Debris-flow hazards in the Blue Ridge of central Virginia

2000 ◽  
Vol 6 (1) ◽  
pp. 3-23 ◽  
Author(s):  
G. F. Wieczorek ◽  
B. A. Morgan ◽  
R. H. Campbell
Keyword(s):  
Debris Flow ◽  
Debris Flows ◽  
Rainfall Intensity ◽  
Granitic Rocks ◽  
Blue Ridge ◽  
Eastern United States ◽  
Storm Events ◽  
Gis Technology ◽  
Madison County ◽  
Physical Examinations

Abstract The June 27, 1995, storm in Madison County, Virginia produced debris flows and floods that devastated a small (130 km 2 ) area of the Blue Ridge in the eastern United States. Although similar debris-flow inducing storm events may return only approximately once every two thousand years to the same given locale, these events affecting a similar small-sized area occur about every three years somewhere in the central and southern Appalachian Mountains. From physical examinations and mapping of debris-flow sources, paths, and deposits in Madison County, we develop methods for identifying areas subject to debris flows using Geographic Information Systems (GIS) technology. We examined the rainfall intensity and duration characteristics of the June 27, 1995, and other storms, in the Blue Ridge of central Virginia, and have defined a minimum threshold necessary to trigger debris flows in granitic rocks. In comparison with thresholds elsewhere, longer and more intense rainfall is necessary to trigger debris flows in the Blue Ridge.

Simulating debris flows triggered by rainfall in Shiyang gully, China

2020 ◽  
Author(s):  
Jiaoyang Li
Keyword(s):  
Debris Flow ◽  
Debris Flows ◽  
Rainfall Intensity ◽  
Intensity Increase ◽  
Short Term ◽  
Rain Area ◽  
Cumulative Rainfall ◽  
Event Flow ◽  
Warning Time ◽  
Main Factor

<p>A debris flow occurred in Shiyang gully, located between Hebei Province and Beijing, on 8 June 2017, resulting in 6 people dead or injured. Short-term heavy rainfall is the main factor that triggered this event, however, the meteorological agency didn’t forecast this event very well. In this study, numerical simulation using FLO-2D was performed to reproduce the debris flow event (flow depths, flow velocities, and sediment depositions)occurred in 2017. The results of the field survey showed that the influential range of debris flow is consistent with the simulation results. Simulated depth accuracy is greater than 70%. Then, we used FLO-2D is calibrated to simulate debris flows disasters under different rainfall scenarios. The results showed that, the Beijing needs to be warned when the accumulated precipitation is 40mm at the rainfall intensity of 1mm/min. As cumulative rainfall and rainfall intensity increase, the risk of Shiyang gully is increasing.  This study used FLO-2D simulated process of debris flows triggered by rainfall. The results showed the early warning time and influential range for different intensity ,accumulated precipitation, and rain area, which is beneficial to the debris flow management in the western mountainous areas of Beijing.</p>

scholarly journals Uji Laboratorium Pengaruh Kemiringan Lereng Terhadap Kejadian Longsoran Aliran Debris Pasir Merapi

2020 ◽  
Vol 16 (1) ◽  
pp. 23-34
Author(s):  
Bayu Seto Waseso Utomo ◽  
Jati Iswardoyo ◽  
Ruzardi Ruzardi
Keyword(s):  
Debris Flow ◽  
Laboratory Test ◽  
Early Warning ◽  
Debris Flows ◽  
Rainfall Intensity ◽  
Laboratory Scale ◽  
Artificial Rainfall ◽  
Rain Simulator

The debris flow that happen on the of Mount Merapi is really hard to be seen, therefore, it is necessary to conduct laboratory-scale simulations to know when debris flows will happen as regard to rainfall intensity and the slope of Mount of Merapi. This research examines the correlation between the slope and the potential for debris flow at 25 mm/h rainfall intensity. This will be a reference for early warning of landslides on Mount of Merapi. This research uses a tool such as flume that sized 3 x 5 x 0,15 m as a model of slope of Mount of Merapi, and artificial rainfall apparatus as the rain simulator. The simulation is conducted using five years rainfall intensity of 25 mm/h in combination of slope i.e. 15, 20, 25, 30 and 35 degrees whereas the material used to represent the sediment is in form of sand taken from Gendol River upstream with 4,75 mm passing mesh sieves. The result of this simulation is the steeper the slope is, the faster the duration for the rain to cause debris flow. This research can be continued with change variation of rainfall intensity to understand the debris flows behavior. Keywords: Debris flow, Mount of Merapi, laboratory test, rainfall intensity, flume model

scholarly journals Evaluating methods for debris-flow prediction based on rainfall in an Alpine catchment

