A SPATIAL-TEMPORAL DOWNSCALING APPROACH TO CONSTRUCTION OF INTENSITY-DURATION-FREQUENCY RELATIONS IN CONSIDERATION OF G

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A SPATIAL-Transient DOWNSCALING Way to deal with Development OF Force Span Recurrence RELATIONS In light of GCM-BASED Environmental CHANGE Situations. Van-Thanh - Van Nguyen (and Understudies) Invested Prop Teacher Seat in Structural Building. Diagram. Presentation

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A SPATIAL-TEMPORAL DOWNSCALING APPROACH TO CONSTRUCTION OF INTENSITY-DURATION-FREQUENCY RELATIONS IN CONSIDERATION OF GCM-BASED CLIMATE CHANGE SCENARIOS Van-Thanh - Van Nguyen (and Students) Endowed Brace Professor Chair in Civil Engineering

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OUTLINE INTRODUCTION Design Rainfall and Design Storm Concept – Current Practices Extreme Rainfall Estimation Issues? Atmosphere Variability and Climate Change Impacts? Destinations DOWNSCALING METHODS Spatial Downscaling Issues Temporal Downscaling Issues Spatial-Temporal Downscaling Method APPLICATIONS CONCLUSIONS

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INTRODUCTION Extreme tempests (and surges) represent a bigger number of misfortunes than whatever other cataclysmic event (both as far as loss of lives and financial expenses). Harms because of Saguenay surge in Quebec (Canada) in 1996: $800 million dollars. Normal yearly surge harms in the U.S. are US$2.1 billion dollars . (US NRC) Information on extraordinary rainfalls is basic for arranging, outline , and administration of different water-asset frameworks. Plan Rainfall = most extreme measure of precipitation at a given site for a predefined length and return period .

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Design Rainfall Estimation Methods The decision of an estimation technique relies on upon the accessibility of verifiable information: Gaged Sites  Sufficient long chronicled records (> 20 years?)  At-site Methods . Mostly Gaged Sites  Limited information records  Regionalization Methods . Ungaged Sites  Data are not accessible  Regionalization Methods .

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Design Rainfall and Design Storm Estimation At-site Frequency Analysis of Precipitation Regional Frequency Analysis of Precipitation ⇒ Intensity-Duration-Frequency (IDF) Relations ⇒ DESIGN STORM CONCEPT for outline of water powered structures (WMO Guides to Hydrological Practices: 1 st Edition 1965 → 6 th Edition: Section 5.7, in press)

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Extreme Rainfall Estimation Issues (1) Current practices: At-site Estimation Methods (for gaged destinations): Annual greatest arrangement (AMS) utilizing 2-parameter Gumbel/Ordinary minutes strategy , or utilizing 3-parameter GEV/L-minutes technique . ⇒ Which likelihood dispersion? ⇒ Which estimation technique? ⇒ How to evaluate display ampleness? B est-fit circulation? Issues: Uncertainties in Data, Model and Estimation Method

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1 2 3 4 Geographically bordering settled districts Geographically non touching settled areas Hydrologic neighborhood sort locales Extreme Rainfall Estimation Issues (2) Regionalization techniques GEV/Index-surge strategy. File Flood Method ( Dalrymple , 1960): Similarity (or homogeneity) of point rainfalls? How to characterize gatherings of homogeneous gages? What are the characterization criteria? Proposed Regional Homogeneity: PCA of precipitation sums at various locales for various time scales . PCA of precipitation events at various destinations. (WMO Guides to Hydrological Practices: first Edition 1965 → sixth Edition: Section 5.7, in press)

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Extreme Rainfall Estimation Issues (3) The "scale" issue The properties of a variable rely on upon the size of estimation or perception . Are there scale-invariance properties? What's more, how to decide these scaling properties? Existing strategies are constrained to the particular time scale related with the information utilized. Existing strategies can't consider the properties of the physical procedure over various scales.

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Extreme Rainfall Estimation Issues (4) Climate Variability and Change will impactsly affect the hydrologic cycle, and specifically the precipitation procedure ! How to measure Climate Change? General Circulation Models (GCMs): A believable reenactment of the " normal " vast scale " occasional dispersion of air weight, temperature, and flow. (AMIP 1 Project, 31 demonstrating bunches) Climate change recreations from GCMs are " insufficient " for effect thinks about on territorial scales: Spatial determination ~ 50,000 km 2 Temporal determination ~ (every day), month, regular Reliability of some GCM yield factors, (for example, darkness  precipitation)?

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… How to create Climate Change situations for effects thinks about in hydrology? Spatial scale ~ a couple km 2 to a few 1000 km 2 Temporal scale ~ minutes to years A scale jumble between the data that GCM can certainly give and scales required by effects thinks about. "Downscaling techniques" are fundamental!!! GCM Climate Simulations Precipitation ( Extremes ) at a Local Site

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IDF Relations At-site Frequency Analysis of Precipitation Regional Frequency Analysis of Precipitation ⇒ Intensity-Duration-Frequency (IDF) Relations ⇒ DESIGN STORM for outline of water powered structures. Customary IDF estimation strategies: Time scaling issue : no thought of precipitation properties at various time scales; Spatial scaling issue : comes about constrained to information accessibility at a nearby site; Climate change : no thought.

