Bastos, A.

Bevacqua, E.

Blesić, S.

Couasnon, A.

Durante, F.

Hillier, J.

Manning, C.

Michele, C.

Oliveira, S.

Pinto, J.

Ragno, E.

Ramos, A.

Ribeiro, A.

Rivoire, P.

Saunders, K.

van der Wiel, K.

Vignotto, E.

Wu, W.

Zhang, T.

Zscheischler, J.

English

MPG.PuRe

JOURNAL OF GEOPHYSICAL RESEARCHSupporting Information for ”Guidelines for studyingdiverse types of compound weather and climateevents”Emanuele Bevacqua1,2, Carlo De Michele3, Colin Manning4, AnäısCouasnon5, Andreia F. S. Ribeiro6,7, Alexandre M. Ramos7, EdoardoVignotto8, Ana Bastos9, Suzana Blesić10, Fabrizio Durante11, John Hillier12,Sérgio C. Oliveira13, Joaquim G. Pinto14, Elisa Ragno15, Pauline Rivoire16,Kate Saunders17, Karin van der Wiel18, Wenyan Wu19, Tianyi Zhang20, andJakob Zscheischler1,21,221Department of Computational Hydrosystems, Helmholtz Centre for Environmental Research - UFZ, Leipzig, Germany2Department of Meteorology, University of Reading, Reading, United Kingdom3Department of Civil and Environmental Engineering, Politecnico di Milano, Milano, Italy4School of Civil Engineering and Geosciences, Newcastle University, Newcastle upon Tyne, United Kingdom5Institute for Environmental Studies, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands6Institute for Atmospheric and Climate Science, ETH Zurich, Universitätstrasse 16, Zurich 8092, Switzerland7Instituto Dom Luiz (IDL), Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal8Research Center for Statistics, University of Geneva, 24 rue du Général-Dufour, Geneva 12119Max Planck Institute for Biogeochemistry, Dept. of Biogeochemical Integration, 07745 Jena, Germany10Institute for Medical Research, University of Belgrade and Center for Participatory Science, Belgrade, Serbia11Department of Economic Sciences, University of Salento, Lecce, Italy.12Geography, Loughborough University, Epinal Way, Loughborough, LE11 3TU, UK13Centre for Geographical Studies, Institute of Geography and Spatial Planning, Universidade de Lisboa, Lisboa, Portugal14Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology, Karlsruhe, GermanyOctober 8, 2021, 3:32pmX - 2 :15Delft University of Technology, Faculty of Civil Engineering and Geosciences, 2628 CN, Delft, Netherlands16Oeschger Centre for Climate Change Research and Institute of Geography, University of Bern, Bern, Switzerland17School of Mathematical Sciences, Queensland University of Technology, Gardens’s Point, Brisbane, Australia, 400018Royal Netherlands Meteorological Institute (KNMI), De Bilt, The Netherlands19Department of Infrastructure Engineering, Faculty of Engineering and Information Technology, The University of Melbourne,Australia20State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics,Chinese Academy of Sciences, Beijing, China21Climate and Environmental Physics, University of Bern, Bern, Switzerland22Oeschger Centre for Climate Change Research, University of Bern, Bern, SwitzerlandContents of this file1. Text S1 to S2October 8, 2021, 3:32pm: X - 3Text S1. Additional information on the tools employed for the preconditionedevent analysisClassification of Crop and Forest Grid Cells. The categorisation of grid cells intoforest or crop grid cells (Figure 2a) is done using the European Space Agency Land Coverdataset (Santoro et al., 2017). This provides a land classification at a 300m resolutionfor the period 1992-2018, which we regrid to a 0.5° lat-lon grid using the LC-CCI UserTool. A grid cell is classified as a crop grid cell if rainfed croplands are the majority class,while it is classified as a forest grid cell if the majority class is either evergreen, deciduousbroadleaf, needle leaved, or mixed forests. To exclude human influence due to land-coverchange, we keep only grid cells where the sum of the absolute changes over the 27 yearperiod is below 5%. We also exclude irrigated croplands. The locations of the resultingcrop and forest grid cells is shown in Figure 2a.Application of Logistic Regression. Using a logistic regression model, we estimatethe probability of extremely low vegetation activity in summer, defined as occurrences ofLAI in summer (LAIJJA) below its 5th percentile. In this case, the impact Y is a binarytime series (1: LAIJJA < 5th percentile; 0: LAIJJA > 5th percentile), while the predictorsX1, X2, ..., Xn are continuous variables. The probability of a low anomaly is estimated as:P [Y = 1] =11 + exp(b0 + b1 X1 + ... + bn Xn)where b0, b1, ... , bn are the regression coefficients. The logistic regression model is fittedusing the glm function from the stats package in R (R Core Team, 2020). We fit twomodels for both cropland and forest grid cells separately, a base