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Impacts of permafrost degradation on streamflow in the northern Himalayas

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Science China Earth Sciences 2024年第6期67卷 1990-2000页

作者： Linfeng FAN Fang JI Xingxing KUANG Zhilin GUO Rui ZHANG Chunmiao ZHENG State Key Laboratory of mountain hazards and Engineering Resilience Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610299China Yangtze institute for Conservation and Development Hohai UniversityNanjing 210024China School of Environmental Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Eastern institute for Advanced Study Ningbo 315200China

The Himalayan water tower provides crucial water resources for *** degradation is deemed to exert important impacts on streamflow in the Himalayan ***,the magnitudes of such impacts remain poorly ***,we established a robust hydrological model that incorporated active layer deepening and ground ice melt for the drainage basin of the largest river in the northern Himalayas-the Yarlung Zangbo River(YZR).We estimated that permafrost degradation led to~0.65 km^(3)/yr decrease in surface runoff and~0.35 km^(3)/yr increase in baseflow and ground ice melt contributed~0.25% to the annual streamflow in the YZR for the period ***“fill-and-spill”mechanism helps explain the seeming contradiction of observed increasing versus decreasing baseflow in different permafrost regions *** propose that the dilution of riverine dissolved organic carbon(DOC)concentrations by baseflow may lead to the riverine DOC hysteresis *** study not only lays solid scientific basis for water resources management in the Himalayas,but also yields new insights into how to interpret measured river discharge and nutrient flux in permafrost regions over the globe.

关键词： Permafrost degradation Hydrology Streamflow Surface runoff Groundwater Himalayas

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Process and Mechanism of Controlling Outburst Floods of Landslide Dams through Artificial Structures: Physical Experiments

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Journal of Earth Science 2024年

作者： Hechun Ruan Huayong Chen Xiaoqing Chen Wanyu Zhao Xiao Li Key Laboratory of mountain hazards and Surface Processes institute of mountain hazards and Environment Chinese Academy of Sciences University of Chinese Academy of Sciences

The scale of the outburst flood resulting from a landslide dam failure significantly influences the extent of the disaster and losses downstream. This study proposes a method for controlling the discharge of landslide dam failure using the artificial structure (AS). The primary approach involves strategically arranging the designed AS on both sides of the excavated spillway. As the breach is eroded， ASs gradually falls into the breach， serving to control the discharge. Fourteen sets of flume experiments were conducted to investigate the impact of various factors， including AS types， distances from the breach boundary， arrangements， columns， numbers， on the discharge control effectiveness. The mechanism how AS controls outburst discharge was revealed. Results indicate a pronounced control effect of AS on landslide dam-break discharge， with the maximum reducing rate of the peak discharge in the experiment reaching 47.4%. In controlling outburst discharge， four-sided framed structures demonstrate superior performance compared to four-sided solid structures， and serial structures outperform discrete structures. When arranging ASs， it is essential to ensure that the structure initiates its descent into the breach at the onset of the rapid increase in outburst discharge and continues until the peak discharge occurs. Building upon this foundation， augmenting the number of ASs and columns significantly enhances the efficacy of discharge control. The fundamental rationale for this discharge control lies in the AS’s capacity to reduce the headward scarp height， constrain sediment transport， and diminish the erosion capacity of the outburst flood， ultimately lowering the breach erosion rate. Lastly， a calculation method for the size design of the AS is established based on mechanical analysis. The research findings offer a new technological option for the emergency disposal of landslide dams.

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Economic Risk Assessment of Future Debris Flows by Machine Learning Method

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International Journal of Disaster Risk Science 2024年第1期15卷 149-164页

作者： Chenchen Qiu Lijun Su Alessandro Pasuto Giulia Bossi Xueyu Geng School of Engineering University of WarwickCoventry CV47ALUK Key Laboratory of mountain hazards and Earth Surface Process Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610041China Research institute for Geo-Hydrological Protection(IRPI-CNR) National Research Council of ItalyPadova 35127Italy

