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Cited by 58

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

An evaluation of impacts of DEM resolution and parameter correlation on TOPMODEL modeling uncertainty

Lin, Kairong, Zhang, Qiang, Chen, Xiaohong·2010·Journal of Hydrology
DOI 10.1016/j.jhydrol.2010.09.012
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Crossref · 10.1016/j.jhydrol.2010.09.012

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  • The reduced error distribution for ANADEM could lead to more accurate predictions of hydrological processes, which are particularly sensitive to topographic variations.

    “The reduced error distribution shown in Figure 3 for ANADEM could lead to more accurate predictions of hydrological processes, which are particularly sensitive to topographic variations [55,56].”

    From ANADEM: A Digital Terrain Model for South America (2024) · cites this paper as [55]

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Research works printed but not linked: 42

  1. Benke, 2007, Parameter uncertainty, sensitivity analysis and prediction error in a water-balance hydrological model, Mathematical and Computer Modeling, 47, 1134
  2. Beven, 1992, The future of distributed models, model calibration and uncertainty prediction, Hydrological Processes, 6, 279, 10.1002/hyp.3360060305 · DOI · Paper page
  3. Beven, 2001, Equifinality, data assimilation, and uncertainty estimation in mechanistic modeling of complex environmental systems using the GLUE methodology, Journal of Hydrology, 249, 11, 10.1016/S0022-1694(01)00421-8 · DOI · Paper page
  4. Beven, 2001, A dynamic TOPMODEL, Hydrology Processes, 15, 1993, 10.1002/hyp.252 · DOI
  5. Beven, 1979, A physically-based variable contributing area model of basin hydrology, Hydrology Science Bulletin, 24, 43, 10.1080/02626667909491834 · DOI · Paper page
  6. Beven, 1983, Catchment geomorphology and the dynamics of runoff contributing areas, Journal of Hydrology, 65, 139, 10.1016/0022-1694(83)90214-7 · DOI
  7. Blasone, 2008, Generalized likelihood uncertainty estimation (GLUE) using adaptive Markov Chain Mote Carlo sampling, Advances in Water Resources, 31, 630, 10.1016/j.advwatres.2007.12.003 · DOI
  8. Blazkova, 2002, Flood frequency estimation by continuous simulation for a catchment treated as ungauged (with uncertainty), Water Resources Research, 38, 1139, 10.1029/2001WR000500 · DOI
  9. Choi, 2007, Multi-period and multi-criteria model conditioning to reduce prediction uncertainty in an application of TOPMODEL within the GLUE framework, Journal of Hydrology, 332, 316, 10.1016/j.jhydrol.2006.07.012 · DOI
  10. Duan, 1997, A generalized power function for the subsurface transmissivity analysis of the TOPMODEL, Journal of Hydrology, 175, 293
  11. Freer, 1996, Bayesian estimation of uncertainty in runoff prediction and the value of data: an application of the GLUE approach, Water Resources Research, 32, 2161, 10.1029/95WR03723 · DOI
  12. Gupta, 1998, Toward improved calibration of hydrologic models: multiple and noncommensurable measures of information, Water Resources Research, 34, 751, 10.1029/97WR03495 · DOI
  13. Jin, 2010, Parameter and modeling uncertainty simulated by GLUE and a formal Bayesian method for a conceptual hydrological model, Journal of Hydrology, 383, 147, 10.1016/j.jhydrol.2009.12.028 · DOI
  14. Keesman, 1990, Set theoretic parameter estimation using random scanning and principal component analysis, Mathematics and Computers in Simulation, 32, 535, 10.1016/0378-4754(90)90009-8 · DOI
  15. Kuczera, 1998, Monte Carlo assessment of parameter uncertainty in conceptual catchment models: the Metropolis algorithm, Journal of Hydrology, 211, 69, 10.1016/S0022-1694(98)00198-X · DOI
  16. Lin, 2007, A new baseflow separation method based on analytical solutions of the Horton infiltration capacity curve, Hydrological Processes, 21, 1719, 10.1002/hyp.6357 · DOI
  17. Madsen, 2003, Parameter estimation in distributed hydrological catchment modeling using automatic calibration with multiple objectives, Advance in Water Resource, 26, 205, 10.1016/S0309-1708(02)00092-1 · DOI
  18. McMichael, 2006, Distributed hydrological modeling in California semi-arid shrublands: MIKE SHE model calibration and uncertainty estimation, Journal of Hydrology, 317, 307, 10.1016/j.jhydrol.2005.05.023 · DOI
  19. Montanari, 2005, Large sample behaviors of the generalized likelihood uncertainty estimation (GLUE) in assessing the uncertainty of rainfall–runoff simulations, Water Resources Research, 41, W08406, 10.1029/2004WR003826 · DOI · Paper page
