Evaluation of rainfall threshold models for debris flow initiation in the Jiangjia Gully,Yunnan Province,China

Systematically determining the discriminatory power of various rainfall properties and their combinations in identifying debris flow occurrence is crucial for early warning systems.In this study,we evaluated the discriminatory power of different univariate and multivariate rainfall threshold models in identifying triggering conditions of debris flow in the Jiangjia Gully,Yunnan Province,China.The univariate models used single rainfall properties as indicators,including total rainfall(R_(tot)),rainfall duration(D),mean intensity(I_(mean)),absolute energy(Eabs),storm kinetic energy(E_(s)),antecedent rainfall(R_(a)),and maximum rainfall intensity over various durations(I_(max_dur)).The evaluation reveals that the I_(max_dur)and Eabs models have the best performance,followed by the E_(s),R_(tot),and I_(mean)models,while the D and R_(a)models have poor performances.Specifically,the I_(max_dur)model has the highest performance metrics at a 40-min duration.We used logistic regression to combine at least two rainfall properties to establish multivariate threshold models.The results show that adding D or R_(a)to the models dominated by Eabs,E_(s),R_(tot),or I_(mean)generally improve their performances,specifically when D is combined with I_(mean)or when R_(a)is combined with Eabs or E_(s).Including R_(a)in the I_(max_dur)model,it performs better than the univariate I_(max_dur)model.A power-law relationship between I_(max_dur)and R_(a)or between Eabs and R_(a)has better performance than the traditional I_(mean)–D model,while the performance of the E_(s)–R_(a)model is moderate.Our evaluation reemphasizes the important role of the maximum intensity over short durations in debris flow occurrence.It also highlights the importance of systematically investigating the role of R_(a)in establishing rainfall thresholds for triggering debris flow.Given the regional variations in rainfall patterns worldwide,it is necessary to evaluate the findings of this study across diverse watersheds.

Climate Variability & Establishment of Rainfall Threshold Line for Landslide Hazards in Rangamati, Bangladesh

This study aims to evaluate the impact of extreme rainfall events on landslides under current and past climate scenarios. Rainfall-triggered landslides are analyzed by rainfall estimates, derived using statistics of events. It is established that recent climate changes, mainly temperature and rainfall patterns have significantly increased the rainfall-induced landslide hazards in the Rangamati district, Bangladesh. It is also observed that the temperature and rainfall of Rangamati had increased gradually during the last 40 years (1981-2021). On 13 June 2017, a series of landslides triggered by heavy monsoon rains (300 mm/24 h) occurred and killed more than 112 people in the Rangamati hill district, Bangladesh. The highest annual decade rainfall is 3816 mm, recorded in 2010-21. A relationship between causalities and the number of events has also been established. The analysis shows that both antecedent and single-day major rainfall patterns can influence sliding events. It is established that monsoonal rainfall (June-September) can significantly influence catastrophic landslide hazard events. Finally, two rainfall threshold lines for the researched area are constructed based on antecedent and single-day major rainfall occurrences, as well as the number of fatalities caused by landslides. Total rainfall of 100 mm (16.66 mm/day) during six days appears to define the minimum rainfall that has led to shallow landslides/slope failures, while 210 mm (35 mm/day) within six days appears to define the lowest rainfall that could be a cause of catastrophic landslide in Rangamati district.

An hourly shallow landslide warning model developed by combining automatic landslide spatial susceptibility and temporal rainfall threshold predictions

