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More than 130 people were killed and severe property damage took place when volcaniclastic debris flows triggered by heavy rainfall inundated various towns located in piedmont areas. This work investigates the suitability of LAHARZ, a GIS-assisted method for the automatic delineation of lahar inundation areas, for reproducing the May 1998 flows at Sarno. It was found that recalibration of the empirical relationship employed by LAHARZ is required in order to realistically hind-cast the inundation areas of considered events. The potential for further improvements in prediction outputs for this type of geomorphic setting is discussed, taking into account the observed lower mobility of these small volcaniclastic debris flows as compared to lahars of similar size.",{"EN":221},"Empirical modelling of the May 1998 small debris flows in Sarno (Italy) using LAHARZ",{"VOID":223},"[\"15558275430794878482\"]",{"VOID":225},"Aleotti P, Polloni G (2003) Two-dimensional model of the 1998 Sarno debris flows (Italy): preliminary results. In: Rickenmann D, Cheng-lung C (eds) Proceed. Third international conference on debris-flow hazards mitigation: mechanics, prediction and assessment. pp 553–563\nBovis MJ, Jakob M (1999) The role of debris supply conditions in predicting debris flow activity. Earth Surf Process Landforms 24:1039–1054\nCalcaterra D, Parise M, Palma B, Pelella L (1999) The May 5th 1998, lansliding event in Campania (southern Italy): inventory of slope movements in the Quindici area. In: Yagi N, Yamagami T, Jiang J (eds) Proceed. International symposium on slope stability engineering. pp 1361–1366\nCalcaterra D, Parise M, Palma B, Pelella L (2000) Multiple debris-flows in volcaniclastic materials mantling carbonate slopes. In: Wieczorek GF, Naeser ND (eds) Debris-flow hazards mitigation: mechanics, prediction, and assessment. Balkema, Rotterdam, pp 99–107\nCrosta GB, Dal Negro P (2003) Observations and modelling of soil slip-debris flow initiation processes in pyroclastic deposits: the Sarno 1998 event. Nat Hazards Earth Syst Sci 3:53–69\nD’Ambrosio D, Di Gregorio S, Iovine G, Lupiano V, Rongo R, Spataro W (2003) First simulations of the Sarno debris flow through Cellular Automata modeling. Geomorphology 54:91–117\nFavalli M, Pareschi MT (2004) Digital elevation model construction from structured topographic data: the DEST algorithm. J Geophys Res 109:F04004, doi: 10.1029\u002F2004JF000150\nIovine G, Di Gregorio S, Lupiano V (2003) Assessing debris-flow susceptibility through cellular automata modelling: an example from the May 1998 disaster at Pizzo d’Alvano (Campania-southern Italy). In: Rickenmann D, Cheng-lung C (eds) Proceed. Third international conference on debris-flow hazards mitigation: mechanics, prediction and assessment. pp 623–634\nIverson RM (1997) The physics of debris flows. Rev Geophys 35(3):245–296\nIverson RM, Schilling SP, Vallance JW (1998) Objective delineation of lahar inundation hazard zones. Geol Soc Am Bull 110(8):972–984\nIverson RM (2003) The debris flow rheology myth. In: Rickenmann D, Cheng-lung C (eds) Proceed. Third international conference on debris-flow hazards mitigation: mechanics, prediction and assessment 1. Davos, Switzerland, pp 303–314\nJakob M, Bovis MJ, Oden M (2005) The significance of channel recharge rates for estimating debris-flow magnitude and frequency. Earth Surf Process Landforms 30:755–766\nKerle N, van Wyk de Vries B, Oppenheimer C (2003) New insight into the factors leading to the 1998 flank collapse and lahar disaster at Casita volcano, Nicaragua. Bull Volcanol 65:331–345\nMalin MC, Sheridan MF (1982) Computer-assisted mapping of pyroclastic surges. Science 217:637–640\nMigale LS, Milone A (1998) Mudflows in pyroclastic deposits of Campania. First results of the historical research. Rass Stor Salernitana 30, 15, 2:235–271\nNewhall CG, Punongbayan RS (1996) Fire and mud: eruptions and lahars of Mount Pinatubo, Philippines. Philippine Institute of Volcanology and Seismology & University of Washington Press, Seattle, p 1125\nO’Brien JS, Julien PY, Fullerton WT (1993) Two-dimensional water flood and mudflow simulation. J Hydraul Eng 119:244–261\nPalumbo A, Pisano L (1966) Analisi dei dati medi degli andamenti annuali di alcuni elementi metereologici a Napoli relativi al periodo 1872–1966. Annali dell’Osservatorio Vesuviano 8:3–17\nPareschi MT, Favalli M, Giannini F, Sulpizio R, Zanchetta G, Santacroce R (2000) May 5, 1998, debris flows in circumvesuvian areas (Southern Italy): insights for hazard assessment. Geology 7:629–642\nPescatore T, Ortolani F (1973) Shema tettonico dell’Appennino campano-lucano. Boll Soc Geol Ital 92:453–473\nPierson TC (1998) An empirical method for estimating travel times for wet volcanic mass flows. Bull Volcanol 60:98–109\nPierson TC, Janda RJ, Thouret JC, Borrero CA (1990) Perturbation and melting of snow and ice by the of snow and ice by the 13 November 1985 eruption of Nevado del Ruiz, Colombia, and consequent mobilization, flow and deposition of lahars. J Volcanol Geotherm Res 41:17–66\nPorfido S, Esposito E, Alaia F, Esposito G, Laccarino G (2002) Il dissesto idrogeologico: inventario e prospettive. Atti Accad Naz Lincei 181:457–466\nRickenmann D (1999) Empirical relationships for debris flows. Nat Hazards 19:47–77\nRosi M, Principe C, Vecci R (1993) The 1631 Vesuvius eruption. A reconstruction based on historical and stratigraphical data. J Volcanol Geotherm Res 58:151–182\nSchilling SP (1998) LAHARZ: GIS programs for automated delineation of lahar-hazard zones. US Geological Survey, Open-file Report 98–638, p 84\nScott KM (1988) Origin, behavior and sedimentology of lahars and lahar-runout flows in the Toutle-Cowlitz River System. US Geol Surv Prof Pap 1447-A\nScott KM, Vallance JW, Pringle PT (1995) Sedimentology, behavior, and hazard of debris flows at Mount Rainer, Washington. US Geol Surv Prof Pap 1547\nScott KM, Macias JL, Naranjo JA, Rodriguez S, McGeehin JP (2001) Catastrophic debris flows transformed from landslide in volcanic terrains: mobility, hazard assessment and mitigation strategies. US Geological Survey Prof. Pap. 1630:1–59\nSmith GA, Fritz WJ (1989) Volcanic influences on terrestrial sedimentation. Geology 17:375–376\nSmith GA, Lowe DR (1991) Lahars: volcano-hydrologic events and deposition in the debris flow-hyperconcentrated flow continuum. Sedimentation in volcanic settings (SEPM Special Publication) 45:59–69\nStevens NF, Manville V, Heron DW (2002) The sensitivity of a volcanic flow model to digital elevation model accuracy: experiments with digitised map