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Analysis of non-stationary climate-related extreme events considering climate change scenarios: an application for multi-hazard assessment in the Dar es Salaam region, Tanzania
Author(s)
Language
English
Obiettivo Specifico
4A. Clima e Oceani
Status
Published
JCR Journal
JCR Journal
Peer review journal
Yes
Title of the book
Issue/vol(year)
/ 75 (2015)
ISSN
0921-030X
Electronic ISSN
1573-0840
Publisher
Springer Science+Business Media B.V.
Pages (printed)
289-320
Issued date
January 2015
Alternative Location
Abstract
In this paper we have put forward a Bayesian framework for the analysis and testing of possible non-stationarities in extreme events. We use the extreme value theory to
model temperature and precipitation data in the Dar es Salaam region, Tanzania. Temporal
trends are modeled writing the location parameter of the generalized extreme value distribution in terms of deterministic functions of explanatory covariates. The analyses are performed using synthetic time series derived from a Regional Climate Model. The simulations, performed in an area around the Dar es Salaam city, Tanzania, take into account two Representative Concentration Pathways scenarios from the Intergovernmental Panel on Climate Change. Our main interest is to analyze extremes with high spatial and temporal resolution and to pursue this requirement we have adopted an individual grid box analysis approach. The approach presented in this paper is composed of the following key
elements: (1) an advanced Bayesian method for the estimation of model parameters, (2) a
rigorous procedure for model selection, and (3) uncertainty assessment and propagation.
The results of our analyses are intended to be used for quantitative hazard and risk
assessment and are presented in terms of hazard curves and probabilistic hazard maps. In the case study we found that for both the temperature and precipitation data, a linear trend in the location parameter was the only model performing better than the stationary one in the areas where evidence against the stationary model exists.
model temperature and precipitation data in the Dar es Salaam region, Tanzania. Temporal
trends are modeled writing the location parameter of the generalized extreme value distribution in terms of deterministic functions of explanatory covariates. The analyses are performed using synthetic time series derived from a Regional Climate Model. The simulations, performed in an area around the Dar es Salaam city, Tanzania, take into account two Representative Concentration Pathways scenarios from the Intergovernmental Panel on Climate Change. Our main interest is to analyze extremes with high spatial and temporal resolution and to pursue this requirement we have adopted an individual grid box analysis approach. The approach presented in this paper is composed of the following key
elements: (1) an advanced Bayesian method for the estimation of model parameters, (2) a
rigorous procedure for model selection, and (3) uncertainty assessment and propagation.
The results of our analyses are intended to be used for quantitative hazard and risk
assessment and are presented in terms of hazard curves and probabilistic hazard maps. In the case study we found that for both the temperature and precipitation data, a linear trend in the location parameter was the only model performing better than the stationary one in the areas where evidence against the stationary model exists.
Sponsors
This research has been developed in the framework of the FP7 European project CLUVA (Climate change and Urban Vulnerability in Africa), Grant No. 265137. This research has been funded by the FP7 European project CLUVA (Climate change and Urban ulnerability in Africa).
