Hydrogeomorphic Recovery and Temporal Changes in Rainfall Thresholds for Debris Flows Following Wildfire

Probabilistic rainfall thresholds for debris flows occurred after the Wenchuan earthquake using a Bayesian technique

Frequency–magnitude distribution of debris flows compiled from global data, and comparison with post-fire debris flows in the western U.S.

The recurrence interval of post-fire debris-flow generating rainfall in the southwestern United States

Preliminary study of the effect of earthquakes on the rainfall threshold of debris flows

Increases in wildfire activity and rainfall intensification are driving more postfire debris flows (PFDF) in many regions around the world.PFDFs are most common in the first postfire year and may even occur before a fire is fully controlled.This underscores the importance of assessing postfire hazards before a fire starts.Evaluation of PFDF hazards prior to fire can help strategize interventions lessening the negative effects of future fires.However, debris-flow runout and inundation analyses are not routine in PFDF hazard assessments, partially due to time constraints and substantial uncertainties in boundary conditions.Here, we propose a prefire PFDF inundation assessment framework using a debris-flow runout model based on the Herschel-Bulkley (HB) rheology (HEC-RAS v6.1).We constrain model inputs and parameters using Bayesian posterior analysis, rainfall-runoff simulations, and a debris-flow volume model.We use observations from recent PFDF incidents in northern Arizona, USA, to calibrate model components and then apply our prefire inundation assessment framework in a nearby unburned area.Specifically, we (a) identify yield stress as the most influential factor on inundation extent and arrival time in a HB model, (b) establish posterior distributions for model parameters suitable for forward modeling by leveraging uncertainties in field observations, and (c) implement a predictive forward analysis in an area that has not burned recently to evaluate PFDF inundation under several future fire scenarios.This study improves our ability to assess postfire debris-flow hazards before a fire begins and provides guidance for future applications of single-phase rheological models when assessing PFDF hazards. Plain Language SummaryWildfires and increased rainfall are causing more dangerous postfire debris flows around the world.These are most likely within the first year after a fire, sometimes even while the fire is ongoing.Recognizing this, it's important to assess these hazards before a fire starts to minimize damage.Current methods for predicting where and how debris flow will happen after a fire are not used often enough, mainly because they take a lot of time and there are many uncertainties involved.Our study proposes a new approach using a model to predict debris flow behavior before a fire occurs.This model, tested with data from northern Arizona, helps us understand factors like the mud's "stickiness" which influences how far and fast it travels.We also improve the way we estimate model parameters by incorporating uncertainties from real-world observations.Finally, we use this model to foresee the impact of debris flow in different fire scenarios that could happen in the future.This work not only gets us better prepared for postfire debris flow but also offers guidance on how to use these models in future hazard assessments.

The Ms 8.0 Wenchuan earthquake greatly altered the threshold for rainfall-triggered debris flows in the affected areas. It is of both scientific and practical significance to determine the rainfall thresholds. This study examines one of the regions most prone to debris flows to analyze the characteristics of rainfall that caused debris flows, and to explore local rainfall thresholds. We applied the relation between rainfall intensity and duration, peak intensity and event amount, and other single factor approaches. Comparison of effectiveness and accuracy indicates that the event rainfall is the most sensitive factor for forecasting. Analysis of the annual rainfall thresholds showed that the rainfall conditions required for debris flows have increased on the continent during the past 6 years. Besides the rainfall fluctuations over the past few years, material changes were the primary reason for threshold variability. Recovery of vegetation plays an important role in reducing potential loose material that supplies volume for debris flows. Natural solidification, decrease of the potential erosion depth, and surface coarsening make it more difficult to initiate a debris flow, and ultimately increased rainfall conditions required. The change in rainfall thresholds can be predicted and verified for the entire earthquake-affected region.

The relationship between median grain size of sediments and the abundance in the wild of green algal mats (Ulva prolifera) on the intertidal flats of Muan, Korea, were investigated. The impact of substratum particle size on the growth and survival of germlings was examined in the laboratory. In the wild, the average annual density of algal mats was 7,950 ind m−2. The algal mats mainly occurred in sands and exhibited patchy distribution. Statistical analysis indicates significant spatial analysis differences and a significant relationship between density and the ratio of sands to silts, suggesting that the distribution and density of this species were related to particle size. In laboratory experiments, the survival rate of U. prolifera germlings was the lowest value (22%) on sediments with a median grain size of 63–125 μm. Laboratory experiments have generally shown a positive relationship between attachment or survival of the alga and substratum particles size. Our laboratory results indicate a clear link between germling settlement/survival and substratum particle size. These results explain the spatial differences in abundance observed in the field in relation to the distribution and ratio of sands to silt on the Muan flats.