contours and interferometric SAR at Ruapehu and Taranaki volcanoes, New Zealand. J Volcanol Geotherm Res 119:89–105\nTakahashi T (1991) Debris flow. Balkema, Rotterdam, Netherlands, p 165\nToyos G, Oppenheimer C, Pareschi MT, Sulpizio R, Zanchetta G, Zuccaro G (2003) Building damage by debris flows in the Sarno area, Southern Italy. In: Rickenmann D, Cheng-lung C (eds) Proceed. Third international conference on debris-flow hazards mitigation: mechanics, prediction and assessment, 2. pp 1209–1220\nVallance JW, Scott KM (1997) The Osceola mudflow from Mount Rainer: sedimentology and hazard implications of a huge clay-rich debris flow. Geol Soc Am Bull 109:143–163\nVallance JW (2000) Lahars. In: Sigurdsson H, Houghton BF, McNutt SR, Rymer H, Stix J (eds) Encyclopaedia of volcanoes. pp 601–616\nZanchetta G, Sulpizio R, Pareschi MT, Leoni FM, Santacroce R (2004) Characteristics of May 5–6, 1998 volcaniclastic debris-flows in the Sarno area of Campania, Southern Italy: relationships to structural damage and hazard zonation. 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The Monte da Lua hill area in Portugal, a tourist destination close to the historic city of Sintra (UNESCO World Heritage), is a typical granite boulder chaos landscape where from time to time rockfalls occur, the last such event having occurred on 29 January 2002. This area is therefore suitable to develop a rockfall study in order to provide hazard and risk maps a basis for mitigation measures. A preliminary investigation of the area leads to the identification of 188 potentially dangerous boulders. Detailed locations and geotechnical characteristics in terms of geometry, strength and context were sampled for each boulder. Digital elevations at 1 × 1 m resolution, known rockfall trajectory and building locations are provided in a GIS project for the study together with the spatial database of boulder characteristics. The modelling approach was conducted in two steps: (1) discrimination of the boulders in terms of static and dynamic mobility behaviour with multivariate analysis; (2) stochastic simulation of rockfall trajectories. The rockfall trajectory algorithm proposed is straightforward and is only dependent on elevation data, initial location of boulders and a friction angle. Due to the slope of the area, it assumes that rockfall is always of the rolling or sliding type. The friction angle was calibrated on the basis of the rockfall travel distance recorded on 29 January 2002 and generates simulated “realistic” trajectories. A smaller friction angle increases all simulated trajectories, leading to more “pessimistic” scenarios. The combined analysis of trajectories and potential damage to buildings and discrimination in terms of static and dynamic behaviour provides a final table in which all 188 sampled boulders are classified in one of the five risk grades.",{"EN":411},"Rockfall hazard and risk analysis for Monte da Lua, Sintra, 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A, Vilaplana JM, Martínez J (2006) Application of a long-range terrestrial laser scanner to a detailed rockfall study at Vall de Núria (Eastern Pyrenees, Spain). Eng Geol 88:136–148",{},{"id":519,"text":520,"url":521,"identifiers":522},"4c68646b-0035-4279-8000-0006b275d4fa","Agliardi F, Crosta GB (2003) High resolution three-dimensional numerical modelling of rock falls. Int J Rock Mech Min Sci 40(4):455–471","https:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs10440-022-00541-7",{"doi":523},"10.1007\u002Fs10440-022-00541-7",{"id":20,"text":525,"url":20,"identifiers":526},"Alejano LR, Stockhausen H, Bastante FG, Alonso E, Ramírez-Oyanguren P (2008) ROFRAQ: an empirical method to estimate the risk of accidents due to rockfalls in quarries. Int J Rock Mech Min Sci 45:1252–1272",{},{"id":528,"text":529,"url":530,"identifiers":531},"d33c0568-b26b-451f-89b1-33fbb3e7deb2","Banks J, Carson J, Nelson B (1996) Discrete-event system simulation. 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Nat Hazards Earth Syst Sci 3:407–422",{"doi":523},{"id":519,"text":546,"url":521,"identifiers":547},"Dorren LKA (2003) A review of rockfall mechanics and modelling approaches. Prog Phys Geogr 27(1):69–87",{"doi":523},{"id":20,"text":549,"url":20,"identifiers":550},"Dussage-Peisser C, Helmsteter A, Grasso JR, Hantz D, Desverreaux P, Jeannin M, Giraud A (2002) Probabilistic approach to rockfall hazard assessment: potential of historical data analysis. Nat Haz Earth Syst Sci 2:15–26",{},{"id":552,"text":553,"url":554,"identifiers":555},"b1ea2f55-f6f1-475a-aaa4-4de90f3ffb49","Frattini O, Crosta G, Carrara A, Agliardi F (2007) Assessment of rockfall susceptibility by integrating statistical and physically-based approaches. 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Environ Manage 34:191–208",{},{"id":20,"text":570,"url":20,"identifiers":571},"ISRM (2007) The Complete ISRM suggested methods for rock characterization, testing and monitoring: 1974–2006. In: Ulusay R, JA Hudson (ed) Commission on testing methods, international society for rock mechanics, p 628",{},{"id":519,"text":573,"url":521,"identifiers":574},"Johnson R, Wichern D (1982) Applied multivariate statistical analysis. Prentice Hall, New Jersey, p 767",{"doi":523},{"id":20,"text":576,"url":20,"identifiers":577},"Jones CL, Higgins JD, Andrew RD (2000) Colorado rockfall simulation program version 4.0 (for windows). Colorado Department of Transportation, Colorado Geological Survey, p 127",{},{"id":20,"text":579,"url":20,"identifiers":580},"Kullberg JC, Terrinha P, Pais J, Reis RP, Legoinha P (2006) Arrábida e Sintra: dois Exemplos de Tectónica Pós-Rifting da Bacia Lusitaniana. In: Dias R, Araújo A, Terrinha P, Kullberg JC (eds) Geologia de Portugal no contexto da Ibéria. Univ. Évora, p 418",{},{"id":20,"text":582,"url":20,"identifiers":583},"Lee S, Pradhan B (2006) Probabilistic landslide risk mapping at Penang Island, Malaysia. J Earth Syst Sci 115(6):1–12",{},{"id":519,"text":585,"url":521,"identifiers":586},"Malamud BD, Turcotte DL (2000) Cellular automata models applied to natural hazards. IEEE Comput Sci Eng 2:42–51",{"doi":523},{"id":588,"text":589,"url":590,"identifiers":591},"c6dc1834-2248-498e-92bb-f1b3d08a4614","Miranda R, Valadares V, Terrinha P, Mata J, Azevedo MR, Gaspar M, Kullberg JC, Ribeiro C (2009) Age constraints on the late cretaceous alkaline magmatism on the West Iberian margin. Cretaceous Res 30:575–586","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0195667108001560",{"doi":592},"10.1016\u002Fj.cretres.2008.11.002",{"id":519,"text":594,"url":521,"identifiers":595},"Miyamoto H, Sasaki S (1998) Simulating lava flows by an improved cellular automata method. 