Hostname: page-component-78c5997874-m6dg7 Total loading time: 0 Render date: 2024-11-08T00:31:55.218Z Has data issue: false hasContentIssue false
  • English
  • Français

Developing Models with More Detail: Do More Algorithms Give More Truth?

Published online by Cambridge University Press:  12 June 2017

Donn G. Decoursey
Donn G. Decoursey*
Affiliation:
U.S. Dep. Agric.-Agric. Res. Serv., Hydro-Ecosystem Research Unit, Fort Collins, CO 80521
Get access Rights & Permissions [Opens in a new window]

Abstract

Do quasi-physically based models with more detail perform better than regression or other empirical models? This is a question that was raised many years ago and still remains. In an effort to respond to this question, the author reviews the needs and concerns of users and then divides the large number of models into three classes: (1) screening, (2) research, and (3) planning, monitoring, and assessment. Empirical and causal (physically based) models are contrasted and the advantages and disadvantages of each described. Sources of model uncertainty (properties of data bases, model structure, parameter estimation methods, algorithmic implementation, verification and validation, and future users) that lead to skepticism about models' performance are investigated. Simulation scale and spatial variability are also important considerations. The leading question is then discussed from the perspective of screening, research, and planning, monitoring, and regulatory models.

Keywords

Research modelsscreening modelsmodel validationsimulation scalesassessment modelsspatial variabilityregulatory modelsmodel uncertaintymodel structure
Type
Symposium
Information
Weed Technology , Volume 6 , Issue 3 , September 1992 , pp. 709 - 715
Copyright
Copyright © 1990 by the Weed Science Society of America 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

LITERATURE CITED

1. Beck, M. B. 1987. Water quality modeling: A review of the analysis of uncertainty. Water Resour. Res. 23:1393–1442.CrossRefGoogle Scholar
2. Beven, K.J. and Jakeman, A. K. 1990. Complexity and uncertainty in predictive models. p. 555–576 in DeCoursey, D. G., ed. Proc. Int. Symp. on Water Qual. Modeling of Agric. Non-Point Sources. Part 2. U.S. Dep. Agric. ARS-81. Logan, UT. June.Google Scholar
3. Dagan, G., Russo, D., and Bresler, E. 1990. Effect of spatial variability upon subsurface transport of solutes from nonpoint sources. p. 523–548 in DeCoursey, D. G., ed. Proc. Int. Symp. on Water Qual. Modeling of Agric. Non-Point Sources. Part 2. U.S. Dep. Agric. ARS-81. Logan, UT. June.Google Scholar
4. DeCoursey, D. G. and Rojas, K. W. 1990. RZWQM—A Model for simulating the movement of water and solutes in the root zone. p. 813–822 in DeCoursey, D. G., ed. Proc. Int. Symp. on Water Qual. Modeling of Agric. Non-Point Sources. Part 2. U.S. Dep. Agric. ARS-81. Logan, UT. June.Google Scholar
5. Engeman, E. T. 1974. A partial area model for simulating surface runoff from small watersheds. Ph.D. thesis. The Pennsylvania State Univ., University Park, PA.Google Scholar
6. Engeman, E. T. and Rogowski, A. S. 1974. A partial area model for storm flow synthesis. Water Resour. Res. 10:464–472.Google Scholar
7. Goss, D. and Wauchope, R. D. 1991. The SCS/ARS/CES pesticide properties data base: II using it with soils data in a screening procedure. p. 471–493 in Weigmann, D. L., ed. Proc. 3rd Nat. Res. Conf. on Pestic. Pesticides in the Next Decade: The Challenges Ahead. Virginia Water Resour. Res. Cent. Blacksburg, VA.Google Scholar
8. Jakeman, A. J. 1988. Features and principles of a systems approach to environmental modeling. p. 380–385 in Vichnevetsky, R., et al. eds. Proc. 12th IMACS World Congr. of Sci. Computation, Vol. 2. Paris, July.Google Scholar
9. Klemes, V. 1982. Empirical and Causal Models in Hydrology, Scientific Basis of Water-Resources Management. p. 95–104 in Studies in Geophysics. National Res. Council, National Academy Press, Washington, DC.Google Scholar
10. Loague, K. M. and Freeze, R. A. 1985. A comparison of rainfall-runoff modeling techniques on small upland catchments. Water Resour. Res. 21:229–248.CrossRefGoogle Scholar
11. Rinaldo, A. and Marani, A. 1987. Basin scale model of solute transport. Water Resour. Res. 23:2107–2118.CrossRefGoogle Scholar
12. Shaw, R. R. and Falco, J. W. 1990. A management perspective on water quality models for agricultural nonpoint sources of pollution. p. 697–704 in DeCoursey, D. G., ed. Proc. Int. Symp. on Water Qual. Modeling of Agric. Non-Point Sources. Part 2. U.S. Dep. Agric. ARS-81. Logan, UT. June.Google Scholar
13. Woolhiser, D. A., Jury, W. A., and Nielsen, D. R. 1990. Effects of spatial and temporal variability on water quality model development. p. 505–522 in DeCoursey, D. G., ed. Proc. Int. Symp. on Water Qual. Modeling of Agric. Non-Point Sources. Part 2. U.S. Dep. Agric. ARS-81. Logan, UT. June.Google Scholar