References
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techniques for precipitation data. Water Resour Res 44(2):W02,425
Bell TL (1987) A space-time stochastic model of rainfall for satellite remote sensing studies. J Geophys Res
92:9631–9643
̈
̈
Bellprat O, Kotlarski S, Luthi D, Schar C (2012) Objective calibration of regional climate models. J Geo-
phys Res Atmos 117(D23). doi:10.1029/2012JD018262
̈
̈
Bohm U, Kucken M, Ahrens W, Block A, Hauffe D, Keuler B, Rockel B, Will A (2006) CLM–the climate
version of LM: brief description and long-term applications. COSMO Newslett 6:225–235
Brussolo E, von Hardenberg J, Ferraris L, Rebora N, Provenzale A (2008) Verification of quantitative
precipitation forecasts via stochastic downscaling. J Hydrometeorol 9:1084–1094
Bucchignani E, Sanna A, Gualdi S, Castellari S, Schiano P (2013) Simulation of the climate of the XX
century in the Alpine space. Nat Hazards 67:981–990. doi:10.1007/s11069-011-9883-82010
Cannon A (2010) A flexible nonlinear modelling framework for nonstationary generalized extreme value
analysis in hydroclimatology. Hydrol Process 24(6):673–685
Carter TR, Jones JN, Lu X, Bhadwal S, Conde C, Mearns LO, O’Neill BC, Rounsevell MDA, Zurek MB
(2007) New assessment methods and the characterisation of future conditions. In: Parry ML, Canziani
OF, Palutikof JP, Linde PJVD, Hanson CE (eds) Climate change 2007. Impacts, adaptation and
vulnerability. Contribution of working group II to the fourth assessment report of the intergovern-
mental panel on climate change. Cambridge University Press, Cambridge, pp 133–171
Coles S (2001) An introduction to statistical modeling of extreme values. Springer series in statistics,
Springer-Verlag London limited
Davison AC, Padoan SA, Ribatet M (2012) Statistical modelling of spatial extremes. Stat Sci
27(2):161–186. doi:10.1214/11-STS376
D’Onofrio D, Palazzi E, von Hardenberg J, Provenzale A, Calmanti S (2014) Stochastic rainfall down-
scaling of climate models. J Hydrometeorol 15:830–843. doi:10.1175/JHM-D-13-096.1
̈
Ekstrom M, Fowler H, Kilsby C, Jones P (2005) New estimates of future changes in extreme rainfall across
the {UK} using regional climate model integrations. 2. Future estimates and use in impact studies.
J Hydrol 300(1–4):234–251, doi: 10.1016/j.jhydrol.2004.06.019
El-Adlouni S, Favre AC, Bobee B (2006) Comparison of methodologies to assess the convergence of
Markov Chain Monte Carlo methods. Comput Stat Data Anal 50(10):2685–2701
́
El Adlouni S, Ouarda T, Zhang X, Roy R, Bobee B (2007) Generalized maximum likelihood estimators for
the nonstationary generalized extreme value model. Water Resour Res 43(3):W03,410
̈
Fowler H, Ekstrom M, Kilsby C, Jones P (2005) New estimates of future changes in extreme rainfall across
the {UK} using regional climate model integrations. 1. Assessment of control climate. J Hydrol
300(1–4): 212–233, doi:10.1016/j.jhydrol.2004.06.017
Gelman A, Rubin D (1992) Inference from iterative simulation using multiple sequences. Stat Sci 7(4):
457–472. doi:10.1214/ss/1177011136
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London
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moments. In: Bernardo JM, Berger JO, Dawid AP, Smith AF (eds) Bayesian statistics 4. Oxford
University Press, Oxford, pp 169–193
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Giorgi F, Hurrell JW, Mariucci MR (1997) Elevation dependency of the surface climate change signal: a
model study. J Clim 10:288–296. doi:10.1175/1520-0442(1997)010
Gualdi S, Somot S, Li L, Artale V, Adani M, Belluci A, Braun A, Calmanti S, Carillo A, Dell’Aquila A,
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change projections with realistic representation of the Mediterranean Sea. Bull Am Meteorol Soc
94:65–81. doi:10.1175/BAMS-D-11-00136.1
Hanel M, Buishand TA, Ferro CAT (2009) A nonstationary index flood model for precipitation extremes in
transient regional climate model simulations. J Geophys Res Atmos 114(D15): doi:10.1029/
2009JD011712
Houghton J, Ding Y, Griggs DJ, Noguer M, van der Linden PJ, Dai X, Maskell K, Johnson CA (eds) (2001)
Climate change 2001: the scientific basis. Cambridge University Press, Cambridge, MA
Hrafnkelsson B, Morris JS, Baladandayuthapani V (2012) Spatial modeling of annual minimum and
maximum temperatures in Iceland. Meteorol Atmos Phys 116(1–2):43–61. doi:10.1007/s00703-010-
0101-0
Huth R, Kysely J, Pokorna L (2000) A GCM simulation of heat waves, dry spells, and their relationships to
circulation. Clim Change 46(1–2):29–60. doi:10.1023/A:1005633925903
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Cambridge University Press, Cambridge, MA
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fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press,
Cambridge, MA
Jain S, Lall U (2001) Floods in a changing climate: does the past represent the future? Water Resour Res
37(12):3193–3205
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Jenkinson AF (1955) The frequency distribution of the annual maximum (or minimum) values of meteo-
rological elements. Q J R Meteorol Soc 81:158–171
Kass RE, Raftery AE (1995) Bayes factors. J Am Stat Assoc 90(430):773–795
Katz R (2010) Statistics of extremes in climate change. Clim Change 100(1):71–76
Katz R, Parlange M, Naveau P (2002) Statistics of extremes in hydrology. Adv Water Resour
25(8–12):1287–1304
́
Khaliq M, Ouarda T, Ondo JC, Gachon P, Bobee B (2006) Frequency analysis of a sequence of dependent
and/or non-stationary hydro-meteorological observations: a review. J Hydrol 329(3–4):534–552.
