Modelling future landslide activity based on general circulation models

Dehn, Martin; Buma, Jelle

doi:10.1016/s0169-555x(99)00053-7

Cited by 62 publications

(38 citation statements)

References 14 publications

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“…The developed relationship has been found to be statistically significant at α=5% significance level using the t-test. These results are comparable with the results of previous studies on statistical monthly precipitation downscaling with more sophisticated methodologies (Dehn and Buma, 1999;Schoof and Pryor, 2001;Buishand et al, 2004;Tatli et al, 2004) and are better than the results obtained by Loukas et al (2008) with the same MLR statistical downscaling method (Eq. 3) for Lake Karla watershed (r=0.55 for development period, 1960-1990, and 0.57 for validation period, 1990-1993).…”

Section: Historical Periodsupporting

confidence: 89%

“…A lot of studies have proved that with a statistical or dynamical downscaling method, it could be explained a large proportion of the monthly observed precipitation for present climatic conditions (i.e. Dehn and Buma, 1999;Schoof and Pryor, 2001;Buishand et al, 2004;Tatli et al, 2004, Loukas et al, 2008. Future work is to test the developed methodology with the results of Regional Climate Models (PRUDENCE, ENSEM-BLES EU), which, for Central Eastern Greece underestimate annual precipitation by about 33±19% (Zanis et al, 2008).…”

Section: Discussionmentioning

confidence: 99%

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Evaluation of a statistical downscaling procedure for the estimation of climate change impacts on droughts

Vasiliades

Λουκάς

Patsonas

2009

Nat. Hazards Earth Syst. Sci.

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Abstract. Despite uncertainties in future climates, there is considerable evidence that there will be substantial impacts on the environment and human interests. Climate change will affect the hydrology of a region through changes in the timing, amount, and form of precipitation, evaporation and transpiration rates, and soil moisture, which in turn affect also the drought characteristics in a region. Droughts are long-term phenomena affecting large regions causing significant damages both in human lives and economic losses. The most widely used approach in regional climate impact studies is to combine the output of the General Circulation Models (GCMs) with an impact model. The outputs of Global Circulation Model CGCMa2 were applied for two socioeconomic scenarios, namely, SRES A2 and SRES B2 for the assessment of climate change impact on droughts. In this study, a statistical downscaling method has been applied for monthly precipitation. The methodology is based on multiple regression of GCM predictant variables with observed precipitation developed in an earlier paper (Loukas et al., 2008) and the application of a stochastic timeseries model for precipitation residuals simulation (white noise). The methodology was developed for historical period and validated against observed monthly precipitation for period 1990-2002 in Lake Karla watershed, Thessaly, Greece. The validation indicated the accuracy of the methodology and the uncertainties propagated by the downscaling procedure in the estimation of a meteorological drought index the Standardized Precipitation Index (SPI) at multiple timescales. Subsequently, monthly precipitation and SPI were estimated for two future periods 2020-2050 and 2070-2100. The results of the present study indicate the accuracy, reliability and uncertainty of the statistical downscaling method for the assessment of climate change on hydrological, agricultural and Correspondence to: A. Loukas (aloukas@civ.uth.gr) water resources droughts. Results show that climate change will have a major impact on droughts but the uncertainty introduced is quite large and is increasing as SPI timescale increases. Larger timescales of SPI, which, are used to monitor hydrological and water resources droughts, are more sensitive to climate change than smaller timescales, which, are used to monitor meteorological and agricultural droughts. Future drought predictions should be handled with caution and their uncertainty should always be evaluated as results demonstrate.

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Section: Historical Periodsupporting

confidence: 89%

Section: Discussionmentioning

confidence: 99%

Evaluation of a statistical downscaling procedure for the estimation of climate change impacts on droughts

Vasiliades

Λουκάς

Patsonas

2009

Nat. Hazards Earth Syst. Sci.