  20. Montanari, 2007, What do we mean by ‘uncertainty’? the need for a consistent wording about uncertainty assessment in hydrology, Hydrological Processes, 21, 841, 10.1002/hyp.6623 · DOI
  21. Moradkhani, 2005, Uncertainty assessment of hydrologic model states and parameters: sequential data assimilation using the particle filter, Water Resources Research, 41, 1, 10.1029/2004WR003604 · DOI
  22. Nash, 1970, River flow forecasting through the conceptual models, 1: a discussion of principles, Journal of Hydrology, 10, 282, 10.1016/0022-1694(70)90255-6 · DOI
  23. Quinn, 1995, The ln(a/tanβ) index: how to calculated it and how to use it within the TOPMODEL framework, Hydrological Processes, 9, 161, 10.1002/hyp.3360090204 · DOI
  24. Salvadori, 2004, Frequency analysis via copulas, theoretical aspects and applications to hydrological events, Water Resources Research, 40, 1, 10.1029/2004WR003133 · DOI
  25. Scanlon, 2000, Shallow subsurface storm flow in a forested headwater catchment: observations and modeling using a modified TOPMODEL, Water Resources Research, 36, 2575, 10.1029/2000WR900125 · DOI
  26. Sklar, K., 1959. Fonctions de repartition à n dimensions et leura marges. Paris, Publications de l’Institut de de l’Université, vol. 8, pp. 229–231.
  27. Sørensen, 2007, Effects of DEM resolution on the calculation of topographical indices: TWI and its components, Journal of Hydrology, 347, 79, 10.1016/j.jhydrol.2007.09.001 · DOI
  28. Thiemann, 2001, Bayesian recursive parameter estimation for hydrological models, Water Resources Research, 7, 21
  29. Vrugt, 2007, Treatment of uncertainty using ensemble methods: comparison of sequential data assimilation and Bayesian model averaging, Water Resources Research, 43, W01411, 10.1029/2005WR004838 · DOI · Paper page
  30. Vrugt, 2003, A Shuffled complex evolution metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters, Water Resources Research, 39, 1201, 10.1029/2002WR001642 · DOI
  31. Vrugt, 2005, Improved treatment of uncertainty in hydrologic modeling: combining the strengths of global optimization and data assimilation, Water Resources Research, 41, 1, 10.1029/2004WR003059 · DOI
  32. Walter, 2002, Refined conceptualization of TOPMODEL for shallow subsurface flows, Hydrology Processes, 16, 2041, 10.1002/hyp.5030 · DOI
  33. Wolock, 1995, Comparison of single and multiple flow direction algorithms for computing topographic parameters in TOPMODEL, Water Resources Research, 31, 1315, 10.1029/95WR00471 · DOI
  34. Wolock, 1994, Effects of digital elevation model map scale and data resolution on a topography-based watershed model, Water Resources Research, 30, 3041, 10.1029/94WR01971 · DOI
  35. Xiong, 2004, Effects of the catchment runoff coefficient on the performance of TOPMODEL in rainfall–runoff modeling, Hydrological Processes, 18, 1823, 10.1002/hyp.1449 · DOI
  36. Xiong, 2008, An empirical method to improve the prediction limits of the GLUE methodology in rainfall–runoff modeling, Journal of Hydrology, 349, 115, 10.1016/j.jhydrol.2007.10.029 · DOI
  37. Yang, 2007, Bayesian uncertainty analysis in distributed hydrologic modeling: a case study in the Thur River basin (Switzerland), Water Resources Research, 43, W10401, 10.1029/2006WR005497 · DOI
  38. Yang, 2008, Comparing uncertainty analysis techniques for a SWAT application to the Chaohe Basin in China, Journal of Hydrology, 358, 1, 10.1016/j.jhydrol.2008.05.012 · DOI · Paper page
  39. Zhang, 1994, Digital elevation model grid size, landscape representation, and hydrologic simulations, Water Resources Research, 30, 1019, 10.1029/93WR03553 · DOI
  40. Zhang, 2006, Bivariate flood frequency analysis using the copula method, Journal of Hydrologic Engineering, 11, 150, 10.1061/(ASCE)1084-0699(2006)11:2(150) · DOI
  41. Zhang, 2009, Multi-scale variability of sediment load and streamflow of the Lower Yangtze River basin: possible causes and implications, Journal of Hydrology, 368, 96, 10.1016/j.jhydrol.2009.01.030 · DOI
  42. Zhang, Q., Xu, C.-Y., Zhang, Z., Chen, Y.D., 2010. Changes of atmospheric water vapor budget in the Pearl River basin and possible implications for hydrological cycle. Theoretical and Applied Climatology. doi:10.1007/s00704-010-0257-z. · DOI

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Metadata

Authors
Lin, K., Zhang, Q., & Chen, X.
Venue
Journal of Hydrology
Publisher
Elsevier BV
Published
2010-11
Volume
394
Issue
3-4
Pages
370-383
ISSN
0022-1694
DOI
10.1016/j.jhydrol.2010.09.012
Cited by
58
References
1
Language
en
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)

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