doi:10.1016/j.jhydrol.2006.03.004
Koutsoyiannis D (2004) Statistics of extremes and estimation of extreme rainfall: II. Empirical investigation
ˆ
́
ˆ
of long rainfall records/Statistiques de valeurs extremes et estimation de precipitations extremes: II.
́
́
Recherche empirique sur de longues series de precipitations. Hydrol Sci J 49(4):591–610. doi:10.1623/
hysj.49.4.591.54424
Kysely J (2002) Probability estimates of extreme temperature events: stochastic modelling approach vs.
extreme value distributions. Stud Geophys Geod 46:93–112
Kysely J (2010) Recent severe heat waves in central Europe: how to view them in a long-term prospect? Int
J Climatol 30(1):89–109. doi:10.1002/joc.1874
Lewis S, Raftery A (1997a) Estimating Bayes factors via posterior simulation with the Laplace–Metropolis
estimator. J Am Stat As 92(438):648–655. doi:10.1080/01621459.1997.10474016
Lewis SM, Raftery AE (1997b) Estimating Bayes factors via posterior simulation with the Laplace–
Metropolis estimator. J Am Stat As 92(438):648–655
Lovejoy S, Mandelbrot B (1985) Fractal properties of rain and a fractal model. Tellus 37A:209–232
Martins E, Stedinger J (2000) Generalized maximum-likelihood generalized extreme-value quantile esti-
mators for hydrologic data. Water Resour Res 36(3):737–744
Marzocchi W, Garcia-Aristizabal A, Gasparini P, Mastellone ML, Di Ruocco A (2012) Basic principles of
multi-risk assessment: a case study in Italy. Nat Hazards 62(2):551–573
Menabde M, Seed A, Harris D, Austin G (1997) Selfsimilar random fields and rainfall simulations.
J Geophys Res C Oceans 102D:13,509–13,515
Metropolis N, Ulam S (1949) The Monte Carlo method. J Am Stat As 44:335–341
Metropolis N, Rosenbluth A, Rosenbluth M, Teller A, Teller E (1953) Equation of state calculations by fast
computing machines. J Chem Phys 21:1081–1092
Moss RH, Edmonds JA, Hibbard KA, Manning MR, Rose SK, van Vuuren DP, Carter TR, Emori S,
Kainuma M, Kram T, Meehl GA, Mitchell JFB, Nakicenovic N, Riahi K, Smith SJ, Stouffer RJ,
Thomson AM, Weyant JP, Wilbanks TJ (2010) The next generation of scenarios for climate change
research and assessment. Nature 463(7282):747–756. doi:10.1038/nature08823
Newton MA, Raftery AE (1994) Approximate Bayesian inference with the weighted likelihood bootstrap.
