (SM) (hydrologic analogues, post-treatment of HM, etc.) Examples of tools addressing the extended time frame horizon (1-8 weeks) : 1. HM :ESPfor. met : Ensemble of met and / or hydrological scenarios - [day 1, 2 weeks] 2. HM + MM :ESPhis. met : Spread of climatic historical variability - [day 3, several weeks] 3. OM :SDP : Stochastic Dynamic Programming - [day 3, several weeks]

6. Issue addressed Classic tools ESP challenges SWE error of HYDROTEL ESP-ECoSS Products Conclusion Issue addressed Initial SWE estimate Later discussed Later discussed  From operational forecasting, we noticed that SWE sim error is occasionaly fairly high after 2 weeks, compared to manual snow survey.  Need to characterize the SWEsim error since ESPhis time span expands over several weeks.

7. Issue addressed Classic tools ESP challenges SWE error of HYDROTEL ESP-ECoSS Products Conclusion Snow measurement issues • In contrast with western Canadian provinces and territories and the U.S., there is no snow pillow measurement program in Quebec. • The manual snow survey network remains the most reliable observation source of SWE. Quebec’s network : ~190 snow sites. • But low temporal resolution of the snow survey network… •  Need to complement the direct SWE observations with other indirect sources of information on SWE that could be available on a daily basis. •  > 2005 : Operational approach combining in-situ obs. of SWE and snow depth from the snow survey network, with HYDROTEL snow module simulating temporal evolution of the snowpack, using precipitation and air temperature as input. Turcotte, R, Fortin, L.G., Fortin, J.P., Fortin, V., Villeneuve, J.P (2007). Nordic hydrology. vol 38(3) 07 •  Recently : ongoing studies for improving futher SWE assimilation (at CEHQ and also at U.Laval )

8. From snow measurement to operational assimilation Sites d’observation Precipitations Temperatures Snow model Correction Vertical inflows (melt water and direct rain on ground) Manual nivometric data Daily simulated SWE

9. Recent work at CEHQ leading to « priceless » database… Monthly variograms of HYDROTEL’s SWE error  Adaptation of the operational SWE correction to the historic 1900-2010 period CEHQ publication, Poirier, C., T.C. Fortier-Filion, R. Turcotte, P. Lacombe, 2014 Historicgridded P& T • [1900, 2010], 24 h time step Griddedsimulated(SWECo & Verticalinflows) [1900, 2010], 24 h time step, HYDROTEL code • Good spatial distribution of nivom. Data > 1965 • 41 years of SWECo • [1970, 2010] south from • 50th parallel Corected SWE The domain grid

10. P et T gauge stations in 1970 : P et T gauge stations in 2010 : Nivometric sites in 1970 : Nivometric sites in 2010 :

11. Issue addressed Classic tools ESP challenges SWE error of HYDROTEL ESP-ECoSS Products Conclusion

12. Issue addressed Classic tools ESP challenges SWE error of HYDROTEL ESP-ECoSS Products Conclusion

13. Lac Kénogami Réserve faunique region Dense coniferous forest cover Estrie region, loose to dense mix forest cover Issue addressed Classic tools ESP challenges SWE error of HYDROTEL ESP-ECoSS Products Conclusion Comparison between SWE and SWECo Recall that both are simulated SWE, but SWECo is forced with historical nivometric data when available. The « SWE correction grids » are the result of interpolation, over the grid, of (SWEsim – SWE in situ measurement). They are produced only when a sufficient amount of nivometric sites can contribute to the interpolation. Otherwise, the evaluation of SWE correction could be under estimated.  WE set that 60% or more of the total nivometric sites have to provide a SWE measurement. We will also categorized 3 types of SWEsim drift : after 1 week, 2 weeks and 3 weeks. Data used for the next figures are issued from these « SWE correction grids ».  For each watershed : the mean value of data at grid points enclosed by watersheds contours.

14. Under catch Signature of Sublimation ? Or under estimating of snow melt for this model or for this set of calib. parameters ? It might be the signature of sublimation, because Hydrotel ignores sublimation in its water budget… It might be the signature of sublimation, because Hydrotel ignores sublimation in its water budget… It might be the signature of sublimation, because Hydrotel ignores sublimation in its water budget… Issue addressed Classic tools ESP challenges SWE error of HYDROTEL ESP-ECoSS Products Conclusion  Illustration of two probable processes acting in opposition : « Snow under-catch» and Sublimation No snow under catch here ! Absence de sous captage No snow under catch here !

15. Issue addressed Classic tools ESP challenges SWE error of HYDROTEL ESP-ECoSS Products Conclusion (m) (m) Signature of nivometric data correcting simulated SWE • Estrie region : • Almost no under catch • Lots of sublimation  Systematic over-estimating of SWE due to ignorance of sublimation (is there a snow model that can really capture sublimation ?...) (m)

16. Issue addressed Classic tools ESP challenges SWE error of HYDROTEL ESP-ECoSS Products Conclusion Lac Kénogami Réserve faunique region Dense coniferous forest cover Estrie region, loose to dense mix forest cover

17. Problématique Approches Enjeux ESP SWE VS SWECo ESP-ECSS Produits Conclusion (m) (m) • Laurentides region : • Lots of under catch • A bit of sublimation  Systematic under-estimating of SWE due to snow under-catch …

