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A Brief Introduction to the Concept of Return Period for Univariate Variables

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A Brief Introduction to the Concept of Return Period for Univariate Variables Research Papers Issue RP0139 A brief introduction to the concept of September 2012 return period for univariate variables Impact on Soil and Coasts Division (ISC) By Renata Vezzoli SUMMARY Here the concept of return period is discussed in terms of the Impact on Soil and Coasts Division (ISC), stationarity of the process. If the process is stationarity the concept of Euro-Mediterranean Center on return period is well defined and ambiguities do not arise. If the process is Climate Change (CMCC), via non stationary, as it could be any climate driven phenomenon under climate Maiorise s.n.c, I-81043, Capua [email protected] change assumption, the meaning of the return period varies because it could be read in a more or a less conservative way. In this work, we refer to Paola Mercogliano Impact on Soil and Coasts the three possible alternatives of non stationary return period concept given Division (ISC), in [5]. Euro-Mediterranean Center on Climate Change (CMCC), via Keywords: Definition, stationarity, non-stationarity, univariate, return period Maiorise s.n.c, I-81043, Capua [email protected] Silvano Pecora Servizio Idro Meteo Clima - Area Idrografia e Idrologia (SIMC), Agenzia Regionale per l’Ambiente Emilia Romagna (ARPA), via Garibaldi, I-43121, Parma [email protected] This report is carried on within a MOU agreement between CMCC and ARPA Emilia Romagna SIMC. CMCC Research Papers Introduction tions are characterised by a linear trend at an- nual scale, i.e. there is a very low evidence Climate is characterized by a natural variabil- of a generalised trend at annual scale, while 02 ity in precipitation and temperature and that at seasonal scale, Catalonia will experience an variability reflects also in floods and droughts increase in spring precipitation and a decrease occurrence and intensity. The detection of cli- in winter events. Since climate change affects mate change signals leads to the question of also the frequency of hydrological extreme phe- the reliability of the long term persistence of nomena (droughts and floods) there is interest climate (hydrological) conditions and to need in studying the climate driven impacts of floods of a non stationary approach to the problem. and droughts on the socio-economical condi- In literature there are several studies on the tion over an area, among the studies available non stationarity of climate variables [4, 6, 9, 1]. in literature we cite [3, 2, 10, 11, 7, 8]. The Changes in temperature and precipitation ex- future behavior of extremes in river discharge tremes simulated using a second-generation under climate change from simulated daily dis- coupled GCM of the Canadian Centre for Cli- charge are investigate by [3]. The results fore- mate Modelling and Analysis are studied by [4]. cast an increase of floods and droughts fre- They estimated the return values of annual ex- quency over many regions, with sometimes an tremes from a generalized extreme value distri- increase in both floods and droughts frequency bution with time-dependent location and scale over the same region. [2] uses climate simu- parameters. The results show that changes lations to project in the future the impacts of in temperature extremes are expressed by climate on extreme droughts in UK. They find, changes in the location parameter of the distri- that in 2070, a maximum increase in drought Centro Euro-Mediterraneo sui Cambiamenti Climatici bution, while changes in precipitation extremes severity of about 125%, and a decrease in max- are reflected in changes in both location and imum drought duration up to 50%. The vari- scale distribution parameters. Moreover it re- ability of flood risk related to climate and land sults that the probability of extreme precipitation use change in the Rhine basin is studied in events almost double from the beginning to the [10, 11] that also provide an estimate of the ending of the simulations, thus the return period costs. As main results they found that an in- of the events decreased along the simulation crease of about 8% - 17% in Rhine extreme period. The changes in extreme European win- flood peak should be expected in 2050 for all ter (December - February) precipitation in the the return period between 10 and 1250 years, last 500 year are studied by [6] that note an high [10]; and, more generally that the flood risk is a variability of the return periods of extremely wet not stationary variable and might considerably and dry winters before and after the onset of the increase over a period of several decades, [11]. anthropogenic influences. The existence of a Both studies [10, 11] agree that the area that