X-MP Radar for Developing a Lahar Rainfall Threshold for the Merapi Volcano Using a Bayesian Approach

2019 ◽  
Vol 14 (5) ◽  
pp. 811-828 ◽  
Author(s):  
Ratih Indri Hapsari ◽  
Satoru Oishi ◽  
Magfira Syarifuddin ◽  
Rosa Andrie Asmara ◽  
Djoko Legono ◽  
...  
Keyword(s):  
Debris Flow ◽  
Warning System ◽  
Rainfall Threshold ◽  
Bayesian Probability ◽  
Good Representation ◽  
Merapi Volcano ◽  
Track Record ◽  
Long Duration ◽  
Short Term Prediction ◽  
Critical Lines

Lahar flow is recognized as among the worst secondary hazards from volcanic disaster. Intense rainfall with long duration is frequently associated with lahar flow. In this study, estimation of a rainfall threshold likely to trigger lahar flow is presented in the first part. The second part discusses its implementation by assessing the growth of observed and predicted rainfall, including the uncertainties. The study area is Merapi Volcano, one of the most active volcanoes in Indonesia, including rivers on the flank of Mount Merapi that are vulnerable to debris flow. The rainfall indices needed to describe the conditions that generate lahars or not were determined empirically by evaluating the hourly and working rainfall using X-band multiparameter (X-MP) weather radar. Using past records of lahar flow, the threshold lines separating rainfall that triggers lahars or not were analyzed for the Putih, Gendol, Pabelan, and Krasak Rivers. The performance of several critical lines was evaluated using Bayesian probability based on skill rates from a contingency matrix. The study shows that the line intercept of the critical lines after a significant eruption in 2010 was higher than those lines developed before 2010, indicating that the rivers are currently at lesser risk. Good representation was shown by the thresholds verified with actual rainfall progression and lahar event information on February 17, 2016, at the Gendol and Pabelan Rivers. These rainfall critical lines were the basis for judging the debris flow occurrence by analyzing the track record of predicted rainfall progression. The uncertainty of rainfall short-term prediction from the extrapolation model was evaluated by perturbing the advection vector of rain echo motion. This ensemble forecast product could provide a plausible range of prediction possibility as assistance in gaining the confidence with which a lahar could be predicted. The scheme presented herein could serve as a useful tool for a lahar early warning system in the area of the Merapi Volcano.

scholarly journals Rainfall Monitoring to Support Temporal and Spatial Information of Debris Flow Initiation in Merapi Volcano

2021 ◽  
Vol 13 (3) ◽  
Author(s):  
Ani Hairani ◽  
◽  
Adam Pamudji Rahardjo ◽  
Djoko Legono ◽  
Istiarto Istiarto ◽  
...  
Keyword(s):  
Numerical Simulation ◽  
Debris Flow ◽  
Rainfall Intensity ◽  
Spatial Information ◽  
Line Graph ◽  
Warning System ◽  
Surface Flow ◽  
Merapi Volcano ◽  
Hourly Rainfall ◽  
Debris Flow Initiation

Debris flow frequently attacks rivers on slopes of Merapi Volcano and causes fatalities and damage of infrastructures. To reduce the risk of debris flow, a warning system has been developed by Sabo Office Center. Critical line and snake line graph are applied in Merapi Volcano to monitor characteristics of rainfall in the upland river basin. However, this warning system cannot predict the arrival time and location of the debris flow occurrence. Numerical simulation seems to be a good tool to improve its performance. This research proposed an idea to combine rainfall-based warning system with the numerical simulation model. This model used slope stability theory to identify debris flow initiation. Results of this research showed that fluctuation of rainfall intensity reflects changes of debris flow initiation area. The more severe rainfall intensity, the larger volume of surface flow, and thus the greater debris flow initiation takes place. When the rainfall monitoring is combined with the debris flow simulation results, there is a tendency of the enlargement of the debris flow area to follow the growth of the hourly rainfall.

scholarly journals Rainfall threshold for triggering debris flow on Merapi volcano area, Yogyakarta, Indonesia

2017 ◽  
Author(s):  
Rini Kusumawardani ◽  
Rizki Kurniadhi ◽  
Muhammad Mukhlisin ◽  
Djoko Legono
Keyword(s):  
Debris Flow ◽  
Rainfall Threshold ◽  
Merapi Volcano

scholarly journals Uncertainty analysis of the estimation of stony debris flow rainfall threshold: the application to the Backward Dynamical Approach

2020 ◽  
Author(s):  
Marta Martinengo ◽  
Daniel Zugliani ◽  
Giorgio Rosatti
Keyword(s):  
Debris Flow ◽  
Uncertainty Analysis ◽  
Flow Characteristics ◽  
Warning System ◽  
Rainfall Threshold ◽  
Rainfall Condition ◽  
Dynamical Approach ◽  
Critical Issues ◽  
Frequentist Method ◽  
The Impact

