On characterizing the temporal dominance patterns of model parameters and processes

Hydrological Processes

Volumn 30, Issue 13, 2016, Pages 2255-2270

  Guse, Björn ^a   Pfannerstill, Matthias ^a   Strauch, Michael ^b   Reusser, Dominik E ^c   Lüdtke, Stefan ^d   Volk, Martin ^b   Gupta, Hoshin ^e   Fohrer, Nicola ^a  

^a UNIVERSITY OF KIEL   (Germany)

^b HELMHOLTZ CENTRE FOR ENVIRONMENTAL RESEARCH UFZ   (Germany)

^c POTSDAM INSTITUTE FOR CLIMATE IMPACT RESEARCH   (Germany)

^d GFZ GERMAN RESEARCH CENTRE FOR GEOSCIENCES   (Germany)

^e UNIVERSITY OF ARIZONA   (United States)

Author keywords

catchment modelling; diagnostic model analysis; model understanding; parameter characterization; temporal parameter sensitivity analysis

Indexed keywords

CATCHMENTS; DYNAMICS; ELECTRIC NETWORK TOPOLOGY; GROUNDWATER; HYDROLOGY; LANDFORMS; RUNOFF; SENSITIVITY ANALYSIS;

CATCHMENT MODELLING; DIAGNOSTIC MODEL; MODEL UNDERSTANDING; PARAMETER CHARACTERIZATION; PARAMETER SENSITIVITY ANALYSIS;

PARAMETER ESTIMATION;

CATCHMENT; HYDROLOGICAL MODELING; HYDROLOGICAL REGIME; SENSITIVITY ANALYSIS;

EID: 84959432211     PISSN: 08856087     EISSN: 10991085     Source Type: Journal    
DOI: 10.1002/hyp.10764     Document Type: Article

References (71)

