What Is a Debris Flood?

2020 ◽  
Vol 56 (8) ◽  
Author(s):  
Michael Church ◽  
Matthias Jakob
Keyword(s):  
Debris Flood

scholarly journals Evaluation of Debris Flows for Flood Plain Estimation in a Small Ungauged Tropical Watershed for Hurricane Otto

Hydrology ◽  
2021 ◽  
Vol 8 (3) ◽  
pp. 122
Author(s):  
Sebastián Fallas Salazar ◽  
Alejandra M. Rojas González
Keyword(s):  
Debris Flows ◽  
Flood Plain ◽  
Rain Gauge ◽  
Economic Losses ◽  
Clean Water ◽  
Newtonian Flows ◽  
Bias Factor ◽  
Input Source ◽  
Debris Flood ◽  
Tropical Watershed

The variability of climate, increase in population, and lack of territorial plans in Costa Rica have caused intense disasters with human and economic losses. In 2016, Hurricane Otto hit the country’s northern area, leaving substantial damages, including landslides, debris flows, and flooding. The present study evaluated different scenarios to estimate flooded areas for Newtonian (clean water), and non-Newtonian flows with volumetric sediment concentrations (Cv) of 0.3, 0.45, 0.55, and 0.65 using Hydro-Estimator (HE), rain gauge station, and the 100-year return period event. HEC–HMS modeled the rainfall products, and FLO-2D modeled the hydrographs and Cv combinations. The simulation results were evaluated with continuous statistics, contingency table, Nash Sutcliffe Efficiency, measure of fit (F), and mean absolute differences (E) in the floodplains. Flow depths, velocities, and hazard intensities were obtained in the floodplain. The debris flood was validated with field data and classified with a Cv of 0.45, presenting lower MAE and RMSE. Results indicated no significant differences in flood depths between hydrological scenarios with clean-water simulations with a difference of 8.38% in the peak flow. The flood plain generated with HE rainfall and clear-water condition presented similar results compared to the rain gauge input source. Additionally, hydraulic results with HE and Cv of 0.45 presented E and F values similar to the simulation of Cv of 0.3, demonstrating that the HE bias did not influence the determination of the floodplain depth and extent. A mean bias factor can be applied to a sub-daily temporal resolution to enhance HE rain rate quantifications and floodplain determination.

scholarly journals Observations on the May 2019 Joffre Peak landslides, British Columbia

Landslides ◽  
2020 ◽  
Vol 17 (4) ◽  
pp. 913-930 ◽  
Author(s):  
Pierre Friele ◽  
Tom H. Millard ◽  
Andrew Mitchell ◽  
Kate E. Allstadt ◽  
Brian Menounos ◽  
...  
Keyword(s):  
British Columbia ◽  
Debris Flow ◽  
Seismic Analysis ◽  
Rock Avalanche ◽  
Permafrost Degradation ◽  
Depth Estimate ◽  
Coast Mountains ◽  
Conditioning Factors ◽  
The North ◽  
Debris Flood

AbstractTwo catastrophic landslides occurred in quick succession on 13 and 16 May 2019, from the north face of Joffre Peak, Cerise Creek, southern Coast Mountains, British Columbia. With headscarps at 2560 m and 2690 m elevation, both began as rock avalanches, rapidly transforming into debris flows along middle Cerise Creek, and finally into debris floods affecting the fan. Beyond the fan margin, a flood surge on Cayoosh Creek reached bankfull and attenuated rapidly downstream; only fine sediment reached Duffey Lake. The toe of the main debris flow deposit reached 4 km from the headscarp, with a travel angle of 0.28, while the debris flood phase reached the fan margin 5.9 km downstream, with a travel angle of 0.22. Photogrammetry indicates the source volume of each event is 2–3 Mm3, with combined volume of 5 Mm3. Lidar differencing, used to assess deposit volume, yielded a similar total result, although error in the depth estimate introduced large volume error masking the expected increase due to dilation and entrainment. The average velocity of the rock avalanche-debris flow phases, from seismic analysis, was ~ 25–30 m/s, and the velocity of the 16 May debris flood on the upper fan, from super-elevation and boulder sizes, was 5–10 m/s. The volume of debris deposited on the fan was ~ 104 m3, 2 orders of magnitude less than the avalanche/debris flow phases. Progressive glacier retreat and permafrost degradation were likely the conditioning factors; precursor rockfall activity was noted at least ~6 months previous; thus, the mountain was primed to fail. The 13 May landslide was apparently triggered by rapid snowmelt, with debuttressing triggering the 16 May event.

scholarly journals A Hybrid Intelligence Model for the Prediction of the Peak Flow of Debris Floods

