We work broadly on the theme of flood nonstationarity, developing approaches to (1) understand how floods are changing, (2) quantify the different drivers of flood nonstationarity, and (3) predict future flooding and hydro-climatic extremes.
(1) Channel conveyance effects on flooding
We examine how channel conveyance affects flooding over management timescales. We quantify the drivers of changes in channel conveyance/morphology (e.g. climate variability) and forecast future changes in conveyance and their effects on the likelihood of flooding.
- 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
(2) Flood attribution: land cover and climate
We investigate why floods are changing. We evaluate influence of land cover change effects, such as urbanization and deforestation, on both the hydrology (from very low to very high flows) and channel morphology/conveyance capacity.
- 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
(3) Flood detection: changes in flood characteristics
We explore how floods are changing by exploring changes in flood characteristics (magnitude, frequency, extent, duration) in the past and future.
- Slater, L.J., Villarini, G. (2016) Recent trends in U.S. flood risk, Geophysical Research Letters
- Villarini, G., Slater, L.J. (2018) Examination of Changes in Annual Maximum Gage Height in the Continental United States Using Quantile Regression, Journal of Hydrologic Engineering (ASCE)
- Slater, L.J., Villarini, G. (2016) On the impact of gaps on trend detection in extreme streamflow time series, International Journal of Climatology
- Villarini, G.,Slater, L.J. (2017) Climatology of flooding in the United States, Oxford Research Encyclopedia of Natural Hazard Science
(4) Ensemble-based statistical 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. (2017) 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)
(6) Data science approaches to understand global extremes in hydro-climatology and geomorphology
- 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