Landslide warning models are important for mitigating landslide risks.The rainfall threshold model is the most widely used early warning model for predicting rainfall-triggered landslides.Recently,the rainfall threshold model has been coupled with the landslide susceptibility(LS)model to improve the accuracy of early warnings in the spatial domain.Existing coupled models,designed based on a matrix including predefined rainfall thresholds and susceptibility levels,have been used to determine the warning level.These predefined classifications inevitably have subjective rainfall thresholds and susceptibility levels,thus affecting the probability distribution information and eventually influencing the reliability of the produced early warning.In this paper,we propose a novel landslide warning model in which the temporal and spatial probabilities of landslides are coupled without predefining the classified levels.The temporal probability of landslides is obtained from the probability distribution of rainfall intensities that triggered historical landslides.The spatial probability of landslides is then obtained from the susceptibility probability distribution.A case study shows that the proposed probability-coupled model can successfully provide hourly warning results before the occurrence of a landslide.Although all three models successfully predicted the landslide,the probability-coupled model produced a warning zone comprising the fewest grid cells.Quantitatively,the probabilitycoupled model produced only 39 grid cells in the warning zone,while the rainfall threshold model and the matrix-coupled model produced warning zones including 81 and 49 grid cells,respectively.The proposed model is also applicable to other regions affected by rainfall-induced landslides and is thus expected to be useful for practical landslide risk management.

Estimation of rainfall thresholds for shallow landslides in the Sierra Madre Oriental, northeastern Mexico

Landslides induced by prolonged rainfalls are frequent mass movements along the northeastern portion of the Sierra Madre Oriental in Mexico,causing significant damage to infrastructure.In this work,we have studied the connection between rainfall and landslides in the Santa Rosa Canyon,a catchment located in the northeastern Mexico.A landslide database triggered by major storms and hurricanes that have hit the region over the past 30 years was analyzed.A total of 92 rainfall events in the Santa Rosa Canyon were studied to determine the amount of precipitation needed to trigger shallow landslides.For each event the duration(D,in hours)and the cumulated rainfall event(E,in mm)were determined by using historical rainfall data from weather stations located near the study area.We have proposed an ED threshold for rainfall-induced landslides with durations 0.5<D<120 hours to address the conditions that trigger these events in the study area.On analyzing the obtained threshold,it has been established that almost 60 mm of a daily rainfall accumulation is required to trigger shallow landslides in the study area.This estimation is consistent with other calculations made for areas close to the Santa Rosa Canyon.Finally,we validated the predictive capability of the threshold with a different set of rainfall data that did not result in landslides performing a straightforward receiver operating characteristic analysis.A good approach was obtained,especially for rainfall events with daily measurements.Results could be used as input information in the design of a landslide early warning system for the northeastern Mexico,and replicated for other landslide prone areas in the region.

A multiobjective evolutionary optimization method based critical rainfall thresholds for debris flows initiation

At present,most researches on the critical rainfall threshold of debris flow initiation use a linear model obtained through regression.With relatively weak fault tolerance,this method not only ignores nonlinear effects but also is susceptible to singular noise samples,which makes it difficult to characterize the true quantization relationship of the rainfall threshold.Besides,the early warning threshold determined by statistical parameters is susceptible to negative samples(samples where no debris flow has occurred),which leads to uncertainty in the reliability of the early warning results by the regression curve.To overcome the above limitations,this study develops a data-driven multiobjective evolutionary optimization method that combines an artificial neural network(ANN)and a multiobjective evolutionary optimization implemented by particle swarm optimization(PSO).Firstly,the Pareto optimality method is used to represent the nonlinear and conflicting critical thresholds for the rainfall intensity I and the rainfall duration D.An ANN is used to construct a dual-target(dual-task)predictive surrogate model,and then a PSO-based multiobjective evolutionary optimization algorithm is applied to train the ANN and stochastically search the trained ANN for obtaining the Pareto front of the I-D surrogate prediction model,which is intended to overcome the limitations of the existing linear regression-based threshold methods.Finally,a double early warning curve model that can effectively control the false alarm rate and negative alarm rate of hazard warnings are proposed based on the decision space and target space maps.This study provides theoretical guidance for the early warning and forecasting of debris flows and has strong applicability.

Study on the Rainfall and Aftershock Threshold for Debris Flow of Post-earthquake

Due to the special condition of provenance and disaster environment after "5·12" Earthquake, the probability and conditions of the occurrence of gully debris flow change greatly after the event, which make it difficult to prevent disaster effectively. In this study the hydrological model of ground water table in loose sediment is established. According to infinite slope theory, the safety factor of deposits is defined as the ratio of resistance force to driving force. The starting condition of post-earthquake gully debris flow is clearly studied by analyzing the effects of rainfall intensity, seismic strength, slope gradient and mechanical properties on the balance of accumulation body. Then the formulas of rainfall and aftershock threshold for starting of gully debris flow are proposed, and an example is given to illustrate the effect of rainfall, aftershocks and their coupling action on a debris flow. The result shows the critical rainfall intensity decreases as the lateral seismic acceleration and channel gradient increases, while the critical intensity linearly increases as the friction angle increases.