Abstract. Rainfall thresholds, namely rainfall intensity-duration conditions beyond which the probability of debris flow occurrence is considered significant, can be used as a forecasting tool in debris-flow early warning system. Many uncertainties may affect the thresholds calibration and, in turn, the reliability and effectiveness of this tool. The purpose of this study is to assess the uncertainty in the determination of the rainfall threshold for stony debris flow based on the Back Dynamical Approach (BDA) (Rosatti et al., 2019), an innovative method to estimate the rainfall duration and averaged intensity strictly related to measured debris flow. The uncertainty analysis has been computed performing two Monte Carlo cascade simulations: (i) to assess the variability in the estimate of rainfall conditions due to the uncertainty of some of the BDA parameters and (ii) to quantify the impact of this variability on the threshold parameters, obtained by using the frequentist method. Then, the deviation between these analysis outcomes and the values obtained in Rosatti et al. (2019) has been examined. The results highlight that the variability in the rainfall condition estimate is strongly related to the debris flow characteristics and the hyetograph shape. Depending on these features, the spreading of the obtained distributions can take both low and high values. Instead, the threshold parameters are characterised by a low statistical spreading. Finally, the consistency between the outcome of this study and the results obtained in Rosatti et al. (2019) has been proved and the critical issues related to the rainfall condition estimation have been discussed.

scholarly journals Learning Case Study of a Shallow-Water Model to Assess an Early-Warning System for Fast Alpine Muddy-Debris-Flow

Water ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 750
Author(s):  
Antonio Pasculli ◽  
Jacopo Cinosi ◽  
Laura Turconi ◽  
Nicola Sciarra
Keyword(s):  
Debris Flow ◽  
Shallow Water ◽  
Early Warning ◽  
Debris Flows ◽  
Early Warning System ◽  
Warning System ◽  
Extreme Weather Events ◽  
Water Model ◽  
Computational Time ◽  
Processing Unit

The current climate change could lead to an intensification of extreme weather events, such as sudden floods and fast flowing debris flows. Accordingly, the availability of an early-warning device system, based on hydrological data and on both accurate and very fast running mathematical-numerical models, would be not only desirable, but also necessary in areas of particular hazard. To this purpose, the 2D Riemann–Godunov shallow-water approach, solved in parallel on a Graphical-Processing-Unit (GPU) (able to drastically reduce calculation time) and implemented with the RiverFlow2D code (version 2017), was selected as a possible tool to be applied within the Alpine contexts. Moreover, it was also necessary to identify a prototype of an actual rainfall monitoring network and an actual debris-flow event, beside the acquisition of an accurate numerical description of the topography. The Marderello’s basin (Alps, Turin, Italy), described by a 5 × 5 m Digital Terrain Model (DTM), equipped with five rain-gauges and one hydrometer and the muddy debris flow event that was monitored on 22 July 2016, were identified as a typical test case, well representative of mountain contexts and the phenomena under study. Several parametric analyses, also including selected infiltration modelling, were carried out in order to individuate the best numerical values fitting the measured data. Different rheological options, such as Coulomb-Turbulent-Yield and others, were tested. Moreover, some useful general suggestions, regarding the improvement of the adopted mathematical modelling, were acquired. The rapidity of the computational time due to the application of the GPU and the comparison between experimental data and numerical results, regarding both the arrival time and the height of the debris wave, clearly show that the selected approaches and methodology can be considered suitable and accurate tools to be included in an early-warning system, based at least on simple acoustic and/or light alarms that can allow rapid evacuation, for fast flowing debris flows.

scholarly journals Application and analysis of debris-flow early warning system in Wenchuan earthquake-affected area

2016 ◽  
Vol 16 (2) ◽  
pp. 483-496 ◽  
Author(s):  
D. L. Liu ◽  
S. J. Zhang ◽  
H. J. Yang ◽  
L. Q. Zhao ◽  
Y. H. Jiang ◽  
...  
Keyword(s):  
Debris Flow ◽  
Wenchuan Earthquake ◽  
Early Warning ◽  
Early Warning System ◽  
Prediction Accuracy ◽  
Warning System ◽  
Sichuan Province ◽  
Affected Area ◽  
The Wenchuan Earthquake ◽  
New System

Abstract. The activities of debris flow (DF) in the Wenchuan earthquake-affected area significantly increased after the earthquake on 12 May 2008. The safety of the lives and property of local people is threatened by DFs. A physics-based early warning system (EWS) for DF forecasting was developed and applied in this earthquake area. This paper introduces an application of the system in the Wenchuan earthquake-affected area and analyzes the prediction results via a comparison to the DF events triggered by the strong rainfall events reported by the local government. The prediction accuracy and efficiency was first compared with a contribution-factor-based system currently used by the weather bureau of Sichuan province. The storm on 17 August 2012 was used as a case study for this comparison. The comparison shows that the false negative rate and false positive rate of the new system is, respectively, 19 and 21 % lower than the system based on the contribution factors. Consequently, the prediction accuracy is obviously higher than the system based on the contribution factors with a higher operational efficiency. On the invitation of the weather bureau of Sichuan province, the authors upgraded their prediction system of DF by using this new system before the monsoon of Wenchuan earthquake-affected area in 2013. Two prediction cases on 9 July 2013 and 10 July 2014 were chosen to further demonstrate that the new EWS has high stability, efficiency, and prediction accuracy.

