Redundant information in the rainfall-runoff relationship.

Transcription

1 Redundant information in the rainfall-runoff relationship. Timothée Michon, Georges-Marie Saulnier, William Castaings Environments, Dynamics and Territories of Mountainous regions CNRS (National Center for Scientific Research) Savoy University (Alps) Catchment Change Network 27th June 212 1/14

2 More data to solve more complex questions Since 1-2 years: More complex questions: Understand and forecast larger catchments. From a few km 2 experimental catchments to a few 1km 2 regional catchments. Say something on ungauged catchments. PUB Couple complex processes Evapotranspiration, land uses, surface/subsurface, groundwater, water quality, etc More available data Not enough of course, but still more available and more accurate e.g. topography, high resolution images, etc More complex models Physically-based, uncertainty estimation, complex mathematical tools (optimization, sensitivity, uncertainties) Catchment Change Network 27th June 212 2/14

3 More data to solve more complex questions Since 1-2 years: More complex questions: Understand and forecast larger catchments. From a few km 2 experimental catchments to a few 1km 2 regional catchments. Say something on ungauged catchments. PUB Couple complex processes Evapotranspiration, land uses, surface/subsurface, groundwater, water quality, etc More available data Not enough of course, but still more available and more accurate e.g. topography, high resolution images, etc More complex models Physically-based, uncertainty estimation, complex mathematical tools (optimization, sensitivity, uncertainties) Catchment Change Network 27th June 212 2/14

4 More data to solve more complex questions Since 1-2 years: More complex questions: Understand and forecast larger catchments. From a few km 2 experimental catchments to a few 1km 2 regional catchments. Say something on ungauged catchments. PUB Couple complex processes Evapotranspiration, land uses, surface/subsurface, groundwater, water quality, etc More available data Not enough of course, but still more available and more accurate e.g. topography, high resolution images, etc More complex models Physically-based, uncertainty estimation, complex mathematical tools (optimization, sensitivity, uncertainties) Catchment Change Network 27th June 212 2/14

5 Example High-resolution flood forecasting everywhere Large catchments > km 2 high resolution 3mn time step, DTM grid size <2m Gauged and ungauged stations Runoff, evapotranspiration, water content, etc Coupled with meteorological operational models Non-hydrostatic, X = 2km 2 Models fit quite well even on wrong data? Data assimilation accurate even when the model is wrong? Complex sensitivity/uncertainty analysis GLUE, Variational approach Catchment Change Network 27th June 212 3/14

6 Real improvements or... Did we become more powerful flood forecasters? Give us: high resolution data, big computers, money and we can simulate what you want and everywhere. (but with uncertainties) or more lazy? claiming for more means to stay in a more comfortable situation Catchment Change Network 27th June 212 4/14

7 Real improvements or... Did we become more powerful flood forecasters? Give us: high resolution data, big computers, money and we can simulate what you want and everywhere. (but with uncertainties) or more lazy? claiming for more means to stay in a more comfortable situation Catchment Change Network 27th June 212 4/14

8 Remaining challenging questions The future is not yet gauged Can models predict the future? Are they good enough to simulate changing climate conditions/hydrological behaviour? How to verify that our models are relevant enough? Can they simulate past changes and not only present stationary conditions? Catchment Change Network 27th June 212 /14

9 Remaining challenging questions The future is not yet gauged Can models predict the future? Are they good enough to simulate changing climate conditions/hydrological behaviour? How to verify that our models are relevant enough? Can they simulate past changes and not only present stationary conditions? Catchment Change Network 27th June 212 /14

10 Hydrological models for varying conditions The future is not yet gauged, the past is partly/not gauged too... What can we do when no supplementary data can be obtained? What can we learn from parsimonious, heterogeneous, indirect data? e.g. historical documents, lakes sediments, etc. Catchment Change Network 27th June 212 6/14

11 Change the paradigm? Instead of doing more with more data can we do the same with less data? Catchment Change Network 27th June 212 7/14

12 Some silly questions Knowing the model parameters values what can we simulate when: no rainfall time-series are available, only the total areal amount? e.g. discharges data in the valley, only cumulative rainfall gauging stations on the higher elevations. no rating curve is available, only river heights measurements? e.g. post-events interviews/historical documents give ideas about the evolution of river levels with time. Can we identify the model with less informations? no rainfall time-series are available, only the areal total amount and the discharges? e.g. discharges data in the valley and only cumulative rainfall gauging stations Catchment Change Network 27th June 212 8/14