Rainfall Threshold Calculation Method for Debris Flow Pre-Warning in Data-Poor Areas

Debris flows are the one type of natural disaster that is most closely associated with hu- man activities. Debris flows are characterized as being widely distributed and frequently activated. Rainfall is an important component of debris flows and is the most active factor when debris flows oc- cur. Rainfall also determines the temporal and spatial distribution characteristics of the hazards. A reasonable rainfall threshold target is essential to ensuring the accuracy of debris flow pre-warning. Such a threshold is important for the study of the mechanisms of debris flow formation, predicting the characteristics of future activities and the design of prevention and engineering control measures. Most mountainous areas have little data regarding rainfall and hazards, especially in debris flow forming re- gions. Therefore, both the traditional demonstration method and frequency calculated method cannot satisfy the debris flow pre-warning requirements. This study presents the characteristics of pre-warning regions, included the rainfall, hydrologic and topographic conditions. An analogous area with abundant data and the same conditions as the pre-warning region was selected, and the rainfall threshold was calculated by proxy. This method resolved the problem of debris flow pre-warning in ar- eas lacking data and provided a new approach for debris flow pre-warning in mountainous areas.

Characteristic Rainfall for Warning of Debris Flows

A characteristic rainfall is introduced to overcome the difficulties encountered in determining a critical rainfall value for triggering debris flow.The characteristic value is defined as the rainfall at which debris-flow occurrence probability shows a rapid increase,and can be used as a warning rainfall threshold for debris flows.Investigation of recorded debris flows and 24-hour rainfall data at Jiangjia basin,Yunnan Province,in southwestern China,demonstrates the existence of such a characteristic rainfall.It was found that the characteristic rainfall corresponds to the daily rainfall of 90% cumulative probability by analyzing the basin's daily rainfall histogram.The result provides a simple and useful method for estimating a debris-flow warning rainfall threshold from the daily rainfall distribution.It was applied to estimate the debris-flow warning rainfall threshold for the Subaohe basin,a watershed in the 2008 Wenchuan earthquake zone with many physical characteristics similar to those of the Jiangjia basin.

Deformation characteristics and thresholds of the Tanjiawan landslide in the Three Gorges Reservoir Area,China

Since the first impoundment of the Three Gorges Reservoir(TGR)in China in 2003,more than 5000 landslides including potential landslides were identified.In this paper,a deep-seated active landslide in TGR area was analyzed.Fourteen years’monitoring data and field investigations from 2006 to 2020 were used to analyze the deformation characteristics,influencing factors,and meteohydrological thresholds.The landslide showed a none-overall periodic movement pattern featuring acceleration during long-duration rainfall and rapid transition to constant creep after rainfall events.Two secondary sliding masses,No.1 and No.2,were defined via field investigation.The reservoir has no impact on the deformation whereas long-duration-low-intensity rainfall is the main factor.At present,the cumulative displacements of the main sliding mass range from 0.9 to 3.2 m,and the deformation during the rainy season is gradually increasing.The boundary of this landslide was formed,and the boundary of No.2 sliding mass became obvious.The probability of the failure of sliding mass No.2 is very high under the conditions of continuous rainfall.The 15-day antecedent rainfall combined with 4-day cumulative rainfall could be the rainfall threshold which could be associated with the groundwater level S1 of 294 m above sea level for forecasting large deformation of Tanjiawan landslide.

