Landslide Multi-Hazard Risk Assessment, Preparedness and Early Warning in South Asia: Integrating Meteorology, Landscape and Society

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About 12.6% of Indian land mass is prone to landslides, with the Himalaya and Western Ghats regions particularly prone due to climate, geomorphology & geology. Rainfall and earthquakes are the main triggers of these landslides. Poor land management practices (e.g., deforestation, slash & burn cultivation, haphazard mining and heavy tilling in agriculture), coupled with increased development and poor settlement location have increased vulnerability of communities in these areas to landslides. The impact of landslides on people, business, culture and heritage can be considerable and wide-ranging, including fatalities, loss of agricultural land and infrastructure, and damage to ecosystems. To build resilience to landslides in these vulnerable communities (a key aim of SHEAR), a root and branch evaluation of human interactions with landslide prone environments, and improved knowledge of the 'physical' processes is required. Developing approaches to integrate weather, landscape and social-dynamic models is fundamental to building an effective hydrologically-controlled landslide early warning system (EWS). LANDSLIP will develop new insights by building on existing scientific research in India, the UK and Italy and using interdisciplinary methodologies and perspectives. Due to complex environmental conditions and triggering processes that cause landslides, the extent and variability of spatial & temporal scales means that landslides are inherently difficult to forecast and manage at site, slope, catchment and regional spatial scales and hourly to decadal temporal scales. LANDSLIP will address this by doing research to understand weather regimes (previously not done in S Asia) and rainfall characteristics that trigger landslides and geomorphological/geological control factors that can enhance landslide susceptibility. Knowledge of where and when historic landslides have occurred and under what environmental conditions, will also be collated and analysed, drawing on extensive consortium experience of developing and managing landslide inventories and impact libraries.An innovative challenge we address in LANDSLIP is how slope and site specific EWS inform wider catchment to national landslide EWS and how early warning information from medium-range forecasts supplement and enhance short-term (day to a week) forecasting approaches. A further innovative aspect of LANDSLIP is improving EWS effectiveness through integrating social dynamics information gathered from both 'Human' (i.e. social media) and physical sensors (remote sensing and pre-existing site-specific wireless networks deployed by AMRITA). LANDSLIP will develop ways of utilising these sources of information to supplement existing inventories and enhance EW information for decision makers. Our programme will operate in partnership with decision makers, in public and private sectors, academics and non-for profit agencies to achieve an overarching aim of contributing to better landslide risk assessment and early warning, in a multi-hazard framework in India, aiming to increase resilience and reduce loss. Tools and services, focussed on a web map interface, will be developed in conjunction with local scientists, decision makers and communities to improve resilience to hydrologically-controlled landslides in India, specifically using two pilot study areas; Darjeeling-East Sikkim in the Himalaya and Nilgiris in the Western Ghats. We will ensure knowledge transfer to other vulnerable communities by assessing how they can be applied, remotely, in Afghanistan.Through advances in interdisciplinary science and application in practise, the collective ambition of this consortium is to contribute to better landslide risk assessment and early warning in a multi-hazard framework, and, by working with communities, better preparedness for hydrologically controlled landslides and related hazards on a slope to regional spatial scale and daily to seasonal temporal scale.