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Earth Sci Front 14(6):143–152",{"doi":523},{"id":519,"text":607,"url":521,"identifiers":608},"Pradhan B, Lee S (2009) Landslide risk analysis using artificial neural network model focusing on different training sites. Int J Phys Sci 3(11):1–15",{"doi":523},{"id":20,"text":610,"url":20,"identifiers":611},"Pradhan B, Lee S (2010) Delineation of landslide hazard areas using frequency ratio, logistic regression and artificial neural network model at Penang Island, Malaysia. Environ Earth Sci 60:1037–1054",{},{"id":519,"text":613,"url":521,"identifiers":614},"Pradhan B, Youssef AM (2010) Manifestation of remote sensing data and GIS for landslide hazard analysis using spatial-based statistical models. Arabian J Geosci 3(3):319–326",{"doi":523},{"id":519,"text":616,"url":521,"identifiers":617},"Rock NMS (1982) The Late cretaceous alkaline igneous province in the Iberian Peninsula and its tectonic significance. Lithos 15:111–131",{"doi":523},{"id":519,"text":619,"url":521,"identifiers":620},"Schweigl J, Ferretti C, Nössing L (2003) Geotechnical characterization and rockfall simulation of a slope: a practical case study from south Tyrol (Italy). Eng Geol 67:281–296",{"doi":523},{"id":519,"text":622,"url":521,"identifiers":623},"Storetvedt KM, Mogstad H, Abranches MC, Mitchell JG, Serralheiro A (1987) Paleomagnetism and isotopic age data from upper cretaceous igneous rocks of W Portugal; geological correlation and plate tectonic aspects. Geophys J R Astron Soc 88:241–263",{"doi":523},{"id":20,"text":625,"url":20,"identifiers":626},"Terrinha P, Aranguren A, Kullberg MC, Pueyo E, Kullberg JC, Casas-Sainz AM, Rillo C (2003) Complexo Ígneo de Sintra—um modelo de instalação constrangido por novos dados de gravimetria e ASM. In: Ciências da Terra, vol. E. V, VI. Congresso Nacional de Geologia, pp 58–59",{},{"id":628,"createTime":629,"updateTime":630,"relativeEntities":631,"slug":632,"properties":633,"entityType":228,"verifyStatus":229,"verifyTime":644,"verifyNote":231,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":645,"fullTextUrl":20,"authors":646,"publicationType":336,"publisherRelationship":664,"citationCount":118,"citationInfo":720,"publishDate":723,"publishYear":721,"citationAnalyzeStatus":397,"lastCitationAnalyze":724,"indexDatabases":725,"openAccess":20,"references":20,"isForceReanalyzing":400},"1f7d1652-8f1e-4167-8104-186054f1fc49","2024-01-26T20:33:14.716+00:00","2026-08-18T06:16:30.846+00:00",[],"Households-disaster-memory-recollection-after-the-2013-Colorado-flood",{"abstract":634,"title":636,"gsPaper":638,"references":640,"doi":642},{"EN":635},"There is some evidence supporting that surveys conducted 12–18 months after a disaster can provide accurate assessments of people’s disaster responses during disasters. Studies suggest that people have good memories of events that are personally relevant to them and that there appear to be reasonable justifications for taking post-event survey data at face value. Nonetheless, according to the Protective Action Decision Model, people’s disaster response activities include behavioral and emotional responses. Since these two types of responses are different in nature, it is unclear whether people have good memory recollection of both types of responses. Thus, it is important to obtain additional evidence to test survey respondents’ memory recollection over time. To do this, this study collected the 2013 Colorado flood household response data 7 months and 14 months after the event. Box’s homogeneity test is used to test the equivalence of covariance matrices. The results indicate that survey respondents’ behavioral responses follow similar patterns between two survey samples, but the emotional responses do not. This finding suggests that survey studies are able to acquire accurate disaster behavioral response data 14 months after a disaster; however, emotional response is considered ephemeral data.\n",{"EN":637},"Households disaster memory recollection after the 2013 Colorado flood",{"VOID":639},"[\"7444793681550595501\"]",{"VOID":641},"Arlikatti S, Lindell M, Prater C (2007) Perceived stakeholder role relationships and adoption of seismic hazard adjustments. Int J Mass Emerg Disasters 25(3):218–256\nAustin PC, Hux JE (2002) A brief note on overlapping confidence intervals. J Vasc Surg 36(1):194–195\nBourque LB, Shoaf KI, Nguyen LH (1997) Survey research. Int J Mass Emerg Disasters 15(1):71–101\nBrown R, Kulik J (1977) Flashbulb memories. Cognition 5(1):73–99\nDillman DA, Smyth JD, Christian LM (2014) Internet, phone, mail, and mixed-mode surveys : the tailored design method, 4th edn. Wiley, Hoboken\nGnanadesikan R (2011) Methods for statistical data analysis of multivariate observations. Wiley, Hoboken\nHarsch N, Neisser U (1989) Substantial and irreversible errors in flashbulb memories of the challenger explosion. Bull Psychon Soc 27(6):519\nHuang S, Lindell M, Prater C, Haoche W, Siebeneck L (2012) Household evacuation decision making in response to hurricane Ike. Nat Hazards Rev 13(4):283–296\nHuang S, Lindell M, Prater C (2016) Who leaves and who stays? A review and statistical meta-analysis of hurricane evacuation studies. Environ Behav 48(8):991–1029\nLarsen SF (1992) Potential flashbulbs: memories of ordinary news as the baseline. In: Winograd E, Neisser U (eds) Emory symposia in cognition. Cambridge University Press, New York, pp 32–64\nLin C, Siebeneck L, Lindell M, Prater C, Haoche W, Huang S (2014) Evacuees’ information sources and reentry decision making in the aftermath of hurricane Ike. Nat Hazards 70(1):865–882\nLindell M, Jingchein L, Prater C (2005) Household decision making and evacuation in response to hurricane Lili. Nat Hazards Rev 6(4):171\nLivingston RB (1967a) Brain circuitry relating to complex behavior. In: Quarton GC, Melnechuck T, Schmitt FO (eds) The neurosciences: a study program. Rockefeller University Press, New York, pp 499–514\nLivingston RB (1967b) Reinforcement. In: Quarton GC, Melnechuch T, Schmitt FO (eds) The neurosciences: a study program. Rockefeller University Press, New York, pp 568–576\nMatthews G, Jones DM, Graham Chamberlain A (1990) Refining the measurement of mood: the UWIST mood adjective checklist. Br J Psychol 81:17–42\nMurphy H, Greer A, Haoche W (2018) Trusting government to mitigate a new hazard: the case of Oklahoma earthquakes. Risk Hazards Crisis Public Policy 9(3):357–380\nNeisser U (1982) Snapshots or benchmarks? In: Neisser U (ed) Memory observed: remembering in natural contexts. Cambridge