Modelling Environmental Change: Quantification of Impacts of Land Use and Land Management Change on UK Flood Risk

  • H. S. Wheater
  • C. Ballard
  • N. Bulygina
  • N. McIntyre
  • B. M. Jackson


Hydrological models have an important role to play in supporting water management. In this paper we consider the insights developed from Pater Young’s research in the context of the particularly challenging problem of predicting the effects of land use and land management change across multiple scales. The strengths and weaknesses of alternative modelling approaches are reviewed. We then consider the utility of physics-based models, firstly applied to issues of upland grazed pasture and the effect of tree shelter belts, supported by an extensive multi-scale field experimental programme at Pontbren, Wales. The models provide useful quantification of local (field-scale) effects; we use meta-modelling emulation to extend the simulations to catchment scale. Secondly we consider the problem of upland peatland management, in the absence of detailed local data. Physics-based modelling provides generic guidance on the effects of drainage and drain blocking on flood risk. We then consider the potential of conceptual models, conditioned by regionalised indices—in this case a Base Flow Index and the US SCS Curve Number. BFI has considerable power in constraining ungauged catchment simulations, and with speculative adjustment of soil categorisation, catchment-scale effects of land management can be simulated. The CN approach is applied through subjective mapping of US to UK soils; although derived for the US, results show that it has considerable utility for UK regional application. Finally, we reflect on the role of Data Based Modelling, and, in application to our detailed experimental data, show its usefulness in identifying appropriate model structures to guide hydrological application.


Flood Risk Catchment Scale Generalise Likelihood Uncertainty Estimation Ungauged Catchment Base Flow Index 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



FRMRC, FREE, Pontbren cooperative. This research was sponsored by NERC Grant NE/F001061/1 and EPSRC Grants EP/FP202511/1 and GR/S76304/01.


  1. 1.
    Abbott, M.B., Bathurst, J.C., Cunge, J.A., O’Connell, P.E., Rasmussen, J.: An introduction to the European Hydrological System—Systeme Hydrologique Europeen, SHE. 1. History and philosophy of a physically-based, distributed modelling system. J. Hydrol. 87, 45–59 (1986) CrossRefGoogle Scholar
  2. 2.
    Abbott, M.B., Bathurst, J.C., Cunge, J.A., O’Connell, P.E., Rasmussen, J.: An introduction to the European Hydrological System—Systeme Hydrologique Europeen, SHE. 2. Structure of a physically-based, distributed modelling system. J. Hydrol. 87, 61–77 (1986) CrossRefGoogle Scholar
  3. 3.
    Ahti, E.: Ditch spacing experiments in estimating the effects of peatland drainage on summer runoff. In: Proceedings of the International Symposium on Influence of Man on Hydrological Regime, Helsinki. IAHS-AISH Publication, vol. 130, pp. 49–53 (1980) Google Scholar
  4. 4.
    Ballard, C., McIntyre, N., Wheater, H.S.: Peatland drain blocking—can it reduce peak flood flows. In: Proceedings of BHS 2010 International Conference, Newcastle, UK (2010) Google Scholar
  5. 5.
    Ballard, C.E., McIntyre, N., Wheater, H.S., Holden, J., Wallage, Z.E.: Hydrological modelling of drained blanket peatland. J. Hydrol. 407, 81–93 (2011) CrossRefGoogle Scholar
  6. 6.
    Beven, K.J.: Changing ideas in hydrology: the case of physically-based models. J. Hydrol. 105, 157–172 (1989) CrossRefGoogle Scholar
  7. 7.
    Beven, K.J.: Prophecy, reality and uncertainty in distributed hydrological modelling. Adv. Water Resour. 16, 41–51 (1993) CrossRefGoogle Scholar
  8. 8.
    Beven, K.J.: How far can we go in distributed hydrological modelling? Hydrol. Earth Syst. Sci. 5(1), 1–12 (2001) CrossRefGoogle Scholar
  9. 9.
    Beven, K.J., Binley, A.: The future of distributed models—model calibration and uncertainty prediction. Hydrol. Process. 6(3), 279–298 (1992) CrossRefGoogle Scholar
  10. 10.