J R Stat Soc Ser B 56(1):3–48
Ouarda T, El-Adlouni S (2011) Bayesian nonstationary frequency analysis of hydrological variables. J Am
Water Resour As 47(3):496–505
Perica S, Foufoula-Georgiou E (1996) Model for multiscale disaggregation of spatial rainfall based on
coupling meteorological and scaling description. J Geophys Res 101(D21):26,347–26,361
Raftery A (1995) Bayesian model selection in social research. Soc Methodol 25:111–163
Raftery AE (1996) Hypothesis testing and model selection. In: Gilks WR, Richardson S, Spiegelhalter DJ
(eds) Markov Chain Monte Carlo in practice. Chapman and Hill, London, pp 165–187
Raftery AE, Newton MA, Satagopan JM, Krivitsky PN (2007) Estimating the integrated likelihood via
posterior simulation using the Harmonic mean identity. Bayesian Stat 8:1–45
Rebora N, Ferraris L, von Hardenberg J, Provenzale A (2006a) Rainfall downscaling and flood forecasting: a
case study in the Mediterranean area. Nat Hazards Earth Syst Sci 6:611–619
Rebora N, Ferraris L, von Hardenberg J, Provenzale A (2006b) RainFARM: rainfall downscaling by a
filtered autoregressive model. J Hydrometeorol 7:724–738
Rockel B, Will A, Hense A (2008) The regional climate model COSMO-CLM (CCLM). Meteorologische
Zeitschrift 17:347–348
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Environ Stat 15:49–65. doi:10.1007/s13253-009-0010-1
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perature series. J Appl Stat 38(12):2793–2804. doi:10.1080/02664763.2011.570317
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peaks simulation using non-stationary frequency analysis. Nat Hazards 1–12
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deterministic simulations and non-stationary frequency analysis. Nat Hazards 1–13. doi:10.1007/
s11069-011-0052-x
̈
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techniques for precipitation data. Water Resour Res 44(2):W02,425
Bell TL (1987) A space-time stochastic model of rainfall for satellite remote sensing studies. J Geophys Res
92:9631–9643
̈
̈
Bellprat O, Kotlarski S, Luthi D, Schar C (2012) Objective calibration of regional climate models. J Geo-
phys Res Atmos 117(D23). doi:10.1029/2012JD018262
̈
̈
Bohm U, Kucken M, Ahrens W, Block A, Hauffe D, Keuler B, Rockel B, Will A (2006) CLM–the climate
version of LM: brief description and long-term applications. COSMO Newslett 6:225–235
Brussolo E, von Hardenberg J, Ferraris L, Rebora N, Provenzale A (2008) Verification of quantitative
precipitation forecasts via stochastic downscaling. J Hydrometeorol 9:1084–1094
Bucchignani E, Sanna A, Gualdi S, Castellari S, Schiano P (2013) Simulation of the climate of the XX
century in the Alpine space. Nat Hazards 67:981–990. doi:10.1007/s11069-011-9883-82010
Cannon A (2010) A flexible nonlinear modelling framework for nonstationary generalized extreme value
analysis in hydroclimatology. Hydrol Process 24(6):673–685
Carter TR, Jones JN, Lu X, Bhadwal S, Conde C, Mearns LO, O’Neill BC, Rounsevell MDA, Zurek MB
(2007) New assessment methods and the characterisation of future conditions. In: Parry ML, Canziani
OF, Palutikof JP, Linde PJVD, Hanson CE (eds) Climate change 2007. Impacts, adaptation and
vulnerability. Contribution of working group II to the fourth assessment report of the intergovern-
mental panel on climate change. Cambridge University Press, Cambridge, pp 133–171
Coles S (2001) An introduction to statistical modeling of extreme values. Springer series in statistics,
Springer-Verlag London limited
Davison AC, Padoan SA, Ribatet M (2012) Statistical modelling of spatial extremes. Stat Sci
27(2):161–186. doi:10.1214/11-STS376
D’Onofrio D, Palazzi E, von Hardenberg J, Provenzale A, Calmanti S (2014) Stochastic rainfall down-
scaling of climate models. J Hydrometeorol 15:830–843. doi:10.1175/JHM-D-13-096.1
̈
Ekstrom M, Fowler H, Kilsby C, Jones P (2005) New estimates of future changes in extreme rainfall across
the {UK} using regional climate model integrations. 2. Future estimates and use in impact studies.
