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CLIMATE CHANGE IMPACT ON PRECIPITATION EXTREME EVENTS IN UNCERTAINTY SITUATION; PASSING FROM ...

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    CLIMATE CHANGE IMPACT ON PRECIPITATION EXTREME EVENTS IN UNCERTAINTY SITUATION; PASSING FROM GLOBAL SCALE TO REGIONAL SCALE
M. J. Khordadi, A. Alizadeh, M. Nassiri Mahallati, H. Ansari and H. Sanaeinejad
Abstract    Global warming and then climate change are important topics studied by researchers throughout the world in the recent decades. In these studies, climatic parameters changes are investigated. Considering large-scaled output of AOGCMs and low precision in computational cells, uncertainty analysis is one of the principles in doing hydrological studies. For this reason, it is tried that investigating the uncertainty due to precision of computational cells and in passing from global scale to regional scale through LARS-WG model and CRU institute, precipitation changes in Mashhad synoptic station located in Ghareghom basin are studied. The results show enough ability of the model to simulate precipitation parameter in the base period. Using downscaled output of HadCM3 generated by CRU with high precision shows gradual decreasing of precipitation trend for frequency and sum values. Comparing the downscaled output of the AOGCM with 2.5*3.75 resolution and the output of CRU with 0.5*0.5 resolution, the uncertainty is due to precision of computational cells from global to regional scale which the latter scale is closer to real values.                                                             

Keywords    climate change, CRU, downscaling, LARS-WG, uncertainty                                                                  

چکیده    گرمايش جهاني و به تبع آن تغيير اقليم موضوع مهمي است که در دهه­هاي اخير توسط محققين در سرتاسر دنيا مورد مطالعه قرار گرفته است. در اين مطالعات تغييرات پارامترهاي اقليمي مورد بررسي قرار مي­گيرد. با توجه به بزرگ مقياس بودن داده­هاي مربوط به AOGCM و کم دقت بودن سلول محاسباتي آن­ها، تحليل عدم قطعيت يکي از موارد اصولي و زيربنايي جهت انجام کليه مطالعات هيدرولوژيکي مي­باشد. بدين منظور در اين مطالعه تلاش شد با بررسي عدم قطعيت ناشي از دقت سلول محاسباتي در حالت گذار از مقياس جهاني به منطقه­اي توسط مدل LARS-WG و مرکز CRU تغييرات پارامتر بارندگي در ايستگاه سينوپتيک مشهد واقع در حوضه قره قوم مطالعه شود. نتايج حاصل نشان دهنده توانايي تقريبا بالاي مدل در شبيه­سازي پارامتر بارندگي در دوره پايه تلقي گرديد. استفاده از خروجي ريز مقياس شده مدل HadCM3 که توسط مرکز CRU با دقت مکاني بالا توليد گرديده بود، کاهش تدريجي روند بارندگي براي مقادير فراواني و مجموع سه شاخص تعريف شده را نشان داد. با مقايسه خروجي ريزمقياس شده مدل مذکور با دقت 3.75×2.5 و خروجي مرکز CRU با دقت 0.5×0.5 عدم قطعيت ايجاد شده مربوط و ناشي از دقت سلول محاسباتي از مقياس جهاني به منطقه­اي ارزيابي مي­گردد که به واقعيت نزديک­تر خواهد بود.                                                                     

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      Hydrologic Data Uncertainty—Evident and Ignored

      Ali Naghibi, Ph.D., P.Eng.1 1 Environmental Resources Management (ERM) Canada, 1111 West Hastings St., Vancouver, BC, Canada V6E 2J3. E-mail: ali.naghibi@erm.com

      Abstract Measured and estimated hydrologic data are key to development of watershed assessment tools including hydrologic, water quality, and water balance models. Although, it is recognized that uncertainty in hydrologic data undermines the reliability of such models, water resources practitioners still do not or cannot adequately incorporate data uncertainty in model calibration and validation. Disconnected and decoupled data collection, analysis, and modeling processes are the primary reason why data uncertainty is neglected during calibration and validation. This work investigates the impacts of data uncertainty on hydrologic analysis and modeling results. An example project is used to demonstrate how the characterization of data uncertainty can inform the data collection, data analysis, and modeling processes. Main sources of data uncertainty (e.g., field measurements, rating curve development, and time-series development) are reviewed, and a discussion is provided about how these uncertainties can be characterized. This work acknowledges that sources of data uncertainty and appropriate methodologies to characterize and reduce uncertainty are case-specific and therefore does not recommend one methodology over another. However, it provides a list of considerations for identifying and characterizing the most critical sources of uncertainty.


