scholarly journals Developing Real-Time Nowcasting System for Regional Landslide Hazard Assessment under Extreme Rainfall Events

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 732
Author(s):  
Yuan-Chang Deng ◽  
Jin-Hung Hwang ◽  
Yu-Da Lyu
Keyword(s):  
Real Time ◽  
Hazard Assessment ◽  
Landslide Susceptibility ◽  
Extreme Rainfall ◽  
Landslide Hazard ◽  
Optimization Techniques ◽  
Disaster Prevention ◽  
Landslide Hazard Assessment ◽  
Landslide Event ◽  
Real Coded Genetic Algorithm

In this research, a real-time nowcasting system for regional landslide-hazard assessment under extreme-rainfall conditions was established by integrating a real-time rainfall data retrieving system, a landslide-susceptibility analysis program (TRISHAL), and a real-time display system to show the stability of regional slopes in real time and provide an alert index under rainstorm conditions for disaster prevention and mitigation. The regional hydrogeological parameters were calibrated using a reverse-optimization analysis based on an RGA (Real-coded Genetic Algorithm) of the optimization techniques and an improved version of the TRIGRS (Transient Rainfall Infiltration and Grid-based Regional Slope-Stability) model. The 2009 landslide event in the Xiaolin area of Taiwan, associated with Typhoon Morakot, was used to test the real-time regional landslide-susceptibility system. The system-testing results showed that the system configuration was feasible for practical applications concerning disaster prevention and mitigation.

Global Landslide Hazard Assessment for Situational Awareness (LHASA) Version 2: New Activities and Future Plans

2020 ◽  
Author(s):  
Dalia Kirschbaum ◽  
Thomas Stanley ◽  
Robert Emberson ◽  
Pukar Amatya ◽  
Sana Khan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Hazard Assessment ◽  
Situational Awareness ◽  
Landslide Hazard ◽  
Susceptibility Map ◽  
Landslide Hazard Assessment ◽  
Landslide Activity ◽  
Burned Areas ◽  
Version 2.0

<p>A remote sensing-based system has been developed to characterize the potential for rainfall-triggered landslides across the globe in near real-time. The Landslide Hazard Assessment for Situational Awareness (LHASA) model uses a decision tree framework to combine a static susceptibility map derived from information on slope, rock characteristics, forest loss, distance to fault zones and distance to road networks with satellite precipitation estimates from the Global Precipitation Measurement (GPM) mission. Since 2016, the LHASA model has been providing near real-time and retrospective estimates of potential landslide activity. Results of this work are available at https://landslides.nasa.gov.</p><p>In order to advance LHASA’s capabilities to characterize landslide hazards and impacts dynamically, we have implemented a new approach that leverages machine learning, new parameters, and new inventories. LHASA 2.0 uses the XGBoost machine learning model to bring in dynamic variables as well as additional static variables to better represent landslide hazard globally. Global rainfall forecasts are also being evaluated to provide a 1-3 day forecast of potential landslide activity. Additional factors such as recent seismicity and burned areas are also being considered to represent the preconditioning or changing interactions with subsequent rainfall over affected areas. A series of parameters are being tested within this structure using NASA’s Global Landslide Catalog as well as many other event-based and multi-temporal inventories mapped by the project team or provided by project partners.</p><p>In addition to estimates of landslide hazard, LHASA Version 2 will incorporate dynamic estimates of exposure including population, roads and infrastructure to highlight the potential impacts that rainfall-triggered landslides. The ultimate goal of LHASA Version 2.0 is to approximate the relative probabilities of landslide hazard and exposure across different space and time scales to inform hazard assessment retrospectively over the past 20 years, in near real-time, and in the future. In addition to the hazard. This presentation will outline the new activities for LHASA Version 2.0 and present some next steps for this system.</p>

scholarly journals A dynamic landslide hazard assessment system for Central America and Hispaniola

