Evaluating enhanced hydrological representations in Noah LSM over transition zones: Implications for model development

Journal of Hydrometeorology

Volumn 10, Issue 3, 2009, Pages 600-622

  Rosero, Enrique ^a   Yang, Zong Liang ^a   Gulden, Lindsey E ^a   Niu, Guo Yue ^a   Gochis, David J ^b  

^a UNIVERSITY OF TEXAS AT AUSTIN   (United States)

^b NATIONAL CENTER FOR ATMOSPHERIC RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EVAPORATION; HYDROLOGICAL MODELING; LAND SURFACE; PARAMETERIZATION; TRANSITION ZONE; TRANSPIRATION;

EID: 68049090694     PISSN: 1525755X     EISSN: None     Source Type: Journal    
DOI: 10.1175/2009JHM1029.1     Document Type: Article

References (65)

1. Abramowitz, G., H. Gupta, A. J. Pitman, Y. Wang, R. Leuning, and H. Cleugh, 2006: Neural Error Regression Diagnosis (NERD): A tool for model bias identification and prognostic data assimilation. J. Hydrometeor., 7, 160-177.
2. Akaike, H., 1974:Anew look at the statistical model identification. IEEE Trans. Autom. Control, 19, 716-723.
3. Bastidas, L. A., H. V. Gupta, and S. Sorooshian, 2001: Bounding the parameters of land-surface schemes using observational data. Land Surface Hydrology, Meteorology, and Climate: Observations and Modeling, V. Lakshmi, J. Albertson, and J. Schaake, Eds.,Water Science and Application Series, Vol. 3, Amer. Geophys. Union, 65-76.
4. Bastidas, L. A., T. S. Hogue, S. Sorooshian, H. V. Gupta, and W. J. Shuttleworth, 2006: Parameter sensitivity analysis for different complexity land surface models using multicriteria methods. J. Geophys. Res., 111, D20101, doi:10.1029/2005JD006377.
5. Beck, M. B., Ed., 2002: Environmental Foresight and Models: A Manifesto. Developments in Environmental Modelling Series, Vol. 22, Elsevier Science, 473 pp.
6. Beven, K. J., 2006: A manifesto for the equifinality thesis. J. Hydrol., 320, 18-36.
7. Beven, K. J., and J. Freer, 2001: Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems. J. Hydrol., 249, 11-29.
8. Boone, A., and Coauthors, 2004: The Rhône-Aggregation Land Surface Scheme intercomparison project: An overview. J. Climate, 17, 187-208.
9. Bowling, L. C., and Coauthors, 2003: Simulation of high-latitude hydrological processes in the Torne-Kalix basin: PILPS Phase 2(e). 1: Experiment description and summary intercomparisons. Global Planet. Change, 38, 1-30.
10. Boyle, D. P., H. V. Gupta, and S. Sorooshian, 2000: Toward improved calibration of hydrologic models: Combining the strengths of manual and automatic methods. Water Resour. Res., 36, 3663-3674.
11. Carlson, J. M., and J. Doyle, 2002: Complexity and robustness. Proc. Natl. Acad. Sci. USA, 99, 2538-2545.
12. Chen, F., and Coauthors, 2007: Description and evaluation of the characteristics of the NCAR high-resolution land data assimilation system. J. Appl. Meteor. Climatol., 46, 694-713.
13. Childs, P., A. Qureshi, S. Raman, K. Alapaty, R. Ellis, R. Boyles, and D. Niyogi, 2006: Simulation of convective initiation during IHOP_2002 using the Flux-Adjusting Surface Data Assimilation System (FASDAS). Mon. Wea. Rev., 134, 134-148.
14. Clarke, R. T., 2008: Issues of experimental design for comparing the performance of hydrologic models. Water Resour. Res., 44, W01409, doi:10.1029/2007WR005927.
15. Cosgrove, B. A., and Coauthors, 2003: Real-time and retrospective forcing in the North American Land Data Assimilation System (NLDAS) project. J. Geophys. Res., 108, 8842, doi:10.1029/ 2002JD003118.
16. De Lannoy, G. J. M., P. R. Houser, V. R. N. Pauwels, and N. E. C. Verhoest, 2006: Assessment of model uncertainty for soil moisture through ensemble verification. J. Geophys. Res., 111, D10101, doi:10.1029/2005JD006367.
17. Demarty, J., C. Ottlé, I. Braud, A. Olioso, J. P. Frangi, L. Bastidas, and H. V. Gupta, 2004: Using a multiobjective approach to retrieve information on surface properties used in a SVAT model. J. Hydrol., 287, 214-236.
18. Dickinson, R. E., M. Shaikh, R. Bryant, and L. Graumlich, 1998: Interactive canopies for a climate model. J. Climate, 11, 2823-2836.
19. Dirmeyer, P. A., F. J. Zeng, A. Ducharne, J. C. Morrill, and R. D. Koster, 2000: The sensitivity of surface fluxes to soil water content in three land surface schemes. J. Hydrometeor., 1, 121-134.