A reliable economic risk map is critical for effective debris-flow ***,the uncertainties surrounding future scenarios in debris-flow frequency and magnitude restrict its *** estimate the economic risks caused by future debris flows,a machine learning-based method was proposed to generate an economic risk map by multiplying a debris-flow hazard map and an economic vulnerability *** selected the Gyirong Zangbo Basin as the study area because frequent severe debris flows impact the area every *** debris-flow hazard map was developed through the multiplication of the annual probability of spatial impact,temporal probability,and annual *** employed a hybrid machine learning model-certainty factor-genetic algorithm-support vector classification-to calculate ***,a Poisson model was applied for temporal probabilities,while the determination of annual probability of spatial impact relied on statistical ***,four major elements at risk were selected for the generation of an economic loss map:roads,vegetation-covered land,residential buildings,and *** economic loss of elements at risk was calculated based on physical vulnerabilities and their economic ***,we proposed a physical vulnerability matrix for residential buildings,factoring in impact pressure on buildings and their horizontal distance and vertical distance to debrisflow *** this context,an ensemble model(XGBoost) was used to predict debris-flow volumes to calculate impact pressures on *** results show that residential buildings occupy 76.7% of the total economic risk,while roadcovered areas contribute approximately 6.85%.Vegetation-covered land and farmland collectively represent 16.45% of the entire *** findings can provide a scientific support for the effective mitigation of future debris flows.

关键词： Economic risk Future debris fows Gyirong Zangbo Basin Machine learning model Physical vulnerability matrix Southwest Tibet,China

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

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Journal of mountain Science 2024年第6期21卷 1799-1813页

作者： YANG Hongjuan ZHANG Shaojie HU Kaiheng WEI Fangqiang LIU Yanhui State Key Laboratory of mountain hazards and Engineering Resilience Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610299China Chongqing institute of Green and Intelligent Technology Chinese Academy of SciencesChongqing 400714China Chongqing School University of Chinese Academy of SciencesChongqing 400714China China institute of Geo-Environment Monitoring Beijing 100081China

Systematically determining the discriminatory power of various rainfall properties and their combinations in identifying debris flow occurrence is crucial for early warning *** 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,*** 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 ***,the I_(max_dur)model has the highest performance metrics at a 40-min *** used logistic regression to combine at least two rainfall properties to establish multivariate threshold *** 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 *** evaluation reemphasizes the important role of the maximum intensity over short durations in debris flow *** also highlights the importance of systematically investigating the role of R_(a)in establishing rainfall thresholds for triggering debris *** the regional variations in rainfall patterns worldwide,it is necessary to evaluate the findings of this study across diverse watersheds.

关键词： Rainfall threshold Logistic regression Maximum rainfall intensity Absolute energy Antecedent rainfall

Dynamics of the spatiotemporal velocity of glaciers on the eastern slope of Mount Gongga,China,under climate change

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Advances in Climate Change Research 2024年第3期15卷 490-499页

作者： Yin FU Bo ZHANG Qiao LIU Guo-Xiang LIU Rui ZHANG institute of mountain hazards and Environment Chinese Academy of SciencesChengdu 610299China Faculty of Geosciences and Environmental Engineering Southwest Jiaotong UniversityChengdu 611756China

The quantitative assessment of glacier flow velocity dynamics plays a pivotal role in understanding its response mechanisms concerning climate *** work provides a systematic quantitative assessment of the deceleration status of glaciers in this region by investigating the motion evolution of typical glaciers in Mount Gongga in recent years,thereby revealing the seasonal dynamics and inter-annual evolution over an extensive time *** used the optical flow-small baseline subset(OF-SBAS)method to compute the time-series velocities of the Hai-luogou Glacier and the Mozigou Glacier using 178 archived Sentinel-1 satellite synthetic aperture radar(SAR)images from 2014 to *** findings revealed a prominent seasonal pattern in glacier motion,characterised by cyclic variations in velocity from cold to warm ***,we identified variations in velocities across distinct regions of the glacier surface,underscored by the lag in the peak time node of glacier flow with increasing *** pattern may have been determined by a combination of internal and external factors,including mass accumulation and ablation-driven subglacial drainage,as well as the glacier geomorphological ***,during 2015-2021,the glaciers on the eastern slope of Mount Gongga exhibited an overarching trend of ***,the ablation area of the Hailuogou Glacier recorded the most substantial deceleration,exceeding 8%per *** study underscores the efficacy of the OF-SBAS method in extracting long-term glacier *** work also establishes a robust foundation for the analysis of spatiotemporal fluctuations in glacier movement within the context of climate warming.