University Press, Cambridge, pp 43–48\nNeisser U, Winograd E, Bergman ET (1996) Remembering the earthquake: direct experience vs. hearing the news. Memory 4(4):337–357\nNorris FH, Kaniasty K (1992) Reliability of delayed self-reports in disaster research. J Trauma Stress 5(4):575–588\nWei H, Lindell M, Prater C (2014) ‘“Certain death”’ from storm surge: a comparative study of household responses to warnings about hurricanes Rita and Ike. Weather Clim Soc 6:425–433\nWei HL, Wu H, Lindell M, Prater C, Shiroshita H, Johnston D, Becker J (2017) Assessment of households’ responses to the tsunami threat: a comparative study of Japan and New Zealand. Int J Disaster Risk Reduct 25:274–282\nWright DB (1993) Recall of the Hillsborough disaster over time: systematic biases of ‘Flashbulb’ memories. Appl Cogn Psychol 7(2):129–138\nWu H, Lindell M, Prater C (2012) Logistics of hurricane evacuation in hurricanes Katrina and Rita. Transp Res Part F Traffic Psychol Behav 15(4):445–461",{"VOID":643},"10.1007\u002Fs11069-020-03951-8","2024-05-16T07:00:53.380+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11069-020-03951-8",[647],{"id":648,"sortIndex":21,"researcher":20,"roles":649,"affiliations":650,"properties":659,"displayName":661,"givenName":20,"familyName":20},"95499393-4f14-4a40-a15d-079227bbed40",[237],[651],{"id":652,"sortIndex":21,"affiliation":653,"properties":20},"fe2c7178-e2d1-4ee5-a8b5-396987567a94",{"id":652,"createTime":20,"updateTime":20,"relativeEntities":654,"slug":20,"properties":655,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":658,"statistic":20},[],{"title":656},{"VI":657},"University of North Texas, Denton, USA",[],{"title":660,"gsAuthor":662},{"VI":661},"Hao-Che 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evaluation of natural catastrophe risk to structures often includes consideration of uncertainty in predictions of some measure of the intensity of the hazard caused by the catastrophe. For example, in the well-established method of probabilistic seismic hazard analysis, uncertainty in the intensity measure for the ground motion is considered through so-called ground motion prediction equations, which predict ground motion intensity and uncertainty as a function of earthquake characteristics. An analogous method for evaluating hurricane risk to offshore structures, referred to herein as probabilistic offshore hurricane hazard analysis, has not been studied extensively, and analogous equations do not exist to predict offshore hurricane wind and wave intensity and uncertainty as a function of hurricane characteristics. Such equations, termed here as wind and wave prediction equations (WWPEs), are developed in this paper by comparing wind and wave estimates from parametric models with corresponding measurements during historical hurricanes from 22 offshore buoys maintained as part of the National Data Buoy Center and located near the US Atlantic and Gulf of Mexico coasts. The considered buoys include observations from 27 historical hurricanes spanning from 1999 to 2012. The 27 hurricanes are characterized by their eye position, translation speed, central pressure, radius to maximum winds, maximum wind speed, Holland B parameter and direction. Most of these parameters are provided for historical hurricanes by the National Hurricane Center’s H*Wind program. The exception is the Holland B parameter, which is calculated using a best-fit procedure based on H*Wind’s surface wind reanalyses. The formulation of the WWPEs is based on two parametric models: the Holland model to estimate hurricane winds and Young’s model to estimate hurricane-induced waves. Model predictions are made for the 27 considered historical hurricanes, and bias and uncertainty of these predictions are characterized by comparing predictions with measurements from buoys. The significance of including uncertainty in the WWPEs is evaluated by applying the WWPEs to a 100,000-year stochastic catalog of synthetic hurricanes at three locations near the US Atlantic coast. The limitations of this approach and remaining work are also discussed.",{"EN":736},"Wind-wave prediction equations for probabilistic offshore hurricane hazard analysis",{"VOID":738},"[\"11361376692989887774\"]",{"VOID":740},"10.1007\u002Fs11069-016-2331-z","2024-05-04T10:53:30.419+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11069-016-2331-z",[744,764,781,798,815,830],{"id":745,"sortIndex":21,"researcher":20,"roles":746,"affiliations":747,"properties":759,"displayName":761,"givenName":20,"familyName":20},"2f78be40-5ed7-4f95-ad78-cbb382b80492",[237],[748],{"id":749,"sortIndex":21,"affiliation":750,"properties":756},"5dac9763-7b90-4239-9f8c-60c05d8a1f0f",{"id":749,"createTime":20,"updateTime":20,"relativeEntities":751,"slug":20,"properties":752,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":755,"statistic":20},[],{"title":753},{"VI":754},"Department of Civil and Environmental Engineering, Northeastern University, Boston, United States",[],{"title":757},{"VI":758},"Department of Civil and Environmental Engineering, Northeastern University, Boston, USA",{"title":760,"gsAuthor":762},{"VI":761},"Vahid Valamanesh",{"VOID":763},"[\"YVIEd5YAAAAJ\"]",{"id":765,"sortIndex":249,"researcher":20,"roles":766,"affiliations":767,"properties":776,"displayName":778,"givenName":20,"familyName":20},"746fd28f-a3f1-453e-902f-734ef2375927",[237],[768],{"id":749,"sortIndex":21,"affiliation":769,"properties":774},{"id":749,"createTime":20,"updateTime":20,"relativeEntities":770,"slug":20,"properties":771,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":773,"statistic":20},[],{"title":772},{"VI":754},[],{"title":775},{"VI":758},{"title":777,"gsAuthor":779},{"VI":778},"Andrew T. 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White Pap 2(1):79",{"doi":523},{"id":519,"text":912,"url":521,"identifiers":913},"Batts ME, Simiu E, Russell LR (1980) Hurricane wind speeds in the United States. J Struct Div 106(10):2001–2016",{"doi":523},{"id":519,"text":915,"url":521,"identifiers":916},"Bender MA, Ginis I (2000) Real-case simulations of hurricane-ocean interaction using a high-resolution coupled model: effects on hurricane intensity. Mon Weather Rev 128(4):917–946",{"doi":523},{"id":918,"text":919,"url":920,"identifiers":921},"60c84438-4b21-4778-bcb3-f1b3f87f05f3","Booij N, Ris RC, Holthuijsen LH (1999) A third generation wave model for coastal regions: 1. Model description and validation. J Geophys Res Oceans 104(C4):7649–7666","https:\u002F\u002Fagupubs.onlinelibrary.wiley.com\u002Fdoi\u002F10.1029\u002F98JC02622",{"doi":922},"10.1029\u002F98jc02622",{"id":924,"text":925,"url":926,"identifiers":927},"00f88fa9-c1ab-4f23-8463-bf481395f69e","Chawla A, Spindler DM, Tolman HL (2013) Validation of a thirty year wave hindcast using the Climate Forecast System Reanalysis winds. Ocean Model 70:189–206","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1463500312001047",{"doi":928},"10.1016\u002Fj.ocemod.2012.07.005",{"id":20,"text":930,"url":20,"identifiers":931},"Cornell CA (1964) Stochastic processes in civil engineering. Doctoral Dissertation, Stanford University, CA",{},{"id":933,"text":934,"url":935,"identifiers":936},"ae66857a-ab36-487d-8fd8-a2a7833b6c66","Cornell CA (1968) Engineering seismic risk analysis. 