Landslide frequency in a changing climate

Landslide frequency in a changing climate:

Synoptic scale weather types as predictors of landslide occurrence 12th April, 2018

Dr Jo Wood1, Stephan Harrison2, Thea Turkington3 and Liam Reinhardt2 1

Kings College London, UK; 2 University of Exeter, UK; 3 Centre for Climate Research, Singapore

Climate change and landslides • Hydro-meteorological trigger is rainfall (Jakob and Weatherly, 2003; Farahmand and Aghakouchak, 2013) • Rely on empirical rainfall thresholds used to define minimum triggering conditions (Peruccacci et al., 2012) • Localized • Depend on quality of rainfall data (Gariano et al., 2015)

• Makes future predictions hard to obtain • Synoptic-scale weather types may provide an alternative (Nikolopoulos et al., 2015; Wood et al., 2016)

Synoptic weather types • Classification of atmospheric circulation patterns • Subjective or automated

• Proposed as effective downscaling tool for GCMs (Conway & Jones, 1998) • Proportion of weather types can change over time • Different circulation patterns becoming more (or less) frequent (Paredes et al., 2006; Boé & Terray, 2008; Wood et al., 2016)

• Under a changing climate, it is probable that the proportion of weather types has, and will change in the future (Lopez Saez et al., 2013)

• Can synoptic weather patterns be used to understand landslide frequency and occurrence in a topographically diverse region such as the European Alps?

• European Cooperation in Science and Technology Action 733 (COST733) provides a classification catalogue for synoptic weather in Europe • 12 European domains • September 1957 to August 2002 • 5076 different classifications • 17 automated • five subjective Philipp et al. (2010). Phy. Chem. Earth, 35, 360-373; Uppala et al. (2005). Q J R Meteorol. Soc., 131, 2961-3012;Wood et al. (2016), Clim. Cha., 136, 297-308.

• Annual classifications • divided into nine, 18 or 27 weather types • Single or 4-day sequences

• Seasonal classifications • 28 weather types (seven types for each season)

Philipp et al. (2010). Phy. Chem. Earth, 35, 360-373; Uppala et al. (2005). Q J R Meteorol. Soc., 131, 2961-3012;Wood et al. (2016), Clim. Cha., 136, 297-308.

Landslide inventory

(European Alps)

• 7919 landslides • 2966 rotational/translational landslides with known • Location • Date of occurrence • 1972-2002

Wood et al. (2015). Geomorph., 228, 398-408.

• Brier Skill Score results Rank COST733 classification (name) 1 2 3 4 5 84 280 439 1750 2542

CKM28_SE_S01_SP.K5_D06 CKM28_SE_S01_SP_D06 CAP28_SE_S01_SP_D06 CAP28_SE_S01_SP.K5_D06 SAN28_SE_S01_SP.Z5.Y5.K5_D06 GWT27_YR_S01_SP_D06 PERo27_YR_S01_SP_D00 SUEo27_YR_S01_SP_D00 OGWo27_YR_S01_SP_D00 PECo09_YR_S01_SP_D00

Brier (1950). Mon.Weather Rev., 78, 1-3; Wood et al. (2016), Clim. Cha., 136, 297-308.

• Brier Skill Score results Rank COST733 classification (name) 1 2 3 4 5 84 280 439 1750 2542

Classification method

CKM28_SE_S01_SP.K5_D06 CKMeans CKM28_SE_S01_SP_D06 CKMeans CAP28_SE_S01_SP_D06 Cluster Analysis of Principal components CAP28_SE_S01_SP.K5_D06 Cluster Analysis of Principal components SAN28_SE_S01_SP.Z5.Y5.K5_D06 Simulated ANeilling clustering GWT27_YR_S01_SP_D06 GrossWetter PERo27_YR_S01_SP_D00 Perret SUEo27_YR_S01_SP_D00 Schüepp OGWo27_YR_S01_SP_D00 Objective GrossWetter PECo09_YR_S01_SP_D00 Péczely


Weather types (n)

BSS

28 28 28 28 28 27 27 27 27 9

0.029 0.029 0.028 0.028 0.028 0.022 0.018 0.017 0.010 0.008

LsD

Wood et al. (2016), Clim. Cha., 136, 297-308.

LLsD

Wood et al. (2016), Clim. Cha., 136, 297-308.

