The MultiHazard package provides tools for stationary multivariate
statistical modeling, for example, to estimate joint occurrence
probabilities of MULTIple co-occurring HAZARDs.
Install the latest version of this package by entering the following in R:
install.packages("remotes")
remotes::install_github("rjaneUCF/MultiHazard")The package contains functions for pre-processing data, including imputing missing values, detrending and declustering time series as well as analyzing pairwise correlations over a range of lags. Functionality is built in to implement the conditional sampling - copula theory approach described in Jane et al. (2020) including the automated threshold selection method from Solari et al. (2017). There is a function that calculates joint probability contours using the method of overlaying conditional contours given in Bender et al. (2016) and extracts design events such as the “most likely” event or an ensemble of possible design events. The package also includes methods from Murphy-Barltrop et al. (2023) and Murphy-Barltrop et al. (2024) for deriving isolines using the Heffernan and Tawn (2004) [HT04] and Wadsworth and Tawn (2013) [WT13] models, together with a novel bootstrap procedure for quantifying sampling uncertainty in the isolines. Three higher dimensional approaches — standard (elliptic/Archimedean) copulas, Pair Copula Constructions (PCCs) and a conditional threshold exceedance approach (HT04) — are coded. Finally, the package can be implemented to derive temporally coherent extreme events comprising a hyetograph and water level curve for simulated peak rainfall and peak sea level events.
For detailed tutorials and examples, see the package vignette.
If you use this package, please cite:
Jane, R., Wahl, T., Peña, F., Obeysekera, J., Murphy-Barltrop, C., Ali, J., Maduwantha, P., Li, H., and Malagón Santos, V. (under review) MultiHazard: Copula-based Joint Probability Analysis in R. Journal of Open Source Software. [under revision]
Contributions to the MultiHazard package are welcome! Please feel free
to submit issues or pull requests on GitHub.
This package has been used in several peer-reviewed publications:
Li, H., Jane, R. A., Eilander, D., Enríquez, A. R., Haer, T., and Ward, P. J. (2025). Assessing the spatial correlation of potential compound flooding in the United States, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-2993.
Amorim, R., Villarini, G., Kim, H., Jane, R., and Wahl, T. (2025). A Practitioner’s approach to process-driven modeling of compound rainfall and storm surge extremes for coastal Texas, J. Hydrol. Eng., 30(5), 04025025, https://doi.org/10.1061/JHYEFF.HEENG-648.
Maduwantha, P., Wahl, T., Santamaria-Aguilar, S., Jane, R., Booth, J. F., Kim, H., and Villarini, G. (2024). A multivariate statistical framework for mixed storm types in compound flood analysis, Nat. Hazards Earth Syst. Sci., 24, 4091–4107, https://doi.org/10.5194/nhess-24-4091-2024.
Nasr, A. A., Wahl, T., Rashid, M. M., Jane, R., Camus, P. and Haigh, I. D. (2023). Temporal changes in dependence between compound coastal and inland flooding drivers around the contiguous United States coastline, Weather Clim. Extrem., 41, 100594, https://doi.org/10.1016/j.wace.2023.100594.
Kim, H., Villarini, G., Jane, R., Wahl, T., Misra, S., and Michalek, A. (2023). On the generation of high‐resolution probabilistic design events capturing the joint occurrence of rainfall and storm surge in coastal basins, Int. J. Climatol, 43(2), 761-771, https://doi.org/10.1002/joc.7825.
Kim, T., Villarini, G., Kim, H., Jane, R., and Wahl, T. (2023). On the compounding of nitrate loads and discharge, J. Environ. Qual., 52, 706–717. https://doi.org/10.1002/jeq2.20458.
Peña, F., Obeysekera, J., Jane R., Nardi, F., Maran, C., Cadogan, A., de Groen, F., and Melesse, A. (2023). Investigating compound flooding in a low elevation coastal karst environment using multivariate statistical and 2D hydrodynamic modeling, Weather Clim. Extrem., 39, 100534. https://doi.org/10.1016/j.wace.2022.100534.
Jane, R., Cadavid, L., Obeysekera, J., and Wahl, T. (2020). Multivariate statistical modelling of the drivers of compound flood events in South Florida, Nat. Hazards Earth Syst. Sci., 20, 2681–2699, https://doi.org/10.5194/nhess-20-2681-2020.