    Beven, K.J., Young, P., Romanowicz, R., O’Connell, P.E., Ewen, J., O’Donnell, G.M.O., Homan, I., Posthumus, H., Morris, J., Hollis, J., Rose, S., Lamb, R., Archer, D.: Analysis of historical data sets to look for impacts of land use and management change on flood generation. Defra R&D Final Report FD2120. Defra, London (2008) Google Scholar
  11. 11.
    Bicknel, B.R., Imhoff, J.C., Kittle, J.L., Jobes, T.H., Donigian, A.S.: Hydrological Simulation Program—FORTRAN HSPF Version 12 User’s Manual. AQUA TERRA Consultants Mountain View, California 94043 (2001) Google Scholar
  12. 12.
    Binley, A.M., Beven, K.J.: A physically based model of heterogeneous hillslopes 2. Effective hydraulic conductivities. Water Resour. Res. 25(6), 1227–1233 (1989) CrossRefGoogle Scholar
  13. 13.
    Bonn, A., Allott, T.E.H., Hubacek, K., Stewart, J.: Introduction: drivers of change in upland environments: concepts, threats and opportunities. In: Bonn, A., Allott, T.E.H., Hubacek, K., Stewart, J. (eds.) Drivers of Change in Upland Environments, Routledge, Oxon, pp. 1–10 (2009) Google Scholar
  14. 14.
    Boorman, D., Hollis, J., Lilly, A.: Hydrology of Soil Types: A Hydrologically-Based Classification of the Soils of the United Kingdom. Institute of Hydrology, Wallingford (1995) Google Scholar
  15. 15.
    Bulygina, N., McIntyre, N., Wheater, H.S.: Conditioning rainfall-runoff model parameters for ungauged catchments and land management impacts analysis. Hydrol. Earth Syst. Sci. 13(6), 893–904 (2009) CrossRefGoogle Scholar
  16. 16.
    Bulygina, N., McIntyre, N., Wheater, H.S.: Bayesian conditioning of a rainfall-runoff model for predicting flows in ungauged catchments and under land use changes. Water Resour. Res. (2010). doi: 10.1029/2010wr009240 Google Scholar
  17. 17.
    Butts, M.B., Payne, J.T., Kristensen, M., Madsen, H.: An evaluation of the impact of model structure on hydrological modelling uncertainty for streamflow simulation. J. Hydrol. 298(1–4), 242–266 (2004) CrossRefGoogle Scholar
  18. 18.
    Calver, A., Crooks, S., Jones, D., Kay, A., Kjeldsen, T., Reynard, N.: National river catchment flood frequency method using continuous simulation. DEFRA (2005) Google Scholar
  19. 19.
    Carroll, Z.L., Bird, S.B., Emmett, B.A., Reynolds, B., Sinclair, F.L.: Can tree shelterbelts on agricultural land reduce flood risk? Soil Use Manag. 20, 357–359 (2004) CrossRefGoogle Scholar
  20. 20.
    Conway, V.M., Millar, A.: The hydrology of some small peat-covered catchments in the Northern Pennines. J. Inst. Water Eng. 14, 415–424 (1960) Google Scholar
  21. 21.
    Coulson, J.C., Butterfield, J.E.L., Henderson, E.: The effect of open drainage ditches on the plant and invertebrate communities of moorland and on the decomposition of peat. J. Appl. Ecol. 27(2), 549–561 (1990) CrossRefGoogle Scholar
  22. 22.
    Crawford, N.H., Linsley, R.K.: Digital simulation in hydrology: Stanford Watershed Model IV. Tech. Rpt 39, Stanford University, California (1996) Google Scholar
  23. 23.
    David, J.S., Valente, F., Gash, J.: Evaporation of intercepted rainfall. In: Anderson, M.G. (ed.) Encyclopedia of Hydrological Sciences. Wiley, New York (2005) Google Scholar
  24. 24.
    Duan, Q., Sorooshian, S., Gupta, V.K.: Shuffled complex evolution approach for effective and efficient global minimization. J. Optim. Theory Appl. 76(3), 501–521 (1993) MathSciNetMATHCrossRefGoogle Scholar
  25. 25.
    Ebel, B.A., Loague, K.: Physics-based hydrologic-response simulation: seeing through the fog of equifinality. Hydrol. Process. 20(13), 2887–2900 (2006) CrossRefGoogle Scholar
  26. 26.
    Freer, J., Beven, K.J., Abroise, B.: Bayesian uncertainty in runoff prediction and the value of data: an application of the GLUE approach. Water Resour. Res. 32, 2163–2173 (1996) CrossRefGoogle Scholar
  27. 27.