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J Wind Eng Ind Aerodyn 89:1058–1470","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0167610501000988",{"doi":1003},"10.1016\u002Fs0167-6105(01)00098-8",{"id":519,"text":1005,"url":521,"identifiers":1006},"Tryggvason BV, Davenport AG, Surry D (1976) Predicting wind-induced response in hurricane zones. J Struct Div 102(12):2333–2350",{"doi":523},{"id":20,"text":1008,"url":20,"identifiers":1009},"Valamanesh V, Myers AT, Arwade SR (2015) Analysis of extreme metocean conditions for offshore wind turbines. Struct Saf 55:60–69",{},{"id":519,"text":1011,"url":521,"identifiers":1012},"Vickery PJ, Twisdale LA (1995) Wind-field and filling models for hurricane wind-speed predictions. J Struct Eng 121(11):1700–1709",{"doi":523},{"id":20,"text":1014,"url":20,"identifiers":1015},"Vickery PJ, Wadhera D (2008a) Development of design wind speed maps for the Caribbean for application with the wind load provisions of ASCE 7. 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J Waterw Port C 114(5):637–652",{"doi":523},{"id":1041,"createTime":1042,"updateTime":1043,"relativeEntities":1044,"slug":1045,"properties":1046,"entityType":228,"verifyStatus":229,"verifyTime":1057,"verifyNote":231,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":1058,"fullTextUrl":20,"authors":1059,"publicationType":336,"publisherRelationship":1148,"citationCount":21,"citationInfo":1204,"publishDate":1206,"publishYear":721,"citationAnalyzeStatus":397,"lastCitationAnalyze":1043,"indexDatabases":1207,"openAccess":20,"references":20,"isForceReanalyzing":400},"b96f5132-66df-464c-8138-3a1e899d5eca","2024-01-12T23:42:45.129+00:00","2026-08-14T23:41:28.911+00:00",[],"Empirical-seismic-fragility-models-for-Nepalese-school-buildings",{"abstract":1047,"title":1049,"gsPaper":1051,"references":1053,"doi":1055},{"EN":1048},"Empirical vulnerability models are fundamental tools to assess the impact of future earthquakes on urban settlements and communities. Generally, they consist of sets of fragility curves that are derived from georeferenced post-earthquake damage data. Following the 2015 Nepal earthquake sequence, the World Bank, through the Global Program for Safer Schools, conducted a Structural Integrity and Damage Assessment (SIDA) of about 18,000 school buildings in the earthquake-affected area. In this work, the database is utilized to identify the main structural characteristics of the Nepalese school building stock. For the first time, extended SIDA school damage data is processed to derive fragility curves for the main structural typologies. Data sets for each structural typology are used for a Bayesian updating of existing fragilities to obtain regional models for Nepalese schools. These fragility estimates can be adopted to assess potential seismic losses of the school infrastructure in Nepal. Additionally, they can be used for calibrating loss assessment studies in the wider Himalayan region where the structural typologies are similar.",{"EN":1050},"Empirical seismic fragility models for Nepalese school buildings",{"VOID":1052},"[\"5204416861267284810\"]",{"VOID":1054},"ARUP (2015) Global program for safer schools—structural typologies. London, UK\nAsian Development Bank (2014) Strategy and plan for increasing disaster resilience for schools in Nepal. Bangkok, Thailand, Thailand\nBajaj K, Anbazhagan P (2019) Regional stochastic GMPE with available recorded data for active region: application to the Himalayan region. Soil Dyn Earthq Eng 126:105825. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.soildyn.2019.105825\nBhattacharyya A (1946) On a measure of divergence between two multinomial populations. Indian J Stat 7(4):401–406\nCalvi GM, Pinho R, Magenes G et al (2006) Development of seismic vulnerability assessment methodologies over the past 30 years. 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Geneva, Switzerland\nUSGS (2017a) M 7.8—36 km E of Khudi, Nepal. https:\u002F\u002Fearthquake.usgs.gov\u002Fearthquakes\u002Feventpage\u002Fus20002926\u002Fexecutive\nUSGS (2017b) M 7.3—19 km SE of Kodari, Nepal. https:\u002F\u002Fearthquake.usgs.gov\u002Fearthquakes\u002Feventpage\u002Fus20002ejl\u002Fexecutive\nWald DJ, Worden BC, Quitoriano V, Pankow KL (2006) ShakeMap manual: technical manual, user’s guide, and software guide. USGS\nWorld Bank (2017) Global program for safer schools. Washington, DC, USA. https:\u002F\u002Fgpss.worldbank.org\u002F\nWorld Bank (2019a) Fragility and vulnerability assessment guide. Washington, DC, USA\nWorld Bank (2019b) Global library of school infrastructure. https:\u002F\u002Fgpss.worldbank.org\u002Fen\u002Fglosi\u002Fabout-glosi",{"VOID":1056},"10.1007\u002Fs11069-020-04312-1","2024-06-24T17:18:07.150+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11069-020-04312-1",[1060,1077,1092,1107,1122,1135],{"id":1061,"sortIndex":21,"researcher":20,"roles":1062,"affiliations":1063,"properties":1072,"displayName":1074,"givenName":20,"familyName":20},"65650d0b-94e9-47f5-b3ec-d112d7148c47",[237],[1064],{"id":1065,"sortIndex":21,"affiliation":1066,"properties":20},"b11fe5b1-6f6c-4995-8a4f-4a8cf2544d91",{"id":1065,"createTime":20,"updateTime":20,"relativeEntities":1067,"slug":20,"properties":1068,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1071,"statistic":20},[],{"title":1069},{"VI":1070},"Department of Civil Engineering, University of Bristol, 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is one of the most frequent and most costly natural disasters that occur throughout Canada, and although there is ongoing work to update and improve flood hazard assessments and mapping of high flood risk rivers throughout the country, most studies only delve into open water flooding. However, many rivers in Canada experience higher peak water levels due to ice jamming, resulting in severe flooding of surrounding areas. Hence, there is an urgency to expand current flood hazard assessments to include ice jam flooding for better flood management practices. One area that is often plagued with ice jam flooding is the lowest reach of Manitoba’s Red River. The Lower Red River is a low-lying river with a terminus inland delta where water levels are governed by Lake Winnipeg. Ice jam floods often divert water into the lower Red River’s floodplain that is continually being encroached by development. RIVICE, Environment Canada’s one-dimensional ice hydraulic model, was set up within a Monte Carlo framework to simulate an envelope of backwater level profiles that result from ice jams within the study site. Non-exceedance probability profiles were created from the envelope of backwater level profiles to assess ice jam flood hazard.",{"EN":1218},"An ice jam flood hazard assessment of a lowland river and its terminus inland delta",{"VOID":1220},"[\"1315702010513501857\"]",{"VOID":1222},"Beltaos S (2011) Alternative method for synthetic frequency analysis of breakup-jam floods. 