Adopting the margin of stability for space–time landslide prediction–A data-driven approach for generating spatial dynamic thresholds

Shallow landslide initiation typically results from an interplay of dynamic triggering and preparatory conditions along with static predisposition factors.While data-driven methods for assessing landslide susceptibility or for establishing rainfall-triggering thresholds are prevalent,integrating spatiotemporal information for dynamic large-area landslide prediction remains a challenge.The main aim of this research is to generate a dynamic spatial landslide initiation model that operates at a daily scale and explicitly counteracts potential errors in the available landslide data.Unlike previous studies focusing on space–time landslide modelling,it places a strong emphasis on reducing the propagation of landslide data errors into the modelling results,while ensuring interpretable outcomes.It introduces also other noteworthy innovations,such as visualizing the final predictions as dynamic spatial thresholds linked to true positive rates and false alarm rates and by using animations for highlighting its application potential for hindcasting and scenario-building.The initial step involves the creation of a spatio-temporally representative sample of landslide presence and absence observations for the study area of South Tyrol,Italy(7400 km2)within well-investigated terrain.Model setup entails integrating landslide controls that operate on various temporal scales through a binomial Generalized Additive Mixed Model.Model relationships are then interpreted based on variable importance and partial effect plots,while predictive performance is evaluated through various crossvalidation techniques.Optimal and user-defined probability cutpoints are used to establish quantitative thresholds that reflect both,the true positive rate(correctly predicted landslides)and the false positive rate(precipitation periods misclassified as landslide-inducing conditions).The resulting dynamic maps directly visualize landslide threshold exceedance.The model demonstrates high predictive performance while revealing geomorphologically plausible prediction patterns largely consistent with current process knowledge.Notably,the model also shows that generally drier hillslopes exhibit a greater sensitivity to certain precipitation events than regions adapted to wetter conditions.The practical applicability of the approach is demonstrated in a hindcasting and scenario-building context.In the currently evolving field of space–time landslide modelling,we recommend focusing on data error handling,model interpretability,and geomorphic plausibility,rather than allocating excessive resources to algorithm and case study comparisons.

Debris Flow Disaster Prevention and Mitigation of NonStructural Strategies in Taiwan

Taiwan has disadvantageous conditions for sediment-related disasters such as debris flows. The construction of engineering structures is an effective strategy for reducing debris flow disasters. However, it is impossible to construct engineering structures in all debris flow areas in a short period. Therefore, the government aims to gradually develop non-structural preventive strategies, including evacuation planning, debris flow disaster emergency action system, disaster resistant community program, recruitment of debris flow professional volunteers, debris flow warning systems, and land management strategies, to mitigate disasters and secure the safety of residents. This review describes the processes and effects of recent debris flow non-structural preventive strategies in Taiwan. The average number of casualties prior to the year 2000 was far higher than the corresponding number after 2000 because debris flow evacuation drills have been promoted since 2000 and the debris flow disaster emergency action system has been progressively improved since 2002. Furthermore, the changes in risks caused by debris flow disasters before and after the implementation of non-structural preventive strategies were used to explain the effectiveness of these strategies at the community level. The results showed that softwarebased non-structural preventive strategies can effectively reduce the casualties caused by debris flows at both the national and community levels.

Non-Structural Mitigation Programs For Sediment-Related Disasters after the Chichi Earthquake in Taiwan

Following the Chichi Earthquake （ML=7.3） in 1999, sediment-related disasters, such as landslides and debris flows, have become more frequent in Taiwan. Because engineering structures cannot be fully and rapidly emplaeed, the government has initiated non-structural hazard mitigation programs. Initially, community debris flow evacuation drills were promoted in 2000. Typhoon Toraji caused numerous debris flow events in July 2001, and some communities evacuated according to the drills, significantly reducing the numbers of possible casualties. Based on that result, the government expanded the program for evacuation drills. Secondly, the early warning system created after the Chichi Earthquake will prevent many potential future casualties. Rainfall threshold values for debris flow warnings in different areas are determined from information received from local weather stations and modified for local geomorphologic situations. Real- time information is gradually being integrated to create a debris flow disaster warning system, the goal of which is to provide warnings to zones in which debris flows are likely. The warning system was launched in 2005 and has two levels of alarms： yellow and red. The final, red alarm triggers enforced evacuation. Overall, the decrease in casualties from debris flows during the decade after the Chichi Earthquake is not the result of a decrease in number or severity of sediment related disasters, but is more directly related to the gradually improved early warning and evacuation system. However, the compound hazards resulting from Typhoon Morakotin 2009 remind us of the ongoing need for improving the existing mitigation system.