2021 ◽  
Vol 21 (9) ◽  
pp. 2773-2789
Author(s):  
Jacob Hirschberg ◽  
Alexandre Badoux ◽  
Brian W. McArdell ◽  
Elena Leonarduzzi ◽  
Peter Molnar
Keyword(s):  
Random Forest ◽  
Debris Flow ◽  
Debris Flows ◽  
Rainfall Intensity ◽  
Multiple Scales ◽  
Learning Algorithm ◽  
Predictive Performance ◽  
Early Warning Systems ◽  
Antecedent Rainfall ◽  
Data Set

Abstract. The prediction of debris flows is relevant because this type of natural hazard can pose a threat to humans and infrastructure. Debris-flow (and landslide) early warning systems often rely on rainfall intensity–duration (ID) thresholds. Multiple competing methods exist for the determination of such ID thresholds but have not been objectively and thoroughly compared at multiple scales, and a validation and uncertainty assessment is often missing in their formulation. As a consequence, updating, interpreting, generalizing and comparing rainfall thresholds is challenging. Using a 17-year record of rainfall and 67 debris flows in a Swiss Alpine catchment (Illgraben), we determined ID thresholds and associated uncertainties as a function of record duration. Furthermore, we compared two methods for rainfall definition based on linear regression and/or true-skill-statistic maximization. The main difference between these approaches and the well-known frequentist method is that non-triggering rainfall events were also considered for obtaining ID-threshold parameters. Depending on the method applied, the ID-threshold parameters and their uncertainties differed significantly. We found that 25 debris flows are sufficient to constrain uncertainties in ID-threshold parameters to ±30 % for our study site. We further demonstrated the change in predictive performance of the two methods if a regional landslide data set with a regional rainfall product was used instead of a local one with local rainfall measurements. Hence, an important finding is that the ideal method for ID-threshold determination depends on the available landslide and rainfall data sets. Furthermore, for the local data set we tested if the ID-threshold performance can be increased by considering other rainfall properties (e.g. antecedent rainfall, maximum intensity) in a multivariate statistical learning algorithm based on decision trees (random forest). The highest predictive power was reached when the peak 30 min rainfall intensity was added to the ID variables, while no improvement was achieved by considering antecedent rainfall for debris-flow predictions in Illgraben. Although the increase in predictive performance with the random forest model over the classical ID threshold was small, such a framework could be valuable for future studies if more predictors are available from measured or modelled data.

scholarly journals Development of Nomogram for Debris Flow Forecasting Based on Critical Accumulated Rainfall in South Korea

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 2181
Author(s):  
Nam ◽  
Lee ◽  
Kim
Keyword(s):  
South Korea ◽  
Debris Flow ◽  
Urban Areas ◽  
Debris Flows ◽  
Rainfall Intensity ◽  
Extreme Weather Events ◽  
Weather Events ◽  
Accumulated Rainfall ◽  
Landslides And Debris Flows ◽  
Heavy Rains

Climate change causes extreme weather events worldwide such as increasing temperatures and changing rainfall patterns. With South Korea facing growing damage from the increased frequency of localized heavy rains. In particular, its steep slope lands, including mountainous areas, are vulnerable to damage from landslides and debris flows. In addition, localized short-term heavy rains that occur in urban areas with extremely high intensity tend to lead a sharp increase in damage from soil-related disasters and cause huge losses of life and property. Currently, South Korea forecasts landslides and debris flows using the standards for forecasting landslides and heavy rains. However, as the forecasting is conducted separately for rainfall intensity and accumulated rainfall, this lacks a technique that reflects both amount and intensity of rainfall in an episode of localized heavy rainfall. In this study, aims to develop such a technique by collecting past cases of debris flow occurrences and rainfall events that accompanied debris flows to calculate the rainfall triggering index (RTI) reflecting accumulated rainfall and rainfall intensity. In addition, the RTI is converted into the critical accumulated rainfall (Rc) to use rainfall information and provide real-time forecasting. The study classifies the standards for flow debris forecasting into three levels: ALERT (10–50%), WARNING (50–70%), and EMERGENCY (70% or higher), to provide a nomogram for 6 h, 12 h, and 24 h. As a result of applying this classification into the actual cases of Seoul, Chuncheon, and Cheongju, it is found that about 2–4 h of response time is secured from the point of the Emergency level to the occurrence of debris flows.