scholarly journals Determination of rainfall thresholds for shallow landslides by a probabilistic and empirical method

2015 ◽  
Vol 3 (5) ◽  
pp. 3487-3508
Author(s):  
J. Huang ◽  
N. P. Ju ◽  
Y. J. Liao ◽  
D. D. Liu
Keyword(s):  
Early Warning ◽  
Regional Scale ◽  
Warning System ◽  
Rainfall Threshold ◽  
Empirical Method ◽  
Shallow Landslides ◽  
Data Sets ◽  
Threshold Values ◽  
Mountainous Regions ◽  
Hourly Rainfall

Abstract. Rainfall-induced landslides not only cause property loss, but also kill and injure large numbers of people every year in mountainous areas in China. These losses and casualties may be avoided to some extent with rainfall threshold values used in an early warning system at a regional scale for the occurrence of landslides. However, the limited availability of data always causes difficulties. In this paper we present a method to calculate rainfall threshold values with limited data sets for the two rainfall parameters: maximum hourly rainfall intensity and accumulated precipitation. The method has been applied to the Huangshan region, in Anhui Province, China. Four early warning levels (Zero, Outlook, Attention, and Warning) have been adopted and the corresponding rainfall threshold values have been defined by probability lines. A validation procedure showed that this method can significantly enhance the effectiveness of a warning system, and finally reduce the risk from shallow landslides in mountainous regions.

Persistent Depressive Disorder/Dysthymia

2017 ◽  
pp. 160-166
Author(s):  
Myrna M. Weissman ◽  
John C. Markowitz ◽  
Gerald L. Klerman
Keyword(s):  
Depressive Symptoms ◽  
Depressive Disorder ◽  
Maintenance Therapy ◽  
Role Transition ◽  
Depressed Woman ◽  
Track Record ◽  
Self Image ◽  
Long Duration ◽  
Current Mood ◽  
Persistent Depressive Disorder

This chapter describes the adaptation of IPT for the treatment of patients with persistent depressive disorder/dysthymia. The usual IPT model connects a recent event in the patient’s life with current mood and symptoms, but for patients who have been depressed for years, this model makes less sense. Instead, the IPT therapist makes the treatment itself a role transition from longstanding depression to euthymia in which patients learn to recognize depressive symptoms of long duration and how they have affected their social functioning. The therapist offers a formulation that shifts the blame for the patient’s situation from the patient to the illness. Treatment includes sixteen weekly sessions to drive these points home, although monthly continuation sessions and maintenance therapy are frequently offered so that patients’ new self-image and track record of healthy interpersonal functioning can sink in. A case example is given of a chronically depressed woman who improves with IPT.

scholarly journals Rainfall Threshold Estimation and Landslide Forecasting for Kalimpong, India Using SIGMA Model

Water ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 1195 ◽  
Author(s):  
Minu Treesa Abraham ◽  
Neelima Satyam ◽  
Sai Kushal ◽  
Ascanio Rosi ◽  
Biswajeet Pradhan ◽  
...  
Keyword(s):  
Early Warning ◽  
Sigma Model ◽  
Warning System ◽  
Daily Rainfall ◽  
Early Warning Systems ◽  
Rainfall Threshold ◽  
Integrated System ◽  
Warning Systems ◽  
Rainfall Forecasting ◽  
Rainfall Thresholds

Rainfall-induced landslides are among the most devastating natural disasters in hilly terrains and the reduction of the related risk has become paramount for public authorities. Between the several possible approaches, one of the most used is the development of early warning systems, so as the population can be rapidly warned, and the loss related to landslide can be reduced. Early warning systems which can forecast such disasters must hence be developed for zones which are susceptible to landslides, and have to be based on reliable scientific bases such as the SIGMA (sistema integrato gestione monitoraggio allerta—integrated system for management, monitoring and alerting) model, which is used in the regional landslide warning system developed for Emilia Romagna in Italy. The model uses statistical distribution of cumulative rainfall values as input and rainfall thresholds are defined as multiples of standard deviation. In this paper, the SIGMA model has been applied to the Kalimpong town in the Darjeeling Himalayas, which is among the regions most affected by landslides. The objectives of the study is twofold: (i) the definition of local rainfall thresholds for landslide occurrences in the Kalimpong region; (ii) testing the applicability of the SIGMA model in a physical setting completely different from one of the areas where it was first conceived and developed. To achieve these purposes, a calibration dataset of daily rainfall and landslides from 2010 to 2015 has been used; the results have then been validated using 2016 and 2017 data, which represent an independent dataset from the calibration one. The validation showed that the model correctly predicted all the reported landslide events in the region. Statistically, the SIGMA model for Kalimpong town is found to have 92% efficiency with a likelihood ratio of 11.28. This performance was deemed satisfactory, thus SIGMA can be integrated with rainfall forecasting and can be used to develop a landslide early warning system.

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