1. Arnold JG, Fohrer N. 2005. SWAT2000: current capabilities and research opportunities in applied watershed modelling. Hydrological Processes 19: 563–572.
2. Arnold JG, Srinivasan R, Muttiah RS, Williams JR. 1998. Large area hydrologic modeling and assessment part I: model development. Journal of the American Water Resources Association 1: 73–89.
3. Beven KJ. 1993. Prophey, reality and uncertainty in distributed hydrological modeling. Advances in Water Resources 16: 41–54.
4. Beven KJ, Binley AM. 1992. The future of distributed models: model calibration and uncertainty prediction. Hydrological Processes 6: 279–298.
5. BGR. 1999a. Bundesanstalt fuer Geowissenschaften und Rohstoffe – Bodenuebersichtskarte im Maßstab 1:1:000.000. Verbreitung der Bodengesellschaften.
6. BGR. 1999b. Bundesanstalt fuer Geowissenschaften und Rohstoffe – Bodenuebersichtskarte im Maßstab 1:200.000. Verbreitung der Bodengesellschaften
7. Boyle DP, Gupta HV, Sorooshian S. 2000. Toward improved calibration of hydrologic models: combining the strengths of manual and automatic methods. Water Resources Research 36(12): 3663–3674.
8. Boyle DP, Gupta HV, Sorooshian S, Koren V, Zhang Z, Smith M. 2001. Toward improved streamflow forecasts: value of semidistributed modeling. Water Resources Research 37(11): 2749–2759.
9. Burnash RJ. 1995. The NWS River Forecast System – catchment model. In Computer Models of Watershed Hydrology Singh VP (ed). Water Resour. Publ. Highlands Ranch: Colorado; 311–366.
10. Castellarin A, Galeati G, Brandimarte L, Montanari A, Brath A. 2004. Regional flow-duration curves: reliability for ungauged basins. Advances in Water Resources 27: 953–965.
11. Cheng L, Yaeger M, Viglione A, Coopersmith E, Ye S, Sivapalan M. 2012. Exploring the physical controls of regional patterns of flow duration curves – part 1: insights from statistical analyses. Hydrology and Earth System Sciences 16: 4435–4446.
12. Cibin R, Sudheer KP, Chaubey I. 2010. Sensitivity and identifiability of stream flow generation parameters of the SWAT model. Hydrological Processes 24(9): 1133–1148.
13. Clark MP, McMillan HK, Collins DB, Kavetski D, Woods RA. 2011. Hydrological field data from a modeller's perspective: part 2: process-based evaluation of model hypotheses. Hydrological Processes 25: 523–543.
14. Cloke H, Pappenberger F, Renaud J-P. 2008. Multi-method global sensitivity analysis (MMGSA) for modelling floodplain hydrological processes. Hydrological Processes 22: 1660–1674.
15. Conradt T, Koch H, Hattermann FF, Wechsung F. 2012. Precipitation or evapotranspiration? Bayesian analysis of potential error sources in the simulation of sub-basin discharges in the Czech Elbe River basin. Regional Environmental Change 12: 649–661.
16. Cukier RI, Fortuin CM, Shuler KE, Petschek AG, Schaibly JH. 1973. Study of sensitivity of coupled reaction systems to uncertainties in rate coefficients.1. Theory. Journal of Chemical Physics 59(8): 3873–3878.
17. Cukier RI, Levine HB, Shuler KE. 1978. Non-linear sensitivity analysis of multi-parameter model systems. Journal of Computational Physics 26(1): 1–42.
18. Cukier RI, Schaibly JH, Shuler KE. 1975. Study of sensitivity of coupled reaction systems to uncertainties in rate coefficients.3. Analysis of approximations. Journal of Chemical Physics 63(3): 1140–1149.
19. De Vos NJ, Rientjes THM, Gupta HV. 2010. Diagnostic evaluation of conceptual rainfall–runoff models using temporal clustering. Hydrological Processes 24(20): 2840–2850.
20. EEA. 1999. CORINE Land Cover provided by the European Environment Agency.
21. Fenicia F, Savenije HH, Winsemius HC. 2008. Moving from model calibration towards process understanding. Physics and Chemistry of the Earth 33: 1057–1060.
22. Gupta HV, Clark MP, Vrugt JA, Abramowitz G, Ye M. 2012. Towards a comprehensive assessment of model structural adequacy. Water Resources Research 48: W08301. DOI: 10.1029/2011WR011044
23. Gupta HV, Wagener T, Liu Y. 2008. Reconciling theory with observations: elements of a diagnostic approach to model evaluation. Hydrological Processes 22: 3802–3813.
24. Guse B, Kail J, Radinger J, Schroeder M, Kiesel J, Hering D, Wolter C, Fohrer N. 2015a. Eco-hydrologic model cascade: simulating land use and climate change impacts on hydrology, hydraulics and habitats for fish and macroinvertebrates. Science of the Total Environment 533: 542–556.
25. Guse B, Pfannerstill M, Fohrer N. 2015b. Dynamic modelling of land use change impacts on nitrate loads in rivers. Environmental Processes 2(4): 575–592.
26. Guse B, Reusser DE, Fohrer N. 2014. How to improve the representation of hydrological processes in SWAT for a lowland catchment – temporal analysis of parameter sensitivity and model performance. Hydrological Processes 28: 2651–2670.