2015 ◽  
Vol 15 (10) ◽  
pp. 2257-2272 ◽  
Author(s):  
D. B. Kirschbaum ◽  
T. Stanley ◽  
J. Simmons
Keyword(s):  
Decision Tree ◽  
Real Time ◽  
Central America ◽  
Hazard Assessment ◽  
Situational Awareness ◽  
Landslide Hazard ◽  
Antecedent Rainfall ◽  
Landslide Hazard Assessment ◽  
Flexible Framework ◽  
The Caribbean

Abstract. Landslides pose a serious threat to life and property in Central America and the Caribbean Islands. In order to allow regionally coordinated situational awareness and disaster response, an online decision support system was created. At its core is a new flexible framework for evaluating potential landslide activity in near real time: Landslide Hazard Assessment for Situational Awareness. This framework was implemented in Central America and the Caribbean by integrating a regional susceptibility map and satellite-based rainfall estimates into a binary decision tree, considering both daily and antecedent rainfall. Using a regionally distributed, percentile-based threshold approach, the model outputs a pixel-by-pixel nowcast in near real time at a resolution of 30 arcsec to identify areas of moderate and high landslide hazard. The daily and antecedent rainfall thresholds in the model are calibrated using a subset of the Global Landslide Catalog in Central America available for 2007–2013. The model was then evaluated with data for 2014. Results suggest reasonable model skill over Central America and poorer performance over Hispaniola due primarily to the limited availability of calibration and validation data. The landslide model framework presented here demonstrates the capability to utilize globally available satellite products for regional landslide hazard assessment. It also provides a flexible framework to interchange the individual model components and adjust or calibrate thresholds based on access to new data and calibration sources. The availability of free satellite-based near real-time rainfall data allows the creation of similar models for any study area with a spatiotemporal record of landslide events. This method may also incorporate other hydrological or atmospheric variables such as numerical weather forecasts or satellite-based soil moisture estimates within this decision tree approach for improved hazard analysis.

A new approach to temporal modelling for landslide hazard assessment using an extreme rainfall induced-landslide index

2016 ◽  
Vol 215 ◽  
pp. 36-49 ◽  
Author(s):  
Nikhil Nedumpallile Vasu ◽  
Seung-Rae Lee ◽  
Ananta Man Singh Pradhan ◽  
Yun-Tae Kim ◽  
Sin- Hang Kang ◽  
...  
Keyword(s):  
Hazard Assessment ◽  
Extreme Rainfall ◽  
Landslide Hazard ◽  
New Approach ◽  
Landslide Hazard Assessment ◽  
Temporal Modelling

scholarly journals Regional landslide hazard assessment through integrating susceptibility index and rainfall process

2020 ◽  
Vol 104 (3) ◽  
pp. 2153-2173
Author(s):  
Zhiheng Wang ◽  
Dongchuan Wang ◽  
Qiaozhen Guo ◽  
Daikun Wang
Keyword(s):  
Environmental Factors ◽  
Hazard Assessment ◽  
Landslide Susceptibility ◽  
Probability Model ◽  
Regional Scale ◽  
Landslide Hazard ◽  
Landslide Hazard Assessment ◽  
Susceptibility Index ◽  
Rainfall Process ◽  
Process Factors

Abstract Due to the difference of the spatial and temporal distribution of rainfall and the complex diversity of the disaster-prone environment (topography, geological, fault, and lithology), it is difficult to assess the hazard of landslides at the regional scale quantitatively only considering rainfall condition. Based on detailed landslide inventory and rainfall data in the hilly area in Sichuan province, this study analyzed the effects of both rainfall process and environmental factors on the occurrence of landslides. Through analyzing environmental factors, a landslide susceptibility index (LSI) was calculated using multiple layer perceptron (MLP) model to reflect the regional landslide susceptibility. Further, the characteristics of rainfall process and landslides were examined quantitatively with statistical analysis. Finally, a probability model integrating LSI and rainfall process was constructed using logistical regression analysis to assess the landslide hazard. Validation showed satisfactory results, and the inclusion of LSI effectively improved the accuracy of the landslide hazard assessment: Compared with only considering the rainfall process factors, the accuracy of the landslide prediction model both considering the rainfall process and landslide susceptibility is improved by 3%. These results indicate that an integration of susceptibility index and rainfall process is essential in improving the timeliness and accuracy of regional landslide early warning.