J Hydrol 300(1–4):234–251, doi: 10.1016/j.jhydrol.2004.06.019
El-Adlouni S, Favre AC, Bobee B (2006) Comparison of methodologies to assess the convergence of
Markov Chain Monte Carlo methods. Comput Stat Data Anal 50(10):2685–2701
́
El Adlouni S, Ouarda T, Zhang X, Roy R, Bobee B (2007) Generalized maximum likelihood estimators for
the nonstationary generalized extreme value model. Water Resour Res 43(3):W03,410
̈
Fowler H, Ekstrom M, Kilsby C, Jones P (2005) New estimates of future changes in extreme rainfall across
the {UK} using regional climate model integrations. 1. Assessment of control climate. J Hydrol
300(1–4): 212–233, doi:10.1016/j.jhydrol.2004.06.017
Gelman A, Rubin D (1992) Inference from iterative simulation using multiple sequences. Stat Sci 7(4):
457–472. doi:10.1214/ss/1177011136
Gelman A, Carlin J, Stern H, Rubin D (2004) Bayesian data analysis, 2nd edn. Chapman and Hall/CRC,
London
Geweke J (1992) Evaluating the accuracy of sampling-based approaches to the calculation of posterior
moments. In: Bernardo JM, Berger JO, Dawid AP, Smith AF (eds) Bayesian statistics 4. Oxford
University Press, Oxford, pp 169–193
Geyer CJ (1992) Practical Markov Chain Monte Carlo. Stat Sci 7(4):473–483. doi:10.1214/ss/1177011137
Giorgi F, Hurrell JW, Mariucci MR (1997) Elevation dependency of the surface climate change signal: a
model study. J Clim 10:288–296. doi:10.1175/1520-0442(1997)010
Gualdi S, Somot S, Li L, Artale V, Adani M, Belluci A, Braun A, Calmanti S, Carillo A, Dell’Aquila A,
Deque M, Dubois C, Elizalde A, Harzallah A, Jacob D, L’Heveder B, May W, Oddo P, Ruti P, Sanna
A, Sannino G, Scoccimarro E, Sevault F, Navarra A (2013) The CIRCE simulations: regional climate
change projections with realistic representation of the Mediterranean Sea. Bull Am Meteorol Soc
94:65–81. doi:10.1175/BAMS-D-11-00136.1
Hanel M, Buishand TA, Ferro CAT (2009) A nonstationary index flood model for precipitation extremes in
transient regional climate model simulations. J Geophys Res Atmos 114(D15): doi:10.1029/
2009JD011712
Houghton J, Ding Y, Griggs DJ, Noguer M, van der Linden PJ, Dai X, Maskell K, Johnson CA (eds) (2001)
Climate change 2001: the scientific basis. Cambridge University Press, Cambridge, MA
Hrafnkelsson B, Morris JS, Baladandayuthapani V (2012) Spatial modeling of annual minimum and
maximum temperatures in Iceland. Meteorol Atmos Phys 116(1–2):43–61. doi:10.1007/s00703-010-
0101-0
Huth R, Kysely J, Pokorna L (2000) A GCM simulation of heat waves, dry spells, and their relationships to
circulation. Clim Change 46(1–2):29–60. doi:10.1023/A:1005633925903
IPCC (2012) Managing the risks of extreme events and disasters to advance climate change adaptation.
Cambridge University Press, Cambridge, MA
IPCC (2013) Climate change 2013: the physical science basis. In: Contribution of working group I to the
fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press,
Cambridge, MA
Jain S, Lall U (2001) Floods in a changing climate: does the past represent the future? Water Resour Res
37(12):3193–3205
Jeffreys H (1961) Theory of probability, 3rd edn. Oxford University Press, Oxford
Jenkinson AF (1955) The frequency distribution of the annual maximum (or minimum) values of meteo-
rological elements. Q J R Meteorol Soc 81:158–171
Kass RE, Raftery AE (1995) Bayes factors. J Am Stat Assoc 90(430):773–795
Katz R (2010) Statistics of extremes in climate change. Clim Change 100(1):71–76
Katz R, Parlange M, Naveau P (2002) Statistics of extremes in hydrology. Adv Water Resour
25(8–12):1287–1304
́
Khaliq M, Ouarda T, Ondo JC, Gachon P, Bobee B (2006) Frequency analysis of a sequence of dependent
and/or non-stationary hydro-meteorological observations: a review. J Hydrol 329(3–4):534–552.
doi:10.1016/j.jhydrol.2006.03.004
Koutsoyiannis D (2004) Statistics of extremes and estimation of extreme rainfall: II. Empirical investigation
ˆ
́
ˆ
of long rainfall records/Statistiques de valeurs extremes et estimation de precipitations extremes: II.