27. Haas MB, Guse B, Pfannerstill M, Fohrer N. 2015. Detection of dominant nitrate processes in eco-hydrological modelling with temporal parameter sensitivity analysis. Ecological Modelling 314: 62–72.
28. Heller K. 2012. Einfluss periglazialer Deckschichten auf die oberflaechennahen Fliesswege am Hang – eine Prozessstudie im Osterzgebirge, Sachsen. Doktorarbeit, Technische Universitaet Dresden.
29. Herman JD, Kollat JB, Reed PM, Wagener T. 2013a. From maps to movies: high resolution time-varying sensitivity analysis for spatially distributed watershed models. Hydrology and Earth System Sciences 17: 5109–5125.
30. Herman JD, Reed PM, Wagener T. 2013b. Time-varying sensitivity analysis clarifies the effects of watershed model formulation on model behavior. Water Resources Research 49. DOI:10.1002/wrcr.20124.
31. Hrachowitz M, Savenije HHG, Blöschl G, McDonnell JJ, Sivapalan M, Pomeroy JW, Arheimer B, Blume T, Clark MP, Ehret U, Fenicia F, Freer JE, Gelfan A, Gupta HV, Hughes DA, Hut RW, Montanari A, Pande S, Tetzlaff D, Troch PA, Uhlenbrook S, Wagener T, Winsemius HC, Woods RA, Zehe E, Cudennec C. 2013. A decade of predictions in ungauged basins (PUB) – a review. Hydrological Sciences Journal 58(6): 1198–1255.
32. Jarvis A, Reuter HI, Nelson A, Guevara E. 2008. Hole-filled SRTM for the globe version 4, available from the CGIAR-CSI SRTM 90 m Database. http://srtm.csi.cgiar.org [Accessed April 22, 2014]
33. Kiesel J, Fohrer N, Schmalz B, White MJ. 2010. Incorporating landscape depressions and tile drainages of a northern German lowland catchment into a semi-distributed model. Hydrological Processes 24(11): 1472–1486.
34. Krysanova V, Bronstert A, Mueller-Wohlfeil DI. 1999. Modelling river discharge for large drainage basins: from lumped to distributed approach. Hydrological Sciences Journal 44(2): 313–331.
35. LVERMA S-H. 1995. Digitales Hoehenmodell DHM25 (25 m × 25 m), Land survey office Schleswig-Holstein: Kiel.
36. LVERMA S-H. 2004. Automatisierte Liegenschaftskarte (ALK), Vermessungs- und Katasterverwaltung Schleswig-Holstein.
37. Massmann C, Holzmann H. 2012. Analysis of the behavior of a rainfall–runoff model using three global sensitivity analysis methods evaluated at different temporal scales. Journal of Hydrology 475: 97–110.
38. Massmann C, Wagener T, Holzmann H. 2014. A new approach to visualizing time-varying sensitivity indices for environmental model diagnostics across evaluation timescales. Environmental Modelling and Software 51: 190–194.
39. Montanari A, Young G, Savenije HHG, Hughes D, Wagener T, Ren LL, Koutsoyiannis D, Cudennec C, Toth E, Grimaldi S, Blöschl G, Sivapalan M, Beven K, Gupta H, Hipsey M, Schaefli B, Arheimer B, Boegh E, Schymanski SJ, Di Baldassarre G, Yu B, Hubert P, Huang Y, Schumann A, Post DA, Srinivasan V, Harman C, Thompson S, Rogger M, Viglione A, McMillan H, Characklis G, Pang Z, Belyaev V. 2013. “Panta rhei – everything flows”: change in hydrology and society – the IAHS scientific decade 2013–2022. Hydrological Sciences Journal 58(6): 1256–1275.
40. Nearing GS, Gupta HV. 2015. The quantity and quality of information in hydrologic models. Water Resources Research 51. DOI:10.1002/2014WR015895.
41. Neitsch SL, Arnold JG, Kiniry JR, Williams JR. 2011. SWAT theoretical documentation version 2009. Grassland, Soil and Water Research Laboratory, Agricultural Research Service. Blackland Research Center, Texas Agricultural Experiment Station, Texas Water Resources Institute Technical Report No. 406.
42. Nossent J, Elsen P, Bauwens W. 2011. Sobol' sensitivity analyses of a complex environmental model. Environmental Modelling and Software 26: 1515–1525.
43. Oesterle H. 2001. Reconstruction of daily global radiation for past years for use in agricultural models. Physics and Chemistry of the Earth - Part B: Hydrology, Oceans and Atmosphere 26(3): 253–256.
44. Pechlivanidis IG, Jackson B, McMillan H, Gupta H. 2014. Use of entropy-based metric in multiobjective calibration to improve model performance. Water Resources Research 50: 8066–8083.
45. Pfannerstill M, Guse B, Fohrer N. 2014a. Smart low flow signature metrics for an improved overall performance evaluation of hydrological models. Journal of Hydrology 510: 447–458.
46. Pfannerstill M, Guse B, Fohrer N. 2014b. A multi-storage groundwater concept for the SWAT model to emphasize nonlinear groundwater dynamics in lowland catchments. Hydrological Processes 28: 5599–5612.
47. Pfannerstill M, Guse B, Reusser D, Fohrer N. 2015. Process verification of a hydrological model using a temporal parameter sensitivity analysis. Hydrology and Earth System Sciences 19: 4365–4376.
