The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Toward improved calibration of hydrologic models: Combining the strengths of manual and automatic methods
Open accessAbstract
Automatic methods for model calibration seek to take advantage of the speed and power of digital computers, while being objective and relatively easy to implement. However, they do not provide parameter estimates and hydrograph simulations that are considered acceptable by the hydrologists responsible for operational forecasting and have therefore not entered into widespread use. In contrast, the manual approach which has been developed and refined over the years to result in excellent model calibrations is complicated and highly labor‐intensive, and the expertise acquired by one individual with a specific model is not easily transferred to another person (or model). In this paper, we propose a hybrid approach that combines the strengths of each. A multicriteria formulation is used to “model” the evaluation techniques and strategies used in manual calibration, and the resulting optimization problem is solved by means of a computerized algorithm. The new approach provides a stronger test of model performance than methods that use a single overall statistic to aggregate model errors over a large range of hydrologie behaviors. The power of the new approach is illustrated by means of a case study using the Sacramento Soil Moisture Accounting model.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Genetic algorithms in search, optimization, and machine learning
(1989) · Choice Reviews Online · doi:10.5860/choice.27-0936 · Cited by 49,010
The future of distributed models: model calibration and uncertainty prediction
Beven, K., & Binley, A. (1992) · Hydrological Processes · doi:10.1002/hyp.3360060305 · Cited by 4,527
Effective and efficient global optimization for conceptual rainfall‐runoff models
Duan, Q., Sorooshian, S., & Gupta, V. (1992) · Water Resources Research · doi:10.1029/91wr02985 · Cited by 3,310
Self-citationStatus of Automatic Calibration for Hydrologic Models: Comparison with Multilevel Expert Calibration
Gupta, H. V., Sorooshian, S., & Yapo, P. O. (1999) · Journal of Hydrologic Engineering · doi:10.1061/(asce)1084-0699(1999)4:2(135 · Cited by 2,216
Self-citationShuffled complex evolution approach for effective and efficient global minimization
Duan, Q. Y., Gupta, V. K., & Sorooshian, S. (1993) · Journal of Optimization Theory and Applications · doi:10.1007/bf00939380 · Cited by 1,722
Self-citationGupta, H. V., Sorooshian, S., & Yapo, P. O. (1998) · Water Resources Research · doi:10.1029/97wr03495 · Cited by 1,426
Self-citationEvaluation of automated techniques for base flow and recession analyses
Nathan, R. J., & McMahon, T. A. (1990) · Water Resources Research · doi:10.1029/wr026i007p01465 · Cited by 1,212
Whitley, L. D. (1989) · international conference on Genetic algorithms · Cited by 992
Multi-objective global optimization for hydrologic models
Yapo, P. O., Gupta, H. V., & Sorooshian, S. (1998) · Journal of Hydrology · doi:10.1016/s0022-1694(97)00107-8 · Cited by 908
Self-citationDevelopment and application of a conceptual runoff model for Scandinavian catchments
Bergström, S. (1976) · Cited by 794
Cited by
Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations
Moriasi, D. N., Arnold, J. G., Liew, M. W. V., Bingner, R. L., Harmel, R. D., & Veith, T. L. (2007) · Transactions of the ASABE · doi:10.13031/2013.23153 · Cited by 13,446
Gupta, H. V., Kling, H., Yilmaz, K. K., & Martinez, G. F. (2009) · Journal of Hydrology · doi:10.1016/j.jhydrol.2009.08.003 · Cited by 6,758
Self-citationSWAT: Model Use, Calibration, and Validation
Arnold, J. G., Moriasi, D. N., Gassman, P. W., Abbaspour, K. C., White, M. J., Srinivasan, R., Santhi, C., Harmel, R. D., van Griensven, A., Liew, M. W. V., et al. (2012) · Transactions of the ASABE · doi:10.13031/2013.42256 · Cited by 3,450
Characterising performance of environmental models
Bennett, N. D., Croke, B. F. W., Guariso, G., Guillaume, J. H. A., Hamilton, S. H., Jakeman, A. J., Marsili-Libelli, S., Newham, L. T. H., Norton, J. P., Perrin, C., et al. (2013) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2012.09.011 · Cited by 1,536
A decade of Predictions in Ungauged Basins (PUB)—a review
Hrachowitz, M., Savenije, H. H. G., Blöschl, G., McDonnell, J. J., Sivapalan, M., Pomeroy, J. W., Arheimer, B., Blume, T., Clark, M. P., Ehret, U., et al. (2013) · Hydrological Sciences Journal · doi:10.1080/02626667.2013.803183 · Cited by 1,295
Self-citation
Metadata
- Authors
- Boyle, D. P., Gupta, H. V., & Sorooshian, S.
- Venue
- Water Resources Research
- Publisher
- American Geophysical Union (AGU)
- Published
- 2000-12
- Volume
- 36
- Issue
- 12
- Pages
- 3663-3674
- ISSN
- 0043-1397 (ISSN-L)
- 1944-7973
- OpenAlex
- W1983083246
- Cited by
- 696
- References
- 22
- FWCI
- 32.83
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Toward improved calibration of hydrologic models: Combining the strengths of manual and automatic methods. https://app.cochat.ai/papers/10.1029/2000wr900207
- The paper
- Boyle, D. P., Gupta, H. V., & Sorooshian, S. (2000). Toward improved calibration of hydrologic models: Combining the strengths of manual and automatic methods. Water Resources Research. https://doi.org/10.1029/2000wr900207
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