Using satellite soil moisture and rainfall in the Landslide Hazard Assessment for Situational Awareness system

2021 ◽  
Author(s):  
Thomas Stanley ◽  
Dalia Kirschbaum ◽  
Robert Emberson
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Real Time ◽  
Hazard Assessment ◽  
Situational Awareness ◽  
Landslide Hazard ◽  
Antecedent Soil Moisture ◽  
Landslide Hazard Assessment ◽  
Landslide Occurrence ◽  
Awareness System

<p>The Landslide Hazard Assessment for Situational Awareness system (LHASA) gives a global view of landslide hazard in nearly real time. Currently, it is being upgraded from version 1 to version 2, which entails improvements along several dimensions. These include the incorporation of new predictors, machine learning, and new event-based landslide inventories. As a result, LHASA version 2 substantially improves on the prior performance and introduces a probabilistic element to the global landslide nowcast.</p><p>Data from the soil moisture active-passive (SMAP) satellite has been assimilated into a globally consistent data product with a latency less than 3 days, known as SMAP Level 4. In LHASA, these data represent the antecedent conditions prior to landslide-triggering rainfall. In some cases, soil moisture may have accumulated over a period of many months. The model behind SMAP Level 4 also estimates the amount of snow on the ground, which is an important factor in some landslide events. LHASA also incorporates this information as an antecedent condition that modulates the response to rainfall. Slope, lithology, and active faults were also used as predictor variables. These factors can have a strong influence on where landslides initiate. LHASA relies on precipitation estimates from the Global Precipitation Measurement mission to identify the locations where landslides are most probable. The low latency and consistent global coverage of these data make them ideal for real-time applications at continental to global scales. LHASA relies primarily on rainfall from the last 24 hours to spot hazardous sites, which is rescaled by the local 99<sup>th</sup> percentile rainfall. However, the multi-day latency of SMAP requires the use of a 2-day antecedent rainfall variable to represent the accumulation of rain between the antecedent soil moisture and current rainfall.</p><p>LHASA merges these predictors with XGBoost, a commonly used machine-learning tool, relying on historical landslide inventories to develop the relationship between landslide occurrence and various risk factors. The resulting model relies heavily on current daily rainfall, but other factors also play an important role. LHASA outputs the probability of landslide occurrence on a grid of roughly one kilometer over all continents from 60 North to 60 South latitude. Evaluation over the period 2019-2020 shows that LHASA version 2 doubles the accuracy of the global landslide nowcast without increasing the global false alarm rate.</p><p>LHASA also identifies the areas where the human exposure to landslide hazard is most intense. Landslide hazard is divided into 4 levels: minimal, low, moderate, and high. Next, the number of persons and the length of major roads (primary and secondary roads) within each of these areas is calculated for every second-level administrative district (county). These results can be viewed through a web portal hosted at the Goddard Space Flight Center. In addition, users can download daily hazard and exposure data.</p><p>LHASA version 2 uses machine learning and satellite data to identify areas of probable landslide hazard within hours of heavy rainfall. Its global maps are significantly more accurate, and it now includes rapid estimates of exposed populations and infrastructure. In addition, a forecast mode will be implemented soon.</p>

scholarly journals A dynamic landslide hazard assessment system for Central America and Hispaniola

2015 ◽  
Vol 3 (4) ◽  
pp. 2847-2882 ◽  
Author(s):  
D. B. Kirschbaum ◽  
T. Stanley ◽  
J. Simmons
Keyword(s):  
Decision Tree ◽  
Real Time ◽  
Central America ◽  
Hazard Assessment ◽  
Situational Awareness ◽  
Landslide Hazard ◽  
Antecedent Rainfall ◽  
Landslide Hazard Assessment ◽  
Flexible Framework ◽  
The Caribbean

