Increasing Flash Floods in a Drying Climate over Southwest China

In a globally warming world, subtropical regions are generally expected to become drier while the tropics and mid-high latitudes become wetter. In line with this, Southwest China, close to 25°N, is expected to become increasingly prone to drought if annual mean precipitation decreases. However, despite this trend, changes in the temporal distribution of moisture supply might actually result in increased extreme rainfall in the region, whose climate is characterized by distinct dry and wet seasons. Using hourly and daily gauge observations, rainfall intensity changes since 1971 are exalnined for a network of 142 locations in the region. From the analysis, dry season changes are negligible but wet season changes exhibit a significantly strong downward trend [-2.4% （10 yr）^-1], particularly during the past 15 years [-17.7% （10 yr）^-1]. However, the intensity of events during the wettest of 5% hours appears to steadily increase during the whole period [1.4% （10 yr）^-1], tying in with government statistical reports of recent droughts and flooding. If the opposing trends are a consequence of a warming climate, it is reasonable to expect the contradictory trend to continue with an enhanced risk of flash flooding in coming decades in the region concerned.

Long-term reconstruction of flash floods in the Qilian Mountains,China,based on dendrogeomorphic methods

In China,flash floods are one of the main natural disasters causing loss of life and damage to infrastructure.The threat of flash floods is exacerbated with climate change and increased human activities,such that the number of disasters has shown a clear upward trend in recent years.However,due to the scarcity of instrumental data or overly short timeseries,we are still lacking critical data to understand spatio-temporal patterns and driving factors of extreme flash floods.This missing knowledge is however crucial for a proper management of these hazards,especially in remote mountain environments.In forested catchments,dendrogeomorphology allows the reconstruction of past process activity based on growth disturbances(GDs)in trees that have been affected by past flash floods.Therefore,in our study,for the first time,we reconstruct past flash floods in the Qilian Mountains,northeast Tibetan Plateau,over past centuries.To this end,we sampled 99 Qinghai spruce(Picea crassifolia)trees affected by flash floods,with a total of 194increment cores,and identified 302 GDs induced by past flash floods.These GDs have been caused by at least 21 flash floods that we are able to reconstruct over the last 170 years.The position of GDs within tree rings and the intra-seasonal dating of past events also allowed discussion of the likely synoptic situations that may have led to the triggering of flash floods in the past.Logistic regression analysis confirms that significant correlation exists between cumulative maximum 5-day August-September precipitation and reconstructed flash floods,which is corresponding to the majority of scars and related tangential rows of traumatic resin ducts(TRDs)found in the latewood portion of growth rings.These results support the idea that abundant precipitation occurring at the end of the summer season and early fall is the key factor driving flash floods in our study area.Our research not only fills the gaps regarding historical flash flood histories in the Qilian Mountains,but also provides a scientific basis for the region's response to climate change and flood prevention and reduction.

Morphometric Analysis and Flash Floods Assessment for Drainage Basins of the Ras En Naqb Area, South Jordan Using GIS