13 Some silly questions Knowing the model parameters values what can we simulate when: no rainfall time-series are available, only the total areal amount? e.g. discharges data in the valley, only cumulative rainfall gauging stations on the higher elevations. no rating curve is available, only river heights measurements? e.g. post-events interviews/historical documents give ideas about the evolution of river levels with time. Can we identify the model with less informations? no rainfall time-series are available, only the areal total amount and the discharges? e.g. discharges data in the valley and only cumulative rainfall gauging stations Catchment Change Network 27th June 212 8/14

14 Discharges and model parameters values Inversing rainfall time series (Gradient-based optimization approach Quasi-Newton, BFGS, Wolf) Event 864 by TOPSIMPL ; iteration 1 Q Obs Q Opt P Obs P Inv 8 1 Rainfall (mm/h) Time (h) Catchment Change Network 27th June 212 9/14

15 Discharges and model parameters values Inversing rainfall time series (Gradient-based optimization approach Quasi-Newton, BFGS, Wolf) Event 864 by TOPSIMPL ; iteration 1 1 Q Obs Q Opt P Obs P Inv 8 1 Rainfall (mm/h) Time (h) Catchment Change Network 27th June 212 9/14

16 Discharges and model parameters values Inversing rainfall time series (Gradient-based optimization approach Quasi-Newton, BFGS, Wolf) Event 864 by TOPSIMPL ; iteration 2 1 Q Obs Q Opt P Obs P Inv 8 1 Rainfall (mm/h) Time (h) Catchment Change Network 27th June 212 9/14

17 Discharges and model parameters values Inversing rainfall time series (Gradient-based optimization approach Quasi-Newton, BFGS, Wolf) Event 864 by TOPSIMPL ; iteration 3 1 Q Obs Q Opt P Obs P Inv 8 1 Rainfall (mm/h) Time (h) Catchment Change Network 27th June 212 9/14

18 Discharges and model parameters values Inversing rainfall time series (Gradient-based optimization approach Quasi-Newton, BFGS, Wolf) Event 864 by TOPSIMPL ; iteration 4 1 Q Obs Q Opt P Obs P Inv 8 1 Rainfall (mm/h) Time (h) Catchment Change Network 27th June 212 9/14

19 Discharges and model parameters values Inversing rainfall time series (Gradient-based optimization approach Quasi-Newton, BFGS, Wolf) Event 864 by TOPSIMPL ; iteration 1 Q Obs Q Opt P Obs P Inv 8 1 Rainfall (mm/h) Time (h) Catchment Change Network 27th June 212 9/14

20 Discharges and model parameters values Inversing rainfall time series (Gradient-based optimization approach Quasi-Newton, BFGS, Wolf) Event 864 by TOPSIMPL ; iteration 1 1 Q Obs Q Opt P Obs P Inv 8 1 Rainfall (mm/h) Time (h) Catchment Change Network 27th June 212 9/14

21 Discharges and model parameters values Inversing rainfall time series (Gradient-based optimization approach Quasi-Newton, BFGS, Wolf) Event 864 by TOPSIMPL ; iteration 2 1 Q Obs Q Opt P Obs P Inv 8 1 Rainfall (mm/h) Time (h) Catchment Change Network 27th June 212 9/14

22 Discharges and model parameters values Inversing rainfall time series (Gradient-based optimization approach Quasi-Newton, BFGS, Wolf) Event 864 by TOPSIMPL ; iteration 3 1 Q Obs Q Opt P Obs P Inv 8 1 Rainfall (mm/h) Time (h) Catchment Change Network 27th June 212 9/14

23 Discharges and model parameters values Inversing rainfall time series (Gradient-based optimization approach Quasi-Newton, BFGS, Wolf) Event 864 by TOPSIMPL ; iteration 4 1 Q Obs Q Opt P Obs P Inv 8 1 Rainfall (mm/h) Time (h) Catchment Change Network 27th June 212 9/14