16th Workshop on river ice organized by CRIPE—Committee on River Ice Processes and the Environment. Winnipeg, Manitoba, September 18–22, 2011. https:\u002F\u002Fwww.cripe.ca\u002Fdocs\u002Fproceedings\u002F16\u002FBeltaos-2011.pdf. Accessed 20 Jun 2019\nDas A, Rokaya P, Lindenschmidt K-E (2017) Assessing the impacts of climate change on ice jams along the Athabasca River at Fort McMurray, Alberta, Canada. 19th Workshop on river ice organized by CRIPE—Committee on River Ice Processes and the Environment. Whitehorse, Yukon, Canada, July 9–12, 2017. https:\u002F\u002Fwww.cripe.ca\u002Fdocs\u002Fproceedings\u002F19\u002FDas-et-al-2017.pdf. Accessed 7 July 2019\nEnvironment and climate change Canada (2020) Historical data. Retrieved from government of Canada: https:\u002F\u002Fclimate.weather.gc.ca\u002Fhistorical_data\u002Fsearch_historic_data_e.html. Accessed 16 August 2018\nEnvironment Canada (2013) RIVICE Model—User's manual. Available from https:\u002F\u002Fgiws.usask.ca\u002Frivice\u002FManual\u002FRIVICE_Manual_2013-01-11.pdf. Accessed 18 June 2018\nLindenschmidt K-E (2017a) RIVICE—A non-proprietary, open source. One-Dimens River-Ice Model Water 9(5):314. https:\u002F\u002Fdoi.org\u002F10.3390\u002Fw9050314\nLindenschmidt K-E (2017b) Using stage frequency distributions as objective functions for model calibration and global sensitivity analyses. Environ Model Softw 92:169–175. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.envsoft.2017.02.027\nLindenschmidt K-E (2020) River ice processes and ice flood forecasting—A guide for practitioners and students. Springer, Switzerland\nLindenschmidt K-E, Das A, Rokaya P, Chu T (2016) Ice jam flood risk assessment and mapping. Hydrol Process 30:3754–3769. https:\u002F\u002Fdoi.org\u002F10.1002\u002Fhyp.10853\nLindenschmidt K-E, Das A, Rokaya P, Chun K, Chu T (2015) Ice jam flood hazard assessment and mapping of the Peace River at the Town of Peace River. 18th Workshop on river ice organized by CRIPE—Committee on River Ice Processes and the Environment. Quebec City, Quebec, August 18–20, 2015. https:\u002F\u002Fwww.cripe.ca\u002Fdocs\u002Fproceedings\u002F18\u002F23_Lindenschmidt_et_al_2015.pdf. Accessed 22 May 2018\nLindenschmidt K-E, Sanden JV, Demski A, Drouin H, Geldsetzer T (2011) Characterising river ice along the Lower Red River using RADARSAT-2 Imagery. 16th Workshop on River Ice organized by CRIPE—Committee on River Ice processes and the environment. Winnipeg, Manitoba, Canada, September 18–22, 2011 https:\u002F\u002Fwww.cripe.ca\u002Fdocs\u002Fproceedings\u002F16\u002FLindenschmidt-et-al-2011a.pdf. Accessed 20 August 2018\nLindenschmidt K-E, Sydor M, Carson RW, Harrison R (2012) Ice jam modelling of the lower red river. J Water Resour Prot 4(1):1–11\nNezhikhovskiy RA (1964) Coefficients of roughness of bottom surface on slush-ice cover. Soviet Hydrol. American Geophysical Union, Washington, pp 127–150\nWater Survey of Canada (2020) Historical data. Retrieved from Government of Canada: https:\u002F\u002Fwateroffice.ec.gc.ca\u002Fsearch\u002Fhistorical_e.html. Accessed 5 June 2019\nWhite KD (2008) Development of ice-affected stage-frequency curves. In: Beltaos S (ed) River ice breakup. Water Resources Publications, Highlands Ranch, Colorado\nWilliams B, Luo B, Lindenschmidt K-E (2019) Modeling overbank flows during ice jam flood events on the Lower Red River. 20th Workshop on River Ice organized by CRIPE—Committee on River Ice Processes and the Environment. Ottawa, Ontario, May 14–16 2019. https:\u002F\u002Fwww.cripe.ca\u002Fdocs\u002Fproceedings\u002F20\u002FWilliams-et-al-2019.pdf. Accessed 25 May 2019\nZhang F, Mosaffa M, Chu T, Lindenschmidt K-E (2017) Using remote sensing data to parameterize ice jam modeling for a Northern Inland Delta. 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2001 and 2005, a large debris rock slide occurred on the western slope of the Cordillera de Santa Cruz in the southeast Andean corner of the Province of San Juan (31°40′ S–70°16′ W). The landslide material accumulated in a downstream gorge as a natural dam of the Santa Cruz river, forming a large-volume lake. In November 2005, probably as a result of the increasing pressure of the water volume, this natural dam breached off with a violent and unexpected flash flood. In addition to life-threatening instances lived by some people downstream, this flood caused great economic loss to main localities of the Department of Calingasta, as well as considerable damage to one of the most relevant projects of the Province, the Caracoles Hydropower Project dam on the San Juan river. Considering the high costs of any physical remediation for a natural dam located in this high, remote, and inaccessible mountain area with no reliable road access, the main protective measures left to be pondered are the installation of a flash-flood early-warning system connected to downstream localities, along with a program of hydrological monitoring at the dam-forming area and annual satellital monitoring to verify the evolution of accumulated mass movements.",{"EN":1360},"Evolution of a debris-rock slide causing a natural dam: the flash flood of Río Santa Cruz, Province of San Juan—November 12, 2005",{"VOID":1362},"[\"9472506295799950758\"]",{"VOID":1364},"Abele G (1974) Bergstürze in den Alpen: the Verbreitung, Morphologie und Folgeerscheinun-gen. Wissenschaftliche Alpenvereinshefte, Heft 25, Munchen\nAlonso R, Wayne W (1992) Riesgos geológicos en el norte Argentino, Congreso Geologico Boliviano. Bol 27:213–216\nCruden D, Varnes D (1996) Landslides types and processes. In: Turner AK, Schuster RL (eds) Landslides: investigation and