scholarly journals Diversity of Rainfall Thresholds for early warning of hydro-geological disasters

2017 ◽  
Vol 44 ◽  
pp. 53-60 ◽  
Author(s):  
Davide L. De Luca ◽  
Pasquale Versace
Keyword(s):  
Early Warning ◽  
Southern Italy ◽  
Critical Value ◽  
Exceedance Probability ◽  
Occurrence Probability ◽  
Rainfall Thresholds ◽  
Landslide Prediction ◽  
Emergency Plans ◽  
Geological Disasters

Abstract. For early warning of disasters induced by precipitation (such as floods and landslides), different kinds of rainfall thresholds are adopted, which vary from each other, on the basis on adopted hypotheses. In some cases, they represent the occurrence probability of an event (landslide or flood), in other cases the exceedance probability of a critical value for an assigned indicator I (a function of rainfall heights), and in further cases they only indicate the exceeding of a prefixed percentage a critical value for I, indicated as Icr. For each scheme, it is usual to define three different criticality levels (ordinary, moderate and severe), which are associated to warning levels, according to emergency plans. This work briefly discusses different schemes of rainfall thresholds, focusing attention on landslide prediction, with some applications to a real case study in Calabria region (southern Italy).

scholarly journals DELIVERING GIS TRAINING USING GEOSPATIAL WEB SERVICE – A CASE STUDY OF LANDSLIDE RISK MAPPING IN HONG KONG

2015 ◽  
Vol XL-6/W1 ◽  
pp. 15-16
Author(s):  
J. Huang ◽  
L. You ◽  
Q. Zhou ◽  
H. Wu
Keyword(s):  
Hong Kong ◽  
Web Service ◽  
Early Warning ◽  
Risk Level ◽  
Landslide Risk ◽  
Landslide Prediction ◽  
Web Based ◽  
Warning Messages ◽  
Geospatial Web Service

This paper sketches a prototype of web-based landslide prediction service for delivering web-based training. The results show that the proposed landslide GWSC model can effectively compute the landslide risk level in different location, and consequently allow for early-warning, which starts with the sensor in the field and ending with user-opitmized warning messages and action advice.

scholarly journals Preface to the Special Issue “Rainfall Thresholds and Other Approaches for Landslide Prediction and Early Warning”

Water ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 323
Author(s):  
Samuele Segoni ◽  
Stefano Luigi Gariano ◽  
Ascanio Rosi
Keyword(s):  
Early Warning ◽  
Special Issue ◽  
Rainfall Thresholds ◽  
Landslide Prediction ◽  
Destructive Processes

Landslides are frequent and widespread destructive processes causing casualties and damage worldwide [...]

scholarly journals Landslide Early Warning in Indonesia: A Gap Analysis

2021 ◽  
Author(s):  
Ratna Satyaningsih ◽  
Victor Jetten ◽  
Janneke Ettema ◽  
Ardhasena Sopaheluwakan ◽  
Danang Eko Nuryanto ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Community Response ◽  
Catchment Scale ◽  
Rainfall Thresholds ◽  
Landslide Prediction ◽  
Rainfall Forecast ◽  
Landslide Early Warning

<p>For the last decade, rainfall-triggered landslides have been one of the major hazards in Indonesia. According to the National Agency for Disaster Management (BNPB) reports, from 2010 to 2020, a total of 5822 landslides occurred in Indonesia and caused 1812 casualties, 1627 injured, and 234 missing. More than 75% of those landslides occurred in Java, the most populous island in the region. Settlements alongside agricultural fields often are located in areas that are susceptible to landslides. As relocation would be costly, a landslide early warning system (LEWS) could provide the necessary information for communities susceptible to landslides to prepare for the upcoming hazard. The objective of this study is to map the issues with the existing landslide early warning system in Indonesia and our plan to improve landslide forecasting by tailoring available rainfall forecasts and monitoring.</p><p>The United Nations International Strategy for Disaster Reduction (UNISDR) has defined an end-to-end early warning system that essentially comprises knowledge risk, hazard forecasting, alerts dissemination, and community response. In the definition, the UNISDR also highlighted timely and meaningful warning information for appropriate preparedness and action in a sufficient time. Landslide prediction itself is challenging in terms of when and where precisely the landslides occur as different landslide types have different characteristics and trigger mechanisms. Moreover, when rainfall forecast data is used as input for a physically-based hydrological and landslide model, the uncertainty and accuracy of the rainfall will affect the forecast skill.</p><p>National LEWS with a longer lead-time is operational, utilizing generic rainfall thresholds derived from 1-day and 3-day cumulative rainfall triggering landslides occurred in Indonesia (mostly in the Java Island) as warning signals. The rainfall thresholds were derived from NASA Tropical Rainfall Measuring Mission (TRMM) rainfall estimates with a spatial resolution of 0.25°×0.25°. Different studies showed that the thresholds derived from that product are lower than those derived from raingauge measurements, potentially leading to more false alerts. These thresholds are applied for all catchments in Indonesia even though the region has different climate regimes and geomorphological characteristics, leading to insufficient accuracy for the local landslide prediction.  As for the forecast, the current LEWS applies rainfall forecast with the same spatial resolution as TRMM, which is not suitable for (sub-)catchment-scale prediction.</p><p>This study proposes an approach to tailor rainfall data from various high-resolution sources, like radar, NWP models, and satellite, where historical landslide data are to be used to derive dynamical rainfall thresholds at local scale.</p>

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.

Normative Ecologies of Planning: Understanding Norms in Action

2021 ◽  
pp. 0739456X2110211
Author(s):  
Laura Lieto
Keyword(s):  
Southern Italy ◽  
Historic Center ◽  
Rules Of The Game

The paper deals with planning norms in action, assuming that planning regulation is one among many kinds of regulation with which planners must contend. Norms operate and co-evolve within a normative ecology where institutions collaborate and compete through overlapping and often incommensurate normative frameworks and rules of the game. The importance of socio-materiality in how different regulations work in practice is emphasized. To develop the normative ecology argument, a case study is presented on the effects of Airbnb tourism on the historic center of Napoli in southern Italy.

Probabilistic nature of storage delay parameter of the hydrologic model RORB: a case study for the Cooper's Creek catchment in Australia

2014 ◽  
Vol 46 (3) ◽  
pp. 400-410 ◽  
Author(s):  
Hitesh Patel ◽  
Ataur Rahman
Keyword(s):  
Hydrologic Model ◽  
Exceedance Probability ◽  
Design Flood ◽  
Flood Peak ◽  
Monte Carlo Simulation Technique ◽  
South Wales ◽  
Accurate Design ◽  
High Degree ◽  
Probabilistic Nature

In rainfall–runoff modeling, Design Event Approach is widely adopted in practice, which assumes that the rainfall depth of a given annual exceedance probability (AEP), can be converted to a flood peak of the same AEP by assuming a representative fixed value for the other model inputs/parameters such as temporal pattern, losses and storage-delay parameter of the runoff routing model. This paper presents a case study which applies Monte Carlo simulation technique (MCST) to assess the probabilistic nature of the storage delay parameter (kc) of the RORB model for the Cooper's Creek catchment in New South Wales, Australia. It has been found that the values of kc exhibit a high degree of variability, and different sets of plausible values of kc result in quite different flood peak estimates. It has been shown that a stochastic kc in the MCST provides more accurate design flood estimates than a fixed representative value of kc. The method presented in this study can be adapted to other catchments/countries to derive more accurate design flood estimates, in particular for important flood study projects, which require a sensitivity analysis to investigate the impacts of parameter uncertainty on design flood estimates.

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