Deciphering seasonal effects of triggering and preparatory precipitation for improved shallow landslide prediction using generalized additive mixed models

The increasing availability of long-term observational data can lead to the development of innovative modelling approaches to determine landslide triggering conditions at regional scale, opening new avenues for landslide prediction and early warning. This research blends the strengths of existing approaches with the capabilities of generalized additive mixed models (GAMMs) to develop an interpretable approach that identifies seasonally dynamic precipitation conditions for shallow landslides. The model builds upon a 21-year record of landslides in South Tyrol (Italy) and separates precipitation that induced landslides from precipitation that did not. The model accounts for effects acting at four temporal scales: short-term &ldquo;triggering&rdquo; precipitation, medium-term &ldquo;preparatory&rdquo; precipitation, seasonal effects and across-year data variability. It provides relative landslide probability scores that were used to establish seasonally dynamic thresholds with optimal performance in terms of hit and false alarm rates, as well as additional thresholds related to user-defined performance scores. The GAMM shows a high predictive performance and indicates that more precipitation is required to induce a landslide in summer than in winter/spring, which can presumably be attributed mainly to vegetation and temperature effects. The discussion illustrates why the quality of input data, study design and model transparency are crucial for landslide prediction using advanced data-driven techniques.</p

Supporting agri-food projects to implement climate change adaptation through the interactive online tool ‘CRISP’

Introduction International agri-food system programmes are increasingly seeking to mainstream climate action across their portfolios. A range of methods and tools exists, but there is no “ready-to-use” tool that allows a cost- and time-effective climate risk assessment for specific agri-food systems and the development of adaptation hypotheses. The Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ), with Eurac Research and the Alliance of Bioversity International and the International Centre for Tropical Agriculture (CIAT), set out to provide an easy-to-use tool that considers the specific characteristics of agri-food systems under a changing climate. Objectives The Climate Risk Planning & Managing Tool for development programmes in agri-food systems (CRISP) is a web-based tool for projects planners and implementers in the agri-food sector. It allows them to identify starting points for climate risk management and develop adaptation hypotheses to backstop their intervention’s design – in a quick and easy way. Methodology Using the impact chain methodology as a framework, we undertook a literature search to identify relevant climate risks in the context of selected agro-ecological systems across five regions. We organised the findings into an extensive knowledge database. We then co-designed a tool with potential users that would allow the database to be queried in different ways depending on the user needs. Findings Potential users of the tool see promise in using it to improve their programming in the agri-food sector. They suggest expanding the knowledge database to include more agro-ecological systems, value chain concepts and national policy-related data. Significance of the work for policy and practice The CRISP tool will help users to identify starting points for climate risk management. The tool provides science-based evidence and linkages to complementary tools and approaches to implement climate actions. It will assist practitioners in the agri-food sector to develop adaptation hypotheses to help guide the project from the planning phase onward

Earth observation-based disaggregation of exposure data for earthquake loss modelling

We use TanDEM-X and Sentinel-2 observations to disaggregate earthquake risk-related exposure data. We use the refined exposure data and model earthquake loss. Results for the city of Santiago de Chile show that earthquake risk has been underestimated before due to aggregated exposure data

Earth observation-based disaggregation of exposure data for earthquake loss modeling

We use TanDEM-X and Sentinel-2 observations to disaggregate earthquake risk-related exposure data. We use the refined exposure data and model earthquake loss. Results for the city of Santiago de Chile show that earthquake risk has been underestimated before due to aggregated exposure data

Benefits of global earth observation missions for disaggregation of exposure data and earthquake loss modeling: evidence from Santiago de Chile

Exposure is an essential component of risk models and describes elements that are endangered by a hazard and susceptible to damage. The associated vulnerability characterizes the likelihood of experiencing damage (which can translate into losses) at a certain level of hazard intensity. Frequently, the compilation of exposure information is the costliest component (in terms of time and labor) of risk assessment procedures. Existing models often describe exposure in an aggregated manner, e.g., by relying on statistical/census data for given administrative entities. Nowadays, earth observation techniques allow the collection of spatially continuous information for large geographic areas while enabling a high geometric and temporal resolution. Consequently, we exploit measurements from the earth observation missions TanDEM-X and Sentinel-2, which collect data on a global scale, to characterize the built environment in terms of constituting morphologic properties, namely built-up density and height. Subsequently, we use this information to constrain existing exposure data in a spatial disaggregation approach. Thereby, we establish dasymetric methods for disaggregation. The results are presented for the city of Santiago de Chile, which is prone to natural hazards such as earthquakes. We present loss estimations due to seismic ground shaking and corresponding sensitivity as a function of the resolution properties of the exposure data used in the model. The experimental results underline the benefits of deploying modern earth observation technologies for refined exposure mapping and related earthquake loss estimation with enhanced accuracy properties

Community Perception and Communication of Volcanic Risk from the Cotopaxi Volcano in Latacunga, Ecuador

he inhabitants of Latacunga living in the surrounding of the Cotopaxi volcano (Ecuador) are exposed to several hazards and related disasters. After the last 2015 volcanic eruption, it became evident once again how important it is for the exposed population to understand their own social, physical, and systemic vulnerability. Effective risk communication is essential before the occurrence of a volcanic crisis. This study integrates quantitative risk and semi-quantitative social risk perceptions, aiming for risk-informed communities. We present the use of the RIESGOS demonstrator for interactive exploration and visualisation of risk scenarios. The development of this demonstrator through an iterative process with the local experts and potential end-users increases both the quality of the technical tool as well as its practical applicability. Moreover, the community risk perception in a focused area was investigated through online and field surveys. Geo-located interviews are used to map the social perception of volcanic risk factors. Scenario-based outcomes from quantitative risk assessment obtained by the RIESGOS demonstrator are compared with the semi-quantitative risk perceptions. We have found that further efforts are required to provide the exposed communities with a better understanding of the concepts of hazard scenario and intensity