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Ian G. Littlewood

5th International Symposium on Integrated Water Resources Management, 3rd International Symposium on Methodology in Hydrology, Hohai University, Nanjing, China, 19-21 November, 2010.

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Ian G. Littlewood

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  1. 5th International Symposium on Integrated Water Resources Management, 3rd International Symposium on Methodology in Hydrology, Hohai University, Nanjing, China, 19-21 November, 2010 The effect of sub-optimal effective rainfall on the accuracy of unit hydrograph parameters estimated from different n-hourly data for a small humid-region catchment: method and implications Ian G. Littlewood IGLEnvironment, Didcot, Oxford, OX11 7XN, United Kingdom

  2. Part 1 – a summary of some published research undertaken with Barry Croke (Australian National University) and Peter Young (Lancaster University, UK) – presented at the Third International Symposium of the British Hydrological Society (Newcastle, UK, July 2010) PUB Part 2 – related work since July 2010 – feedback very welcome Part 1 … Littlewood, Nanjing, Nov 2010

  3. IHACRES – 6 parameters (DRCs) Identification of unit Hydrographs And Component flows from Rainfall, Evaporation and Streamflow data Jakeman, Littlewood and Whitehead (1990) Journ. Hydrol., 117, 275‑300 (ek) (uk) rk Qk Non-linear 'loss' Linear UH module module (3 parameters) tk (3 parameters)  effective rainfall in time step k (uk) is the portion of rainfall (rk) that eventually leaves the catchment as streamflow  uk is un-observable – generated during model calibration Littlewood, Nanjing, Nov 2010

  4. IHACRES – 6 parameters (DRCs)  (q) – quick flow response decay time constant [T] c– depth of a conceptual catchment wetness store [L]  (s) – slow flow response decay time constant [T] w – catchment drying time constant [T] (ek) (uk) rk Qk Non-linear 'loss' Linear UH module module (3 parameters) tk (3 parameters)  (s)= SFI (analogous to BFI)  (s) – proportional volumetric contribution of slow flow to streamflow [-] f– temperature modulation factor (oC-1) Littlewood, Nanjing, Nov 2010

  5. loss module s0 = 0 UH module quick slow Littlewood, Nanjing, Nov 2010

  6. Calculating unit hydrograph DRCs from a (.) and b(.) parameters DRC Quick flow Slow flow Characteristic decay response times Relative volumetric flow, where ν(q) + ν(s) = 1, ν(s) = SFI and Relative magnitudes of unit hydrograph peaks Littlewood, Nanjing, Nov 2010

  7. Plynlimon – forest and moorland Severn 8.7 km2 10.6 km2 Wye Cefn Brwyn Littlewood, Nanjing, Nov 2010

  8. Littlewood, Nanjing, Nov 2010

  9. Wye at Cefn Brwyn model calibration: 6 December 1987 – 2 July 1988 D = 0.89 Littlewood, Nanjing, Nov 2010

  10. Littlewood, (2007), Tech. Doc. Hydr. No. 81, UNESCO, 149-155. Littlewood & Croke (2008), HSJ, 53(4), 685-695 Littlewood, Nanjing, Nov 2010

  11. Wye at Cefn Brwyn DRCs against data interval: model calibration 6 December 1987 – 2 July 1988 data interval (hours) data interval (hours) (SFI) ν(s) (-) data interval (hours) data interval (hours) data interval (hours) Littlewood, (2007), Tech. Doc. Hydr. No.81, UNESCO, 149-155. Littlewood & Croke (2008), HSJ, 53(4), 685-695 Littlewood, Nanjing, Nov 2010

  12. Wye at Cefn Brwyn IHACRES (q) r2 = 0.999  (q) = 0.692t + 3.27 (t) data interval (hours) extrapolating to t= 0 gives the Instantaneous Unit Hydrograph (IUH) parameter  (q)= 3.27 hr Littlewood, Nanjing, Nov 2010