Water ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2246
Author(s):  
Mohammad Ebrahim Banihabib ◽  
Lubos Jurik ◽  
Mahsa Sheikh Kazemi ◽  
Jaber Soltani ◽  
Mitra Tanhapour
Keyword(s):  
Artificial Intelligence ◽  
Hybrid Model ◽  
Sediment Concentration ◽  
Peak Flows ◽  
Flood Peak ◽  
Flood Warning ◽  
Intelligence Models ◽  
Artificial Intelligence Models ◽  
Debris Flood ◽  
Debris Floods

Debris floods, as one of the most significant natural hazards, often threaten the lives and property of many people worldwide. Predicting models are essential for flood warning systems to minimize casualties of debris floods. Since HEC-HMS (Hydrologic Engineering Center’s Hydrological Modelling System) cannot simulate debris flow, this study proposes a new hybrid model that uses artificial intelligence models to overcome HEC-HMS’s insufficiency in reflecting the sediment concentration effect on the debris floods. A sediment concentration is an effective factor for evaluating debris flood peak flows. This led to the proposal of new hybrid models for predicting the debris flood peak flows on the basis of hybridization of the artificial intelligence models (Bayesian Network (BN) and Support Vector Regression–Particle Swarm Optimization (SVR-PSO)) and HEC-HMS. To estimate the sediment concentration of floods by using the proposed artificial intelligence models, we nominated an average basin elevation, an average basin slope, a basin area, the current day rainfall, the antecedent rainfall of the past 3 days, and the streamflow of the previous day the previous day as the effective variables. In the validation stage, the average of the Mean Absolute Relative Error (MARE) of the estimated values were 0.024, 0.038, and 0.024 for the typical floods that occurred in the Navrood, Kasilian, and the Amameh basins in the north of Iran, respectively. Similarly, we obtained values of 0.038, 0.073, and 0.040 for the debris flood events for the three respective locations. After predicting the debris flood peak flows by the proposed hybrid HMS-BN and HMS-SVR-PSO models, the average of the MAREs for all debris flood events was reduced to 0.013 and 0.014, respectively. The comparison of MAREs of the examined hybrid models shows that the HMS-BN model results in higher accuracy than the HMS-SVR-PSO model in the prediction of the debris flood peak flows. Generally, the absolute error of prediction by the proposed hybrid model is reduced to one-third of the HEC-HMS. The prediction of the debris flood peak flows using the proposed hybrid model can be examined in the debris flood warning systems to reduce the potential damages and casualties in similar basins.

scholarly journals Recognition of debris flow, debris flood and flood hazard through watershed morphometrics

Landslides ◽  
2004 ◽  
Vol 1 (1) ◽  
pp. 61-66 ◽  
Author(s):  
D. J. Wilford ◽  
M. E. Sakals ◽  
J. L. Innes ◽  
R. C. Sidle ◽  
W. A. Bergerud
Keyword(s):  
Debris Flow ◽  
Flood Hazard ◽  
Debris Flood

Estimation of debris flood magnitudes based on dendrogeomorphic data and semi-empirical relationships

Geomorphology ◽  
2013 ◽  
Vol 201 ◽  
pp. 80-85 ◽  
Author(s):  
Klaus Schraml ◽  
Barbara Kogelnig ◽  
Christian Scheidl ◽  
Markus Stoffel ◽  
Roland Kaitna
Keyword(s):  
Empirical Relationships ◽  
Debris Flood ◽  
Semi Empirical

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 Determining the Precipitation Intensity Threshold of Debris Flood Occurrence

2021 ◽  
pp. 473-489
Author(s):  
Mohammad Ebrahim Banihabib ◽  
Mitra Tanhapour
Keyword(s):  
Layer Thickness ◽  
Flow Direction ◽  
Average Diameter ◽  
Precipitation Intensity ◽  
Sediment Layer ◽  
Intensity Threshold ◽  
Precipitation Threshold ◽  
Debris Flood ◽  
Bed Slope ◽  
Debris Floods

AbstractIn this chapter, the precipitation threshold at which debris floods occur was evaluated experimentally, and the factors that influence debris flood occurrence, including the bed slope, sediment layer thickness, sediment grain size, length of alluvial flow direction, precipitation intensity, and time of debris flood occurrence, were examined. The impacts of these factors on debris flood initiation were investigated through dimensional analysis. Then, a method was developed to estimate the precipitation intensity threshold based on a set of laboratory tests. Furthermore, different methods for determining the precipitation intensity threshold at which debris floods are initiated were assessed and discussed. The results of the experiments showed that the effect of the sediment layer thickness on debris flood occurrence can be ignored. Moreover, by independently evaluating the effect of each factor on debris flood occurrence, it was found that the sediment length and average diameter of sediments are influential to debris flood initiation. The results of this research provide a better understanding of debris flood mechanisms and occurrence thresholds of debris floods and can be employed to prepare a forecasting model.

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