mitigation. Transportation Research Board, National Research Council, Washington, pp 36–75\nDepartamento Hidráulica (Hydraulics Department) (2005) Aluvión de los días 12 y 13 de noviembre del 2005. Unpublished report, 16 pp\nDiario de Cuyo Newspaper November (2005) http:\u002F\u002Fwww.diariodecuyo.com.ar\nDikau R, Brunsden D, Schrott L, Ibsen M (1996) Landslide recognition, Report no. 1. European Commission Environment Programme, Contract no. EV5V-CT94-0454. Identification, Movement and Causes, Chichester, 251 pp\nGonzález Díaz EF, Giaccardi AD, Costa CH (2001) La avalancha de rocas del río Barrancas (Cerro Pelán), norte del Neuquén: su relación con la catástrofe del río Colorado (29\u002F12\u002F1914). Rev Asoc Geol Argent 56(4):466–480\nGroeber P (1916) Informe sobre las causas han producido las crecientes del Río Colorado (territorios del Neuquen y La Pampa) en 1914, Ministerio de Agricultura de la Nación (Argentina), Dirección General de Minas. Buenos Aires. Geología e Hidrología, Serie B (Geología), Bull 11, pp 1–29\nGroeber P (1933) Confluencia de los Río Grande y Barrancas (Mendoza y Neuquén). Ministerio de Agricultura de la Nación (Argentina), Dirección de Minas y Geología, Buenos Aires, Bull 38, pp 1–72\nHermanns RL, Niedermann S, Ivy-Ochs S, Kubik P (2004) Rock avalanching into a landslide-dammed lake causing multiple dam failure in Las Conchas valley (NW Argentina)—evidence from surface exposure dating and stratigraphic analyses. Landslides 1(2):113–122. doi:10.1007\u002Fs10346-004-0013-5\nHermanns RL, Folguera A, Penna I, Naumann R, Niedermann S (2006) Morphologic characterization of giant flood deposits downriver landslide dams in the Northern Patagonian Andes. Geophys Res Abstr 8:09181\nHermanns RL, Folguera A, Penna IM, González Díaz FE, Fauque L, Niedermann S (2008) Landslides damns in Central Andes of Argentina (northern Patagonia and the Argentine northwest). In: Evans S, Hermanns R, Strom A, Scarascia Mugnozza G (eds) NATO, Series Publication Security of Natural and Artificial Rock Slide Dams, Springer, Berlin, pp 113–122\nIAHS-UNESCO-WMO (1974) Flash floods. In: Proceedings of the Paris symposium, Publication no. 112\nIgarzábal AP (1979) Los flujos densos de la Quebrada de Escoipe. Proc VII Congr Geol Argent 2:109–117\nKeefer DK (1984) Landslides caused by earthquakes. Bull Geol Soc Am 95:406–421. doi:10.1130\u002F0016-7606(1984)95\u003C406:LCBE>2.0.CO;2\nKeefer DK (2002) Investigating landslides caused by earthquakes—a historical review. Surv Geophys 23:473–510\nKimio I, Toshio M, Tatsuhei I, Yoshihisa K (2005) Outbursts and disasters of Takaisoyama and Hose landslide dams (1892) in East Shikoku. J Jpn Soc Eros Control Eng 58(4):3–12\nKoeppen W (1931) Grundriss der Klimakunde, vol 12. Walter de Gruyter Co., Berlin, 388 pp\nLencinas A (1982) Características estructurales del extremo sur de la Cordillera Sanjuanina, Argentina. 5º Congreso Latinoamericano de Geología. Actas 1:489–498\nLliboutry L, González O, Simken J (1958) Les glaciers du désert chilien. Assoc Int Hydrol Sci 46:291–300\nMinetti J, Barbieri P, Carleto M, Poblete A, Sierra E (1986) El régimen de precipitación de la provincia de San Juan. Informe técnico 8. CIRSAJ-CONICET, San Juan\nMoreiras S, Banchig A (2008) Eventos de deslizamientos-endicamientos reiterados de ocurrencia histórica. Cuenca del río Villavil, Sierra de Aconquija, Andalgalá, Catamarca. 17º Congreso Geológico Argentino. Actas 1:257–258\nNational Weather Service (2006) U.S. NOAA’s. Definitions of flash flood. www.srh.noaa.gov\u002Fmrx\u002Fhydro\u002Fflooddef.php\nPenna IM, Hermanns RL, Folguera A (2007) Determinación del área inmediata afectada por el desagote de la Laguna Navarrete, provincia de Neuquén (36°30′ S–71° O). Rev Asoc Geol Argent 62(3):460–466\nPenna IM, Hermanns R, Folguera A (2008) Remoción en masa y colapso catastrófico de diques naturales generados en el frente orogénico andino (36°–38° S): Los casos Navarrete y río Barrancas. Rev Asoc Geol Argent 63(2):172–180\nPérez D (1995) Estudio geológico del Cordón del Espinacito y regiones adyacentes, Provincia de San Juan. Tesis Doctoral de la Universidad de Buenos Aires. Unpublished, 262 pp\nSelby MJ (1993) Hillslope materials and processes, 2nd edn. Oxford University Press, New York, 451 pp\nSepúlveda SA (2000) Methodology for debris flow hazard evaluation in mountainous environments. Comunicaciones 51:3–18 (in Spanish)\nUSGS (2000) Shuttle Radar Topography Mission, 3 Arc Second, Global Land Cover Facility, University of Maryland, College Park, Maryland\nUSGS\u002FNEIC (2007) National Earthquake Information Center, World Data Center A for Seismology, Global Earthquake Search. United States Geological Survey, National Earthquake Information Center. http:\u002F\u002Fwww.neic.cr.usgs.gov\u002Fneis\u002Fepic\u002Fepic_global.html\nVarnes D (1978) Slope movement. Types and processes, Chap. 2. In: Schuster R, Krizek R (eds) Landslides: analysis and control. Transportation Research Board Special Report 176, National Academy of Sciences, Washington, DC, pp 11–33\nWayne WJ (1987) Aluviones al acecho. 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Wiley, New York",{"doi":1641},"10.1002\u002F9780470316665",{"id":20,"text":1643,"url":20,"identifiers":1644},"Giardini D (1984) Systematic analysis of deep seismicity: 200 centroid-moment tensor solutions for earthquakes between 1977 and 1980. Geophys J R Astr Soc 77:883–914",{"doi":1645},"10.1111\u002Fj.1365-246X.1984.tb02228.x",{"id":20,"text":1647,"url":20,"identifiers":1648},"Gutenberg B (1945a) Amplitudes of P, PP and S and magnitudes of shallow earthquakes. Bull Seismol Soc Am 35:57–69",{"doi":1649},"10.1785\u002FBSSA0350020057",{"id":20,"text":1651,"url":20,"identifiers":1652},"Gutenberg B (1945b) Magnitude determination for deep focus earthquakes. Bull Seismol Soc Am 35:117–130",{"doi":1653},"10.1785\u002FBSSA0350030117",{"id":20,"text":1655,"url":20,"identifiers":1656},"Gutenberg B, Richter CF (1956) Magnitude and energy earthquakes. Ann Geofis 9:1–15",{},{"id":20,"text":1658,"url":20,"identifiers":1659},"Hanks TC, Kanamori H (1979) A moment magnitude scale. J Geophys Res 84:2350–2438",{},{"id":20,"text":1661,"url":20,"identifiers":1662},"Heaton TH, Tajima F, Mori AW (1986) Estimating ground motions using recorded accelerograms. Surv Geophys 8:25–83",{"doi":1663},"10.1007\u002FBF01904051",{"id":20,"text":1665,"url":20,"identifiers":1666},"Joshi GC, Sharma ML (2008) Uncertainties in the estimation of M max. J Earth Syst Sci 117(S2):671–682",{"doi":1667},"10.1007\u002Fs12040-008-0063-5",{"id":20,"text":1669,"url":20,"identifiers":1670},"Kagan YY (2003) Accuracy of