Landslide inventory

(European Alps)

• 7919 landslides • 656 rockfalls with known • Location • Date of occurrence • 1970-2002

• Primarily located in French Alps

Wood et al. (2015). Geomorph., 228, 398-408.

• Brier Skill Score results (rockfall) Rank COST733 classification (name) 1 2 3 4 5 95 236 270 1658 1813

RAC27_SE_S01_SP.Z5.Y5.K5_D07 CKMo27_SE_S01_Z1.Z5_D06 CKM27_SE_S01_SP.K5_D06 CKM27_SE_S01_SP_D06 CAP27_SE_S01_SP_D06 GWT27_YR_S01_SP_D06 PERo27_YR_S01_SP_D00 SUEo27_YR_S01_SP_D00 OGWo27_YR_S01_SP_D00 PECo09_YR_S01_SP_D00


• Brier Skill Score results (rockfall) Rank COST733 classification (name) 1 2 3 4 5 95 236 270 1658 1813

RAC27_SE_S01_SP.Z5.Y5.K5_D07 CKMo27_SE_S01_Z1.Z5_D06 CKM27_SE_S01_SP.K5_D06 CKM27_SE_S01_SP_D06 CAP27_SE_S01_SP_D06 GWT27_YR_S01_SP_D06 PERo27_YR_S01_SP_D00 SUEo27_YR_S01_SP_D00 OGWo27_YR_S01_SP_D00 PECo09_YR_S01_SP_D00


COST733 classification (name) Classification method (Wood et al., 2015 inventory) CKM28_SE_S01_SP.K5_D06 CKMeans CKM28_SE_S01_SP_D06 CKMeans CAP28_SE_S01_SP_D06 CKMeans CAP28_SE_S01_SP.K5_D06 CKMeans SAN28_SE_S01_SP.Z5.Y5.K5_D06 CKMeans GrossWetter Perret Schüepp Objective GrossWetter Péczely

Weather BSS types (n) 0.029 28 0.029 28 0.028 28 0.028 28 0.028 28 27 27 27 27 9

BSS 0.021 0.021 0.020 0.020 0.020 0.016 0.013 0.013 0.008 0.007

RfD

LRfD

?

Landslide inventory

(UK, BGS)

• 314 landslides • 121 between 1957 and 2002 • Location • Date of occurrence • 1970-2002

Personal comm. Freeborough (2018); Wood et al. (2015). Geomorph., 228, 398-408;Taylor et al. (2015). Geomorph., 249, 52-68.

• Brier Skill Score results Rank COST733 classification (name) 22 41 55 56 87 960 983 1211 2031 2067 na

CKMo28_SE_S01_Z1.Z5_D04 CKM28_SE_S01_SP.K5_D04 ERP28_SE_S01_SP.Z5_D04 ERP28_SE_S01_SP.K5_D04 CKM28_SE_S01_SP.Y5_D04 LWTo27_YR_S01_SP_D04 LWTo27_YR_S01_SP_D09 LWTo18_YR_S01_SP_D09 LWTo27_YR_S01_SP_D11 LWTo27_YR_S01_SP_D07` Lamb weather classification (CRU data)

D04

Brier (1950). Mon.Weather Rev., 78, 1-3; CRU (2018). LWT data. https://crudata.uea.ac.uk/cru/data/lwt/; Jones et al. (2013). Int. Jou. Clim., 33, 1129-1139.

• Brier Skill Score results Rank COST733 classification (name) 22 41 55 56 87 960 983 1211 2031 2067 na

CKMo28_SE_S01_Z1.Z5_D04 CKM28_SE_S01_SP.K5_D04 ERP28_SE_S01_SP.Z5_D04 ERP28_SE_S01_SP.K5_D04 CKM28_SE_S01_SP.Y5_D04 LWTo27_YR_S01_SP_D04 LWTo27_YR_S01_SP_D09 LWTo18_YR_S01_SP_D09 LWTo27_YR_S01_SP_D11 LWTo27_YR_S01_SP_D07` Lamb weather classification (CRU data)

COST733 classification (name) Weather types BSS Classification method (Wood et al., 2015 inventory) (n) CKM28_SE_S01_SP.K5_D06 CKMeans 28 0.029 CKM28_SE_S01_SP_D06 CKMeans 28 0.029 CAP28_SE_S01_SP_D06 ERPicum 28 0.028 ERPicum 28 0.028 CAP28_SE_S01_SP.K5_D06 CKMeans 28 0.028 SAN28_SE_S01_SP.Z5.Y5.K5_D06 Objective 27 Objective 27 Objective 18 Objective 27 Objective 27 Objective 26

Brier (1950). Mon.Weather Rev., 78, 1-3; CRU (2018). LWT data. https://crudata.uea.ac.uk/cru/data/lwt/; Jones et al. (2013). Int. Jou. Clim., 33, 1129-1139.