24 Discharges and model parameters values Inversing rainfall time series (Gradient-based optimization approach Quasi-Newton, BFGS, Wolf) Event 864 by TOPSIMPL ; iteration 6 1 Q Obs Q Opt P Obs P Inv 8 1 Rainfall (mm/h) Time (h) Catchment Change Network 27th June 212 9/14

25 Rainfall and river heights time series Retrieving parameters values and a rating curve 8 Rating Curve by GR4H ; iteration 7 H Obs VS Q Sim RC Obs RC Sim 6 Water level (m) 4 3 Param1 =,127 Param2 =,1949 Param3 = -,3993 Param4 =6, Catchment Change Network 27th June 212 1/14

26 Rainfall and river heights time series Retrieving parameters values and a rating curve 8 Rating Curve by GR4H ; iteration 6 7 H Obs VS Q Sim RC Obs RC Sim 6 Water level (m) 4 3 Param1 =,17662 Param2 =,1379 Param3 = -,488 Param4 =7, Catchment Change Network 27th June 212 1/14

27 Rainfall and river heights time series Retrieving parameters values and a rating curve 8 Rating Curve by GR4H ; iteration 7 7 H Obs VS Q Sim RC Obs RC Sim 6 Water level (m) 4 3 Param1 =,1247 Param2 =,8248 Param3 = -,899 Param4 =3, Catchment Change Network 27th June 212 1/14

28 Rainfall and river heights time series Retrieving parameters values and a rating curve 8 Rating Curve by GR4H ; iteration 8 7 H Obs VS Q Sim RC Obs RC Sim 6 Water level (m) 4 3 Param1 =, Param2 =,342 Param3 = -,4237 Param4 =3, Catchment Change Network 27th June 212 1/14

29 Rainfall and river heights time series Retrieving parameters values and a rating curve 8 Rating Curve by GR4H 7 H Obs VS Q Sim RC Obs RC Sim 6 Water level (m) Catchment Change Network 27th June 212 1/14

30 Rainfall and river heights time series Retrieving parameters values and a rating curve 8 Rating Curves All Models ; NO Gauging 7 Observed GR4H SCSRES TOPSIMPL 6 Water level (m) Catchment Change Network 27th June 212 1/14

31 Discharges and cumulative areal rainfall Retrieving parameters values and rainfall time series Event 874 by SCSRES ; iteration 7 Q Obs Q Opt P Obs P Inv 6 Rainfall (mm/h) Param1 =,6333 Param2 =,1937 Param3 =, Time (h) Catchment Change Network 27th June /14

32 Discharges and cumulative areal rainfall Retrieving parameters values and rainfall time series Event 874 by SCSRES ; iteration 1 7 Q Obs Q Opt P Obs P Inv 6 Rainfall (mm/h) Param1 =,6333 Param2 =,1937 Param3 =, Time (h) Catchment Change Network 27th June /14

33 Discharges and cumulative areal rainfall Retrieving parameters values and rainfall time series Event 874 by SCSRES ; iteration 2 7 Q Obs Q Opt P Obs P Inv 6 Rainfall (mm/h) Param1 =,6333 Param2 =,1937 Param3 =, Time (h) Catchment Change Network 27th June /14

34 Discharges and cumulative areal rainfall Retrieving parameters values and rainfall time series Event 874 by SCSRES ; iteration 3 7 Q Obs Q Opt P Obs P Inv 6 Rainfall (mm/h) Param1 =,6333 Param2 =,1937 Param3 =, Time (h) Catchment Change Network 27th June /14

35 Discharges and cumulative areal rainfall Retrieving parameters values and rainfall time series Event 874 by SCSRES ; iteration 4 7 Q Obs Q Opt P Obs P Inv 6 Rainfall (mm/h) Param1 =, Param2 =,1498 Param3 =, Time (h) Catchment Change Network 27th June /14

36 Discharges and cumulative areal rainfall Retrieving parameters values and rainfall time series Event 874 by SCSRES ; iteration 7 Q Obs Q Opt P Obs P Inv 6 Rainfall (mm/h) Param1 =,24119 Param2 =,413 Param3 =, Time (h) Catchment Change Network 27th June /14

37 Discharges and cumulative areal rainfall Retrieving parameters values and rainfall time series Event 874 by SCSRES ; iteration 1 7 Q Obs Q Opt P Obs P Inv 6 Rainfall (mm/h) Param1 =,23277 Param2 =,36838 Param3 =, Time (h) Catchment Change Network 27th June /14