Field observation of debris-flow activities in the initiation area of the Jiangjia Gully, Yunnan Province, China

The Jiangjia Gully, which is located in Dongchuan District, Yunnan Province, China, is a watershed prone to debris flows and has long-term recorded data of debris-flow occurrence. However, the initiation mechanism has mainly been studied by experiments in this watershed. To further reveal debris-flow formation mechanism in the Jiangjia Gully, debris-flow activities in the initiation zone were observed with hand-held video cameras in the summer of 2016 and 2017. In these two years, six debris-flow events were triggered in Menqian Gully, a major tributary of the Jiangjia Gully, while debrisflow activities in some sub-watersheds of Menqian Gully were recorded with video cameras in four events. The video recording shows that landslides constituted an important source for sediment supply in debris flow. Some landslides directly evolved into debris flows, while the others released sediment into rills and channels, where debris flows were generated for sediment entrainment by water flow. Therefore, debris-flow occurrence in the Jiangjia Gully is influenced both by infiltration-dominated processes and by runoff-dominated processes. In addition, rainfall data from four gauges installed in Menqian Gully were analyzed using mean intensity(I), duration(D), peak 10-minute rainfall(R10min) and antecedent rainfall(AR) up to 15 days prior to peak 10-minute rainfall. It reveals that debris-flow triggering events can be discriminated from nontriggering events either by an I-D threshold or by an R10min-AR threshold. However, false alarms can be greatly reduced if these two kinds of thresholds are used together. Moreover, behaviors including intermittency of debris flow, variance in moisture content and volume among surges, and coalescence of multiple surges by temporary damming were observed, indicating the complexity of debris-flow initiation processes. These findings are expected to enhance our knowledge on debris-flow formation mechanism in regions with similar environmental settings.

Characteristics of Clustering Debris Flows in Wenchuan Earthquake Zone

Clustering debris-flow events, namely many debris flows simultaneously triggered by a regional rainstorm in a large-scale mountainous area,occurred in four regions of Wenchuan earthquake stricken areas in 2008 and 2010. The characteristics of the clustering debris flows are examined with regard to triggering rainfall, formation process, and relationship with the earthquake by field survey and remote sensing interpretation. It is found that the clustering events occurred nearly at the same time with the local peak rainstorms, and the rainfall intensity-duration bottom limit line for clustering debris flows is higher than the worldwide line. It means that more rainfall is needed for the occurrence of the clustering debris flows. Four kinds of major formation processes for these debris flows are summarized: tributary-dominated, mainstreamdominated, transformation from slope failures, and mobilization or liquefaction of landslide. The four regions has a spatial correlation with the strongquake-influenced zone with the peak ground acceleration = 0.2 g and the seismic intensity > X.

Outlining a stepwise,multi-parameter debris flow monitoring and warning system:an example of application in Aizi Valley,China

In recent years, the increasing frequency of debris flow demands enhanced effectiveness and efficiency of warning systems. Effective warning systems are essential not only from an economic point of view but are also considered as a frontline approach to alleviate hazards. Currently, the key issues are the imbalance between the limited lifespan of equipment, the relatively long period between the recurrences of such hazards, and the wide range of critical rainfall that trigger these disasters. This paper attempts to provide a stepwise multi-parameter debris flow warning system after taking into account the shortcomings observed in other warning systems. The whole system is divided into five stages. Differentwarning levels can be issued based on the critical rainfall thresholds. Monitoring starts when early warning is issued and it continues with debris flow near warning, triggering warning, movement warning and hazard warning stages. For early warning, historical archives of earthquake and drought are used to choose a debris flow-susceptible site for further monitoring. Secondly, weather forecasts provide an alert of possible near warning. Hazardous precipitation, model calculation and debris flow initiation tests, pore pressure sensors and water content sensors are combined to check the critical rainfall and to publically announce a triggering warning. In the final two stages, equipment such as rainfall gauges, flow stage sensors, vibration sensors, low sound sensors and infrasound meters are used to assess movement processes and issue hazardwarnings. In addition to these warnings, communitybased knowledge and information is also obtained and discussed in detail. The proposed stepwise, multiparameter debris flow monitoring and warning system has been applied in Aizi valley China which continuously monitors the debris flow activities.