Abstract. Landslides pose a serious threat to life and property in Central America and the Caribbean Islands. In order to allow regionally coordinated situational awareness and disaster response, an online decision support system was created. At its core is a new flexible framework for evaluating potential landslide activity in near real-time: Landslide Hazard Assessment for Situational Awareness. This framework was implemented in Central America and the Caribbean by integrating a regional susceptibility map and satellite-based rainfall estimates into a binary decision tree, considering both daily and antecedent rainfall. Using a regionally distributed, percentile-based threshold approach, the model outputs a pixel-by-pixel nowcast in near real-time at a resolution of 30 arcsec to identify areas of moderate and high landslide hazard. The daily and antecedent rainfall thresholds in the model are calibrated using a subset of the Global Landslide Catalog in Central America available for 2007–2013. The model was then evaluated with data for 2014. Results suggest reasonable model skill over Central America and poorer performance over Hispaniola, due primarily to the limited availability of calibration and validation data. The landslide model framework presented here demonstrates the capability to utilize globally available satellite products for regional landslide hazard assessment. It also provides a flexible framework to interchange the indiviual model components and adjust or calibrate thresholds based on access to new data and calibration sources. The availability of free, satellite-based near real-time rainfall data allows the creation of similar models for any study area with a spatiotemporal record of landslide events. This method may also incorporate other hydrological or atmospheric variables such as numerical weather forecasts or satellite-based soil moisture estimates within this decision tree approach for improved hazard analysis.

Landslide Hazard and Exposure Modeling for Situational Awareness and Response in Rio de Janeiro

2021 ◽  
Author(s):  
Dalia Kirschbaum ◽  
Felipe Mandarino ◽  
Raquel Fonseca ◽  
Ricardo D'Orsi ◽  
Robert Emberson ◽  
...  
Keyword(s):  
Real Time ◽  
Landslide Susceptibility ◽  
Rio De Janeiro ◽  
Situational Awareness ◽  
Extreme Rainfall ◽  
Early Warning Systems ◽  
Landslide Hazard ◽  
Precipitation Forecast ◽  
Rainfall Thresholds ◽  
The City

<p>The city of Rio de Janeiro is situated within a coastal region with steep slopes, intense seasonal rainfall, and vulnerable populations located on marginal slopes. Landslides are a seasonal challenge within the city and proximate regions and increasing real-time awareness of the hazard and exposure is paramount to saving lives and mitigating damage. A local alerting system has been developed for the city that leverages a global landslide hazard assessment for situational awareness (LHASA) framework, developed by NASA, with local rainfall thresholds and landslide susceptibility information. The LHASA-Rio system uses a decision tree approach to first identify extreme rainfall based on a series of rainfall thresholds established by Geo-Rio (the City’s agency responsible for landslide hazards) for 1 hour, 1 day or 1 hour and 4 day thresholds. This is then coupled with information on landslide susceptibility also developed by the Geo-Rio team. The LHASA-Rio system has been running operationally since 2017 within the city to provide real-time, high resolution estimates of areas within the city at higher hazard at 15-minute intervals consistent with the rainfall gauge network distributed throughout the city. Results of the LHASA-Rio system indicate excellent performance for several case studies where extreme rainfall triggered landslides within the city over areas identified as high hazard zones by LHASA-Rio. The model has recently been updated to accommodate additional rainfall thresholds to differentiate moderate to very high and critical intensities. The modeling effort is also incorporating information on landslide exposure by connecting the hazard estimates to city-wide data on population, road networks and other infrastructure. The goal of this system is ultimately to provide key tools to emergency response teams, civil protection and other hazard monitoring organizations within Rio’s City Government in real-time and provide  actionable information for key communities, city management and planning. Future work of this system is the application of a regional precipitation forecast to improve the lead time.</p><p>This work has been done in partnership through an agreement established between NASA and the City of Rio de Janeiro in 2015 that was recently extended in 2020. This agreement seeks to support innovative efforts to better understand, anticipate, and monitor hazards and environmental issues, including heavy rainfall and landslides, urban flooding, air quality and water quality in and around the city. This collaboration leverages the unique attributes of NASA's satellite data and modeling frameworks and Rio de Janeiro's management and monitoring capabilities to improve awareness of how the city of Rio may be impacted by hazards and affected by climate change. If the success of this technology is demonstrated, other cities in the world with physiographic and socioeconomic characteristics similar to Rio de Janeiro may benefit by implementing, or strengthening, their own Early Warning Systems for landslides triggered by heavy rains using LHASA's open source algorithms and the experience gathered by the use of LHASA-Rio. This presentation highlights the achievements and advancements of the LHASA-Rio system and discusses lessons learned regarding the applications of the landslide modeling systems to advance decision-relevant science at the city level.</p>