    Freeze, RA: Role of subsurface flow in generating surface runoff. 2: Upstream source areas. Water Resour. Res. 8, 1272–1283 (1972) CrossRefGoogle Scholar
  28. 28.
    Freeze, R.A., Harlan, R.L.: Blueprint for a physically-based, digitally simulated hydrologic response model. J. Hydrol. 9, 237–258 (1969) CrossRefGoogle Scholar
  29. 29.
    Gupta, H.V., Sorooshian, S., Yapo, P.O.: Towards improved calibration of hydrological models: multiple and non-commensurable measures of information. Water Resour. Res. 34(4), 751–763 (1998) CrossRefGoogle Scholar
  30. 30.
    Gustard, A., Bullock, A., Dickson, J.: Low flow estimation in the United Kingdom. Report no 101, Institute of Hydrology, Wallingford (1992) Google Scholar
  31. 31.
    Hawkins, R.H.: The importance of accurate curve numbers in the estimation of storm runoff. Water Resour. Bull. 11(5), 887–891 (1975) Google Scholar
  32. 32.
    Hawkins, R.H.: Asymptotic determination of runoff curve numbers from data. J. Irrig. Drain E 119(2), 334–345 (1993) CrossRefGoogle Scholar
  33. 33.
    Holden, J., Chapman, P.J., Labadz, J.C.: Artificial drainage of peatlands: hydrological and hydrochemical process and wetland restoration. Prog. Phys. Geogr. 28(1), 95–123 (2004) CrossRefGoogle Scholar
  34. 34.
    Holden, J., Evans, M.G., Burt, T.P., Horton, M.M.: Impact of land drainage on peatland hydrology. J. Environ. Qual. 35(5), 1764–1778 (2006) CrossRefGoogle Scholar
  35. 35.
    Holden, J., Gascoign, M., Bosanko, N.R.: Erosion and natural revegetation associated with surface land drains in upland peatlands. Earth Surf. Processes Landf. 32(10), 1547–1557 (2007) CrossRefGoogle Scholar
  36. 36.
    Institute of Hydrology: Flood Estimation Handbook. CEH Wallingford, Wallingford (1999) Google Scholar
  37. 37.
    Jackson, B.M., Chell, J., Francis, O., Frogbrook, Z., Marshall, M., McIntyre, N., Reynolds, B., Solloway, I., Wheater, H.S.: The impact of upland land management on flooding: insights from a multi-scale experimental and modelling programme. J. Flood. Risk Man. 1(2), 71–80 (2008) CrossRefGoogle Scholar
  38. 38.
    Karavokyris, I., Butler, A.P., Wheater, H.S.: The development and validation of a coupled soil-plant-water model (SPW1). Nirex Safety Series Report, NSS/R225 (1990) Google Scholar
  39. 39.
    Kirby, C., Newson, M.D., Gilman, K.: Plynlimon Research: The First Two Decades. Institute of Hydrology, Wallingford (1991) Google Scholar
  40. 40.
    Lamb, R., Kay, A.L.: Confidence intervals for a spatially generalized, continuous simulation flood frequency model for Great Britain. Water Resour. Res. (2004). doi: 10.1029/2003WR002428 Google Scholar
  41. 41.
    Lee, H., McIntyre, N., Wheater, H.S., Young, A.: Selection of conceptual models for regionalization of the rainfall-runoff relationship. J. Hydrol. 312(1–4), 125–147 (2005) CrossRefGoogle Scholar
  42. 42.
    Lees, M.J.: Data-based mechanistic modelling and forecasting of hydrological systems. J. Hydroinform. 2, 15–34 (2000) Google Scholar
  43. 43.
    Lees, M.J., Wagener, T.: A Monte-Carlo Analysis Toolbox (MCAT) for Matlab—User Manual. Imperial College, UK (1999) Google Scholar
  44. 44.
    Marc, V., Robinson, M.: The long-term water balance (1972–2004) of upland forestry and grassland at Plynlimon, mid-Wales. Paper presented at McCulloch Symposium on a View from the Watershed Revisited held at the General Assembly of the European-Geosciences-Union, European Geosciences Union, Vienna, Austria (2006) Google Scholar
  45. 45.