  13. precision ~ +/-11% accuracy ~ +140% data interval (hours) Littlewood, Nanjing, Nov 2010

  14. precision ~ +/-2% accuracy ~ +425% data interval (hours) Littlewood, Nanjing, Nov 2010

  15. the normalised IHACRES discrete-time UH values have been compared with corresponding DBM continuous-time values (previous work by Littlewood, Croke and Young, 2010) data interval (hours) data interval (hours) (SFI)  (s) (-) data interval (hours) data interval (hours) data interval (hours) (comparison of the IHACRES normalised loss module values with DBM continuous-time values has yet to be done) Littlewood, Nanjing, Nov 2010

  16.  (q)  (s) SFI Data time-step (hours) Data time-step (hours) Data time-step (hours) data interval (hours) data interval (hours) data interval (hours) convergence corroborates the IHACRES empirical/graphical method of estimating data interval-invariant (IUH) model parameters proposed by Littlewood (2007) and Littlewood and Croke (2008) CT/DBM parameters are much less sensitive to t why are IHACRES parameters so sensitive to t? Littlewood, Nanjing, Nov 2010

  17. Part 2 Littlewood, Nanjing, Nov 2010

  18. (uk) rk Qk Non-linear 'loss' Linear UH module module (3 parameters) tk (3 parameters) PC-IHACRES (1997) – PCI (v1.03) rk , tk Qk uk PCI allows by-passing of the loss module, i.e. it can use effective rainfall generated externally IHACRES Classic Plus (2006) – ICP(v2.1) rk , tk Qk ICP does not allow by-passing of the loss module IHACRES software packages downloadable (free) … Littlewood, Nanjing, Nov 2010

  19. ICP (5 DRCs) (previous work) Littlewood, Nanjing, Nov 2010

  20. ICP (5 DRCs) (previous work) e.g. u*2,Δ4 = u5,Δ1+ u6,Δ1+ u7,Δ1+ u8,Δ1 Littlewood, Nanjing, Nov 2010

  21. ICP (5 DRCs) (previous work) Compare UH DRCs and DRC*s PCI (3 UH DRC*s) (this paper) e.g. u*2,Δ4 = u5,Δ1+ u6,Δ1+ u7,Δ1+ u8,Δ1 Littlewood, Nanjing, Nov 2010

  22. sub-optimal effective rainfall (> hourly) quasi-optimal effective rainfall  (q)  (s) SFI uk u*k ~ +425%~ +250% ~ +110%~ +17% ~ -18~ -6 (pp) Littlewood, Nanjing, Nov 2010

  23. Concluding remarks  a large part of the inaccuracy associated with an IHACRES UH parameter (DRCs –  (q),  (s)or SFI) calibrated using daily Cefn Brwyn data is due to sub-optimal effective rainfall similar results are likely for other discrete-time conceptual rainfall–streamflow models,and for other catchments with highly dynamic responses to rainfall • there appears to be no escaping the fact that to calibrate discrete-time R-S model parameters with good precision and good accuracy (i.e. low uncertainty), the data must be at a sufficiently high frequency (f *) • f * = hourly (or more frequent) for Cefn Brwyn Littlewood, Nanjing, Nov 2010

  24.  f * will be different for different catchments  this has been largely ignored/overlooked e.g. in several published R-S model parameter regionalisation studies towards predicting streamflow from rainfall in ungauged (flow) catchments –it has been common for daily data to be used for all gauged catchments irrespective of their different dynamic responses to rainfall inaccuracy associated with a given R-S model parameter for gauged catchments (large for some catchments) introduces imprecision in that parameter estimated for ungauged catchments Littlewood, Nanjing, Nov 2010

  25. by minimising or eliminating inaccuracy in calibrated R-S model parameters for gauged basins (as demonstrated) it should be possible to improve the precision of estimates for ungauged basins via model parameter regionalisation but this has not been tried yet …

  26. Thank you

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