20. Dirmeyer, P. A., R. D. Koster, and Z. C. Guo, 2006: Do global models properly represent the feedback between land and atmosphere? J. Hydrometeor., 7, 1177-1198.
21. Ek, M. B., and Coauthors, 2003: Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model. J. Geophys. Res., 108, 8851, doi:10.1029/2002JD003296.
22. Gulden, L. E., E. Rosero, Z.-L. Yang, M. Rodell, C. S. Jackson, G.-Y. Niu, P. J.-F. Yeh, and J. Famiglietti, 2007a: Improving land-surface model hydrology: Is an explicit aquifer model better than a deeper soil profile? Geophys. Res. Lett., 34, L09402, doi:10.1029/ 2007GL029804.
23. Gulden, L. E., Z.-L. Yang, and G.-Y. Niu, 2007b: Interannual variation in biogenic emissions on a regional scale. J. Geophys. Res., 112, D14103, doi:10.1029/2006JD008231.
24. Gulden, L. E., E. Rosero, Z.-L. Yang, T. Wagener, and G.-Y. Niu, 2008: Model performance, model robustness, and model fitness scores: A new method for identifying good land-surface models. Geophys. Res. Lett., 35, L11404, doi:10.1029/2008GL033721.
25. Gupta, H. V., S. Sorooshian, and P. O. Yapo, 1998: Toward improved calibration of hydrologic models: Multiple and non-commensurable measures of information. Water Resour. Res., 34, 751-763.
26. Gupta, H. V, L. A. Bastidas, S. Sorooshian, W. J. Shuttleworth, and Z. L. Yang, 1999: Parameter estimation of a land surface scheme using multicriteria methods. J. Geophys. Res., 104 (D16), 19 491-19 504.
27. Gupta, H. V, T. Wagener, and Y. Liu, 2008: Reconciling theory with observations: Elements of a diagnostic approach to model evaluation. Hydrol. Processes, 22, 3802-3813, doi:10.1002/hyp.6989.
28. Hogue, T. S., L. A. Bastidas, H. V. Gupta, S. Sorooshian, K. Mitchell, and W. Emmerich, 2005: Evaluation and transferability of the Noah land surface model in semiarid environments. J. Hydrometeor., 6, 68-84.
29. Hogue, T. S., L. A. Bastidas, H. V. Gupta, and S. Sorooshian, 2006: Evaluating model performance and parameter behavior for varying levels of land surface model complexity. Water Resour. Res., 42, W08430, doi:10.1029/2005WR004440.
30. Holt, T., D. Niyogi, F. Chen, K. Manning, M. A. LeMone, and A. Qureshi, 2006: Effect of land-atmosphere interactions on the IHOP 24-25 May 2002 convection case. Mon. Wea. Rev., 134, 113-133.
31. Jakeman, A. J., R. A. Letcher, and J. P. Norton, 2006: Ten iterative steps in development and evaluation of environmental models. Environ. Modell. Software, 21, 602-614, doi:10.1016/ j.envsoft.2006.01.004.
32. Jensen, M. J. W., 1998: Prediction error through modelling concepts and uncertainty from basic data. Nutr. Cycling Agroecosyst., 50, 247-253.
33. Kim, Y., and G. Wang, 2007: Impact of vegetation feedback on the response of precipitation to antecedent soil moisture anomalies over North America. J. Hydrometeor., 8, 534-550.
34. Kirchner, J. W., R. P. Hooper, C. Kendall, C. Neal, and G. Leavesley, 1996: Testing and validating environmental models. Sci. Total Environ., 183, 33-47.
35. Koster, R. D., and Coauthors, 2004: Regions of strong coupling between soil moisture and precipitation. Science, 305, 1138-1140.
36. Legates, D. R., and G. J. McCabe Jr., 1999: Evaluating the use of "goodness-of-fit" measures in hydrologic and hydroclimatic model validation. Water Resour. Res., 35, 233-241.
37. 2O Project 2002 field campaign. Bull. Amer. Meteor. Soc., 88, 65-81.
38. Leplastrier, M., A. J. Pitman, H. Gupta, and Y. Xia, 2002: Exploring the relationship between complexity and performance in a land surface model using the multicriteria method. J. Geophys. Res., 107, 4443, doi:10.1029/2001JD000931.
39. Lyon, S. W., and Coauthors, 2008: Coupling terrestrial and atmospheric water dynamics to improve prediction in a changing environment. Bull. Amer. Meteor. Soc., 89, 1275-1279.
40. Mitchell, K. E., and Coauthors, 2004: The multi-institution North American Land Data Assimilation System (NLDAS): Utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system. J. Geophys. Res., 109, D07S90, doi:10.1029/ 2003JD003823.
41. Nijseen, B., and L. A. Bastidas, 2005: Land-atmosphere models for water and energy cycle studies. Encyclopedia of Hydrological Sciences, Part 17: Climate Change, M. G. Anderson and J. J. McDonnell, Ed., Vol. 5, John Wiley, 3089-3102.