Consideration of the Validity of Debris-flow Bulking Factors

2017 ◽  
Vol 23 (4) ◽  
pp. 291-298
Author(s):  
Holly Brunkal ◽  
Paul Santi
Keyword(s):  
Debris Flow ◽  
Debris Flows ◽  
Rainfall Intensity ◽  
Peak Discharge ◽  
Data Set ◽  
Burned Areas ◽  
The Given ◽  
Basin Area ◽  
Bulking Factor ◽  
Significant Underestimation

Abstract Compilation of a database of debris-flow peak discharges (Q) allowed for a comparison with the expected basin discharge, as computed using the rational equation, Q=CIA. The observed values of Q for debris flows in unburned and burned areas were divided by the computed Q values of runoff using the rational method. This ratio is the bulking factor for that debris-flow event. Unburned and burned basins constitute two distinct populations; analysis shows that the bulking factors for burned areas are consistently higher than those for unburned basins. Previously published bulking factors for unburned areas fit the data set in about 50 percent of the observed cases in our compiled data set. Bulking factors for burned areas that were found in the published literature were well below the observed increases in peak discharge in over 50 percent of the cases investigated. If used for design purposes, these bulking factors would result in a significant underestimation of the peak discharge from a burned basin for the given rainfall intensity. Peak discharge bulking rates were found to be inversely related to basin area.

scholarly journals Quantitative Analysis of the Effects of an Earthquake on Rainfall Thresholds for Triggering Debris-Flow Events

2021 ◽  
Vol 9 ◽  
Author(s):  
Shuang Liu ◽  
Kaiheng Hu ◽  
Qun Zhang ◽  
Shaojie Zhang ◽  
Xudong Hu ◽  
...  
Keyword(s):  
Debris Flow ◽  
Debris Flows ◽  
Rainfall Intensity ◽  
Focal Depth ◽  
Rainfall Threshold ◽  
Effective Rainfall ◽  
Rainfall Thresholds ◽  
Jiuzhaigou Earthquake ◽  
The Wenchuan Earthquake ◽  
The Moment

The impacts of destructive earthquakes on rainfall thresholds for triggering the debris flows have not yet been well investigated, due to lacks of data. In this study, we have collected the debris-flow records from the Wenchuan, Lushan, and Jiuzhaigou earthquake-affected areas in Sichuan Province, China. By using a meteorological dataset with 3 h and 0.1° resolutions, the dimensionless effective rainfall and rainfall intensity-duration relationships were calculated as the possible thresholds for triggering the debris flows. The pre- and post-seismic thresholds were compared to evaluate the impacts of the various intensities of earthquakes. Our results indicate that the post-quake thresholds are much smaller than the pre-seismic ones. The dimensionless effective rainfall shows the impacts of the Wenchuan, Lushan, and Jiuzhaigou earthquakes to be ca. 26, 27, and 16%, respectively. The Wenchuan earthquake has the most significant effect on lowering the rainfall intensity-duration curve. Rainfall threshold changes related to the moment magnitude and focal depth are discussed as well. Generally, this work may lead to an improved post-quake debris-flow warning strategy especially in sparsely instrumented regions.

Hydrologic conditions leading to debris-flow initiation

1990 ◽  
Vol 27 (6) ◽  
pp. 789-801 ◽  
Author(s):  
K. A. Johnson ◽  
N. Sitar
Keyword(s):  
Debris Flow ◽  
Pore Pressure ◽  
Debris Flows ◽  
Field Investigation ◽  
Hydrologic Response ◽  
Storm Events ◽  
Hillslope Hydrology ◽  
Antecedent Moisture ◽  
Pressure Pulses ◽  
Flow Activity

Mitigation of the hazards posed by debris flows requires an understanding of the mechanisms leading to their initiation. The objectives of this study were to evaluate and document the hydrologic response of a potential debris-flow source area to major rainstorms and to evaluate whether traditional models of hillslope hydrology can account for the observed response. A field site in an area of previous debris-flow activity was instrumented and monitored for two winter seasons. Hydrologic responses for a wide variety of antecedent conditions were recorded, including two storm events that produced well-defined positive pore-pressure pulses at the site and initiated numerous debris flows in the immediate vicinity of the site. The observed hydrologic response was highly dependent on antecedent moisture conditions which can be characterized by soil matric suction measurements. The pressure-head pulses observed had a magnitude of approximately 50 cm of water, were transient, traveled downslope, and exhibited some spatial variability. Traditional models of hillslope hydrology do not fully account for the positive pore-pressure pulses observed high on the hillslope. Key words: debris flow, hillslope hydrology, pore pressure, antecedent moisture, tensiometer, piezometer, field investigation.