Morphometric analysis and flash floods assessment were conducted for the watersheds of Ras En Naqb escarpment, south Jordan. The study area comprises of twelve small watersheds occupying the faulted-erosional slopes, and the dip slopes. The drainage network shows that dendritic and sub-dendritic patterns dominated the dip slopes, whereas trellis pattern characterized the faulted-erosional slopes. Stream orders range from fourth to sixth order. The mean bifurcation ratios vary between 4.2 and 5.38 for the dip slope basins, and between 3.5 and 5.0 for the faulted-erosional slope watersheds, indicating a noticeable influence of structural disturbances (i.e., faulting and uplifting), and rejuvenation of drainage networks. All watersheds have short basin lengths, ranging from 23.8 km to 42.2 km for the dip slope basins, and between 15.3 km and 45.4 km for the faulted-erosional slope catchments. This is indicative of high flooding susceptibility associated with heavy rainstorms of short duration. The circularity ratios range from 0.177 to 0.704 which denote that the catchments are moderately circular on the faulted-erosional slopes, and to some extent elongated on the dip slopes. The length of overland flow values ranges from 0.854 to 0.924 for the dip slope catchments, whereas LO values for the faulted-erosional slopes vary from 0.793 to 0.945 denoting steep slopes and shorter paths on both dip slope and faulted-erosional slope watersheds. Values of stream frequency range from 1.509 to 1.692 for the dip slope, and from 1.688 to 2.0 for the faulted-erosional slope catchments. FS values are also indicative of slope steepness, low infiltration rate, and high flooding potential. The watersheds of the dip slopes show lower values of form factor varying from 0.079 to 0.364, indicating elongated shape and suggesting a relatively flat hydrograph peak for longer duration. Similarly, values of Dd are high for catchments on the dip slope basins (1.709 - 1.85) and the faulted-erosional slope watersheds (1.587 - 2.0) indicating highly dissected topography, high surface runoff, low infiltration rate, and consequently high flooding potential. Furthermore, high relief values exist, ranging from 388 m to 714 m for the dip slope basins, and from 421 m to 846 m for the faulted-erosional slope catchments indicting high relief and steep slopes. Morphometric analysis, and flash flood assessment suggest that ten watersheds (83.3%) are categorized under high and intermediate flooding susceptibility, and the faulted-erosional slope catchments are more hazardous in terms of flooding. Thus the protection of Ma’an, El Jafr rural Bedouin settlements, and Amman-Aqaba highway from recurrent flooding is essential to ensure sustainable future development in Ras En Naqb-Ma’an area.

Failure criteria of unreinforced masonry walls of rural buildings under the impact of flash floods in mountainous regions

Damage to rural buildings in mountainous regions caused by flash floods accounts for a significant proportion of economic losses from disasters.The unreinforced masonry(URM)wall is the most vulnerable structural element of rural buildings exposed to flash floods.The failure of a URM wall indicates damage to rural buildings in flash floods.Based on the yield line theory of out-of-plane damage of URM walls and the virtual work method,brittle failure criteria for URM walls under the impact of flash floods were established.According to the field investigation data of the 26 June 2020 flash flood event in Damawu Gully and the corresponding simulation results of FLO-2D,the disaster-causing process was analysed,and the failure criteria were validated.Three building parameters were identified to influence the flood-resistance of URM walls,including the mortar grade,the span-to-height ratio of the wall,and the number of floors of the rural building.The results showed that the cause of the 26June disaster was the diversion of a 50-year flash flood into the residential community on the alluvial fan.The affected buildings were constructed with hollow blocks and lacked flood-resistance reinforcement.The critical failure depth of a URM wall restrained at the top by ring beams(RBs)under hydrostatic load conditions is 1.17 to 1.20 times greater than that of a URM wall without RBs,and the difference is even more pronounced when lowerstrength mortar is used.The flood-resistance of a URM wall constructed with Mb 7.5 mortar and restrained by RBs is almost as strong as that of a URM wall constructed with Mb 20 mortar and without RBs.The span-to-height ratio of a URM wall should not be greater than 1.875 in this case.However,the flood-resistance of a URM wall with RB restraint is almost independent of the span-to-height ratio.The brittle fracture energy of masonry mortar is more crucial to the flood-resistance of 4-edge restrained URM walls if L/Z>1.875.The flood-resistance of the URM wall of the first storey increases linearly with the number of floors.Single-storey rural buildings should be given priority to the use of high-grade masonry mortar and high-density blocks to improve flood-resistance.The failure criteria and the influence laws of building parameters on the flood-resistance of URM walls can provide references for flash flood mitigation and flood-resistance reinforcement of rural buildings in mountainous regions of Southwest China.

Risk of Flash Floods in Urban and Rural Municipalities Triggered by Intense Precipitation in Wielkopolska of Poland