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      Estimation of Uncertain Parameters using Static Pushover Methods
      Michalis Fragiadakis* Institute of Structural Analysis & Seismic Research, School of Civil Engineering, National Technical University of Athens, Zografou Campus, Athens, 15771, Greece Dimitrios Vamvatsikos Department of Civil and Environmental Engineering, University of Cyprus, Nicosia 1678, Cyprus   ABSTRACT: Following recent guidelines (e.g. FEMA-350) seismic performance uncertainty is an essential ingredient for performance-based earthquake engineering. Uncertainty refers to both aleatory uncertainty, raised by the random record-to-record variability, and also to epistemic uncertainty primarily introduced by modeling assumptions or errors. A methodology for the performance-based estimation of the dispersion introduced by parameter uncertainties is developed. The methodology proposed provides an inexpensive alternative to the use of tabulated values, or to performing a series of time-consuming nonlinear response history analyses to obtain parameter uncertainty. As a testbed, the well-known 9-storey LA9 2D steel frame is employed using beam-hinges with uncertain backbone properties. The monotonic backbone is fully described by six parameters, which are considered as random variables with given mean and standard deviation. Using point-estimate methods, first-order-second-moment techniques and latin hypercube sampling with Monte Carlo simulation, the pushover curve is shown to be a powerful tool that can help accurately estimating the uncertainty in the seismic capacity. Coupled with SPO2IDA, a powerful R-μ-T relationship, such estimates can be applied at the level of the results of nonlinear dynamic analysis, allowing the evaluation of seismic capacity uncertainty even close to global dynamic instability. Hidden Content
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      Analytical Evaluation of Uncertainty Propagation in Seismic Vulnerability Assessment of Tehran Using GIS
      Original Article, A2
      Jahanpeyma MH, Delavar M.R.
      Journal. Civil Eng. Urban. 1(1): 05-09. 2011.
      ABSTRACT: One of the properties of geospatial information systems is their use in supporting spatial decision making under uncertainty. It is a complex process which is considered in different situations. The existence of uncertainty in geospatial data and various analyses has the potential to expose users to undesirable consequences in their decision making. Nowadays, natural disasters, particularly earthquake, are among the most important disturbances to sustainable development of countries and governments try to manage them in an optimum manner. They would typically try to decrease the amount of financial damages and loss of lives that would occur because of the events. In this research, geospatial information science/system has been implemented to estimate the seismic vulnerability and its probable damages for a particular scenario in Tehran. We applied fuzzy logic concepts to well-known analytical hierarchical process for the damage assessment. Based on this modified approach we developed a hierarchy of effective factors in earthquake vulnerability due to definition of their priorities against a given earthquake scenario.The effect of uncertainty in geospatial data and analysis functions which are applied in estimating Tehran seismic vulnerability would affect the quality of decision making for estimating the damages. In this paper, we analyzed the implemented geospatial data of population statistics, building information, maps, digital terrain models, and satellite images in the process of studying Tehran’s seismic vulnerability. In this research, we used Monte Carlo simulation approach for uncertainty modeling. We compute the statistical parameters for seismic vulnerability in various iterations of Monte Carlo process. After that we extract the relationship between the number of Monte Carlo process and relative variation of seismic vulnerability’s layer variance. For instance, in order to achieve double precision, the number of iterations must increase four times.
      Keywords: Uncertainty, GIS, Seismic Vulnerability, Analytic Hierarchy Process, Monte Carlo Simulation
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