48. Pokhrel P, Yilmaz KK, Gupta HV. 2012. Multiple-criteria calibration of a distributed watershed model using spatial regularization and response signatures. Journal of Hydrology 418: 49–60.
49. R CoreTeam. 2013. R – a language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria.
50. Razavi S, Gupta HV. 2015. What do we mean by sensitivity analysis? The need for comprehensive characterization of global sensitivity in earth and environmental systems models. Water Resources Research 51: 3070–3092.
51. Reusser D. 2013. Fast: implementation of the Fourier amplitude sensitivity test (FAST). R package version 0.63.
52. Reusser DE, Blume T, Schaefli B, Zehe E. 2009. Analysing the temporal dynamics of model performance for hydrological models. Hydrology and Earth System Sciences 13: 999–1018.
53. Reusser DE, Buytaert W, Zehe E. 2011. Temporal dynamics of model parameter sensitivity for computationally expensive models with FAST (Fourier amplitude sensitivity test). Water Resources Research 47(7): DOI:10.1029/2010WR009947.
54. Reusser DE, Zehe E. 2011. Inferring model structural deficits by analyzing temporal dynamics of model performance and parameter sensitivity. Water Resources Research 47(7). DOI:10.1029/2010WR009946.
55. Richter D. 1995. Ergebnisse methodischer Untersuchungen zur Korrektur des systematischen Messfehlers des Hellmann-Niederschlagsmesser. Bericht des DWD 194.
56. Rosolem R, Gupta HV, Shuttleworth WJ, Zeng X, de Goncalves LG. 2012. A fully multiple-criteria implementation of the Sobol' method for parameter sensitivity analysis. Journal of Geophysical Research 117: D07013. DOI: 10.1029/2011JD016355
57. Saltelli A, Bolado R. 1998. An alternative way to compute Fourier amplitude sensitivity test (FAST). Computational Statistics and Data Analysis 26(4): 445–460.
58. Saltelli A, Ratto M, Tarantola S, Campolongo F. 2006. Sensitivity analysis practices: strategies for model-based inference. Reliability Engineering & System Safety 91(10-11): 1109–1125.
59. Sawicz K, Wagener T, Sivapalan M, Troch PA, Carrillo G. 2011. Catchment classification: empirical analysis of hydrologic similarity based on catchment function in the eastern USA. Hydrology and Earth System Sciences 15: 2585–2911.
60. Shamir E, Imam B, Gupta HV, Sorooshian S. 2005. Application of temporal streamflow descriptors in hydrologic model parameter estimation. Water Resources Research 41: W06021. DOI: 10.1029/2004WR003409
61. Sieber A, Uhlenbrook S. 2005. Sensitivity analyses of a distributed catchment model to verify the model structure. Journal of Hydrology 310(1-4): 216–235.
62. van Griensven A, Meixner T, Grunwald S, Bishop T, Di Luzio M, Srinivasan R. 2006. A global sensitivity analysis tool for the parameters of multi-variable catchment models. Journal of Hydrology 324: 10–23.
63. van Werkhoven K, Wagener T, Reed P, Tang Y. 2008. Characterization of watershed model behavior across a hydroclimatic gradient. Water Resources Research 44: W01429. DOI: 10.1029/2007WR006271
64. van Werkhoven K, Wagener T, Reed P, Tang Y. 2009. Sensitivity-guided reduction of parametric dimensionality for multi-objective calibration of watershed models. Advances in Water Resources 32(8): 1154–1169.
65. Vogel RM, Fennessey NM. 1996. Flow-duration curves II: a review of applications in water resources planning. Water Resources Bulletin 31(6): 1029–1039.
66. Wagener T, McIntyre N, Lees MJ, Wheater HS, Gupta HV. 2003. Towards reduced uncertainty in conceptual rainfall–runoff modelling: dynamic identifiability analysis. Hydrological Processes 17: 455–476.
67. Westerberg IK, Guerrero J-L, Younger PM, Beven KJ, Seibert J, Halldin S, Freer JE, Xu C-Y. 2011. Calibration of hydrological models using flow-duration curves. Hydrology and Earth System Sciences 15: 2205–2227.
68. White KL, Chaubey I. 2005. Sensitivity analysis, calibration, and validations for a multisite and multivariable SWAT model. Journal of the American Water Resources Association 41(5): 1077–1089.
69. Yaeger M, Coopersmith E, Ye S, Cheng L, Viglione A, Sivapalan M. 2012. Exploring the physical controls of regional patterns of flow duration curves – part 4: a synthesis of empirical analysis, process modeling and catchment classification. Hydrology and Earth System Sciences 16: 4483–4498.
70. Yilmaz KK, Gupta HV, Wagener T. 2008. A process-based diagnostic approach to model evaluation: application to the NWS distributed hydrologic model. Water Resources Research 44(9), W09417.
71. Yokoo Y, Sivapalan M. 2011. Towards reconstruction of the flow duration curve: development of a conceptual framework with a physical basis. Hydrology and Earth System Sciences 15: 2805–2819.