18. Pre-conclusion : • SWE simulated with Hydrotel can become significant erroneousfairly rapidly (after one week). At spring time, relative SWE correction often >100%. • The problem is not only from Hydrotel. Most hydrologic models make use of an accumulation / melt sub model that does not explicitely and precisely simulate complex processes such as sublimation, and the climatologic stations do under-catch snow occasionaly. We tend to ignore the severe consequences of this reality. • Fairly detailed snow models such as CROCUS may deal with such complexity, but we know it can’t be used operationally at large scale due to a lack of particular data measurement (winds, sun radiation, etc.). But do simulation data from atmospheric models could be used as alternative inputs ? • Regarding the snow under catch, improving assimilation techniques will probably help. • Meanwhile, how can we really consider to run ESP over a time span of several weeks ? Dangerous !!! • If SWE simulated is biased during spring (as it seems frequent), then snow melt volume and runoff are biased, and eventually management decisions may be biased too. • We propose ESP-ECoSS as an alternative approach Recall that we use a fairly severe criterion to estimate the SWE simulation error : « min 60% of total nivometric sites must provide a SWE measurment in order to produce a SWE correction grid, from which we evaluate a mean SWE correction over a particular waterhed». Back in 1970, it means that at least 120 SWE in situ data contributed. The purpose of ESP is to show the domain of possible scenarios. But what is the usfulness of this domain if it is severely erroneous (drifted) compare to the runoff flows associated to the corrected SWE domain ?...

19. Issue addressed Classic tools ESP challenges SWE VS SWEco ESP-ECoSS Products Conclusion No need if we use ESP-ECoSS ! ESP-ECoSS Concepts : Ensembleof Corrected SWE Simulated Deterministic (P,T) forecasts (1-6 days) Historical SWECo simulations from the 41 yrs 24h database Model free : Instead of simulated data, this method make use of the management historical data « his » Sub sampler for historical SWECo analogues matching : 1- SWEsim Day -1 and 2- SWEsimtrend day 1-6 Main filter criteria : ± 10 % SWEsim Day -1 and±10 days sliding Operational Snow model : yesterday’s SWEsim SWE Co Inflows his Releases his Levels his Operational Snow model : SWEsim Day -1 and SWEsimtrend day 1-6 Upon availa-bility ECoSS : Ensemble of Corrected SWE simulations Classic ESP Concepts : Deterministic (P,T) forecasts (1-2 days) Historical (P,T) his (day 2 – several weeks) from the 41 yrs 24h database Reservoir decision model Operational Snow model SWE set to SWE init SWE sim Inflows sim Vertical water budget model (ET, Infiltration, water contend…) Streamflow routing Releases sim Levelssim

20. Issue addressed Classic tools ESP challenges SWE VS SWEco ESP-ECoSS Products Conclusion Criteria defining an ECoSS : Ensemble of Corrected SWE Simulations (… that appear to be similar to the current latest SWE estimation) Yesterday’s SWEsim (produced from the operational snow model) Plot of all SWECo spags available (41) EEN (mm) dates

21. Issue addressed Classic tools ESP challenges SWE VS SWEco ESP-ECoSS Products Conclusion Criteria defining an ECoSS : Ensemble of Corrected SWE Simulations (… that appear to be similar to the current latest SWE estimation) Main criteria of the Sub sampler of SWECo historical analogues EEN (mm) EEN (mm) ± 10 days ± 10% allowed dates

22. Issue addressed Classic tools ESP challenges SWE VS SWEco ESP-ECoSS Products Conclusion Criteria defining an Ensemble of Corrected SWE Simulations The ECoSS for that specific day. - Ensemble of 5 members - Specific years retained - One member / year EEN (mm) EEN (mm) dates

23. Issue addressed Classic tools ESP challenges SWE VS SWEco ESP-ECoSS Products Conclusion ECoss – raw extracts (un-sequenced) EEN (mm) Complementary information Advantage of the sequenced ECoSS : allows conditional probabilities ECoss – sequenced EEN (mm)

24. Issue addressed Classic tools ESP challenges SWE VS SWEco ESP-ECoSSProducts Conclusion

25. Issue addressed Classic tools ESP challenges SWE VS SWEco ESP-ECoSSProducts Conclusion Sub ensemble of historical corrected SWE simulations that are analogues the current date SWE estimation

26. Issue addressed Classic tools ESP challenges SWE VS SWEco ESP-ECoSSProducts Conclusion All these graphs display the real historic management data associated to the ESP sub ensemble obtained by the ESP-ECoSS method

27. Issue addressed Classic tools ESP challenges SWE VS SWEco ESP-ECoSSProducts Conclusion

28. Issue addressed Classic tools ESP challenges SWE VS SWEco ESP-ECoSSProducts Conclusion

29. Issue addressed Classic tools ESP challenges SWE VS SWEco ESP-ECoSSProducts Conclusion

30. Issue addressed Classic tools ESP challenges SWE VS SWEco ESP-ECoSSProducts Conclusion

31. Issue addressed Classic tools ESP challenges SWE VS SWEco ESP-ECoSS Products Conclusion There is often a huge error with SWE simulations. Within lots of hydrogeologic models. Classic ESP simulate SWE while ignoring or under-estimating that reality. ESP-ECoSS is a new approach developped to add value to the historical corrected SWE data that the CEHQ have – 40 years of realible SWE data. ESP-ECoss is currently operational and so far the dam managers’ feedback is excellent. More to come… THANKS ! charles.poirier@mddelcc.gouv.qc.ca or poiriercharles2@gmail.com