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Table of contents
PART I TIME VARYING FREQUE NCY ANALYSIS FRAMEWO RK: LOCAL MODEL
CHAPTER 2 DEVELOPMENT OF A GEN ERAL TIME VARYING MODELING FRA MEWORK AT THE LOCAL SCALE
1L OCAL MODEL CONSTRUCT ION
1.1 Parent distribution for local model
1.2 Regression with temporal covariates
1.3 An illustration of local model construction
1.4 Relationship with other modeling frameworks
2 P OSTERIOR DISTRIBUTIO N AND PARAMETER INFE RENCE
2.1 Parameter estimation methods
2.2 Posterior distribution
2.3 Quantile computation based on the posterior distribution
3 M ODEL DIAGNOSIS AND S ELECTION
3.1 Diagnostic tools
3.2 Model comparison tools
4 S YNTHETIC CASE STUDIE S
4.1 Synthetic study 1
4.2 Synthetic study 2
5 C ONCLUS ION ON THE LOCAL CLI MATE INFORMED FRAMEWORK
CHAPTER 3 CASE STUDIES WITH LO CAL TIME VARYING MODELS
1 P ROJECTED CHANGES IN T HE PRECIPITATION REG IME OF THE D URANCE CATCHMENT
1.1 Data
1.2 Precipitation variables
1.3 Parent distribution selection
1.4 Regression models
1.5 Posterior distribution of regre ssion parameters
1.6 Goodness of fit
1.7 Model comparison
1.8 Accounting for non stationarity in GCM projections: stationary sub periods vs. continuous trend
1.9 Conclusion and discussion
2 NAO EFFECTS AND TEMPORAL TRENDS IN EXTREME PR ECIPITATION IN M EDITERRANEAN F RANCE
2.1 Data
2.2 Regression modeRegression models under three competing hypothesesls under three competing hypotheses
2.3 Posterior distribution of regression parametersPosterior distribution of regression parameters
2.4 GoodnessGoodness–ofof–fitfit
2.5 Conditional predictionsConditional predictions
2.6 Temporal trend and NAO impact for all 92 sitesTemporal trend and NAO impact for all 92 sites
2.7 Conditional quantiles for all 92 sites and their uncertaintyConditional quantiles for all 92 sites and their uncertainty
2.8 Model comparisonModel comparison
2.9 Discussion and conclusDiscussion and conclusionion
PART II TIMETIME–VARYING FREQUENCY ANVARYING FREQUENCY ANALYSIS FRAMEWORK: REALYSIS FRAMEWORK: REGIONAL MODELGIONAL MODEL
CHAPTER 4 DEVELOPMENT OF A GENDEVELOPMENT OF A GENERAL SPATIOERAL SPATIO–TEMPORAL RTEMPORAL REGIONAL FREQUENCY ANEGIONAL FREQUENCY ANALYSIS ALYSIS FRAMEWORKFRAMEWORK
1 RREGIONAL MODEL CONSTREGIONAL MODEL CONSTRUCTIONUCTION
1.1 Parent distributionParent distribution
1.2 SpatioSpatio–temporal regressiontemporal regression
1.3 An illustration of the regional modelAn illustration of the regional model
1.4 AccountinAccounting for spatial dependence between sitesg for spatial dependence between sites
1.5 Parameter inferenceParameter inference
1.6 Missing valuesMissing values
1.7 MCMC sampling, model diagnosis and model comparisonMCMC sampling, model diagnosis and model comparison
2 CCAN THE MODEL DETECT AN THE MODEL DETECT SPATIOSPATIO–TEMPORAL VARIATIONSTEMPORAL VARIATIONS?? SSYNTHETIC CASE STUDIEYNTHETIC CASE STUDIESS
2.1 Synthetic study 1Synthetic study 1
2.2 Synthetic study 2Synthetic study 2
3 CCONCLUSION ON THE SPAONCLUSION ON THE SPATIOTIO–TEMPORAL REGIONATEMPORAL REGIONAL MODELL MODEL
CHAPTER 5 ON THE TREATMENT OF ON THE TREATMENT OF SPATIAL DEPENDENCESPATIAL DEPENDENCE
1 DDOES IGNORING SPATIALOES IGNORING SPATIAL DEPENDENCE LEADS TO DEPENDENCE LEADS TO AN UNDERAN UNDER–ESTIMATION OESTIMATION OF UNCERTAINTIESF UNCERTAINTIES??