This research analyzed interventions of State Fire Service(SFS)units in the Wielkopolska region of Poland that were triggered by extreme precipitation for the period 2010-2021.Our results demonstrated that the most populated and urbanized towns in the Wielkopolska(Greater Poland,west of Warsaw)region are at the most risk in the event of extreme precipitation occurrence as measured by the total number of interventions made by the SFS.The number of SFS unit interventions in towns,standardized to 10,000 inhabitants,indicates that the highest proportional volume of interventions also occurred in smaller towns.In the rural municipalities the number of SFS unit interventions increases along with higher population density and proportion of infrastructure areas.As analyzed in this study,the 12 years from 2010 to 2021 were characterized by a higher number of days with heavy precipitation,for example,20,30,40,and 50 mm,in comparison to the previous periods 1961-2010 and 1981-2010.Intervention databases collected by emergency services are a valuable source of information for hazard mapping.Based on those and other available data,a statistical model was created and factors influencing the local and regional occurrence of interventions were determined.Increasing suburbanization,the rising proportion of impermeable surfaces,and the impact of climate change are of considerable importance in urban flood risk.It is necessary to help municipalities develop abilities to absorb larger amounts of rainwater.

Exploring spatial relationships between stream channel features,water depths and flow velocities during flash floods using HEC-GeoRAS and Geographic Information Systems

Water depths and flow velocities decisively influence the damage caused by flash floods.Geographic Information System(GIS)is a powerful and useful tool,allowing the spatial analysis of results obtained by hydraulic modelling,namely from the HEC-RAS/HEC-GeoRAS software.The GIS spatial analysis performed in this study seeks to explain and quantify the spatial relationships between the stream channel features and flow components during flash flood events.Despite these relationships are generically known,there are few studies exploring this subject in different geographic contexts.A 1D hydraulic model was applied in a small watershed in Portugal,providing good results in the definition of floodable areas,water depths and longitudinal velocities.No direct relationship was found between water depths and velocities in the floodable areas;however,negative strong correlations were found between the two flow components along the stream centerlines.Bed slope,channel and flood width,and roughness prove to be highly relevant on the longitudinal variations of water depths and velocities and on the location of maximum values.Increasing peak discharges and return periods(R;)can change the relationships between water depths and velocities at the same location.Results can be improved with more accurate elevation data for stream channels and floodplains.

GIS-based flash flooding susceptibility analysis and water management in arid mountain ranges:Safaga Region,Red Sea Mountains,Egypt

The Safaga Region(SR)is part of the Red Sea mountain range in Egypt.Catastrophic flash flooding is now an inescapable event,wreaking havoc and causing massive loss of life and property.The majority of the floodwater,however,has been wasted as runoff to the Red Sea,which,if used wisely,could meet a fraction of the water demands for a variety of applications in this area.The current work aims to use GIS techniques to integrate remote sensing data for evaluating,mitigating,and managing flash floods in SR.The data set comprised Tropical Rainfall Measuring Mission(TRMM)thematic rainfall data,1:50,000 scale topographical map sheets,geological maps,the ASTER Digital Elevation Model(ASTER GDEM),Landsat 7 Enhanced Thematic Mapper"(ETM7+),and Landsat 8 Operational Land Imager.The flash flood risk model of SR is developed using ArcGIS-10.3 geoprocessing tools integrating all the causal factors thematic maps.The final flood risk model for the SR suggests that 57%of the total basins in the SR are at high risk of flooding.Almost 38%of all basins are at moderate flood risk.The remaining 5%of basins are less prone to flooding.Flood-prone zones were identified,suitable dam-building sites were located,and extremely probable areas for water recharge were recognized.On the basis of reliable scientific data,structural and non-structural mitigation strategies that might reduce the damage susceptibility,alleviate the sensitivity of the flash flood,and best utilize its water supply were recommended.

Prediction of Flash Flood Susceptibility of Hilly Terrain Using Deep Neural Network:A Case Study of Vietnam

Flash floods are one of the most dangerous natural disasters,especially in hilly terrain,causing loss of life,property,and infrastructures and sudden disruption of traffic.These types of floods are mostly associated with landslides and erosion of roads within a short time.Most of Vietnamis hilly and mountainous;thus,the problem due to flash flood is severe and requires systematic studies to correctly identify flood susceptible areas for proper landuse planning and traffic management.In this study,three Machine Learning(ML)methods namely Deep Learning Neural Network(DL),Correlation-based FeatureWeighted Naive Bayes(CFWNB),and Adaboost(AB-CFWNB)were used for the development of flash flood susceptibility maps for hilly road section(115 km length)of National Highway(NH)-6 inHoa Binh province,Vietnam.In the proposedmodels,88 past flash flood events were used together with 14 flash floods affecting topographical and geo-environmental factors.The performance of themodels was evaluated using standard statisticalmeasures including Receiver Operating Characteristic(ROC)Curve,Area Under Curve(AUC)and Root Mean Square Error(RMSE).The results revealed that all the models performed well(AUC>0.80)in predicting flash flood susceptibility zones,but the performance of the DL model is the best(AUC:0.972,RMSE:0.352).Therefore,the DL model can be applied to develop an accurate flash flood susceptibility map of hilly terrain which can be used for proper planning and designing of the highways and other infrastructure facilities besides landuse management of the area.

