scholarly journals EVALUATION ON THE IMPLEMENTATION OF EARLY WARNING SYSTEM FOR LAHAR IN MERAPI AREA (CASE STUDY AT BOYONG RIVER)

2015 ◽  
Vol 1 (3) ◽  
pp. 77
Author(s):  
Ali Cahyadi Achmad
Keyword(s):  
Debris Flow ◽  
Qualitative Analysis ◽  
Volcanic Activity ◽  
Early Warning ◽  
Early Warning System ◽  
Rainfall Intensity ◽  
Warning System ◽  
Actual Condition ◽  
Telemetry System ◽  
Debris Flood

One of disasters caused by volcanic activity of Mount Merapi is secondary disaster. The disaster usually occurs after eruption and this volcanic activity produces volcanic and pyroclastic material deposit around the top of the mountain as a result of previous eruption. This material might collapse downward in the form of debris flow as it is affected by natural event such as high intensity rainfall. Therefore, a research is needed to analyze whether existing forecasting and early warning system are capable to provide information for the people living in hazardous area before the debris flood occur. This research was carried out using field survey, observation and interview method. Data analysis used qualitative descriptive method by making description of actual condition of the researched location general condition and qualitative analysis of telemetry system installed on Mount Merapi. The qualitative analysis of telemetry system covers network, hardware, software, power supply, security system, operation and maintenance, also human resources. Research analysis used primary and secondary data. Research results revealed that mean rainfall intensity above of 60 mm/hour might trigger debris flood. Early warning should be given at the rainfall intensity level of 50-55 mm/hour, and debris flood time travel from the upstream to the observed location in Pulowatu Village is 45 minute. Based on the analysis of the present forecasting and early warning system, it is known that some of the equipment is not well functioned, so that debris flow cannot be predicted and detected. This is caused by the lack of human resource quality of the officers in operating and maintaining the equipment. Concerning that matter, it is necessary to conduct some improvement to achieve better forecasting and early warning system in order to give information regarding occurrence of debris flow.

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.

Development of a Hydrological Telemetry System in Bago River

2018 ◽  
Vol 13 (1) ◽  
pp. 116-124 ◽  
Author(s):  
Ralph Allen Acierto ◽  
Akiyuki Kawasaki ◽  
Win Win Zin ◽  
Aung Than Oo ◽  
Khon Ra ◽  
...  
Keyword(s):  
Real Time ◽  
Disaster Management ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Early Warning Systems ◽  
Probability Of Detection ◽  
Future Climate Change ◽  
Telemetry System ◽  
Production Type

Hydrological monitoring is one of the key aspects in early warning systems that are vital to flood disaster management in flood-prone areas such as Bago River Basin in Myanmar. Thousands of people are affected due to the perennial flooding. Owing to the increasing pressure of rapid urbanization in the region and future climate change impacts, an early warning system in the basin is urgently required for disaster risk mitigation. This paper introduces the co-establishment of the telemetry system by a group of stakeholders. The co-establishment of the system through intensive consultations, proactive roles in responsibility sharing, and capacity building efforts, is essential in developing a base platform for flood forecasting and an early warning system in the basin. Herein, we identify the key challenges that have been central to the participatory approach in co-establishing the system. We also highlight opportunities as a result of the ongoing process and future impact on the disaster management system in the basin. We also highlight the potential for scientific contributions in understanding the local weather and hydrological characteristics through the establishment of the high-temporal resolution observation network. Using the observation at Zaung Tu Weir, Global Satellite Mapping of Precipitation (GSMaP) and Global Precipitation Measurement (GPM) satellite estimates were assessed. Near real-time and standard versions of both satellite estimates show potential utility over the basin. Hourly aggregation shows slightly higher than 40% probability of detection (POD), on average, for both satellite estimates regardless of the production type. Thus, the hourly aggregation requires correction before usage. The results show useful skills at 60% POD for standard GSMaP (GSMAP-ST), 55% POD for near real-time GSMaP (GSMAP-NR), and 46% POD for GPM, at 3-hourly aggregations. Six-hourly aggregations show maximum benefit for providing useful skill and good correspondence to gauge the observation with GSMAP-ST showing the best true skill score (TSS) at 0.54 and an equitable threat score (ETS) at 0.37. While, both final run GPM and GSMAP-NR show lower POD, TSS, and ETS scores. Considering the latency of near real-time satellite estimates, the GSMAP-NR shows the best potential with a 4-hour latency period for monitoring and forecasting purposes in the basin. The result of the GSMAP-NR does not vary significantly from the GSMAP-ST and all GPM estimates. However, it requires some correction before its usage in any applications, for modeling and forecasting purposes.

Debris-flow Indicator for an early warning system in the Aosta valley region

2020 ◽  
Vol 104 (2) ◽  
pp. 1819-1839
Author(s):  
Michel Ponziani ◽  
Paolo Pogliotti ◽  
Hervé Stevenin ◽  
Sara Maria Ratto
Keyword(s):  
Debris Flow ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System

Exploration of the characteristics of landslide triggering rainfall using rain gauge and numerical weather prediction for Yogyakarta and Central Java, Indonesia

2020 ◽  
Author(s):  
Ratna Satyaningsih ◽  
Ardhasena Sopaheluwakan ◽  
Danang Eko Nuryanto ◽  
Tri Astuti Nuraini ◽  
Arif Rahmat Mulyana ◽  
...  
Keyword(s):  
Numerical Weather Prediction ◽  
Early Warning ◽  
Early Warning System ◽  
Rainfall Intensity ◽  
Weather Prediction ◽  
Warning System ◽  
Rainfall Data ◽  
Numerical Weather ◽  
Central Java ◽  
Landslide Early Warning

<p>The existing Landslide Early Warning System (LEWS) for Indonesia was developed using rainfall thresholds, which were derived from the relationship between rainfall inducing landslides and landslide events in the past. The system utilized the median values of 1-day and 3-day cumulative observed rainfall for determining the threshold and a relatively limited number of landslide events throughout Indonesia during the period of the system development. The system employed a single set of threshold values for all regions despite the possibility of differences in rainfall intensity characteristics for each region. For prediction, the system used rainfall data derived from satellite products and rainfall forecast data with a spatial resolution of 0.25° x 0.25°, which is not adequate for catchment-scale landslide analysis.</p><p> </p><p>We attempt to improve the LEWS by applying a statistical approach based on rainfall intensity and duration for a longer time-series of data set. Instead of determining the thresholds for national scale, we focus on the Special Region of Yogyakarta and surrounding cities in Central Java which are prone to landslides but have high population density. In addition to that, we also perform preliminary exploration of the potential of the output of high-resolution numerical weather prediction in simulating the rainfall inducing the landslides for several historical landslide events. This study is part of a project called BILEWS, a Blueprint for an Indonesian Landslide Early Warning System, which aims to develop threshold for landslides and debris flows as the basis for early warning to be applied at several test sites in Java, using tailored rainfall data, combined with empirical and physically-based hydrological and landslide models, as well as historical landslide data.</p>

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