Critical rainfall thresholds can be the key to ensuring effective debris-flow forecasting. They are significant for the study of the trigger mechanisms of debris flows, for the forecast of the characteristics of future events, and for the development of engineering guidance for mitigation. Using a hydrological approach, we first calculated the flood peak discharge at different frequencies and then the corresponding rainfall thresholds for the initiation of different scales of debris flows in Zhouqu County, China. This was followed by the establishment of a functional relation between the intensity and duration of rainfall events that trigger debris flows of different warning levels in two initial soil conditions (dry and moist). This, in turn, yielded four warning levels and two preliminary warning levels. For the two early soil conditions (dry and moist) in the Sanyanyu Gully of Zhouqu County, the Level I (red) warning values for rainfall triggering debris flow are 56 and 51 mm h −1 , respectively; the Level II (orange) warning values are 41 and 38 mm h −1 , respectively; the Level III (yellow) warning values are 32 and 30 mm h −1 , respectively; and the Level IV (blue) warning values are 24 and 22 mm h −1 , respectively. The Level V preliminary warning values are 17 and 16 mm h −1 , respectively; and the Level VI preliminary warning values are 10 and 9.5 mm h −1 , respectively. The rainfall intensity and duration were found to exhibit a power function relation, I = αD β , where the values of α and β vary with the warning levels. Rainfall events capable of triggering debris flows in the new thresholds and intensity–duration relations presented here can be used for forecasting purposes and in operational geohazard warning systems. These research results also provide a scientific basis for regional hazard mitigation and reduction in Zhouqu County.

Sources of debris flow material in burned areas

There is an increased risk in post-fire debris flow (DF) occurrences in the western USA with recent increase in wildfire frequencies. DFs are destructive, causing high loss to lives and infrastructure. A lot of effort is going into possible preventive and/or mitigation measures. Recent research efforts in this niche focus on developing statistical models that assist emergency responders in isolating high-hazard locations after fires. There are two general approaches to this statistical modeling: linear and nonlinear. This study has looked into applying a linear-based logistic regression model and a nonlinear-based C5.0 decision tree model to assess the strength of each in predicting the locations within the Thomas Fire boundary that produced DFs. To do this, DF scars were delineated by running a change detection protocol known as delta normalized difference vegetation index (dNDVI), using high spatial resolution data from Planet Labs. These scars were further validated with data gathered by Santa Barbara County officials on affected areas. Results from the two statistical models were then overlain on the delineated DF scars and compared against each other to determine predictive strengths. The results revealed both models to perform well in predicting high probabilities for locations that were shown to produce DFs. The logistic regression model predicted an overall ~ 44,800 ha (49%) more high-hazard coverage compared to the C5.0 tree and therefore showed greater urgency. However, a closer look at the basin predictions with the delineated DF scars showed that most of these high-hazard basins identified by the logistic regression did not to have any discernible scars. It was projected that most of these locations were likely false positives and further fine-tuning of the model was recommended. Further recommendation was made concerning the development of additional models to predict potential DF inundation paths. Combining origination and inundation models will be most beneficial since the greatest associated danger with DFs is not necessarily where they start, but rather further downstream where most communities and infrastructure are situated.

This work examines the period affected by extreme rainfall that triggers debris flow and the modification of the critical rainfall threshold for debris flows after extreme events in the Chenyulan watershed in central Taiwan. Three rainfall indices, Im (maximum hourly rainfall depth), Rd (maximum 24-h rainfall amount), and RI (= Im × Rd), were used to analyze the variations in rainfall thresholds for triggering debris flow. Five extreme rainfall events with RI > 365 cm2/h that triggered multiple debris flows are discussed. Critical rainfall (the rainfall threshold of Im, Rd, and RI for triggering debris flow) drops significantly at the initial stage after an extreme rainfall event; it then increases to revert to its original value after subsequent rainfall events. The lowered critical rainfall and the required recovery period were associated with the RI driven by an extreme rainfall event. Empirical relationships for modified critical rainfall and recovery period after an extreme rainfall event are also presented. The critical RI and Im (or Rd) could be reduced by 70% and 40%, respectively. The maximum recovery period was approximately three years after an extreme rainfall. The recovery period has an increasing trend with the increasing ratio of landslide area bought by an extreme rainfall.