Variation of hydro-environment during past four decades with underground sponge city planning to control flash floods in Wuhan,China:An overview

This paper presents a review of the variation in the hydro-environment over the past four decades in Wuhan,China,and discusses its close relationship with flash flood events.These flash flood events have inundated underground infrastructures and resulted in significant economic losses and casualties,which are also discussed in relation to various external and internal factors.The external factors are predominantly owing to heavy rainstorms resulting from extreme climate change.The internal factors predominantly include a decrease in wetland areas,changes in topography,and disrepair of drainage systems in urban areas.Since the 1970s,lakes in Wuhan have been significantly reduced in size(by approximately 40%).This reduction in wetland area decreases the water storage capacity in urban areas,and the frequency of large flooding events thus increased from 0.1 event/year to approximately 0.2 event/year.Additionally,the disrepair of drainage systems in urban areas has reduced their ability to resist flood hazards.Changes in topography have also intensified the flood volume per unit time,further burdening the drainage systems.In 2016,Wuhan suffered from several large flooding events that led to some of the most severe economic losses in recorded history.The drainage system at the urban center of Wuhan and proposed flood prevention methods are discussed in detail in this paper.The sponge city(SPC)concept has been attempted in Qingshan District,Wuhan,and its success proved the reliability of the theory.A SPC is believed to reduce the impacts of future flash floods in China and other developed and developing countries.

Implementation of Remote Sensing and GIS Techniques to Study the Flash Flood Risk at NEOM Mega-City, Saudi Arabia

Southern Red Sea flooding is common. Assessing flood-prone development risks helps decrease life and property threats. It tries to improve flood awareness and advocate property owner steps to lessen risk. DEMs and topography data were analyzed by RS and GIS. Fifth-through seventh-order rivers were studied. Morphometric analysis assessed the area’s flash flood danger. NEOM has 14 catchments. We determined each catchment’s area, perimeter, maximum length, total stream length, minimum and maximum elevations. It also uses remote sensing. It classifies Landsat 8 photos for land use and cover maps. Image categorization involves high-quality Landsat satellite images and secondary data, plus user experience and knowledge. This study used the wetness index, elevation, slope, stream power index, topographic roughness index, normalized difference vegetation index, sediment transport index, stream order, flow accumulation, and geological formation. Analytic hierarchy considered all earlier criteria (AHP). The geometric consistency index GCI (0.15) and the consistency ratio CR (4.3%) are calculated. The study showed five degrees of flooding risk for Wadi Zawhi and four for Wadi Surr, from very high to very low. 9.16% of Wadi Surr is vulnerable to very high flooding, 50% to high flooding, 40% to low flooding, and 0.3% to very low flooding. Wadi Zawhi’s flood risk is 0.23% high, moderate, low, or extremely low. They’re in Wadi Surr and Wadi Zawhi. Flood mapping helps prepare for emergencies. Flood-prone areas should prioritize resilience.

Flash Flood Risk Estimation of Wadi Yutum (Southern Jordan) Watershed Using GIS Based Morphometric Analysis and Remote Sensing Techniques

Flash flood disasters associated with heavy rainstorms are common in dry lands of Jordan. This causes inestimable damage to life and infrastructure. In the present investigation, flash floods were assessed in Wadi Yutum watershed, southern Jordan. Assessment was conducted using remote sensing and GIS techniques, combined with geological and geomorphic field data to evaluate the probability of flooding risk spatially. Two methods were used to assess the flooding risk for seventeen sub-basins of W. Yutum: the morphometric ranking method;and El-Shamy’s approach. Both methods utilized twenty morphometric parameters of paramount interest for flash flood risk estimation. The results achieved based on the two methods enabled identification of sub-basins with a high potential of flash flooding, and served to reveal the common sub-basins falling under each category of flooding risk. Morphometric analysis and GIS were employed to produce flood hazard maps which displayed sub-basins exposed to harmful flooding in Wadi Yutum. The adopted methodology can be applied to estimate flooding risk in other comparable watersheds and region in Jordan. Further, preparedness measures can be proposed in a timely manner in order to minimize destructive flood effects.

