Accounting for satellite rainfall uncertainty in rainfall-triggered landslide forecasting

Geomorphology ◽  
2021 ◽  
pp. 108051
Author(s):  
Binru Zhao ◽  
Qiang Dai ◽  
Lu Zhuo ◽  
Jingqiao Mao ◽  
Shaonan Zhu ◽  
...  
Keyword(s):  
Landslide Forecasting ◽  
Satellite Rainfall

scholarly journals How far are we from the use of satellite rainfall products in landslide forecasting?

2018 ◽  
Vol 210 ◽  
pp. 65-75 ◽  
Author(s):  
M.T. Brunetti ◽  
M. Melillo ◽  
S. Peruccacci ◽  
L. Ciabatta ◽  
L. Brocca
Keyword(s):  
Landslide Forecasting ◽  
Satellite Rainfall

scholarly journals Satellite rainfall products outperform ground observations for landslide forecasting in India

2021 ◽  
Author(s):  
Maria Teresa Brunetti ◽  
Massimo Melillo ◽  
Stefano Luigi Gariano ◽  
Luca Ciabatta ◽  
Luca Brocca ◽  
...  
Keyword(s):  
Early Warning ◽  
Daily Rainfall ◽  
Early Warning Systems ◽  
Rain Gauge ◽  
Warning Systems ◽  
Landslide Occurrence ◽  
Landslide Forecasting ◽  
Satellite Rainfall ◽  
Global Precipitation ◽  
The Impact

Abstract. Landslides are among the most dangerous natural hazards, particularly in developing countries where ground observations for operative early warning systems are lacking. In these areas, remote sensing can represent an important tool to forecast landslide occurrence in space and time, particularly satellite rainfall products that have improved in terms of accuracy and resolution in recent times. Surprisingly, only a few studies have investigated the capability and effectiveness of these products in landslide forecasting, to reduce the impact of this hazard on the population. We have performed a comparative study of ground- and satellite-based rainfall products for landslide forecasting in India by using empirical rainfall thresholds derived from the analysis of historical landslide events. Specifically, we have tested Global Precipitation Measurement (GPM) and SM2RAIN-ASCAT satellite rainfall products, and their merging, at daily and hourly temporal resolution, and Indian Meteorological Department (IMD) daily rain gauge observations. A catalogue of 197 rainfall-induced landslides occurred throughout India in the 13-year period between April 2007 and October 2019 has been used. Results indicate that satellite rainfall products outperform ground observations thanks to their better spatial (10 km vs 25 km) and temporal (hourly vs daily) resolution. The better performance is obtained through the merged GPM and SM2RAIN-ASCAT products, even though improvements in reproducing the daily rainfall (e.g., overestimation of the number of rainy days) are likely needed. These findings open a new avenue for using such satellite products in landslide early warning systems, particularly in poorly gauged areas.

scholarly journals A susceptibility-based rainfall threshold approach for landslide occurrence

2019 ◽  
Vol 19 (4) ◽  
pp. 775-789 ◽  
Author(s):  
Elise Monsieurs ◽  
Olivier Dewitte ◽  
Alain Demoulin
Keyword(s):  
Landslide Susceptibility ◽  
Rainfall Threshold ◽  
Exceedance Probability ◽  
Equal Weight ◽  
Antecedent Rainfall ◽  
Rainfall Thresholds ◽  
Landslide Occurrence ◽  
Continental Scale ◽  
Satellite Rainfall ◽  
Rainfall Estimates

Abstract. Rainfall threshold determination is a pressing issue in the landslide scientific community. While major improvements have been made towards more reproducible techniques for the identification of triggering conditions for landsliding, the now well-established rainfall intensity or event-duration thresholds for landsliding suffer from several limitations. Here, we propose a new approach of the frequentist method for threshold definition based on satellite-derived antecedent rainfall estimates directly coupled with landslide susceptibility data. Adopting a bootstrap statistical technique for the identification of threshold uncertainties at different exceedance probability levels, it results in thresholds expressed as AR = (α±Δα)⋅S(β±Δβ), where AR is antecedent rainfall (mm), S is landslide susceptibility, α and β are scaling parameters, and Δα and Δβ are their uncertainties. The main improvements of this approach consist in (1) using spatially continuous satellite rainfall data, (2) giving equal weight to rainfall characteristics and ground susceptibility factors in the definition of spatially varying rainfall thresholds, (3) proposing an exponential antecedent rainfall function that involves past daily rainfall in the exponent to account for the different lasting effect of large versus small rainfall, (4) quantitatively exploiting the lower parts of the cloud of data points, most meaningful for threshold estimation, and (5) merging the uncertainty on landslide date with the fit uncertainty in a single error estimation. We apply our approach in the western branch of the East African Rift based on landslides that occurred between 2001 and 2018, satellite rainfall estimates from the Tropical Rainfall Measurement Mission Multi-satellite Precipitation Analysis (TMPA 3B42 RT), and the continental-scale map of landslide susceptibility of Broeckx et al. (2018) and provide the first regional rainfall thresholds for landsliding in tropical Africa.

