Anonymous reviewer "Technical notes: rainfall threshold calculation for debris flow early warning in areas with scarcity of data"

2017 ◽  
Author(s):  
Anonymous
Keyword(s):  
Debris Flow ◽  
Early Warning ◽  
Rainfall Threshold

scholarly journals Early warning of debris flow using optimized self-organizing feature mapping network

2020 ◽  
Vol 20 (7) ◽  
pp. 2455-2470
Author(s):  
Xuedong Wang ◽  
Cui Wang ◽  
Chaobiao Zhang
Keyword(s):  
Neural Network ◽  
Debris Flow ◽  
Early Warning ◽  
Rainfall Threshold ◽  
Liaoning Province ◽  
Feature Mapping ◽  
Threshold Method ◽  
Flow Area ◽  
Competitive Neural Network ◽  
Self Organizing

Abstract Early warning of debris flow is one of the core contents of disaster prevention and mitigation work for debris flow disasters. There are few early warning methods based on the combination of rainfall threshold and geological environment conditions. In this paper, we presented an early warning method for debris flow based on the infinite irrelevance method (IIM) and self-organizing feature mapping (SOFM), and applied it to Liaoning Province, China. The proposed model consisted of three stages. Firstly, eight geological environmental conditions and two rainfall-inducing conditions were selected by analyzing the factors affecting the development of debris flow in the study area, and the rainfall threshold for debris flow outbreak was 150 mm. Secondly, the correlation between various factors was analyzed by IIM, which prevented the blindness of parameter selection and improved the prediction accuracy of the model. Finally, SOFM was employed to predict the test data. Experimental results showed that the IIM-SOFM model had a strong early warning ability. When 25 samples of low-frequency debris flow area were selected, the accuracy rate of the IIM-SOFM model with optimized network structure parameters was 100%, which it was obviously superior to the rainfall threshold method, BP neural network and competitive neural network. Consequently, it is feasible to use the IIM-SOFM model for early warning of debris flow, outperforming traditional machine learning methods.

scholarly journals Technical notes: Rainfall threshold calculation for debris flow early warning in areas with scarcity of data

2017 ◽  
Author(s):  
Hua-li Pan ◽  
Yuan-jun Jiang ◽  
Jun Wang ◽  
Guo-qiang Ou
Keyword(s):  
Debris Flow ◽  
Early Warning ◽  
Debris Flows ◽  
Empirical Relationship ◽  
Early Warning Systems ◽  
Rainfall Threshold ◽  
Negative Slope ◽  
Observation Data ◽  
Flow Forming ◽  
Initiation Mechanism

Abstract. Debris flows are one of the natural disasters that frequently occur in mountain areas, usually accompanied by serious loss of lives and properties. One of the most used approaches to mitigate the risk associated to debris flows is the implementation of early warning systems based on well calibrated rainfall thresholds. However, many mountainous areas have little data regarding rainfall and hazards, especially in debris flow forming regions. Therefore, the traditional statistical analysis method that determines the empirical relationship between rainfall and debris flow events cannot be effectively used to calculate reliable rainfall thre-shold in these areas. To solve this problem, this paper developed a quantitative method to identify rainfall threshold for debris flow early warning in data-poor areas based on the initiation mechanism of hydraulic-driven debris flow. First, we studied the characteristics of the study area, including meteorology, hydrology, topography and physical characteristics of the loose solid materials. Then, the rainfall threshold was calculated by the initiation me-chanism of the hydraulic debris flow. The results show that the proposed rainfall threshold curve is a function of the antecedent precipitation index and 1-h rainfall. The function is a line with a negative slope. To test the proposed method, we selected the Guojuanyan gully, a typical debris flow valley that during the 2008–2013 period experienced several debris flow events and that is located in the meizoseismal areas of Wenchuan earthquake, as a case study. We compared the calculated threshold with observation data, showing that the accuracy of the method is satisfying and thus can be used for debris flow early warning in areas with scaricty of data.

scholarly journals Rainfall threshold calculation for debris flow early warning in areas with scarcity of data

2018 ◽  
Vol 18 (5) ◽  
pp. 1395-1409 ◽  
Author(s):  
Hua-Li Pan ◽  
Yuan-Jun Jiang ◽  
Jun Wang ◽  
Guo-Qiang Ou
Keyword(s):  
Debris Flow ◽  
Wenchuan Earthquake ◽  
Early Warning ◽  
Debris Flows ◽  
Early Warning Systems ◽  
Rainfall Threshold ◽  
Warning Systems ◽  
Mountainous Areas ◽  
Rainfall Thresholds ◽  
Initiation Mechanism