Identification on threshold and efficiency of rainfall replenishment to soil water in semi-arid loess hilly areas

As one critical source of water for maintaining ecosystems in arid and semi-arid regions, rainfall replenishment to soil water can determine vegetation growth and ecosystem functions. However, the limited rainfall resources were often not used effectively in the semi-arid loess hilly areas due to random temporal and spatial distribution of rainfall and specific vegetation features. Thus, it is highly significant to determine the threshold and efficiency of rainfall replenishment to soil water under different vegetation types. The threshold and efficiency can offer scientific evidence for rehabilitating vegetation and improving efficiency of using rainfall resources. In this study, the efficiency and threshold of rainfall replenishment to soil water were determined under natural grassland, wheat, artificial grassland, sea buckthorn shrubland and Chinese pine forestland based on consecutive measurements. The results indicated that the lag-time, rate, efficiency of rainfall replenishment to soil water were closely related to vegetation type, with significant differences existing among different vegetation types. The lag-time for natural grassland in the soil horizon of 20 cm was the shortest one （26.4 h）, followed by wheat （27.8 h）, sea buckthorn （41.8 h）, artificial grassland （50.0 h） and Chinese pine （81.8 h）.The value of replenishment rate, followed the order of wheat （0.40 mm h-l）〉 natural grassland （0.30 mm h-~）〉 sea buckthorn （0.17 mm h-t）〉 artificial grassland （0.14 mm h-l）〉 Chinese pine （0.09 mm fit）. As for the efficiency of rainfall replenishment to soil water, natural grassland was the most efficient one （35.1%）, followed by wheat （29.2%）, sea buckthorn （16.8%）, artificial grassland （11.5%）, Chinese pine （4.2%）. At last, it was found that wheat had the lowest threshold （6.8 mm） of rainfall replenishment to soil water, which was followed by natural grassland （10.5 mm）, sea buckthorn （20.5 mm）, artificial grassland （22.6 mm） and Chinese pine （26.4 mm）. These results implied that soil water in natural grassland was sensitive to rainfall and easily to be replenished, while soil water in Chinese pine was harder to be replenished by rainfall compared to other vegetation types.

Two types of summertime heating over Asian large-scale orography and excitation of potential-vorticity forcing II.Sensible heating over Tibetan-Iranian Plateau

Based on analysis and simulation,the interaction of thermal forcing between the Tibetan Plateau(TP) and Iranian Plateau(IP) in summer is investigated.Associated influences on water vapor transport in the Asian subtropical monsoon region and the formation of a cold center in the lower stratosphere over Eurasia are also investigated.Results show that surface sensible heating(SH) over the two plateaus not only have mutual influences but also feedback to each other.SH over the IP can reduce the SH and increase the LH over the TP,whereas the SH over the TP can increase surface heating over the IP,thereby reaching quasi-equilibrium among the SH and LH over the TP,IP SH and atmosphere vertical motion.Therefore,the so-called Tibetan-Iranian Plateau coupling system(TIPS) is constructed,which influences atmosphere circulation.In the TIPS system,interaction between surface SH and LH over the TP plays a leading role.SH of the IP and TP influences on other regions not only have superimposed effects but also mutually offset.Accounting for contributions to the convergence of water vapor transport in the Asian subtropical monsoon region,TP SH contributes more than twice that of the IP.The combined influence of SH over TP and IP represents the major contribution to the convergence of water vapor transport in that region.In addition,the heating effect of TIPS increases the upper tropospheric temperature maximum and lifts the tropopause,cooling the lower stratosphere.Combined with large-scale thermal forcing of the Eurasian continent,the TIPS produces a strong anticyclonic circulation and the South Asian High that warms the upper troposphere and cools the lower stratosphere,thereby affecting regional and global weather and climate.