Some reading


This list is far from complete. We will add to it over time. Let us know of anything that you think should be included.

Papers

Haley, L., Schumacher, J., MacMillan, G.J. and Boutin, L.C., 2014. Highly parameterized model calibration with cloud computing: an example of regional flow model calibration in northeast Alberta, Canada. Hydrogeology Journal. 22(3):729-737. https://doi.org/10.1007/s10040-014-1110-8

Haley, K., Valenza, A., White, E., Hutchison, B. and Schumacher, J., 2019. Application of the iterative ensemble smoother method and cloud computing: a groundwater modelling case study. Water, 11 (8), 1649. https://doi.org/10.3390/w11081649

Doherty, J. and Simmons, C.T., 2013. Groundwater modelling in decision support: reflections on a unified conceptual framework. Hydrogeology Journal 21: 1531–1537

Doherty, J. and Moore, C., 2020. Decision support modelling: data assimilation, uncertainty quantification and strategic abstraction. Groundwater, 58(3), 327-337. https://doi.org/10.1111/gwat.12969

Middlemis, H. and Peeters, L.J.M., 2018. Uncertainty analysis—Guidance for groundwater modelling within a risk management framework. A report prepared for the Independent Expert Scientific Committee on Coal Seam Gas and Large Coal Mining Development through the Department of the Environment and Energy, Commonwealth of Australia 2018. Download.

White, J.T., 2018. A model-independent iterative ensemble smoother for efficient history-matching and uncertainty quantification in very high dimensions. Environmental modelling and software. 109:191-201. https://doi.org/10.1016/j.envsoft.2018.06.009

Internet Resources

Listen to an excellent YouTube course in linear algebra by Gilbert Strang:  MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018