Abstract. Debris flows are natural disasters that frequently occur in mountainous areas, usually accompanied by serious loss of lives and properties. One of the most commonly used approaches to mitigate the risk associated with debris flows is the implementation of early warning systems based on well-calibrated rainfall thresholds. However, many mountainous areas have little data regarding rainfall and hazards, especially in debris-flow-forming regions. Therefore, the traditional statistical analysis method that determines the empirical relationship between rainstorms and debris flow events cannot be effectively used to calculate reliable rainfall thresholds in these areas. After the severe Wenchuan earthquake, there were plenty of deposits deposited in the gullies, which resulted in several debris flow events. The triggering rainfall threshold has decreased obviously. To get a reliable and accurate rainfall threshold and improve the accuracy of debris flow early warning, this paper developed a quantitative method, which is suitable for debris flow triggering mechanisms in meizoseismal areas, to identify rainfall threshold for debris flow early warning in areas with a scarcity of data based on the initiation mechanism of hydraulic-driven debris flow. First, we studied the characteristics of the study area, including meteorology, hydrology, topography and physical characteristics of the loose solid materials. Then, the rainfall threshold was calculated by the initiation mechanism of the hydraulic debris flow. The comparison with other models and with alternate configurations demonstrates that the proposed rainfall threshold curve is a function of the antecedent precipitation index (API) and 1 h rainfall. To test the proposed method, we selected the Guojuanyan gully, a typical debris flow valley that during the 2008–2013 period experienced several debris flow events, located in the meizoseismal areas of the Wenchuan earthquake, as a case study. The comparison with other threshold models and configurations shows that the selected approach is the most promising starting point for further studies on debris flow early warning systems in areas with a scarcity of data.

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.

Application of a combined and automated monitoring and early warning system for debris flows at the Dawinbach

2021 ◽  
Author(s):  
Tobias Schöffl ◽  
Richard Koschuch ◽  
Philipp Jocham ◽  
Johannes Hübl
Keyword(s):  
Debris Flow ◽  
Early Warning ◽  
Pulse Compression ◽  
Mitigation Measures ◽  
Heavy Rainfall Event ◽  
Monitoring Site ◽  
Traffic Lights ◽  
The Road ◽  
Pulse Compression Radar ◽  
Monitoring And Early Warning

<p>After a heavy rainfall event on August 31<sup>st</sup>, 2019, a debris flow at the Dawinbach in the municipality of Strengen (Tyrol, Austria) caused a blockage of the culvert below the provincial road B-316 and deposition in the residential area. The debris deposition raised up to 2 to 3 meters on the road and led to property damage to real estate. The total volume of the debris flow was approximately 15 000 cubic meters.</p><p>In order to control a further debris flow of this magnitude, the Austrian Service of Torrent and Avalanche Control started to construct mitigation measures. They include a channel relocation in order to significantly increase the channel crosssection. Hence the construction company STRABAG is also relocating the provincial road bridge.</p><p>Since the risk for this road section and for the workers on site is particularly high during the construction period, a combined monitoring and early warning concept was developed and implemented by the BOKU, Vienna and the company IBTP Koschuch.</p><p>The monitoring site consisting of a pulse compression radar and a pull rope system was installed 800m upstream from the fan. The combination of the two sensors now results in three major advantages.</p><ul><li>At sensor level, the system operates redundantly.</li> <li>A more reliable differentiation between increased discharge or debris flow is given.</li> <li>In the event of a false alarm, the system provides easier diagnosis and assignment of the fault.</li> </ul><p>Two events of increased runoff occurred during the deployment period. Both were successfully detected by the pulse compression radar. Here, the first event was used for threshold validation of the radar unit. Thus, an alarm could already be sent out automatically for the second one. The road is controlled by an integrated light signal system consisting of three traffic lights. A siren near the construction site can warn workers of an impending event by means of an acoustic signal. The reaction time after the alarm has been triggered is between 75 and 150 seconds, depending on the speed of the debris flow. The responsible authorities are informed by sending an SMS chain, which includes details about the type of process and the type of the activated triggering system.</p>

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.

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