Flash flood hazard mapping: A pilot case study in Xiapu River Basin, China

Flash flood hazard mapping is a supporting component of non-structural measures for flash flood prevention. Pilot case studies are necessary to develop more practicable methods for the technical support systems of flash flood hazard mapping. In this study, the headwater catchment of the Xiapu River Basin in central China was selected as a pilot study area for flash flood hazard mapping. A conceptual distributed hydrological model was developed for flood calculation based on the framework of the Xinanjiang model, which is widely used in humid and semi-humid regions in China. The developed model employs the geomorphological unit hydrograph method, which is extremely valuable when simulating the overland flow process in ungauged catchments, as compared with the original Xinanjiang model. The model was tested in the pilot study area, and the results agree with the measured data on the whole. After calibration and validation, the model is shown to be a useful tool for flash flood calculation. A practicable method for flash flood hazard mapping using the calculated peak discharge and digital elevation model data was presented, and three levels of flood hazards were classified. The resulting flash flood hazard maps indicate that the method successfully predicts the spatial distribution of flash flood hazards, and it can meet the current requirements in China.

Flash flood susceptibility mapping using a novel deep learning model based on deep belief network,back propagation and genetic algorithm

Flash floods are responsible for loss of life and considerable property damage in many countries.Flood susceptibility maps contribute to flood risk reduction in areas that are prone to this hazard if appropriately used by landuse planners and emergency managers.The main objective of this study is to prepare an accurate flood susceptibility map for the Haraz watershed in Iran using a novel modeling approach(DBPGA)based on Deep Belief Network(DBN)with Back Propagation(BP)algorithm optimized by the Genetic Algorithm(GA).For this task,a database comprising ten conditioning factors and 194 flood locations was created using the One-R Attribute Evaluation(ORAE)technique.Various well-known machine learning and optimization algorithms were used as benchmarks to compare the prediction accuracy of the proposed model.Statistical metrics include sensitivity,specificity accuracy,root mean square error(RMSE),and area under the receiver operatic characteristic curve(AUC)were used to assess the validity of the proposed model.The result shows that the proposed model has the highest goodness-of-fit(AUC=0.989)and prediction accuracy(AUC=0.985),and based on the validation dataset it outperforms benchmark models including LR(0.885),LMT(0.934),BLR(0.936),ADT(0.976),NBT(0.974),REPTree(0.811),ANFIS-BAT(0.944),ANFIS-CA(0.921),ANFIS-IWO(0.939),ANFIS-ICA(0.947),and ANFIS-FA(0.917).We conclude that the DBPGA model is an excellent alternative tool for predicting flash flood susceptibility for other regions prone to flash floods.

Regionalization and the Method for Risk Grading for Flash Flood Disaster in Tibet Region

Flash flood is one of the major meteorological disasters on the Tibet Plateau (TP). Flash flood risk regionalization based on the theory of flash flood occurrence risk is the essential basis for relative risk management. The flash flood risk regionalization and the high-resolution grid mountain flood risk level in TP is carried out by using ArcGIS with the indicators of rainfall, days of heavy rain, vegetation cover, slope, relative elevation difference, river network density, population density, average GDP and traffic density. The areas with high mountain flood risk are mainly located in the middle and downstream of Yarlung, the Nujiang River Valley, the Jinsha River and Lancang River Basin. Besides, the results of flash flood disaster risk regionalization were tested by using historical flash flood disaster data and calamity census data. The disasters occurred in high-risk and sub-high-risk regions are accounted for 73%. Flash floods that cause casualties and economic losses of more than 100,000 CNY (Chinese Yuan) occurred in high-risk areas. Flash flood risk assessment may provide reference for the prevention and control of geological disasters in TP, improve disaster prevention and mitigation capabilities, reduce the hazards of flash floods to social development.

