Invited perspectives: Integrating hydrologic information into the next generation of landslide early warning systems

Natural hazards and earth system sciences, Год журнала: 2025, Номер 25(1), С. 169 - 182

Опубликована: Янв. 7, 2025

Abstract. Although rainfall-triggered landslides are initiated by subsurface hydro-mechanical processes related to the loading, weakening, and eventual failure of slope materials, most landslide early warning systems (LEWSs) have relied solely on rainfall event information. In previous decades, several studies demonstrated value integrating proxies for hydrologic information improve rainfall-based forecasting shallow landslides. More recently, broader access commercial sensors telemetry real-time data transmission has invigorated new research into hydrometeorological thresholds LEWSs. Given increasing number across globe using monitoring, mathematical modeling, or both in combination, it is now possible make some insights advantages versus limitations this approach. The extensive progress demonstrates situ reducing failed false alarms through ability characterize infiltration during – as well drainage drying between major storm events. There also areas caution surrounding long-term sustainability monitoring landslide-prone terrain, unresolved questions hillslope which relies heavily assumptions diffuse flow vertical but often ignores preferential lateral drainage. Here, we share a collective perspective based our collaborative work Europe, North America, Africa, Asia discuss these challenges provide guidelines knowledge hydrology climate next generation We propose that greatest opportunity improvement measure-and-model approach develop an understanding hydro-climatology accounts local controls storage dynamics. Additionally, efforts focused complementary existing methods, so leveraging with near-term precipitation forecasts priority lead times.

Язык: Английский

---

Towards a holistic assessment of landslide susceptibility models: insights from the Central Eastern Alps

Environmental Earth Sciences, Год журнала: 2025, Номер 84(4)

Опубликована: Фев. 1, 2025

Язык: Английский

---

Derivation of characteristic physioclimatic regions through density-based spatial clustering of high-dimensional data

Environmental Modelling & Software, Год журнала: 2025, Номер unknown, С. 106324 - 106324

Опубликована: Янв. 1, 2025

Язык: Английский

---

First Pockmark Susceptibility map of the Italian continental margins

Marine and Petroleum Geology, Год журнала: 2025, Номер unknown, С. 107337 - 107337

Опубликована: Фев. 1, 2025

Язык: Английский

---

Towards physics-informed neural networks for landslide prediction

Engineering Geology, Год журнала: 2024, Номер unknown, С. 107852 - 107852

Опубликована: Дек. 1, 2024

Язык: Английский

---

Ground instability effects

Elsevier eBooks, Год журнала: 2025, Номер unknown, С. 47 - 75

Опубликована: Янв. 1, 2025

Язык: Английский

---

Pan‐European Landslide Risk Assessment: From Theory to Practice

Reviews of Geophysics, Год журнала: 2025, Номер 63(1)

Опубликована: Фев. 21, 2025

Abstract Assessing landslide risk is a fundamental requirement to plan suitable prevention actions. To date, most studies focus on individual slopes or catchments. Whereas regional, national continental scale assessments are hardly available because of methodological and/or data limitations. In this contribution, we present an overview all requirements and limitations in across spatial scales, by means hybrid form that combines elements original research with the comprehensive characteristics review study. The critically analyses each component analysis providing detailed explanation their state‐of‐the‐art, dedicated sections susceptibility, hazard, exposure, vulnerability. put theoretical framework test, also dive into case study, expressed at scale. Specifically, take main European mountain ranges provide reader textbook example assessment for such large territory. doing so, account issues associated cross‐national differences mapping. As result, identify landslide‐prone landscape explore possible economic consequences (human settlements agricultural areas). We analyze population during daytime nighttime. Moreover, modern view problem explored how outcomes should be delivered master planners geoscientific personnel alike. convert our output interactive Web Application ( https://pan‐european‐landslide‐risk.github.io/ ) include notions scientific communication both public as well technical audience.

Язык: Английский

---

Enhanced Landslide Risk Evaluation in Hydroelectric Reservoir Zones Utilizing an Improved Random Forest Approach

Water, Год журнала: 2025, Номер 17(7), С. 946 - 946

Опубликована: Март 25, 2025

Landslides on reservoir slopes are one of the key geologic hazards that threaten safe operation hydropower plants. The aim our study was to reduce limitations existing methods landslide risk assessment when dealing with complex nonlinear relationships and difficulty quantifying uncertainty predictions. We established a multidimensional system covers geological settings, meteorological conditions, ecological environment, we proposed model integrates Bayesian theory random forest algorithm. In addition, quantifies through probability distributions provides confidence intervals for prediction results, thus significantly improving usefulness reliability assessment. this study, adopted Gini index SHAP (SHapley Additive exPlanations) value, an analytical methodology, reveal factors affecting slope stability their interaction. empirical results obtained show effectively identifies also accurate risk, enhancing scientific targeted decision making. This offers strong support managing providing more solid guarantee station sites.

Язык: Английский

---

Functional Regression for Space‐Time Prediction of Precipitation‐Induced Shallow Landslides in South Tyrol, Italy

Journal of Geophysical Research Earth Surface, Год журнала: 2025, Номер 130(4)

Опубликована: Апрель 1, 2025

Abstract Landslides are geomorphic hazards in mountainous terrains across the globe, driven by a complex interplay of static and dynamic controls. Data‐driven approaches have been employed to assess landslide occurrence at regional scales analyzing spatial aspects time‐varying conditions separately. However, joint assessment landslides space time remains challenging. This study aims predict precipitation‐induced shallow within Italian province South Tyrol (7,400 km 2 ). We introduce functional predictor framework where precipitation is represented as continuous series, contrast conventional that treat scalar predictor. Using hourly data past occurrences from 2012 2021, we implemented generalized additive model derive statistical relationships between occurrence, various factors, preceding evaluated resulting predictions through several cross‐validation routines, yielding performance scores frequently exceeding 0.90. To demonstrate predictive capabilities, performed hindcast for storm event Passeier Valley on 4–5 August 2016, capturing observed locations illustrating evolution predicted probabilities. Compared standard early warning approaches, this eliminates need predefine fixed windows aggregation while inherently accounting lagged effects. By integrating controls, research advances prediction large areas, addressing seasonal effects underlying limitations.

Язык: Английский

---

Brief communication: Visualizing uncertainties in landslide susceptibility modelling using bivariate mapping

Natural hazards and earth system sciences, Год журнала: 2025, Номер 25(4), С. 1425 - 1437

Опубликована: Апрель 14, 2025

Abstract. Effectively communicating uncertainties inherent to statistical models is a challenging yet crucial aspect of the modelling process. This particularly important in applied research, where output used and interpreted by scientists decision-makers alike. In disaster risk reduction, susceptibility maps for natural hazards are vital spatial planning assessment. We present novel type landslide map that jointly visualizes estimated corresponding prediction uncertainty, using an example from mountainous region Carinthia, Austria. also provide implementation guidelines create such popular free open-source software packages.

Язык: Английский

---