We work broadly on the theme of climatic and water-related extremes – with a specific focus on flood nonstationarity. Some examples of publications are provided below for each theme.
(1) Detection and attribution of changes in extremes
We explore how and why the characteristics of extremes are changing (magnitude, frequency, extent, duration) in the past and future.
- Slater, L.J., Villarini, G. (2017) Evaluating the drivers of seasonal streamflow rates in the U.S. Midwest, Water (MDPI).
- Berghuijs, W., Harrigan, S., Molnar, P., Slater, L. , Kirchner, J., (2019) The relative importance of different flood-generating mechanisms across Europe, Water Resources Research
- Neri, A., Villarini, G., Slater, L.J., Napolitano, F. (2019) On the statistical attribution of the frequency of flood events across the U.S. Midwest
- Slater, L.J., Villarini, G. (2016) Recent trends in U.S. flood risk, Geophysical Research Letters
- Slater, L.J., Villarini, G. (2016) On the impact of gaps on trend detection in extreme streamflow time series, International Journal of Climatology
(2) Measuring the imprint of climate on rivers and freshwater systems
We quantify the drivers of changes in river systems (fluvial geomorphology) and forecast their effects on water-related extremes, over human/management timescales.
- Slater, L.J., Khouakhi, A., Wilby, R.L. (in press) River channel conveyance capacity adjusts to modes of climate variability, Scientific Reports.
- Slater, L.J., Singer, M.B., Kirchner J.W. (2015) Hydrologic versus geomorphic drivers of trends in flood hazard, Geophysical Research Letters
- Slater L.J.(2016) To what extent have changes in channel capacity contributed to flood hazard trends in England and Wales? Earth Surface Processes and Landforms
- Slater, L.J. and Singer, M.B. (2013) Imprint of climate and climate change in alluvial riverbeds: Continental United States, Geology
(3) Ensemble-based forecasting
We develop dynamical, statistical, probabilistic and ensemble-based approaches using climate forecasts to predict hydrological and geomorphic change (floods, streamflow, sea levels).
- Slater, L.J., Villarini, G. (2018) Enhancing the predictability of seasonal streamflow with a statistical dynamical approach. Geophysical Research Letters
- Slater, L.J., Villarini, G., Bradley, A., Vecchi G. (2018) A dynamical statistical framework for seasonal streamflow forecasting in an agricultural watershed, Climate Dynamics
- Khouakhi, A., Villarini, G., Zhang, W., Slater, L. (2019) Seasonal predictability of high sea level frequency from Nino3.4 along the U.S. West coast, Advances in Water Resources.
- Neri, A., Villarini, G., Salvi, K.A., Slater, L.J.and Napolitano, F. (2019) On the decadal predictability of the frequency of flood events across the U.S. Midwest. International Journal of Climatology
- Slater, L.J., Villarini, G., Bradley, A.A. (2017) Weighting of NMME temperature and precipitation forecasts across Europe, Journal of Hydrology
- Slater, L.J., Villarini, G., Bradley, A. (2016) Evaluation of the skill of North-American Multi-Model Ensemble (NMME) Global Climate Models in predicting average and extreme precipitation and temperature over the continental USA
- Zhang, W., Villarini, G., Slater, L.J., Vecchi, G., Bradley, A.A. (2017) Improved ENSO Forecasting using Bayesian Updating and the North American Multi Model Ensemble (NMME)
(4) Data science approaches to understand global climatic and water-related extremes
- Slater, L. J., Thirel, G., Harrigan, S., Delaigue, O., Hurley, A., Khouakhi, A., Prodoscimi, I., Vitolo, C., and Smith, K. (2019) Using R in hydrology: a review of recent developments and future directions, Hydrology and Earth System Sciences, 23, 2939-2963, doi: 10.5194/hess-23-2939-2019
- Courty, L., Wilby, R., Hillier, J., Slater, L.J.(2019) Intensity-Duration-Frequency curves at the global scale, Environmental Research Letters, ERL-106833.R2. Preprint available at: https://eartharxiv.org/w56b8
Key themes: Flood nonstationarity, Flood drivers, Channel conveyance capacity, Land cover change, Statistical modelling, Flood forecasting, Ensemble approaches