42. Niu, G.-Y., Z.-L. Yang, R. E. Dickinson, and L. E. Gulden, 2005: A simple TOPMODEL-based runoff parameterization (SIMTOP) for use in global climate models. J. Geophys. Res., 110, D21106, doi:10.1029/ 2005JD006111.
43. Niu, G.-Y., Z.-L. Yang, R. E. Dickinson, L. E. Gulden, and H. Su, 2007: Development of a simple groundwater model for use in climate models and evaluation with Gravity Recovery and Climate Experiment data. J. Geophys. Res., 112, D07103, doi:10.1029/2006JD007522.
44. Niyogi, D. S., and S. Raman, 1997: Comparison of stomatal resistance simulated by four different schemes using FIFE observations. J. Appl. Meteor., 36, 903-917.
45. Oleson, K. W., and Coauthors, 2008: Improvements to the Community Land Model and their impact on the hydrological cycle. J. Geophys. Res., 113, G01021, doi:10.1029/2007JG000563.
46. Pielke, R. A., Sr., 2001: Influence of the spatial distribution of vegetation and soils on the prediction of cumulus convective rainfall. Rev. Geophys., 39, 151-177.
47. Pitman, A. J., 2003: The evolution of, and revolution in, land surface schemes designed for climate models. Int. J. Climatol., 23, 479-510, doi:10.1002/joc.893.
48. Randall, D. A., and Coauthors, 2007: Climate models and their evaluation. Climate Change 2007: The Physical Science Basis, S. D. Solomon et al., Eds., Cambridge University Press, 589-662.
49. Refsgaard, J. C., J. P. van der Sluijs, J. Brown, and P. van der Keur, 2006: A framework for dealing with uncertainty due to model structure error. Adv. Water Resour., 29, 1586-1597.
50. Rodell, M., P. R. Houser, A. A. Berg, and J. S. Famiglietti, 2005: Evaluation of 10 methods for initializing a land surface model. J. Hydrometeor., 6, 146-155.
51. Schultz, K., and K. Beven, 2003: Data-supported robust parameterizations in land surface-atmosphere flux predictions: Towards a top-down approach. Hydrol. Processes, 17, 2259-2277.
52. Schwarz, G., 1978: Estimating the dimension of a model. Ann. Stat., 6, 461-464.
53. Shuttleworth, W. J., 2007: Putting the "vap" into evaporation. Hydrol. Earth Syst. Sci., 11, 210-244.
54. Smith, L. A., 2002: What might we learn from climate forecasts? Proc. Natl. Acad. Sci. USA, 99, 2487-2492, doi:10.1073/ pnas.012580599.
55. Stöckli, R., and Coauthors, 2008: Use of FLUXNET in the community land model development. J. Geophys. Res., 113, G01025, doi:10.1029/2007JG000562.
56. Talagrand, O., R. Vautar, and B. Strauss, 1997: Evaluation of probabilistic prediction systems. Proc. ECMWF Workshop on Predictability, Reading, United Kingdom, ECMWF, 1-25.
57. Viterbo, P., 2002: A review of parameterization schemes for land surface processes. Meteorological Training Course Lecture Series, ECMWF. [Available online at http://www.ecmwf.int/newsevents/training/ lecture_notes/pdf_files/PARAM/Land_surf.pdf.]
58. Vrugt, J. A.,H.V.Gupta, L. A. Bastidas,W.Bouten, and S. Sorooshian, 2003: Effective and efficient algorithm for multiobjective optimization of hydrologic models. Water Resour. Res., 39, 1214, doi:10.1029/2002WR001746.
59. Wagener, T., and H. V. Gupta, 2005: Model identification for hydrological forecasting under uncertainty. Stochastic Environ. Res. Risk Assess., 19, 378-387.
60. Weckwerth, T. M., and D. B. Parsons, 2006: Areview of convection initiation and motivation for IHOP 2002. Mon. Wea. Rev., 134, 5-22.
61. 2O Project (IHOP_2002) and some preliminary highlights. Bull. Amer. Meteor. Soc., 85, 253-277.
62. Wood, E. F., and Coauthors, 1998: The Project for Intercomparison of Land-Surface Parameterization Schemes (PILPS) Phase 2(c) Red-Arkansas River basin experiment: 1. Experimental description and summary intercomparisons. Global Planet. Change, 19, 115-135.
63. Xia, Y., A. J. Pitman, H. V. Gupta, M. Lepastrier, A. Henderson-Sellers, and L. A. Bastidas, 2002: Calibrating a land surface model of varying complexity using multicriteria methods and the Cabauw dataset. J. Hydrometeor., 3, 181-194.
64. Yang, Z.-L., 2004: Modeling land surface processes in shortterm weather and climate studies. Observation, Theory and Modeling of Atmospheric Variability, X. Zhu et al., Eds., World Scientific Series on Meteorology of East Asia, Vol. 3, World Scientific, 288-313.
65. Yang, Z.-L., and G.-Y. Niu, 2003: The versatile integrator of surface and atmosphere processes. Part 1: Model description. Global Planet. Change, 38, 175-189.