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Summary Recent improvements: Successful uses of the scale invariant idea in precipitation demonstrating to allow measurable induction of precipitation properties between different lengths . Worldwide atmosphere models (GCMs) could sensibly reenact some atmosphere factors for current period and could give different environmental change situations to future periods . Different spatial downscaling strategies have been created to give the linkage between (GCM) vast scale information and neighborhood scale information. Scale Issues: GCMs deliver information over worldwide spatial scales ( many kilometers ) which are exceptionally coarse for water assets and hydrology applications at point or nearby scale. GCMs deliver information at day by day fleeting scale, while numerous applications require information at sub-day by day scales (hourly, 15 minutes, … ).

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OBJECTIVES To survey late advance in downscaling techniques from both hypothetical and viable perspectives. To evaluate the execution of measurable downscaling techniques to discover the " best " strategy in the reproduction of every day precipitation time arrangement for environmental change affect contemplates . To build up an approach that could connect day by day recreated atmosphere factors from GCMs to sub-every day precipitation attributes at a local or neighborhood scale ( a spatial-fleeting downscaling strategy ). To survey the environmental change impacts on the outrageous precipitation forms at a territorial or nearby scale.

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DOWNSCALING METHODS Scenarios

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(SPATIAL) DYNAMIC DOWNSCALING METHODS Coarse GCM + High determination AGCM Variable determination GCM (high determination over the range of intrigue) GCM + RCM or LAM (Nested Modeling Approach) More exact downscaled comes about when contrasted with the utilization of GCM yields alone. Spatial scales for RCM comes about ~ 20 to 50 km  still larges for some hydrologic models . Extensive processing asset prerequisite.

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(SPATIAL) STATISTICAL DOWNSCALING METHODS Weather Typing or Classification Generation day by day climate arrangement at a neighborhood site. Characterization plans are fairly subjective . Stochastic Weather Generators Generation of practical measurable properties of day by day climate arrangement at a nearby site. Reasonable processing assets Climate change situations in view of results anticipated by GCM ( problematic for precipitation ) Regression-Based Approaches Generation day by day climate arrangement at a neighborhood site. Comes about constrained to nearby climatic conditions. Long arrangement of recorded information required. Expansive scale and nearby scale parameter relations stay substantial for future atmosphere conditions . Basic computational prerequisites .

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APPLICATIONS LARS-WG Stochastic Weather Generator (Semenov et al., 1998) Generation of engineered arrangement of every day climate information at a nearby site (day by day precipitation, most extreme and least temperature, and day by day sunlight based radiation) Procedure : Use semi-experimental likelihood circulations to depict the condition of a day (wet or dry). Utilize semi-experimental appropriations for precipitation sums (parameters assessed for every month). Utilize typical appropriations for day by day least and most extreme temperatures. These appropriations are molded on the wet/dry status of the day. Steady Lag-1 autocorrelation and cross-connection are accepted. Utilize semi-experimental dispersion for day by day sun powered radiation. This circulation is adapted on the wet/dry status of the day. Steady Lag-1 autocorrelation is expected.

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Statistical Downscaling Model (SDSM) (Wilby et al., 2001) Generation of engineered arrangement of day by day climate information at a nearby site in light of experimental connections between neighborhood scale predictands (every day temperature and precipitation) and vast scale indicators (air factors) Procedure: Identify huge scale indicators (X) that could control the neighborhood parameters (Y). Locate a factual connection amongst X and Y. Approve the association with autonomous information. Produce Y utilizing estimations of X from GCM information.

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Geographical areas of destinations under review. Land directions of the stations

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DATA: Observed day by day precipitation and temperature extremes at four destinations in the Greater Montreal Region (Quebec, Canada) for the 1961-1990 period. NCEP re-investigation day by day information for the 1961-1990 period. Alignment : 1961-1975; approval : 1976-1990.

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Evaluation records and insights

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The mean of day by day precipitation for the time of 1961-1975 BIAS = Mean (Obs.) – Mean (Est.)

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The mean of every day precipitation for the time of 1976-1990 BIAS = Mean (Obs.) – Mean (Est.)

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The 90 th percentile of day by day precipitation for the time of 1976-1990 BIAS = Mean (Obs.) – Mean (Est.)

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GCM and Downscaling Results (Precipitation Extremes ) 1-Observed 2-SDSM [CGCM1] 3-SDSM [HADCM3] 4-CGCM1-Raw information 5-HADCM3-Raw information From CCAF Project Report by Gachon et al. (2005) .:tslidesep.

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