scholarly journals The Norwegian forecasting and warning service for rainfall- and snowmelt-induced landslides

2018 ◽  
Vol 18 (5) ◽  
pp. 1427-1450 ◽  
Author(s):  
Ingeborg K. Krøgli ◽  
Graziella Devoli ◽  
Hervé Colleuille ◽  
Søren Boje ◽  
Monica Sund ◽  
...  
Keyword(s):  
Flood Forecasting ◽  
Landslide Risk ◽  
Public Agencies ◽  
Positive Feedbacks ◽  
Public Road ◽  
Landslide Forecasting ◽  
Close Cooperation ◽  
Forecasting And Warning ◽  
Main Components ◽  
Recent Evaluation

Abstract. The Norwegian Water Resources and Energy Directorate (NVE) have run a national flood forecasting and warning service since 1989. In 2009, the directorate was given the responsibility of also initiating a national forecasting service for rainfall-induced landslides. Both services are part of a political effort to improve flood and landslide risk prevention. The Landslide Forecasting and Warning Service was officially launched in 2013 and is developed as a joint initiative across public agencies between NVE, the Norwegian Meteorological Institute (MET), the Norwegian Public Road Administration (NPRA) and the Norwegian Rail Administration (Bane NOR). The main goal of the service is to reduce economic and human losses caused by landslides. The service performs daily a national landslide hazard assessment describing the expected awareness level at a regional level (i.e. for a county and/or group of municipalities). The service is operative 7 days a week throughout the year. Assessments and updates are published at the warning portal http://www.varsom.no/ at least twice a day, for the three coming days. The service delivers continuous updates on the current situation and future development to national and regional stakeholders and to the general public. The service is run in close cooperation with the flood forecasting service. Both services are based on the five pillars: automatic hydrological and meteorological stations, landslide and flood historical database, hydro-meteorological forecasting models, thresholds or return periods, and a trained group of forecasters. The main components of the service are herein described. A recent evaluation, conducted on the 4 years of operation, shows a rate of over 95 % correct daily assessments. In addition positive feedbacks have been received from users through a questionnaire. The capability of the service to forecast landslides by following the hydro-meteorological conditions is illustrated by an example from autumn 2017. The case shows how the landslide service has developed into a well-functioning system providing useful information, effectively and on time.

Application of SIGMA model for landslide forecasting in Darjeeling Himalayas

2021 ◽  
Author(s):  
Samuele Segoni ◽  
Minu Treesa Abraham ◽  
Neelima Satyam ◽  
Ascanio Rosi ◽  
Biswajeet Pradhan
Keyword(s):  
Sigma Model ◽  
Confusion Matrix ◽  
Regional Scale ◽  
Geographical Location ◽  
Daily Rainfall ◽  
Rain Gauge ◽  
Integrated System ◽  
Rainfall Thresholds ◽  
Skill Scores ◽  
Landslide Forecasting

<p>SIGMA (Sistema Integrato Gestione Monitoraggio Allerta – integrated system for management, monitoring and alerting) is a landslide forecasting model at regional scale which is operational in Emilia Romagna (Italy) for more than 20 years. It was conceived to be operated with a sparse rain gauge network with coarse (daily) temporal resolution and to account for both shallow landslides (typically triggered by short and intense rainstorms) and deep seated landslides (typically triggered by long and less intense rainfalls). SIGMA model is based on the statistical distribution of cumulative rainfall values (calculated over varying time windows), and rainfall thresholds are defined as the multiples of standard deviation of the same, to identify anomalous rainfalls with the potential of triggering landslides.</p><p>In this study, SIGMA model is applied for the first time in a geographical location outside of Italy, i.e. Kalimpong town in India. The SIGMA algorithm is customized using the historical rainfall and landslide data of Kalimpong from 2010 to 2015 and has been validated using the data from 2016 to 2017. The model was validated by building a confusion matrix and calculating statistical skill scores, which were compared with those of the state-of-the-art intensity-duration rainfall thresholds derived for the region.</p><p>Results of the comparison clearly show that SIGMA performs much better than the other models in forecasting landslides: all instances of the validation confusion matrix are improved, and all skill scores are higher than I-D thresholds, with an efficiency of 92% and a likelihood ratio of 11.28. We explain this outcome mainly with technical characteristics of the site: when only daily rainfall measurements from a spare gauge network are available, SIGMA outperforms other approaches based on peak measurements, like intensity – duration thresholds, which cannot be captured adequately by daily measurements. SIGMA model thus showed a good potential to be used as a part of the local Landslide Early Warning System (LEWS).</p>

scholarly journals Regional landslide forecasting in Piemonte (Italy) and in Norway: experiences from 2013 late spring