1.1 First simulation with a Gaussian parent distributionFirst simulation with a Gaussian parent distribution
1.2 Second simulation with a GEV parent distriSecond simulation with a GEV parent distributionbution
1.3 ConclusionConclusion
2 SSPATIAL DEPENDENCE FOPATIAL DEPENDENCE FOR EXTREMESR EXTREMES:: CCOPULAS VSOPULAS VS.. MAXIMUM STABLE PROCEMAXIMUM STABLE PROCESSESSSES
2.1 Basics of maximum stable processesBasics of maximum stable processes
2.2 Gaussian copula inference with various spatial dataGaussian copula inference with various spatial data
2.3 Comparison with different spatial dependence modelsComparison with different spatial dependence models
2.4 ConclusionConclusion
PART III GENERAL APPLICATIONSGENERAL APPLICATIONS: E: ENSO IMPACT ON PRECIPNSO IMPACT ON PRECIPITATIONSITATIONS
GENERAL ENERAL IINTRODUCTION ABOUT THNTRODUCTION ABOUT THE IMPACT OF E IMPACT OF ENSOENSO ON PRECIPITATIONON PRECIPITATION
CHAPTER 6 QUANTIFYING THE IMQUANTIFYING THE IMPACT OF ENSO ON SUMMPACT OF ENSO ON SUMMER RAINFALL IN SOUTHER RAINFALL IN SOUTHEAST QUEENSLAND, EAST QUEENSLAND, AUSTRALIAAUSTRALIA
1 QQUANTIFYING THE IMPACUANTIFYING THE IMPACT OF T OF ENSOENSO ON SUMMER RAINFALL TON SUMMER RAINFALL TOTALS USING LOCAL MOOTALS USING LOCAL MODELSDELS
1.1 Data and covariatesData and covariates
1.2 Local model for the summer rainfall totalsLocal model for the summer rainfall totals
1.3 ResultsResults
1.4 SummarySummary
2 QQUANTIFYING THE IMPACUANTIFYING THE IMPACT OF T OF ENSOENSO ON SUMMER MAXIMUM DAON SUMMER MAXIMUM DAILY RAINFALLS USING ILY RAINFALLS USING LOCAL AND REGIONAL MLOCAL AND REGIONAL
2.1 Data and covariatesData and covariates
2.2 Models for summer rainfall maximumModels for summer rainfall maximum
2.3 AssessAssessing competing hypotheses of ENSO impact on summer maximum daily rainfallsing competing hypotheses of ENSO impact on summer maximum daily rainfalls
2.4 ResultsResults
2.5 SummarySummary
3 DDISCUSSIONISCUSSION
3.1 Assumption of homogeneous regionsAssumption of homogeneous regions
3.2 Spatial dependence Spatial dependence modelingmodeling
3.3 Spatial regression modelingSpatial regression modeling
3.4 Practical Implications: utilizing predictions of extreme rainfall distributPractical Implications: utilizing predictions of extreme rainfall distributions from the climateions f
4 CCONCLUSIONSONCLUSIONS
CHAPTER 7 A GLOBAL ANALYSIS OFA GLOBAL ANALYSIS OF THE ASYMMETRIC IMPACTHE ASYMMETRIC IMPACT OF ENSO ON ET OF ENSO ON EXTREME PRECIPITATIONXTREME PRECIPITATION
1 DDATA AND METHODATA AND METHOD
1.1 DataData
1.2 A regional extreme value modelA regional extreme value model
2 RRESULTSESULTS
2.1 Regional parameter estimatesRegional parameter estimates
2.2 The impact of ENSO on precipitation quantilesThe impact of ENSO on precipitation quantiles
2.3 Asymmetry of the impact of ENSO on extreme precipitationsAsymmetry of the impact of ENSO on extreme precipitations
2.4 Seasonality of the impact of ENSO on extreme precipitationsSeasonality of the impact of ENSO on extreme precipitations
3 DDISCUSSIONISCUSSION
3.1 Limitation of the model aLimitation of the model and reliability of the definition of a regionnd reliability of the definition of a region
3.2 Changes in ENSO teleconnectionsChanges in ENSO teleconnections
3.3 Impact of other large scale modes oImpact of other large scale modes of climate variabilityf climate variability
4 CCONCLUSIONSONCLUSIONS
5.1 Slope of the location parameter with respect to SOISlope of the location parameter with respect to SOI
5.2 Percentage change for the intensity of 1 in 10 year precipitation relative to SOI=0Percentage change for the intensity of 1 in 10 year precipitation relative to SOI=0
5.3 Difference between the slope of SOI during La Niña and El Niño phasesDifference between the slope of SOI during La Niña and El Niño phases
CONCLUSION
PERSPECTIVES
BIBLIOGRAPHY