BSS 0.007 0.006 0.006 0.006 0.006 0.003 0.003 0.003 0.002 0.002 0.002

LsD

type #06

type #22

Conclusions

Wood et al. (2015)

• Can synoptic weather patterns be used to understand landslide frequency and occurrence in a topographically diverse region such as the European Alps? YES • Caveat: • Must include substantially complete inventory

• Provides the potential for future forecasts under a changing climate • Next steps: • Continued analysis with rockfalls in the Alps • Verify applicability to other areas • Working with researchers from project Wood et al. (2015). Geomorph., 228, 398-408.

BGS (2018)

Thanks for listening Brier (1950). Verification of forecasts expressed in terms of probability. Monthly Weather Review, 78, 1-3. Boé & Terray (2008). A weather-type approach to analysing winter precipitation in France: twentiethcentury trends and the role of anthropogenic forcing. Journal of Climate, 21, 3118-3133. Conway & Jones (1998). The use of weather types and air flow indices for GCM downscaling. Journal of Hydrology, 212-213, 348–361. COST733 (2013). Cost733cat-2.0 centroid plots [Online] http://cost733.geo.uniaugsburg.de/cgi/cost733plot.cgi

Lopez Saez et al. (2013). Climate change increases frequency of shallow spring landslides in the French Alps. Geology, 41(5), 619-622. Nikolopoulos et al. (2015). Debris flows in the Eastern Italian Alps: seasonality and atmospheric circulation patterns. Natural Hazards and Earth System Sciences, 15, 647-656. Paredes et al. (2006). Understanding precipitation changes in Iberia in Early Spring: Weather Typing and Storm-Tracking Approaches. Journal of Hydrometeorology, 7, 101-113. Perry & Mayes (1998). The Lamb weather type catalogue. Weather, 53(7), 222-229.

Farahmand & AghaKouchak (2013). A satellite-based global landslide model. Natural Hazards and Earth System Sciences, 13, 1259-1267.

Philipp et al. (2010). Cost733cat – A database of weather and circulation type classifications. Physics and Chemistry of the Earth, 35, 360-373.

Gariano et al. (2015). Calibration and validation of rainfall thresholds for shallow landslide forecasting in Sicily, southern Italy. Geomorphology 228, 653-665.

Philipp et al. (2014). Cost733class-1.2 User guide [Online] http://cost733.geo.uniaugsburg.de/download/cost733class-1.2/cost733class_userguide.pdf

Isotta et al. (2014). The climate of daily precipitation in the Alps: development and analysis of a highresolution grid dataset from pan-alpine rain-gauge data. International Journal of Climatology, 34, 1657-1675.

Taylor et al. (2015). Enriching Great Britain's National Landslide Database by searching newspaper archives. Geomorphology, 249, 52-68.

Jakob & Weatherly (2003). A hydroclimatic threshold for landslide initiation on the North Shore Mountains of Vancouver, British Columbia. Geomorphology, 54, 137-156.

Uppala et al., (2005). The ERA-40 re-analysis. Quaterly Jounal of the Royal Meteorological Society, 131, 29613012.

James (2006). Second generation lamb weather types – a new generic classification with evenly tempered type frequencies. In: 6th Annual Meeting of the EMS/6th ECAC, EMS2006A00441, Ljubljana, Slovenia.

Wood et al. (2015). Landslide inventories for climate impacts research in the European Alps. Geomorphology, 228, 398-408.

Jones et al. (2013). Lamb weather types derived from reanalysis products. International Journal of Climatology, Wood et al. (2016). Landslides and synoptic weather trends in the European Alps. Climatic Change, 136, 29733, 1129-1139. 308.

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