2017 ◽  
Author(s):  
Davide Tiranti ◽  
Graziella Devoli ◽  
Roberto Cremonini ◽  
Monica Sund ◽  
Søren Boje
Keyword(s):  
National Level ◽  
Daily Rainfall ◽  
Landslide Hazard ◽  
Weather Type ◽  
Late Spring ◽  
Large Area ◽  
Pressure System ◽  
Weather System ◽  
Landslide Forecasting ◽  
Hazard Assessments

Abstract. A few countries in the world operate systematically national and regional forecasting services for rainfall-induced landslides (i.e. shallow landslides, debris flows and debris avalanches), among them: Norway and Italy. In Norway, the Norwegian Water Resources and Energy Directorate (NVE) operates a landslide forecasting service at national level. A daily national hazard assessment is performed, describing both expected awareness level and type of landslide hazard for a selected warning region. In Italy, each administrative region has its own regional environmental agency (Regional Agency for Environmental Protection, ARPA) that is responsible of the daily landslide hazard assessments and emission of landslide warnings for one or more catchments within the region. One of these agencies, the ARPA Piemonte, is responsible for issuing landslide warnings for the Piemonte region, located in Northwestern Italy. Both services provide regular landslide hazard assessments founded on a combination of quantitative thresholds and daily rainfall forecasts together with qualitative expert analysis. Daily warning reports are published at http://www.arpa.piemonte.gov.it/rischinaturali and www.varsom.no. On spring 2013, the ARPA Piemonte, and the NVE issued warnings for hydro-meteorological hazards due to the arrival of a deep and large low-pressure system, called herein Vb cyclone. This kind of weather system is known to produce the largest floods in Europe. Less known is that the weather type can trigger landslides as well. In this study, we present the experiences acquired in late spring 2013 by NVE and ARPA Piemonte. From 27th April to 19nd May 2013, more than 400 mm rain in Piemonte caused severe floods and diffused landslides. In Norway, the same weather type lasted from 15th May to 2nd June 2013 and brought warm winds with high temperatures that caused intense snow melt over a large area, and brought a lot of rain in the Southeastern Norway, initiating large flood along Glomma river and several landslides. Floods and landslides produced significant damages to roads and railways along with buildings and other infrastructure in both countries.

scholarly journals Experiences in using the TMPA-3B42R satellite data to complement rain gauge measurements in the Ecuadorian coastal foothills

2013 ◽  
Vol 17 (7) ◽  
pp. 2905-2915 ◽  
Author(s):  
M. Arias-Hidalgo ◽  
B. Bhattacharya ◽  
A. E. Mynett ◽  
A. van Griensven
Keyword(s):  
Satellite Data ◽  
Bias Correction ◽  
Hydrological Modeling ◽  
New Technologies ◽  
Simple Procedure ◽  
Rain Gauge ◽  
Experimental Methodology ◽  
Rain Gauge Data ◽  
Gauge Data ◽  
Satellite Rainfall

Abstract. At present, new technologies are becoming available to extend the coverage of conventional meteorological datasets. An example is the TMPA-3B42R dataset (research – v6). The usefulness of this satellite rainfall product has been investigated in the hydrological modeling of the Vinces River catchment (Ecuadorian lowlands). The initial TMPA-3B42R information exhibited some features of the precipitation spatial pattern (e.g., decreasing southwards and westwards). It showed a remarkable bias compared to the ground-based rainfall values. Several time scales (annual, seasonal, monthly, etc.) were considered for bias correction. High correlations between the TMPA-3B42R and the rain gauge data were still found for the monthly resolution, and accordingly a bias correction at that level was performed. Bias correction factors were calculated, and, adopting a simple procedure, they were spatially distributed to enhance the satellite data. By means of rain gauge hyetographs, the bias-corrected monthly TMPA-3B42R data were disaggregated to daily resolution. These synthetic time series were inserted in a hydrological model to complement the available rain gauge data to assess the model performance. The results were quite comparable with those using only the rain gauge data. Although the model outcomes did not improve remarkably, the contribution of this experimental methodology was that, despite a high bias, the satellite rainfall data could still be corrected for use in rainfall-runoff modeling at catchment and daily level. In absence of rain gauge data, the approach may have the potential to provide useful data at scales larger than the present modeling resolution (e.g., monthly/basin).

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