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“…Previous studies typically have analysed the impacts of uncertainty related to slope characteristics and future climate independently. For example, Dehn and Buma (1999), Collison et al (2000) and Ciabatta et al (2016) consider the impacts of climate change on slope stability, but ignore uncertainty around soil properties; while Rubio et al (2004) account for uncertainty introduced by slope hydrology and geotechnical properties, but ignore uncertainty relating to design storms. However, our results suggest that the failure to consider both sources of uncertainty simultaneously may lead to a significant underestimation of slope susceptibility to landslides under potential climate change.…”

Section: Can Deep Uncertainty In Future Rainfall Exceed Other Uncertamentioning

confidence: 99%

Dealing with deep uncertainties in landslide modelling for disaster risk reduction under climate change

Almeida

Holcombe

Pianosi

et al. 2017

Nat. Hazards Earth Syst. Sci.

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Abstract. Landslides have large negative economic and societal impacts, including loss of life and damage to infrastructure. Slope stability assessment is a vital tool for landslide risk management, but high levels of uncertainty often challenge its usefulness. Uncertainties are associated with the numerical model used to assess slope stability and its parameters, with the data characterizing the geometric, geotechnic and hydrologic properties of the slope, and with hazard triggers (e.g. rainfall). Uncertainties associated with many of these factors are also likely to be exacerbated further by future climatic and socio-economic changes, such as increased urbanization and resultant land use change. In this study, we illustrate how numerical models can be used to explore the uncertain factors that influence potential future landslide hazard using a bottom-up strategy. Specifically, we link the Combined Hydrology And Stability Model (CHASM) with sensitivity analysis and Classification And Regression Trees (CART) to identify critical thresholds in slope properties and climatic (rainfall) drivers that lead to slope failure. We apply our approach to a slope in the Caribbean, an area that is naturally susceptible to landslides due to a combination of high rainfall rates, steep slopes, and highly weathered residual soils. For this particular slope, we find that uncertainties regarding some slope properties (namely thickness and effective cohesion of topsoil) are as important as the uncertainties related to future rainfall conditions. Furthermore, we show that 89 % of the expected behaviour of the studied slope can be characterized based on only two variables -the ratio of topsoil thickness to cohesion and the ratio of rainfall intensity to duration.

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“…Landslides are triggered by a number of factors, including earthquakes (Keefer 2002;Malamud et al 2004;Meunier et al 2008), rainfall (Iverson 2000;Zêzere et al 2005;Guzzetti et al 2007;Keefer and Larsen 2007;Marques et al 2008), temperature change (Dehn and Buma 1999;Chemenda et al 2005), glacial recession and permafrost degradation (Dramis et al 1995;Stoffel et al 2014) and anthropogenic factors such as the removal of slope toes at road cuts (Barnard et al 2001). For landslides, the hydro-meteorological trigger is often rainfall (Jakob and Weatherly 2003;Farahmand and Aghakouchak 2013), and empirical rainfall thresholds are often used to define minimum triggering conditions for landslides (Peruccacci et al 2012); however these are often localized, and depend greatly on the quality of rainfall data (Gariano et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Landslides and synoptic weather trends in the European Alps

2016

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Landslides present a substantial geomorphological hazard in Alpine regions and there are expectations that climate change will alter their frequency and magnitude in the future. Understanding the spatial distribution and timing of landslides in the context of past change is therefore necessary if we are to assess their future behaviour. Using a regional landslide inventory for the European Alps we analyse the influence of weather types, specifically the COST733 database, on landslides. Monte Carlo permutation tests are used to assess which weather types are most likely associated with landslides. Weather types with high precipitation are consistent with more landslides, although there are also seasonal differences. Over the duration of the COST733 catalogue there has been a significant decrease in the number of days with weather types associated with low frequencies of landslides. During the spring and autumn months, the trend in observed landslide frequency and weather types are well matched. However while there is potential for weather typing to be used as a proxy for future landslide frequency, other external factors must be carefully considered.

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Modelling future landslide activity based on general circulation models

Cited by 62 publications

References 14 publications

Evaluation of a statistical downscaling procedure for the estimation of climate change impacts on droughts

Evaluation of a statistical downscaling procedure for the estimation of climate change impacts on droughts

Dealing with deep uncertainties in landslide modelling for disaster risk reduction under climate change

Landslides and synoptic weather trends in the European Alps

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