关键词： Monsoonal temperate glaciers Synthetic aperture radar(SAR) Glacier movement Hailuogou glacier Mozigou glacier Optical flow-small baseline subset(OF-SBAS)

Dynamic-based model for calculating the boulder impact force in debris flow

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Journal of mountain Science 2024年第6期21卷 1930-1940页

作者： YANG Chaoping ZHANG Shaojie YIN Yueping YANG Hongjuan WEI Fangqiang State Key Laboratory of mountain hazards and Engineering Resilience Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610299China University of Chinese Academy of Sciences Beijing 100049China China institute of Geological Environment Monitoring Beijing 100081China Chongqing institute of Green and Intelligent Technology Chinese Academy of SciencesChongqing 400714China Chongqing School University of Chinese Academy of SciencesChongqing 400714China

The boulder impact force in debris flow is generally calculated by static methods such as the cantilever beam ***,these methods cannot describe the dynamic scenario of boulder collision on structures,so the inertia and damping effects of the structures are not involved causing an overestimation on the boulder impact *** order to address this issue,a dynamic-based model for calculating the boulder impact force of a debris flow was proposed in this study,and the dynamic characteristics of a cantilever beam with multiple degrees of freedom under boulder collision were *** using the drop-weight method to simulate boulders within debris flow,seven experiments of drop-weight impacting the cantilever beam were used to calibrate the error of the dynamicbased *** indicate that the dynamic-based model is able to reconstruct the impact force history on the cantilever beam during impact time and the error of dynamic-based model is 15.3%in calculating boulder impact force,significantly outperforming the cantilever beam model’s error of 285%.Therefore,the dynamic-based model can overcome the drawbacks of the static-based models and provide a more reliable theoretical foundation for the engineering design of debris flow control structures.

关键词： Debris flow Impact force Boulder collision Dynamic-based model Engineering design

Quick and automatic detection of co-seismic landslides with multifeature deep learning model

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Science China Earth Sciences 2024年第7期67卷 2311-2325页

作者： Wenchao HUANGFU Haijun QIU Peng CUI Dongdong YANG Ya LIU Bingzhe TANG Zijing LIU Mohib ULLAH College of Urban and Environmental Sciences Northwest UniversityXi'an 710127China institute of mountain hazards and Environment Chinese Academy of SciencesChengdu 610041China Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity Northwest UniversityXi'an 710127China institute of Earth Surface System and hazards Northwest UniversityXi'an 710127China

Co-seismic landslide detection is essential for post-disaster rescue and risk assessment after an earthquake ***,a variety of ground objects,including roads and bare land,have spectral characteristics similar to those of co-seismic landslides,making it difficult to gather information and assess their impact rapidly and ***,an automatic detection method based on a deep learning model,named ENVINet5,with multiple features(ENVINet5_MF)was proposed to solve this problem and improve the detection accuracy of co-seismic *** ENVINet5_MF method is advantageous for co-seismic landslide detection because it features a landslide gain index(LGI)that effectively eliminates the spectral interference of bare land and *** conducted two experiments using multi-temporal PlanetScope images acquired in Hokkaido,Japan,and Mainling,*** accuracy evaluation and rationality analysis show that ENVINet5_MF performed better than comparative methods and that the co-seismic landslide areas detected by ENVINet5_MF were the most consistent with ground reference *** findings of this study suggest that ENVINet5_MF can provide an efficient and accurate method for coseismic landslide detection to ensure a rapid response to co-seismic landslide disasters.