Sequential Damming Induced Winter Season Flash Flood in Uttarakhand Province of India

204 persons were killed while two hydropower projects located in close proximity at Rishiganga(13.2 MW),and Tapoban(520 MW)were damaged in Dhauliganga flood of February 7,2021 in the Indian Himalaya.This incidence occurred during the winter season when the discharge of the glacier fed rivers is minimal,and no rain was experienced in the region around the time of the flood.Despite discharge of the main river,Rishiganga,not involved in the flood due to damming upstream of its confluence with Raunthi Gadhera,based on field evidences massive volume of around 6 million cu m water involved in this flood is attributed to sequential intermittent damming at three different places;(i)Raunthi Gadhera was dammed first in its upper reaches,(ii)Rishiganga river was then dammed to the north of Murunna,and(iii)finally Dhauliganga river was dammed around Rini village to the upstream of its confluence with Rishiganga river.Lacking warning system only enhanced the flood-induced devastation.Legally binding disaster risk assessment regime,together with robust warning generation,and dissemination infrastructure are therefore recommended for all major infrastructure projects.

Modelling and validation of flash flood inundation in drylands

In the context of climate change and human activities,flood disasters in arid mountainous areas have become increasingly frequent,and seriously threatened the safety of people's lives and property.Rapid and accurate flash flood inundation modelling is an essential foundational research area,which can aid in the reduction of casualties and the minimization of disaster losses;however,this modelling is also very difficult,and models need to be urgently developed to address flash flood forecasting and warnings.The objective of this study is to construct a numerical modelling method for flash floods in drylands.Based on a 2D high-resolution flood numerical model(Flood Map-Hydro Inundation2D),we hindcasted the dynamic process of flash flooding and show the spatio-temporal characteristics of flash flood inundation for the“8·18”flash flood disaster that occurred in Datong county,Qinghai province.The results showed that the model output effectively agreed with the observed inundation after the event in terms of both spatial extent and temporal process.Extensive flooding mainly occurred between 00:00 and 01:00 on August 18,2022.Qingshan,Hejiazhuang and Longwo villages were affected most heavily.We further conducted model sensitivity analysis and found that the model was highly sensitive to both roughness and hydraulic conductivity in drylands,and the effect of hydraulic conductivity was more pronounced.Our study confirmed the good performance of our model for the simulation of flash flooding in arid areas and provides a potential method for flash flood assessment and management in arid areas.

Flood susceptibility modelling using advanced ensemble machine learning models

Floods are one of nature's most destructive disasters because of the immense damage to land,buildings,and human fatalities.It is difficult to forecast the areas that are vulnerable to flash flooding due to the dynamic and complex nature of the flash floods.Therefore,earlier identification of flash flood susceptible sites can be performed using advanced machine learning models for managing flood disasters.In this study,we applied and assessed two new hybrid ensemble models,namely Dagging and Random Subspace(RS)coupled with Artificial Neural Network(ANN),Random Forest(RF),and Support Vector Machine(SVM)which are the other three state-of-the-art machine learning models for modelling flood susceptibility maps at the Teesta River basin,the northern region of Bangladesh.The application of these models includes twelve flood influencing factors with 413 current and former flooding points,which were transferred in a GIS environment.The information gain ratio,the multicollinearity diagnostics tests were employed to determine the association between the occurrences and flood influential factors.For the validation and the comparison of these models,for the ability to predict the statistical appraisal measures such as Freidman,Wilcoxon signed-rank,and t-paired tests and Receiver Operating Characteristic Curve(ROC)were employed.The value of the Area Under the Curve(AUC)of ROC was above 0.80 for all models.For flood susceptibility modelling,the Dagging model performs superior,followed by RF,the ANN,the SVM,and the RS,then the several benchmark models.The approach and solution-oriented outcomes outlined in this paper will assist state and local authorities as well as policy makers in reducing flood-related threats and will also assist in the implementation of effective mitigation strategies to mitigate future damage.

