BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Memento EPFL// BEGIN:VEVENT SUMMARY:Modeling Daily Rainfall Occurrence and Amount Conditional on Atmos pheric Predictors: Improved Assessment of Rainfall Statistics Based on Cli mate Model Results DTSTART:20150929T120000 DTEND:20150929T131500 DTSTAMP:20260509T005911Z UID:71a6d8d3fb972fdb89079a5e8fc7bd7f084ab4e1a30164b76b10bc40 CATEGORIES:Conferences - Seminars DESCRIPTION:Dr Andreas Langousis\, Department of Civil Engineering\, Unive rsity of Patras\, Greece\nAbstract:\nDue to its intermittent and highly va riable character\, and the modeling parameterizations used\, precipitation is one of the least well reproduced hydrologic variables by both Global C limate Models (GCMs) and Regional Climate Models (RCMs). This is especiall y the case at a regional level (where hydrologic risks are assessed) and a t small temporal scales (e.g. daily) used to run hydrologic models.\nTo im prove the level skill of GCMs and RCMs in reproducing the statistics of ra infall at a basin level and at hydrologically relevant temporal scales\, t wo types of statistical approaches have been suggested. One is the use of distribution mapping (i.e. quantile-quantile\, Q-Q\, plots) to statistical ly correct climate model rainfall outputs based on historical series of pr ecipitation. The other is the use of stochastic models of rainfall to cond itionally simulate precipitation series\, based on large-scale atmospheric predictors produced by climate models (e.g. geopotential height\, relativ e vorticity\, divergence\, mean sea level pressure). The latter approach\, usually referred to as statistical rainfall downscaling\, aims at reprodu cing the statistical character of rainfall\, while accounting for the effe cts of large-scale atmospheric circulation (and\, therefore\, climate forc ing) on rainfall statistics. \nWhile promising\, statistical rainfall dow nscaling has not attracted much attention in recent years\, since the sugg ested approaches involved complex (i.e. subjective or computationally inte nse) identification procedures of the local weather\, in addition to demon strating limited success in reproducing several statistical features of ra infall\, such as seasonal variations\, the distributions of dry and wet sp ell lengths\, the distribution of the mean rainfall intensity inside wet p eriods\, and the distribution of rainfall extremes.\nIn an effort to remed y those shortcomings\, Langousis and Kaleris (2014\, WRR\, 50\, doi:10.100 2/2013WR014936) developed a statistical framework for simulation of daily rainfall intensities conditional on upper air indices. Here\, we test the developed downscaling scheme using atmospheric data from the ERA-Interim a rchive (http://www.ecmwf.int/research/era/do/get/index) and daily rainfall measurements from western Greece\, and find that it accurately reproduces several statistical properties of actual rainfall records\, at both annua l and seasonal levels\, including: wet day fractions\, the alternation of wet and dry intervals\, the distributions of dry and wet spell lengths\, t he distribution of rainfall intensities in wet days\, the distribution of yearly rainfall maxima\, dependencies of rainfall statistics on the observ ation scale\, and long-term climatic features of rainfall. This is done so lely by conditioning rainfall simulation on a vector of atmospheric predic tors\, properly selected to reflect the relative influence of upper-air va riables on ground-level rainfall statistics.\nIn a follow up application s tudy\, we assess the relative effectiveness of: a) the developed statistic al downscaling scheme\, and b) quantile-quantile (Q-Q) correction of clima te model rainfall products (i.e. an approach commonly used in climate chan ge impact studies) in reproducing the statistical structure of rainfall\, as well as rainfall extremes\, at a regional level\, based on climate mode l results. This is done for an intermediate-sized catchment in Italy\, i.e . the Flumendosa catchment\, using climate model rainfall and atmospheric data from the ENSEMBLES project (http://ensembleseu.metoffice.com). In doi ng so\, we split the historical rainfall record of mean areal precipitatio n (MAP) in 15-year calibration and 45-year validation periods\, and compar e the historical rainfall statistics to those