modern global earthquake catalogs. Phys Earth Planet Inter 135:173–209",{"doi":1671},"10.1016\u002FS0031-9201(02)00214-5",{"id":20,"text":1673,"url":20,"identifiers":1674},"Kanamori H (1977) The energy release in great earthquakes. J Geophys Res 82:2981–2987",{"doi":1675},"10.1029\u002FJB082i020p02981",{"id":20,"text":1677,"url":20,"identifiers":1678},"Karnik V (1973) Magnitude differences. Pure Appl Geophys 103(II):362–369",{"doi":1679},"10.1007\u002FBF00876413",{"id":20,"text":1681,"url":20,"identifiers":1682},"Kendall MG, Stuart A (1979) The advanced theory of statistics, vol 2, 4th edn. Griffin, London",{},{"id":20,"text":1684,"url":20,"identifiers":1685},"Kiratzi AA, Karakaisis GF, Papadimitriou EE, Papazachos BC (1985) Seismic source parameter relations for earthquakes in Greece. Pageoph 123:27–41",{"doi":1686},"10.1007\u002FBF00877047",{"id":20,"text":1688,"url":20,"identifiers":1689},"Madansky A (1959) The fitting of straight lines when both variables are subject to error. Am Statist As J 54:173–205",{"doi":1690},"10.2307\u002F2282145",{"id":20,"text":1692,"url":20,"identifiers":1693},"Nuttli OW (1983) Average seismic source-parameter relations for mid-plate earthquakes. Bull Seismol Soc Am 73:519–535",{},{"id":20,"text":1695,"url":20,"identifiers":1696},"Nuttli OW (1985) Average seismic source-parameter relations for plate-margin earthquakes. Tectonophysics 118:161–174",{"doi":1697},"10.1016\u002F0040-1951(85)90118-0",{"id":20,"text":1699,"url":20,"identifiers":1700},"Papazachos BC, Kiratzi AA, Karakostas BG (1997) Toward a homogencous moment-magnitude determination for earthquakes in Greece and the surrounding area. Bull Seismol Soc Am 87:474–483",{"doi":1701},"10.1785\u002FBSSA0870020474",{"id":20,"text":1703,"url":20,"identifiers":1704},"Richter C (1935) An instrumental earthquake magnitude scale. Bull Seismol Soc Am B11(1):2302",{},{"id":20,"text":1706,"url":20,"identifiers":1707},"Ristau J (2009) Comparison of magnitude estimates for New Zealand earthquakes: moment magnitude, local magnitude, and teleseismic body-wave magnitude. Bull Seismol Soc Am 99:1841–1852",{"doi":1708},"10.1785\u002F0120080237",{"id":20,"text":1710,"url":20,"identifiers":1711},"Scordilis EM (2006) Empirical global relations converting M S and m b to moment magnitude. J Seism 10:225–236",{"doi":1712},"10.1007\u002Fs10950-006-9012-4",{"id":20,"text":1714,"url":20,"identifiers":1715},"Stromeyer D, Grünthal G, Wahlström R (2004) Chi-square regression for seismic strength parameter relation, and their uncertainties, with application to an m w based earthquake catalogue for central, northern and northwestern Europe. J Seism 8:143–153",{"doi":1716},"10.1023\u002FB:JOSE.0000009503.80673.51",{"id":20,"text":1718,"url":20,"identifiers":1719},"Thingbaijam KKS, Nath SK, Yadav A, Raj A, Walling MY, Mohanty WK (2008) Recent seismicity in Northeast India and its adjoining region. J Seism 12:107–123",{"doi":1720},"10.1007\u002Fs10950-007-9074-y",{"id":20,"text":1722,"url":20,"identifiers":1723},"Utsu T (2002) Relationships between magnitude scales. In: Lee WHK, Kanamori H, Jennings PC, Kisslinger C (eds) International handbook of earthquake and engineering seismology part A. Academic Press, Amsterdam, pp 733–746",{"doi":1724},"10.1016\u002FS0074-6142(02)80247-9",{"id":20,"text":1726,"url":20,"identifiers":1727},"Vanek J, Zatopek A, Karnik V, Kondorskaya NV, Savarensky EF, Riznichenko YV, Soloviev SL, Shebalin NV (1962) Standardization of magnitude scales. Bull Acad Sci USSR Geophys Ser 57:108–111",{},{"id":1729,"createTime":1730,"updateTime":1731,"relativeEntities":1732,"slug":1733,"properties":1734,"entityType":228,"verifyStatus":229,"verifyTime":1745,"verifyNote":231,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":1746,"fullTextUrl":20,"authors":1747,"publicationType":336,"publisherRelationship":1861,"citationCount":21,"citationInfo":1917,"publishDate":1920,"publishYear":1918,"citationAnalyzeStatus":1921,"lastCitationAnalyze":1922,"indexDatabases":1923,"openAccess":20,"references":20,"isForceReanalyzing":400},"8dfeb4a4-4286-4313-813a-9fd562e1125a","2024-02-18T17:47:38.806+00:00","2026-07-28T09:31:31.401+00:00",[],"Impacts-of-climate-and-reservoirs-on-the-downstream-design-flood-hydrograph-a-case-study-of-Yichang-Station",{"abstract":1735,"title":1737,"gsPaper":1739,"references":1741,"doi":1743},{"EN":1736},"The Upper Yangtze River (above Yichang) in China has constructed the world's largest reservoir group with the Three Gorges Reservoir (TGR) as the core, the operation of these reservoirs and future climate change will no doubt alter the downstream hydrological processes and pose a challenge to the downstream flood design. As Yichang Hydrologic Station is 44 km downstream of TGR, how the design flood at Yichang Station would be impacted in the future by climate and upstream reservoirs has rarely been investigated. In this study, the climate and upstream reservoirs effects on design flood at Yichang Station are evaluated under six future climate and reservoir scenarios (S1, S2, S3, S4, S5 and S6) with different combinations of summer precipitation anomaly (SPA) and reservoir index (RI), in which SPA is obtained from global climate models under the three emission scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) of CMIP6 and RI is calculated under the two reservoir conditions (RI at current level and RI at planning level). The SPA and RI of S1, S2, S3, S4, S5 and S6 are, respectively, substituted into the optimal nonstationary GEV probability model, and the corresponding 1000-year design floods are estimated by using average annual reliability method. Under the same future reservoir condition, the flood peak discharge, 3-day, 7-day, 15-day and 30-day flood volume (denoted as Qm, W3, W7, W15 and W30, respectively) under SSP2-4.5 and SSP5-8.5 are 0.2% ~ 2.5% larger than those under SSP1-2.6. The change rates of Qm, W3, W7, W15 and W30 under six scenarios relative to the stationary design flood values calculated by Changjiang Water Resources Commission range from −11.4% to −23.9%, and the reduction amount of Qm is more than 16,000 m3\u002Fs even under SSP5-8.5. Therefore, reservoirs impact on the design flood of Yichang Station is quite prominent.",{"EN":1738},"Impacts of climate and reservoirs on the downstream design flood hydrograph: a case study of Yichang Station",{"VOID":1740},"[\"13621229763418901261\"]",{"VOID":1742},"Almazroui M, Saeed F, Saeed S, Islam MN, Ismail M, Klutse NAB, Siddiqui MH (2020) Projected change in temperature and precipitation over Africa from CMIP6. 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