关键词： Co-seismic landslide Automatic detection Deep learning Landslide gain index PlanetScope images

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Socio-scientific quantification of the comprehensive benefits of debris flow mitigation measures for villages in western Sichuan, China

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Journal of mountain Science 2024年第5期21卷 1598-1612页

作者： DENG Ting XU Pei LI Ming LU Yafeng WANG Yukuan LI Zhengyang SHRAVAN Kumar Ghimire institute of mountain hazards and Environment Chinese Academy of SciencesChengdu 610299China University of Chinese Academy of Sciences Beijing 100049China

Debris flow hazards seriously threaten thesafety and sustainable development of mountainousareas. Numerous debris flow mitigation measures havebeen implemented worldwide;however, acomprehensive assessment of the specific disasterreduction effects of these measures and their economic,social and ecological benefits is yet to be *** western region of Sichuan Province frequentlysuffers from geohazards such as debris flow, and thegovernment has adopted many mitigation *** study assessed the benefits of debris flowmitigation measures and identified the key influencingfactors via a field-based study conducted in 81 villagesin western Sichuan province, China. A framework forthe evaluation of the benefits of rural debris flowmitigation measures was constructed andquantitatively evaluated using a survey. Snowballsampling was performed to recruit 81 village leadersand 468 farmers. The results showed that managementand engineering measures were the main methodsused to mitigate debris flow;ecological measures wereauxiliary. The average satisfaction scores of farmers forthese three types of measures were 4.07, 3.90, and 3.56,respectively (as measured on a five-point Likert scale).In contrast, in terms of the benefits of these mitigationmeasures, only a small proportion of villages (11.11%)obtained a high level of comprehensive benefits fromthe debris flow mitigation measures, while the majority(88.89%) received medium to low-level benefits. Toimprove this situation, we further studied and foundthat the main factors that restricted villages fromachieving high-level comprehensive benefits were theunpredictable nature of debris flows, labour forceoutflow and remoteness. Effective control measures, agood economic environment and strong governmentassistance were reported as crucial factors forimproving these comprehensive benefits. This studyprovides socio-scientific references for decisionmakingon rural debris flow mitigation measures while keeping villages at the

关键词： Mitigation measures Comprehensive benefit evaluation Debris flow mountain development Rural areas

Unsupervised Color Segmentation with Reconstructed Spatial Weighted Gaussian Mixture Model and Random Color Histogram

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Computers, Materials & Continua 2024年第3期78卷 3323-3348页

作者： Umer Sadiq Khan Zhen Liu Fang Xu Muhib Ullah Khan Lerui Chen Touseef Ahmed Khan Muhammad Kashif Khattak Yuquan Zhang School of Computer and Information Science Hubei Engineering UniversityXiaogan432000China institute for AI Industrial Technology Research Hubei Engineering UniversityXiaogan432000China Key Lab of mountain hazards and Surface Processes Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu610041China University of Chinese Academy of Sciences Beijing100049China College of Aviation Zhongyuan University of TechnologyZhengzhou451191China Research Center of Digital mountain and Remote Sensing Application Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu610041China School of Computer Science University of PoonchAzad Jammu Kashmir12350Pakistan School of Computer Science and Information Engineering Hubei UniversityWuhan430062China

Image classification and unsupervised image segmentation can be achieved using the Gaussian mixture *** the Gaussian mixture model enhances the flexibility of image segmentation,it does not reflect spatial information and is sensitive to the segmentation *** this study,we first present an efficient algorithm that incorporates spatial information into the Gaussian mixture model(GMM)without parameter *** proposed model highlights the residual region with considerable information and constructs color ***,we incorporate the content-based color saliency as spatial information in the Gaussian mixture *** segmentation is performed by clustering each pixel into an appropriate component according to the expectation maximization and maximum ***,the random color histogram assigns a unique color to each cluster and creates an attractive color by default for segmentation.A random color histogram serves as an effective tool for data visualization and is instrumental in the creation of generative art,facilitating both analytical and aesthetic *** experiments,we have used the Berkeley segmentation dataset BSDS-500 and Microsoft Research in Cambridge *** the study,the proposed model showcases notable advancements in unsupervised image segmentation,with probabilistic rand index(PRI)values reaching 0.80,BDE scores as low as 12.25 and 12.02,compactness variations at 0.59 and 0.7,and variation of information(VI)reduced to 2.0 and 1.49 for the BSDS-500 and MSRC datasets,respectively,outperforming current leading-edge methods and yielding more precise segmentations.

关键词： Unsupervised segmentation color saliency spatial weighted GMM random color histogram