obtained from: a) Q-Q correc ted climate model rainfall products\, and b) synthetic rainfall series gen erated by the suggested downscaling scheme. To our knowledge\, this is the first time that a detailed statistical comparison of climate model rainfa ll\, statistically downscaled precipitation\, and catchment averaged MAP i s performed at a daily resolution. \nThe obtained results are promising\, since the proposed downscaling scheme is more accurate and robust in repr oducing a number of historical rainfall statistics\, independent of the cl imate model used and the length of the calibration period. This is particu larly the case for the yearly rainfall maxima\, where direct statistical c orrection of climate model rainfall outputs shows increased sensitivity to the length of the calibration period and the climate model used. The robu stness of the suggested downscaling scheme in modeling rainfall extremes a t a daily resolution\, is a notable feature that can effectively be used t o assess hydrologic risk at a regional level under changing climatic condi tions.Short biography:\nDr. Andreas Langousis was born in Athens in 1981. He is a Civil Engineer (National Technical University of Athens\, NTUA\, 2 003)\, with Master of Science (MSc\, 2005) and Doctor of Science (ScD\, 20 08) degrees from the Department of Civil and Environmental Engineering at the Massachusetts Institute of Technology (MIT). Currently he serves as an Assistant Professor in the Department of Civil Engineering at the Univers ity of Patras\, Greece\, in the area of Stochastic Processes and Hydrologi c Risk Assessment. He has received numerous academic awards\, including th e Schoettler Fellowship of MIT\, a 6-year scholarship from the Alexander S . Onassis Public Benefit Foundation\, the 1st prize at the 1st Internation al Summit on Hurricanes and Climate Change\, and a 3-year Postdoctoral Fel lowship from the General Secretariat of Research and Technology (Greece). In addition\, he has more than 10 years of teaching experience in undergra duate and graduate courses related to engineering hydrology and hydraulics \, environmental data analysis\, risk modeling\, and stochastic processes. He has participated in 8 research projects and has co-authored 19 researc h papers in well known scientific journals\, more than 30 presentations in international conferences\, 3 book chapters\, 4 newspaper articles\, and he has served as an active member of the organizing and scientific committ ees of more than 15 international conferences. In addition\, he has given 11 invited talks in Greece and abroad\, he serves as Reviewer in 16 intern ational scientific journals\, as Guest Editor in JoH (Journal of Hydrology \, Jun. 2015- Jan. 2016)\, and as Associate Editor in WRR (Water Resources Research) and SERRA (Stochastic Environmental Research and Risk Assessmen t). During the period Jun. 2012 - Dec. 2013\, he also served as Guest Asso ciate Editor in HESS (Hydrology and Earth System Sciences). He is an activ e member of the Technical Chamber of Greece (TCG)\, the American Society o f Civil Engineers (ASCE)\, the American Geophysical Union (AGU)\, the Euro pean Geosciences Union (EGU)\, the International Association of Hydrologic al Sciences (IAHS)\, the International Commission on Statistical Hydrology (ICSH-IAHS) and\, since 2008\, he serves as an active member of the scien tific committee of the Precipitation and Climate Sub-Division of the Europ ean Geosciences Union (EGU). During the period 2010-2012\, he was an activ e member of the Board of Directors of the Hellenic Chapter of ASCE\, for t he period 2011-2013 he served as the Treasurer of the Alexander S. Onassis Scholar’s Association and\, since March 2014\, he serves as a member of the examination committee of the Technical Chamber of Greece (TCG) for th e conferment of professional rights to Civil Engineers. Dr. Andreas Langou sis’ area of expertise is the development of stochastic models for hydro logic risk analysis\, engineering and environmental design and prediction. His current research interests include (but are not limited to) environme ntal and health risk\, hydrologic scaling\, estimation of hydrologic extre mes\, statistical downscaling and forecasting. LOCATION:GR A3 32 http://plan.epfl.ch/?room=GR%20A3%2032 STATUS:CONFIRMED END:VEVENT END:VCALENDAR