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.

Indigenous Peoples (IPs) are believed to be at particularly high risk from COVID, exacerbated by climate risks and socio-economic stresses. There is emerging evidence that national responses to the pandemic are compounding the vulnerability of IPs, exacerbated by little--if any--understanding on the unique pathways through which COVID will affect IPs. This project will address this knowledge and policy gap by documenting, monitoring, and examining how COVID is interacting with multiple stresses to affect the food systems of IPs globally, co-generating knowledge and capacity to strengthen resilience. Our focus on food reflects the fact that many of the risks posed by COVID stem from interactions with food systems, which for IPs are composed of a mix of traditional and modern elements. The work will be undertaken in collaboration with 24 distinct Indigenous peoples in 14 countries, and is structured around objectives which will: document the emergence of COVID and examine its impacts on food systems to-date; monitor and examine the real-time lived experiences, responses, and observations on COVIDs impact on food systems; compile and assess how COVID is being officially communicated and responded to; identify, examine, and promote interventions to strengthen resilience; and examine scalable insights for vulnerable populations across LMICs. Qualitative data collection is underpinned by a network of 'COVID Observers' within communities, in decision making roles, and researchers already located in the study regions, who will document their experiences and observations in reflective diaries over a 12 month period, capturing different stages of the pandemic and how multiple factors interact over time to create vulnerability and resilience. The global scope of the work builds upon ongoing and completed projects by team members in the study regions, leveraging considerable capacity and networks developed in work funded by DFID, UKRI, Wellcome Trust, FAO, and IDRC, among others.