model that includesOctober 8, 2021, 3:32pmX - 4 :summer precipitation and temperature as predictors and a second model which includesthe two summer predictors along with spring radiation. After an iterative process, this wasfound to be the most parsimonious model with good performance. Similar performance isachieved using spring precipitation instead of spring radiation, though we choose to usespring radiation (see discussion in the main text). Finally, no improvement is achievedwhen adding other variables (i.e summer radiation or spring temperature). Before fitting,all predictors are checked for multi-collinearity using the variation inflation factor (VIF).Some variables have moderate correlation but it is small enough that it will not adverselyaffect the results.October 8, 2021, 3:32pm: X - 5Text S2. Additional information on the tools employed for the temporallycompounding event analysisWe looked for clustering of precipitation events over multiple windows before the land-slide of interest. The amplitude of the shortest window ([-4,0] days) is in line with the 3days thresholds used to disentangle nearby daily precipitation amounts arising from indi-vidual weather systems (i.e., used for removing high-frequency clustered events). Due tothe multiple windows, multiple tests are involved when assessing whether the number ofprecipitation events is above the 95th percentile of the Binomial distribution (where sometests may be dependent due to the overlapping of time windows), hence we consideredthe Fuzzy Benjamini-Hochberg correction (Kulinskaya & Lewin, 2009).Given that we look for precipitation clustering up to 90 days before the landslide,which occurred during the November-March period in each hydrological year, we searchedfor clustering backward until August when considering landslides occurred in November.Hence, the parameter p (equal to 0.056) of the Binomial distribution was estimated basedon data during August-March, specifically as Ntot/L, where Ntot is the total number ofselected precipitation events and L is the length of the August-March time series. Notethat in further analysis, a dependency of the parameter p from time could be considered,such as to account for seasonality effects. The goodness of fit of the Binomial distribu-tion was tested (significance level of 0.05) for all temporal windows using the Chi-squaregoodness-of-fit test.October 8, 2021, 3:32pmX - 6 :We quantified the association between precipitation clustering and landslide occurrencesas the fraction of landslide events preceded by precipitation clustering. To assess whetherthis association is significant, we compared the observed fraction with that 95th percentileof the fraction obtained in 5000 synthetic datasets that assume no association betweenclustered precipitation events and landslides. In these synthetic datasets, the landslidesoccurrence is shuffled in time. In particular, we shuffled the year of occurrence of thelandslide events but left that day and month of occurrence as in the original dataset topreserve the temporal structure of landslide occurrences in the synthetic dataset (Witt etal., 2010; Bevacqua et al., 2019). The resulting increase of the confidence interval widthwith the temporal window in Figure 7b is in line with the tendency of precipitation eventsto cluster more at higher temporal windows.October 8, 2021, 3:32pm: X - 7ReferencesBevacqua, E., Maraun, D., Vousdoukas, M. I., Voukouvalas, E., Vrac, M., Mentaschi, L.,& Widmann, M. (2019). Higher probability of compound flooding from precipitationand storm surge in Europe under anthropogenic climate change. Science Advances ,5 (9), eaaw5531.Kulinskaya, E., & Lewin, A. (2009). On fuzzy familywise error rate and false discoveryrate procedures for discrete distributions. Biometrika, 96 (1), 201–211.R Core Team. (2020). R: A language and environment for statistical computing [Com-puter software manual]. Vienna, Austria. Retrieved from https://www.R-project.org/Santoro, M., Kirches, G., Wevers, J., Boettcher, M., Brockmann, C., Lamarche, C., &Defourny, P. (2017). Land Cover CCI: Product User Guide Version 2.0. ClimateChange Initiative Belgium: Belgium, Leuven.Witt, A., Malamud, B. D., Rossi, M., Guzzetti, F., & Peruccacci, S. (2010). Temporalcorrelations and clustering of landslides. Earth Surface Processes and Landforms ,35 (10), 1138–1156.October 8, 2021, 3:32pm

Guidelines for studying diverse types of compound weather and climate events

http://hdl.handle.net/21.11116/0000-0009-7A89-4

Guidelines for studying diverse types of compound weather and climate events

Abstract

Similar works

Full text

Available Versions

MPG.PuRe