    Marshall, M.R., Francis, O.J., Frogbrook, Z.L., Jackson, B.M., McIntyre, N., Reynolds, B., Solloway, I., Wheater, H.S., Chell, J.: The impact of upland land management on flooding: results from an improved pasture hillslope. Hydrol. Process. 23(3), 464–475 (2009) CrossRefGoogle Scholar
  46. 46.
    McIntyre, N., Marshall, M.R.: Identification of rural land management signals in runoff response. Hydrol. Process. (2010). doi: 10.1002/hyp.7774 MATHGoogle Scholar
  47. 47.
    McIntyre, N., Young, P.C., Orellana, B., Marshall, M.R., Reynolds, B., Wheater, H.S.: Identification of nonlinearity in rainfall-flow response using data-based mechanistic modelling. Water Resour. Res. 47, W03515 (2011) CrossRefGoogle Scholar
  48. 48.
    Moore, R.J.: The probability-distributed principle and runoff production at point and basin scales. Hydrol. Sci. J. 30, 273–297 (1985) CrossRefGoogle Scholar
  49. 49.
    Moore, R.J.: The PDM rainfall-runoff model. Hydrol. Earth Syst. Sci. 11(1), 483–499 (2007) CrossRefGoogle Scholar
  50. 50.
    Mwakalila, S., Campling, P., Feyen, J., Wyseure, G., Beven, K.J.: Application of a data-based mechanistic modelling (DBM) approach for predicting runoff generation in semi-arid regions. Hydrol. Process. 15, 2281–2295 (2001) CrossRefGoogle Scholar
  51. 51.
    NERC: Flood Studies Report, vols. I–V, Natural Environment Research Council, London, UK (1975) Google Scholar
  52. 52.
    O’Connell, P.E., Beven, K.J., Carney, J.N., Clements, R.O., Ewen, J., Fowler, H., Harris, G., Hollis, J., Morris, J., O’Donnell, G.M.O., Packman, J.C., Parkin, A., Quinn, P.F., Rose, S.C., Shepher, M., Tellier, S.: Review of Impacts of Rural Land Use and Management on Flood Generation. Report A: Impact Study Report. R&D Technical Report FD2114/TR, DEFRA, London, UK. 152 pages (2004) Google Scholar
  53. 53.
    O’Connell, P.E., Ewen, J., O’Donnel, G.M.O., Quinn, P.: Is there a link between agricultural land-use and flooding?. Hydrol. Earth Syst. Sci. 11, 96–107 (2007) CrossRefGoogle Scholar
  54. 54.
    Orellana, B., McIntyre, N., Wheater, H.S., Sarkar, A., Young, P.C.: Comparison of lumped rainfall-runoff modelling approaches for a semiarid basin. In: Proceedings of “Water Environment Energy and Society”, New Delhi, 12–16 January 2009, pp. 713–721 (2009) Google Scholar
  55. 55.
    Packman, J., Quinn, P., Hollis, J., O’Connell, P.E.: Review of impacts of rural land use and management on flood generation. Short term improvement to the FEH rainfall-runoff model: technical background, pp. 1–66, DEFRA (2004) Google Scholar
  56. 56.
    Pedregal, D.J., Taylor, C.J., Young, P.C.: System Identification, Time Series Analysis and Forecasting. The Captain Toolbox. Handbook v2.0, Centre for Research on Environmental Systems and Statistics, Lancaster University, UK (2007) Google Scholar
  57. 57.
    Pilgrim, D.H., Huff, D.D., Steele, T.D.: A field evaluation of subsurface and surface runoff: II. Runoff processes. J. Hydrol. 38(3–4), 319–341 (1978) CrossRefGoogle Scholar
  58. 58.
    Ratto, M., Young, P.C., Romanowicz, R., Pappenberger, F., Saltelli, A., Pagano, A.: Uncertainty, sensitivity analysis and the role of data based mechanistic modeling in hydrology. Hydrol. Earth Syst. Sci. 11, 1249–1266 (2007) CrossRefGoogle Scholar
  59. 59.
    Refsgaard, J.C., Storm, B., Clausen, T.: Systeme Hydrologique Europeen (SHE): review and perspectives after 30 years development in physically-base hydrological modelling. Hydrol. Res. 41(5), 355–377 (2010) CrossRefGoogle Scholar
  60. 60.
    Robinson, M.: Changes in catchment runoff following drainage and afforestation. J. Hydrol. 86(1–2), 71–84 (1986) CrossRefGoogle Scholar
  61. 61.