38 Discharges and cumulative areal rainfall Retrieving parameters values and rainfall time series Event 874 by SCSRES ; iteration 2 7 Q Obs Q Opt P Obs P Inv 6 Rainfall (mm/h) Param1 =, Param2 =,187 Param3 =, Time (h) Catchment Change Network 27th June /14

39 Discharges and cumulative areal rainfall Retrieving parameters values and rainfall time series Event 874 by SCSRES ; iteration 3 7 Q Obs Q Opt P Obs P Inv 6 Rainfall (mm/h) Param1 =,16643 Param2 =,14424 Param3 =, Time (h) Catchment Change Network 27th June /14

40 Discharges and cumulative areal rainfall Retrieving parameters values and rainfall time series Event 874 by SCSRES ; iteration 4 7 Q Obs Q Opt P Obs P Inv 6 Rainfall (mm/h) Param1 =,11 Param2 =,11919 Param3 =, Time (h) Catchment Change Network 27th June /14

41 Discharges and cumulative areal rainfall Retrieving parameters values and rainfall time series Event 874 by SCSRES ; iteration 7 Q Obs Q Opt P Obs P Inv 6 Rainfall (mm/h) Param1 =,1619 Param2 =,188 Param3 =, Time (h) Catchment Change Network 27th June /14

42 Discharges and cumulative areal rainfall Retrieving parameters values and rainfall time series Event 874 by SCSRES ; iteration 1 7 Q Obs Q Opt P Obs P Inv 6 Rainfall (mm/h) Param1 =, Param2 =,9923 Param3 = 1, Time (h) Catchment Change Network 27th June /14

43 Discharges and cumulative areal rainfall Retrieving parameters values and rainfall time series Event 874 by SCSRES ; iteration 1 7 Q Obs Q Opt P Obs P Inv 6 Rainfall (mm/h) Param1 =, Param2 =,9893 Param3 = 1, Time (h) Catchment Change Network 27th June /14

44 Discharges and cumulative areal rainfall Retrieving parameters values and rainfall time series Event 874 by SCSRES ; iteration 24 7 Q Obs Q Opt P Obs P Inv 6 Rainfall (mm/h) Param1 =,17347 Param2 =,136 Param3 = 1, Time (h) Catchment Change Network 27th June /14

45 Some silly questions Knowing the model parameters values what can we simulate when: no rainfall time-series are available, only the total areal amount? e.g. discharges data in the valley, only cumulative rainfall gauging stations on the higher elevations. no rating curve is available, only river heights measurements? e.g. post-events interviews/historical documents give ideas about the evolution of river levels with time. Can we identify the model with less informations? no rainfall time-series are available, only the areal total amount and the discharges? e.g. discharges data in the valley and only cumulative rainfall gauging stations Are these questions independent questions? Catchment Change Network 27th June /14

46 Redundant informations surface For a given objective and a given catchment. Catchment Change Network 27th June /14

47 Redundant informations surface For a given objective and a given catchment. Catchment Change Network 27th June /14

48 Redundant informations surface For a given objective and a given catchment. Catchment Change Network 27th June /14

49 Redundant informations surface For a given objective and a given catchment. Catchment Change Network 27th June /14

50 Redundant informations surface For a given objective and a given catchment. Catchment Change Network 27th June /14

51 Redundant informations surface For a given objective and a given catchment. Catchment Change Network 27th June /14

52 Redundant informations surface For a given objective and a given catchment. Catchment Change Network 27th June /14

53 Redundant informations surface For a given objective and a given catchment. Catchment Change Network 27th June /14

54 Redundant informations surface For a given objective and a given catchment. Catchment Change Network 27th June /14

55 Conclusions By the means of tailored-made algorithms of increasing complexity when decreasing information content it is possible to decrease the amount of needed informations to set up a model on a given region and for a given objective. By this kind of approach: Can we contribute to design experimental supplementary effort? 2 rivers height gauges are more or less informative than 1 discharge station? Can we extract valuable informations from non-used data/documents? Historical, qualitative informations (time to peak), paleo-hydrology, etc Thank you! Catchment Change Network 27th June /14