Energy-Efficient Scheduling for a Cognitive IoT-Based Early Warning System

Flash floods are deemed the most fatal and disastrous natural hazards globally due to their prompt onset that requires a short prime time for emergency response.Cognitive Internet of things(CIoT)technologies including inherent characteristics of cognitive radio(CR)are potential candidates to develop a monitoring and early warning system(MEWS)that helps in efficiently utilizing the short response time to save lives during flash floods.However,most CIoT devices are battery-limited and thus,it reduces the lifetime of the MEWS.To tackle these problems,we propose a CIoTbased MEWS to slash the fatalities of flash floods.To extend the lifetime of the MEWS by conserving the limited battery energy of CIoT sensors,we formulate a resource assignment problem for maximizing energy efficiency.To solve the problem,at first,we devise a polynomial-time heuristic energyefficient scheduler(EES-1).However,its performance can be unsatisfactory since it requires an exhaustive search to find local optimum values without consideration of the overall network energy efficiency.To enhance the energy efficiency of the proposed EES-1 scheme,we additionally formulate an optimization problem based on a maximum weight matching bipartite graph.Then,we additionally propose a Hungarian algorithm-based energy-efficient scheduler(EES-2),solvable in polynomial time.The simulation results show that the proposed EES-2 scheme achieves considerably high energy efficiency in the CIoT-based MEWS,leading to the extended lifetime of the MEWS without loss of throughput performance.

Hurricane Camille 1969 and Storm-Triggered Landslides in the Appalachians and a Perspective in a Warmer Climate

This study analyzes storm-triggered landslides in the US Appalachians, in the current geological setting. Concave valleys that favor the convergence of surface runoff are the primary locales for landslides. If the slopes are weathered to the same degree and have the same vegetation coverage, slope orientation (azimuthal) is not critical for slope stability. However, it is found that for the region south of the Black Mountains (North Carolina), north-facing slopes are more prone to slide, because, for the regions not limited by water availability (annual precipitation), the northern slopes usually are grass slopes. For the slopes of the Blue Ridge Mountains, south facing slopes are more prone to slide. Gravity measurements over the past decade reveal that geological conditions, the chute system and underground cracks over the region are stable. Future changes in storm-triggered landslide frequency are primarily controlled by changes in extreme precipitation. Thus, a series of ensemble climate model experiments is carried out to investigate possible changes in future extreme precipitation events, using a weather model forced by atmospheric perturbations from ensemble climate models. Over 50 locations are identified as prone to future landslides. Many of these locales are natural habitats to the Appalachian salamanders. In a future warmer climate, more severe extreme precipitation events are projected because of increased atmospheric water vapor and more frequent passages of tropical cyclone remnants. There is also a likely shift of tropical cyclone tracks and associated extreme precipitations, and the cluster center of Appalachians’s scarps is expected to move westward, with ecological consequences for the endemic salamanders.

Hurricane Camille 1969 and Storm-Triggered Landslides in the Appalachians and a Perspective in a Warmer Climate

This study analyzes storm-triggered landslides in the US Appalachians, in the current geological setting. Concave valleys that favor the convergence of surface runoff are the primary locales for landslides. If the slopes are weathered to the same degree and have the same vegetation coverage, slope orientation (azimuthal) is not critical for slope stability. However, it is found that for the region south of the Black Mountains (North Carolina), north-facing slopes are more prone to slide, because, for the regions not limited by water availability (annual precipitation), the northern slopes usually are grass slopes. For the slopes of the Blue Ridge Mountains, south facing slopes are more prone to slide. Gravity measurements over the past decade reveal that geological conditions, the chute system and underground cracks over the region are stable. Future changes in storm-triggered landslide frequency are primarily controlled by changes in extreme precipitation. Thus, a series of ensemble climate model experiments is carried out to investigate possible changes in future extreme precipitation events, using a weather model forced by atmospheric perturbations from ensemble climate models. Over 50 locations are identified as prone to future landslides. Many of these locales are natural habitats to the Appalachian salamanders. In a future warmer climate, more severe extreme precipitation events are projected because of increased atmospheric water vapor and more frequent passages of tropical cyclone remnants. There is also a likely shift of tropical cyclone tracks and associated extreme precipitations, and the cluster center of Appalachians’s scarps is expected to move westward, with ecological consequences for the endemic salamanders.