    Robinson, M., Dupeyrat, A.: Effects of commercial timber harvesting on streamflow regimes in the Plynlimon catchments, mid-Wales. Hydrol. Process. 19(6), 1213–1226 (2005) CrossRefGoogle Scholar
  62. 62.
    Romanowicz, R.J., Young, P.C., Beven, K.J.: Data assimilation and adaptive forecasting of water levels in the river Severn catchment, United Kingdom. Water Resour. Res. (2006). doi: 10.1029/2005WR004373 Google Scholar
  63. 63.
    Spear, R.C., Hornberger, G.M.: Eutrophication in Peel inlet, II, Identification of critical uncertainties via generalised sensitivity analysis. Water Resour. Res. 14, 43–49 (1980) Google Scholar
  64. 64.
    Stewart, A.J., Lance, A.N.: Moor-draining: a review of impacts on land use. J. Environ. Manag. 17(1), 81–99 (1983) Google Scholar
  65. 65.
    Stewart, A.J., Lance, A.N.: Effects of moor-draining on the hydrology and vegetation of northern Pennine blanket bog. J. Appl. Ecol. 28(3), 1105–1117 (1991) CrossRefGoogle Scholar
  66. 66.
    Taylor, C.J., Pedregal, D.J., Young, P.C., Tych, W.: Environmental time series analysis and forecasting with the Captain toolbox. Environ. Model. Softw. 22, 797–814 (2007) CrossRefGoogle Scholar
  67. 67.
    USDA: Urban hydrology for small watersheds. Technical Release 55, 2nd edn., NTIS PB87-101580. US Department of Agriculture Soil Conservation Service, Springfield, Virginia (1986) Google Scholar
  68. 68.
    U.S. Soil Conservation Service: Hydrology. In: Nat. Eng. Handbook, Sec. 4:547 p. U.S. Govt. Print Off. Washington, DC (1972) Google Scholar
  69. 69.
    Wagener, T., Boyle, D.P., Lees, M.J., Wheater, H.S., Gupta, H.V., Sorooshian, S.: A framework for the development and application of hydrological models. Hydrol. Earth Syst. Sci. 5(1), 13–26 (2001) CrossRefGoogle Scholar
  70. 70.
    Wagener, T., Boyle, D.P., Lees, M.J., Wheater, H.S., Gupta, H.V., Sorooshian, S.: A framework for the development and application of hydrological models. In: Proc. BHS 7th National Symp., Newcastle upon Tyne, pp. 3.75–3.81 (2000) Google Scholar
  71. 71.
    Wagener, T., Lees, M.J., Wheater, H.S.: A Rainfall-Runoff Modelling Toolbox (RRMT) for Matlab—User Manual. Imperial College, UK (1999) Google Scholar
  72. 72.
    Wagener, T., McIntyre, N., Lees, M.J., Wheater, H.S., Gupta, H.V.: Towards reduced uncertainty in conceptual rainfall-runoff modelling: dynamic identifiability analysis. Hydrol. Process. 17, 455–476 (2003) CrossRefGoogle Scholar
  73. 73.
    Wagener, T., Lees, M.J., Wheater, H.S.: Reducing conceptual rainfall-runoff modelling uncertainty. In: Proc. of Workshop on “Runoff Generation and Implications for River Basin Modelling”, Freiburg, Germany (2000) Google Scholar
  74. 74.
    Weiler, M., McDonnell, J.: Virtual experiments: a new approach for improving process conceptualization in hillslope hydrology. J. Hydrol. 285(1–4), 3–18 (2004) CrossRefGoogle Scholar
  75. 75.
    Wheater, H.S.: Flood hazard and management: a UK perspective. Philos. Trans. R. Soc. A 364, 2135–2145 (2006) CrossRefGoogle Scholar
  76. 76.
    Wheater, H.S., Beck, M.B., Kleissen, F.M.: Identifiability of conceptual hydrochemical models. Water Resour. Res. 26(12), 2979–2992 (1990) CrossRefGoogle Scholar
  77. 77.
    Wheater, H.S., Shaw, T.L., Rutherford, J.C.: Storm runoff from small lowland catchments in South West England. J. Hydrol. 55, 321–337 (1982) CrossRefGoogle Scholar
  78. 78.