Landslide Hazard Information System for Landslide Disaster Risk Financing: Earth Observation and Modelling Products for Near-Real-Time Assessment

2021 ◽  
Author(s):  
Clément Michoud ◽  
Jean-Philippe Malet ◽  
Dalia Kirschbaum ◽  
Thierry Oppikofer ◽  
Robert Emberson ◽  
...  
Keyword(s):  
Information System ◽  
Impact Assessment ◽  
Real Time ◽  
Hazard Assessment ◽  
Disaster Risk ◽  
Earth Observation ◽  
Landslide Hazard ◽  
Hazard Maps ◽  
Landslide Hazard Assessment ◽  
Disaster Risk Financing

<p>The frequency and impact of disasters rise at the global scale, calling for effective disaster risk management and innovative risk financing solutions. Disaster Risk Financing (DRF) can increase the ability of national and local governments, homeowners, businesses, agricultural producers, and low-income populations to respond more quickly and resiliently to disasters by strengthening public financial management and promoting market-based disaster risk financing. For landslide events, the usage of DRF products is not yet extensive, mainly due to challenges in capturing the appropriate destabilization factors and triggers, as well as forecasting the physical properties of a landslide event (such as its type, location, size, number of people affected, and/or exposed infrastructure). The availability and quality of satellite EO derived data on rainfall that triggers landslides (Global Precipitation Measurement mission / GPM) and observations of the landslides themselves (Copernicus Sentinel radar and multispectral sensors, very high resolution -VHR- optical sensors) greatly improved in recent years. In the same time, effective models are refined and support near-real time landslide hazard assessment (e.g. Landslide Hazard Assessment for Situational Awareness / LHASA; Flow path assessment of gravitational hazards at a Regional scale / FLOW-R).</p><p>The objective of this work is to present the prototype platform LANDSLIDE HAZARD INFORMATION SYSTEM (LHIS) which aims to support landslide DRF priorities using Earth Observation data and models. The functions of the platform are to be able to anticipate, forecast and respond to incipient landslide events (in Near-Real Time, NRT) by providing estimates of parameters suitable for parametric insurance calculations, including landslide inventories, susceptibility and hazard maps, potential damages and costs analyses. The LHIS prototype is accessible on the GEP / Geohazards Exploitation Platform allowing easy access, processing and visualization of EO-derived products. The prototype consists of three modular components with respectively: 1) a Landslide Detection component to create Landslide Inventories, 2) a Landslide Hazard Assessment component using global and national geospatial datasets leading to Landslide Susceptibility Maps, Scenario-based Hazard Maps and NRT Rainfall-based Hazard Maps, and 3) Landslide Impact Assessment component combining landslide hazard maps with population and infrastructure datasets to derive Landslide Exposure Maps and Landslide Impact Index. The landslide detection module is based on the analysis of time series of optical and SAR data; the landslide hazard and impact assessment modules are based on the LHASA, FLOW-R and PDI models.</p><p>The information system is being developed and tested in Morocco in collaboration with the solidarity fund against catastrophic events (FSEC) and the World Bank for two contrasting use cases in the Rif area (North Morocco) and the Safi area (Central Morocco) exposed to various landslide situations occurring in different environmental and climatic contexts.</p>

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