The steep, tectonically active terrain along the Central California (USA) coast is well known to produce deadly and destructive debris flows. However, the extent to which fire affects debris-flow susceptibility in this region is an open question. We documented the occurrence of postfire debris floods and flows following the landfall of a storm that delivered intense rainfall across multiple burn areas. We used this inventory to evaluate the predictive performance of the US Geological Survey M1 likelihood model, a tool that presently underlies the emergency assessment of postfire debris-flow hazards in the western USA. To test model performance, we used the threat score skill statistic and found that the rainfall thresholds estimated by the M1 model for the Central California coast performed similarly to training (Southern California) and testing (Intermountain West) data associated with the original model calibration. Model performance decreased when differentiating between “minor” and “major” postfire hydrologic response types, which weigh effects on human life and infrastructure. Our results underscore that the problem of false positives is a major challenge for developing accurate rainfall thresholds for the occurrence of postfire debris flows. As wildfire activity increases throughout the western USA, so too will the demand for the assessment of postfire debris-flow hazards. We conclude that additional collection of field-verified inventories of postfire hydrologic response will be critical to prioritize which model variables may be suitable candidates for regional calibration or replacement.

Experimental investigation of the effects of shrub filter strips on debris flow trapping and interception

The authors predicted mudflow and debris flow velocities with 350 field and laboratory measurements and obtained a slight trend for V=u to increase with h=d50, and the ratio of V=u is approximately 10 and rarely exceeds 30. The logarithmic relationship of turbulent resistance agrees reasonably well with the measurements. Good results also are obtained with the Manning-Strickler approach. The dispersive stress equation also compares well with the measurements, but only when h=d50 < 50. For individual series data such as Hashimoto and Puerco, there is a trend for V=u to increase with h=d50. But for other individual series data, such as Davies, Rickenmann, Paris, St. Helens, Nevado del Ruiz, Wanglin, Wang, and Wenhai, there is a trend for V=u to decrease with h=d50 (Fig. 1 of the original paper). The discussers have some field data that also show the same phenomena, which are presented in Fig. 1. The database includes a total of 119 flow velocity measurements, in which each point includes flow depth, density (volume concentration), median grain diameter d50, grain size of less than 10% d10, and slope. The data of Jiangjia were obtained from the field measurements in Jiangjia Ravine, Yunnan, China, in 1999; the data of Hunshui were obtained from the field measurements in Hunshui Ravine, Yunnan, China, in 1976–1978; and the data of Liuwan were obtained from the field measurements in Liuwan Ravine, Gansu, China, in 1963–1964. Debris flow is a gravity flow, and the gravity force plays an important role in the movement of debris flow. So debris flow moves on a large slope and deposits on a small slope. Fei and Su (2004) showed that the main driving force of debris flow is provided by particles, not by water. When the volume concentration C 1⁄4 0:27 (or density ρ 1⁄4 1:46 g=cm3), the particle driving force is equal to the water driving force in debris flow. They defined that the minimum density of debris flow is 1:46 g=cm3, and 1:46–1:80 g=cm3 is the range of less viscous debris flow. Therefore, the minimum density of viscous debris flow is 1:80 g=cm3 (C 1⁄4 0:47) (Fei and Su 2004; Yu 2008a). Two types of shear stresses describe these two kinds of debris flows: (1) the viscous stress for viscous debris flow, and (2) the turbulent stress for less viscous debris flow (Fei and Su 2004). As the driving force of debris flow is provided primarily by particles, the coarse particle plays an important role in the movement of debris flow, especially for viscous debris flow with a large volume concentration. So the larger particle diameter, the larger is the velocity. There is a trend for V to increase with d50, and there is a trend for V=u to increase with d50=h. Two empirical equations of mean velocity of viscous debris flow described this relationship (Wu et al. 1993; Yu 2001): V u 1⁄4 27:57 d50 h 0:245 ð1Þ

Abstract. Many shallow landslides and debris flows are precipitation initiated. Therefore, regional landslide hazard assessment is often based on empirically derived precipitation intensity-duration (ID) thresholds and landslide inventories. Generally, two features of precipitation events are plotted and labeled with (shallow) landslide occurrence or non-occurrence. Hereafter, a separation line or zone is drawn, mostly in logarithmic space. The practical background of ID is that often only meteorological information is available when analyzing (non-)occurrence of shallow landslides and, at the same time, it could be that precipitation information is a good proxy for both meteorological trigger and hydrological cause. Although applied in many case studies, this approach suffers from many false positives as well as limited physical process understanding. Some first steps towards a more hydrologically based approach have been proposed in the past, but these efforts received limited follow-up. Therefore, the objective of our paper is to (a) critically analyze the concept of precipitation ID thresholds for shallow landslides and debris flows from a hydro-meteorological point of view and (b) propose a trigger–cause conceptual framework for lumped regional hydro-meteorological hazard assessment based on published examples and associated discussion. We discuss the ID thresholds in relation to return periods of precipitation, soil physics, and slope and catchment water balance. With this paper, we aim to contribute to the development of a stronger conceptual model for regional landslide hazard assessment based on physical process understanding and empirical data.