Rainfall Spatial variability, rainfall intensity–duration (ID) thresholds, and the initiation of debris flows in the eastern Italian Alps

2021 ◽  
Author(s):  
Oliver Francis ◽  
Hui Tang ◽  
Carlo Gregoretti ◽  
Matteo Berti ◽  
Martino Bernard ◽  
...  
Keyword(s):  
Debris Flow ◽  
Spatial Variability ◽  
Spatial Variation ◽  
Debris Flows ◽  
Rainfall Intensity ◽  
Shallow Water Equation ◽  
Italian Alps ◽  
Mountain Ranges ◽  
Mountainous Regions ◽  
The Impact

<p>Runoff-generated debris flows are a significant hazard in steep mountain ranges across the world. During intense rainfall storms, runoff can rapidly form in small steep basins and mobilise large volumes of sediment triggering debris flows which can damage infrastructure and endanger lives. A common method for forecasting debris flows is deriving empirical rainfall intensity–duration (ID) thresholds from previously recorded debris flow events in a given area. However, the storms which trigger debris flows usually are short and intense with high spatial variation making an accurate recording of the conditions responsible for initiation difficult.</p><p>In this study, we investigate the impact of the spatial variability of rainfall on debris flow initiation in small, steep, and debris flow prone catchments in the eastern Italian Alps (Dolomites) using the SWEHR (Shallow Water Equation Hairsine-Rose) numerical model. The modelled catchments are monitored by multiple rain gages which we use to quantify the uncertainty of the rainfall ID thresholds due to the spatial variation of rainfall by comparing empirical and numerically modelled thresholds. We also compare simulated triggering discharges for debris flows with available field observations in the study area. This study will help to improve the quality of hazard forecasting of debris flows in mountainous regions</p>

scholarly journals Predicting storm-triggered debris flow events: application to the 2009 Ionian Peloritan disaster (Sicily, Italy)

2015 ◽  
Vol 15 (8) ◽  
pp. 1785-1806 ◽  
Author(s):  
M. Cama ◽  
L. Lombardo ◽  
C. Conoscenti ◽  
V. Agnesi ◽  
E. Rotigliano
Keyword(s):  
Debris Flow ◽  
Debris Flows ◽  
Stochastic Modelling ◽  
Storm Events ◽  
Catchment Scale ◽  
M Cell ◽  
Small Catchment ◽  
Digital Elevation ◽  
Geologic Map ◽  
Elevation Model

Abstract. The main assumption on which landslide susceptibility assessment by means of stochastic modelling lies is that the past is the key to the future. As a consequence, a stochastic model able to classify past known landslide events should be able to predict a future unknown scenario as well. However, storm-triggered multiple debris flow events in the Mediterranean region could pose some limits on the operative validity of such an expectation, as they are typically resultant of a randomness in time recurrence and magnitude and a great spatial variability, even at the scale of small catchments. This is the case for the 2007 and 2009 storm events, which recently hit north-eastern Sicily with different intensities, resulting in largely different disaster scenarios. The study area is the small catchment of the Itala torrent (10 km2), which drains from the southern Peloritani Mountains eastward to the Ionian Sea, in the territory of the Messina province (Sicily, Italy). Landslides have been mapped by integrating remote and field surveys, producing two event inventories which include 73 debris flows, activated in 2007, and 616 debris flows, triggered by the 2009 storm. Logistic regression was applied in order to obtain susceptibility models which utilize a set of predictors derived from a 2 m cell digital elevation model and a 1 : 50 000 scale geologic map. The research topic was explored by performing two types of validation procedures: self-validation, based on the random partition of each event inventory, and chrono-validation, based on the time partition of the landslide inventory. It was therefore possible to analyse and compare the performances both of the 2007 calibrated model in predicting the 2009 debris flows (forward chrono-validation), and vice versa of the 2009 calibrated model in predicting the 2007 debris flows (backward chrono-validation). Both of the two predictions resulted in largely acceptable performances in terms of fitting, skill and reliability. However, a loss of performance and differences in the selected predictors arose between the self-validated and the chrono-validated models. These are interpreted as effects of the non-linearity in the domain of the trigger intensity of the relationships between predictors and slope response, as well as in terms of the different spatial paths of the two triggering storms at the catchment scale.

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