    Wheater, H.S., Bishop, K.H., Beck, M.B.: The identification of conceptual hydrological models for surface water acidification. J. Hydrol. Process. 1, 89–109 (1986) CrossRefGoogle Scholar
  79. 79.
    Wheater, H.S., Jakeman, A.J., Beven, K.J.: Progress and directions in rainfall-runoff modelling. In: Jakeman, A.J., Beck, M.B., McAleer, M.J. (eds.) Modelling Change in Environmental Systems, pp. 101–132. Wiley, New York (1993) Google Scholar
  80. 80.
    Wheater, H.S., Reynolds, B., McIntyre, N., Marshall, M.R., Jackson, B.J., Frogbrook, Z.L., Solloway, I., Francis, O.J., Chell, J.: Impacts of land management on flood risk: FRMRC RPA2 at Pontbren. FRMRC Final Report and UFMO (2008) Google Scholar
  81. 81.
    Worrall, F., Armstrong, A., Holden, J.: Short-term impact of peat drain-blocking on water colour, dissolved organic carbon concentration, and water table depth. J. Hydrol. 337(3–4), 315–325 (2007) CrossRefGoogle Scholar
  82. 82.
    Whitehead, P.G., Young, P.C., Hornberger, G.H.: A systems model of stream flow and water quality in the Bedford-Ouse river—Part l: Stream flow modelling. Water Res. 13, 1155–1169 (1976) CrossRefGoogle Scholar
  83. 83.
    Young, P.C.: Recursive Estimation and Time-Series Analysis. Springer, New York (1984) MATHGoogle Scholar
  84. 84.
    Young, P.C.: Data-based mechanistic modelling of environmental, ecological, economic and engineering systems. Environ. Model. Softw. 13, 105–122 (1998) CrossRefGoogle Scholar
  85. 85.
    Young, P.C.: Advances in real-time flood forecasting. Philos. Trans. R. Soc. A 360, 1433–1450 (2002) CrossRefGoogle Scholar
  86. 86.
    Young, P.C.: Top-down and data-based mechanistic modelling of rainfall-flow dynamics at the catchment scale. Hydrol. Process. 17, 2195–2217 (2003) CrossRefGoogle Scholar
  87. 87.
    Young, P.C.: Rainfall-runoff modeling: transfer function models. In: Anderson, M.G. (ed.) Encyclopedia of Hydrological Sciences, vol. 3, part II, pp. 1985–2000. Wiley, Hoboken (2005) Google Scholar
  88. 88.
    Young, P.C.: Real time flow forecasting. In: Wheater, H.S., Sorooshian, S., Sharma, K.D. (eds.) Hydrological Modelling in Arid and Semi-Arid Areas, pp. 113–137. Cambridge University Press, Cambridge (2008) Google Scholar
  89. 89.
    Young, P.C.: The refined instrumental variable method: unified estimation of discrete and continuous-time transfer function models. J. Eur. Syst. Autom. 42, 149–179 (2008) Google Scholar
  90. 90.
    Young, P.C.: Gauss, Kalman and advances in recursive parameter estimation. J Forecasting 30, 104–146 (2011) (special issue celebrating 50 years of the Kalman Filter) MATHCrossRefGoogle Scholar
  91. 91.
    Young, P.C., Beven, K.J.: Data-based mechanistic modelling and the rainfall-flow nonlinearity. Environmetrics 5, 335–363 (1994) CrossRefGoogle Scholar
  92. 92.
    Young, P.C., Ratto, M.: A unified approach to environmental systems modelling. Stoch. Environ. Res. Risk Assess. 23, 1037–1057 (2009) CrossRefGoogle Scholar
  93. 93.
    Young, P.C., Jakeman, A.J., Post, D.A.: Recent advances in data-based modelling and analysis of hydrological systems. Water Sci. Technol. 36, 99–116 (1997) Google Scholar
  94. 94.
    Young, P.C., McKenna, P., Bruun, J.: Identification of non-linear stochastic systems by state dependent parameter estimation. Int. J. Control 74, 1837–1857 (2001) MathSciNetMATHCrossRefGoogle Scholar

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© Springer-Verlag London Limited 2012

Authors and Affiliations

  • H. S. Wheater
    • 1
  • C. Ballard
    • 1
  • N. Bulygina
    • 1
  • N. McIntyre
    • 1
  • B. M. Jackson
    • 1
  1. 1.Imperial College LondonLondonUK

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