́
́
Recherche empirique sur de longues series de precipitations. Hydrol Sci J 49(4):591–610. doi:10.1623/
hysj.49.4.591.54424
Kysely J (2002) Probability estimates of extreme temperature events: stochastic modelling approach vs.
extreme value distributions. Stud Geophys Geod 46:93–112
Kysely J (2010) Recent severe heat waves in central Europe: how to view them in a long-term prospect? Int
J Climatol 30(1):89–109. doi:10.1002/joc.1874
Lewis S, Raftery A (1997a) Estimating Bayes factors via posterior simulation with the Laplace–Metropolis
estimator. J Am Stat As 92(438):648–655. doi:10.1080/01621459.1997.10474016
Lewis SM, Raftery AE (1997b) Estimating Bayes factors via posterior simulation with the Laplace–
Metropolis estimator. J Am Stat As 92(438):648–655
Lovejoy S, Mandelbrot B (1985) Fractal properties of rain and a fractal model. Tellus 37A:209–232
Martins E, Stedinger J (2000) Generalized maximum-likelihood generalized extreme-value quantile esti-
mators for hydrologic data. Water Resour Res 36(3):737–744
Marzocchi W, Garcia-Aristizabal A, Gasparini P, Mastellone ML, Di Ruocco A (2012) Basic principles of
multi-risk assessment: a case study in Italy. Nat Hazards 62(2):551–573
Menabde M, Seed A, Harris D, Austin G (1997) Selfsimilar random fields and rainfall simulations.
J Geophys Res C Oceans 102D:13,509–13,515
Metropolis N, Ulam S (1949) The Monte Carlo method. J Am Stat As 44:335–341
Metropolis N, Rosenbluth A, Rosenbluth M, Teller A, Teller E (1953) Equation of state calculations by fast
computing machines. J Chem Phys 21:1081–1092
Moss RH, Edmonds JA, Hibbard KA, Manning MR, Rose SK, van Vuuren DP, Carter TR, Emori S,
Kainuma M, Kram T, Meehl GA, Mitchell JFB, Nakicenovic N, Riahi K, Smith SJ, Stouffer RJ,
Thomson AM, Weyant JP, Wilbanks TJ (2010) The next generation of scenarios for climate change
research and assessment. Nature 463(7282):747–756. doi:10.1038/nature08823
Newton MA, Raftery AE (1994) Approximate Bayesian inference with the weighted likelihood bootstrap.
J R Stat Soc Ser B 56(1):3–48
Ouarda T, El-Adlouni S (2011) Bayesian nonstationary frequency analysis of hydrological variables. J Am
Water Resour As 47(3):496–505
Perica S, Foufoula-Georgiou E (1996) Model for multiscale disaggregation of spatial rainfall based on
coupling meteorological and scaling description. J Geophys Res 101(D21):26,347–26,361
Raftery A (1995) Bayesian model selection in social research. Soc Methodol 25:111–163
Raftery AE (1996) Hypothesis testing and model selection. In: Gilks WR, Richardson S, Spiegelhalter DJ
(eds) Markov Chain Monte Carlo in practice. Chapman and Hill, London, pp 165–187
Raftery AE, Newton MA, Satagopan JM, Krivitsky PN (2007) Estimating the integrated likelihood via
posterior simulation using the Harmonic mean identity. Bayesian Stat 8:1–45
Rebora N, Ferraris L, von Hardenberg J, Provenzale A (2006a) Rainfall downscaling and flood forecasting: a
case study in the Mediterranean area. Nat Hazards Earth Syst Sci 6:611–619
Rebora N, Ferraris L, von Hardenberg J, Provenzale A (2006b) RainFARM: rainfall downscaling by a
filtered autoregressive model. J Hydrometeorol 7:724–738
Rockel B, Will A, Hense A (2008) The regional climate model COSMO-CLM (CCLM). Meteorologische
Zeitschrift 17:347–348
Sang H, Gelfand A (2010) Continuous spatial process models for spatial extreme values. J Agric Biol
Environ Stat 15:49–65. doi:10.1007/s13253-009-0010-1
Scotto MG, Barbosa SM, Alonso AM (2011) Extreme value and cluster analysis of European daily tem-
perature series. J Appl Stat 38(12):2793–2804. doi:10.1080/02664763.2011.570317
Seidou O, Ramsay A, Nistor I (2011) Climate change impacts on extreme floods II: improving flood future
peaks simulation using non-stationary frequency analysis. Nat Hazards 1–12
Seidou O, Ramsay A, Nistor I (2012) Climate change impacts on extreme floods I: combining imperfect
deterministic simulations and non-stationary frequency analysis. Nat Hazards 1–13. doi:10.1007/
s11069-011-0052-x
̈
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