Hilary McMillan: Hydrology Information and Downloads

Hilary McMillan

Profile

I work for NIWA: the National Institute of Water and Atmospheric Research, in Christchurch.

Contact me at work:

Hilary McMillan
National Institute of Water and Atmospheric Research Ltd
PO Box 8602, Christchurch, New Zealand

+64 3 343 8071

My research interests include rainfall-runoff modelling, process studies, model calibration, uncertainty estimation and floodplain inundation modelling. Some current projects:

  1. Improving process representation in rainfall-runoff models using multi-response data from NZ and abroad.
  2. Setting up an experimental catchment in the Canterbury high country, to study catchment processes in NZ upland environments.
  3. Using multi-response, multi-scale data to calibrate rainfall-runoff models, including uncertainty caused by scale differences between model and data.
  4. Calibrating rainfall-runoff models against uncertain discharge data, particularly focusing on rating curve uncertainty in braided, gravel-bed rivers.
  5. Calibrating rainfall-runoff models in catchments with heterogeneous geology, using Markov Chain Monte Carlo methods with informal likelihood measures to explore the response surface.
  6. Coupling medium-range Numerical Weather Prediction model forecasts to rainfall-runoff models, and exploring relative contributions to discharge prediction uncertainty from errors in initial conditions, model parameteristation, rainfall forecasts and validation data.

PhD Thesis

Download my thesis here: 'End-to-End Flood Risk Assessment: A Model Cascade with Uncertainty Estimation'.

 

Papers

Here you can download pre-prints of my journal and conference papers. I also include links to the journal sites for final articles.

1. McMillan, H., M. Clark, W. Bowden, M. Duncan, R.Woods (2010). Hydrological field data from a modeller's perspective: Part 1. Diagnostic tests for model structure. Hydrological Processes: in press

2. Clark, M., McMillan, H. , Collins, D., Kavetski, D., Woods, R. (2010). Hydrological field data from a modeller's perspective: Part 2. Process-based evaluation of model hypotheses . Hydrological Processes: in press

3. Pechlivanidis I.G., Jackson B., McMillan H. (2010) The Use of Entropy as a Model Diagnostic in Rainfall-Runoff Modelling . Proceedings of the 2010 International Congress on Environmental Modelling and Software, Ottawa, Canada

4. McMillan, H., B. Jackson, M. Clark, D. Kavetski, R. Woods (2010). Input Uncertainty in Hydrological Models: An Evaluation of Error Models for Rainfall . In review.

5. McMillan, H., J. Freer, F. Pappenberger, T. Krueger, M. Clark (2010). Impacts of Uncertain River Flow Data on Rainfall-Runoff Model Calibration and Discharge Predictions . Hydrological Processes DOI: 10.1002/hyp.7587

Link to journal site: http://dx.doi.org/doi:10.1002/hyp.7587

6. McMillan, H., M. Clark, R. Woods, M. Duncan, MS. Srinivasan, A. Western, D. Goodrich (2010). Improving Perceptual and Conceptual Hydrological Models using Data from Small Basins . Status and Perspectives of Hydrology in Small Basins (Proceedings of the Workshop held at Goslar-Hahnenklee, Germany, 30 March-2 April 2009). IAHS Publ. 336-08, 2010.

Link to journal site: http://iahs.info/redbooks/336.htm

7. McMillan, H., and M. Clark (2009), Rainfall-runoff model calibration using informal likelihood measures within a Markov chain Monte Carlo sampling scheme, Water Resour. Res., 45, W04418, doi:10.1029/2008WR007288.

Link to journal site: http://www.agu.org/pubs/crossref/2009/2008WR007288.shtml

8. McMillan H., J. Brasington (2008), End-to-end flood risk assessment: A coupled model cascade with uncertainty estimation, Water Resour. Res., 44, W03419, doi:10.1029/2007WR005995.

Link to journal site: http://www.agu.org/pubs/crossref/2008/2007WR005995.shtml

9. McMillan H., J. Brasington (2007). Reduced complexity strategies for modelling urban floodplain inundation. Geomorphology, Volume 90, Issues 3-4, Pages 226-243.

Link to journal site: http://dx.doi.org/doi:10.1016/j.geomorph.2006.10.031

10. Freer, J.E., McMillan, H., McDonnell, J.J., Beven, K.J. (2004). Constraining dynamic TOPMODEL responses for imprecise water table information using fuzzy rule based performance measures. Journal of Hydrology, Volume 291, Issues 3-4, Pages 254-277

Link to journal site: http://dx.doi.org/doi:10.1016/j.jhydrol.2003.12.037

Posters

1. McMillan, H.K., Clark, M.P., Martinez, G., Goodrich, D., Srinivasan, M.S., Duncan, M., Woods, R., Western, A.
Diagnostics for Model Structure: Improving Hydrological Models using Data from Experimental Basins.
Presented at European Geophysical Union Conference, Vienna, 2009.

2. McMillan, H.K., Freer, J., Pappenberger, F., Krueger, T., Clark, M.P.
Impacts of Uncertain Flow Data on Rainfall-Runoff Model Calibration and Discharge Predictions in a Mobile-Bed River
Presented at American Geophysical Union Fall Meeting, San Francisco, 2008.

3. McMillan, H.K., Clark, M.P., Ibbitt, R.P.
Calibration strategies for distributed hydrological models using qualitative knowledge of spatially variable process characteristics.
Presented at European Geophysical Union Conference, Vienna, 2008.

4. Freer, J.E., McMillan, H. K., McDonnell, J.J., Beven, K.J.
Constraining dynamic TOPMODEL responses for imprecise water table information using fuzzy rule based performance measures.
Presented at European Geophysical Union Conference, Nice, 2003.