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:: برگزاری سمینار تحلیل و مدلسازی سیستم‌های حوضه‌ی آبریز ::
 | تاریخ ارسال: ۱۳۹۵/۲/۲۰ | 
AWT IMAGE سمینار تحلیل و مدلسازی سیستم‌های حوضه‌ی آبریز توسط جناب آقای دکتر سید سامان رضوی، استادیار دانشگاه ساسکاچوان کانادا روز سه‌شنبه مورخ 21 اردیبهشت‌ماه 1395 ساعت 8:30 صبح در کلاس شماره 9 دانشکده مهندسی عمران دانشگاه علم و صنعت ایران برگزار خواهد گردید.

Seminar 
Watershed Systems Analysis and Modelling
تحلیل و مدلسازی سیستم‌های حوضه‌ی آبریز


Abstract: This talk will have three foci: 1- global sensitivity analysis, 2- non-stationarity and change in hydrology, and 3- surrogate modelling and computational efficiency. Global sensitivity analysis (GSA) is a systems theoretic approach to characterizing the overall (average) sensitivity of one or more model responses across the factor space, by attributing the variability of those responses to different controlling (but uncertain) factors (e.g., model parameters, forcings, and boundary and initial conditions). GSA can be very helpful to improve the credibility and utility of Earth and Environmental System Models (EESMs), as these models are continually growing in complexity and dimensionality with continuous advances in understanding and computing power.
The design and management of water resource infrastructure have been generally based on the information available in the observational record, with the central, default assumption of stationarity. In practice, observational records of hydrologic variables have been commonly assumed to be a realization of a stationary stochastic process whose statistical characteristics are contained in the records. The stationarity assumption implies an assumption of a physical constancy of the mechanisms participating in the formation of the streamflow, from the regimes of precipitation and evaporation in the river basin, to geomorphological, pedological, and other physical conditions. Natural proxy records of hydroclimatic behavior, such as tree ring chronologies, have introduced new opportunities as they are a rich source of information of past climate-driven nonstationarities in hydrologic variables. Given such new opportunities, there is a pressing need to abandon the use of the stationarity assumption in the design and management of water resource systems and to broaden the understanding of hydrologic characteristics in any basin beyond the limited observational records, because of the substantial anthropogenic changes to the Earth’s climate.
Computer simulation models, which simulate abstract representations of physically based systems using mathematical concepts and language, are playing a key role in engineering tasks and decision making processes. There are various types of problems utilizing computer simulation models including prediction, optimization, operational management, design space exploration, sensitivity analysis, and uncertainty analysis. There are also problems such as model calibration and model parameter sensitivity analysis dealing with simulation models to enhance their fidelity to the real world system. Surrogate modeling, which is a second level of abstraction, is concerned with developing and utilizing cheaper-to-run ‘‘surrogates’’ of the ‘‘original’’ simulation model. Surrogate modeling, also called metamodeling, has evolved and been extensively used over the past decades.

 

Bio: Dr. Saman Razavi is an assistant professor with School of Environment and Sustainability and Department of Civil and Geological Engineering at the University of Saskatchewan. He leads Watershed Systems Analysis and Modelling Lab at the Global Institute for Water Security. He received the PhD degree (2013) in civil engineering from the University of Waterloo, Ontario, and the MSc (2004) and BSc (2002) degrees in civil engineering from Amirkabir University and Iran University of Science and Technology in Iran. Dr. Razavi is an Associate Editor of Journal of Hydrology and an Editorial Board Member of Environmental Modelling & Software. His research interests include environmental and water resources systems analysis, hydrologic modelling, single- and multiple-objective optimization, sensitivity and uncertainty analysis, and climate change and impacts on hydrology and water resources.

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