positive trend in the precipitation observations will be more affected by floods will be located in Sao Pao (Brasil) from 1933 to 2005 with an in the Lower Rhine in Germany because of the increase of about 40 mm in the 0.99 quantile different flood protection standards used in the (100 yr return period) is the results of [9] analy- Netherlands and Germany. [7, 8] address the sis. More recently [1] investigates the non sta- question of the non stationarity distribution of tionarity of daily rainfall in northeast Spain us- hydrologic time series in Canada using a com- ing the peaks-over-threshold (POT) approach. bination of both non stationary probability distri- Results indicates that less than 5% of the sta- butions and deterministic rainfall-runoff models. The non stationary behavior of daily maximum over a threshold (also known as POT analysis flow is modelled from the annual maximum sim- in hydrology) the return period T can be ex- ulated by the deterministic hydrologic model. pressed as a function of the probability distri- The results show that a non-stationary distribu- bution function FX and of the average waiting 03 tion better estimates the peak flood quantiles time between two events µT : (return level) than a stationary one, [7, 8]. Notwithstanding, new infrastructures are typi- µ cally designed on the basis of normative values T = T (1) (maximum value observed or quantile) derived 1 − FX (x) from historical information under the hypoth- esis of climate stationarity, neglecting climate For example in hydrology, in to the case of peak change effects, [5]. At the same time adapta- flood analysis the sample size is increased tion strategies to climate change need to incor- by considering all the events above a certain porate the foreseen changes in extreme values threshold, so the sample x is composed by to buffer the impacts of climate change on the all the observations above the threshold and failure risk of infrastructure itself, [5, 10, 11]. µT > 1. For this reason we propose a brief discussion on the concept of return period, often used If the variable of interest are annual maxima to define the normative value for infrastructure (minimum) values µT is equal to 1 yr/event and design, for stationary and non stationary pro- equation (1) simplifies as: cesses. If the process is stationarity the con- cept of return period is well defined and any Centro Euro-Mediterraneo sui Cambiamenti Climatici ambiguities arise. If the process is non station- 1 ary the concept of “average waiting time” can T = (2) 1 − FX (x) be interpreted in more or less conservative way. We present the possible interpretations given in [5] and we suggest an alternative way to pro- Theoretically, from equations (1) and (2) is pos- vide a complete description of the return level sible to estimate long return period, but these for a non stationary process, i.e. providing not values are highly affected by the uncertainties only its average value but also a measure of its associated to the size of the sample and the dispersion within the reference period. shape of the tails of the estimated distribu- tion, i.e. to the capacity of the distribution to Return period in the stationary case well describe the data. The confidence in the estimated values of the return period rapidly If a process is stationary the return period of decays for return period more than twice the a given event is defined as the average time length of the observation period, [5]. For ex- elapsing between two successive realizations ample, with reference to equation (2), the re- of the event itself or alternatively the return level turn period associated to FX (x) = 0:99 and is the value expected to be exceeded on aver- FX (x) = 0:995 are 100 yr and 200 yr respec- age, once every return period, or with probabil- tively; in this case an uncertainty of 5% in the ity 1/(return period) in any given year, [5]. If the value of the distribution function corresponds to variable of interest is expressed as exceedence an error of 100% in the estimated return period. CMCC Research Papers BOX 1: SOME DEFINITIONS OF STATIONARITY 04 A stochastic process is defined stationary if its mean, variance, and auto-covariance are time indepen- dent. There are different types of stationarity according to the different interpretation of the dependence concept: Weak stationarity the mean and variance of the process are constant, and for any t, h ≥ 1 Cov(xt; xt+h) depends only on h and not on t; Weakly dependent for increasing values of h, xt and xt+h are “almost independent”, meaning that limh!+1 Corr(xt; xt+h) = 0; Higher order stationarity all the moments of the distribution are stationary, not only mean and variance. Usually with “stationarity” is indicated the weak stationarity. Centro Euro-Mediterraneo sui Cambiamenti Climatici Return period in a non stationarity case difference between the maximum and minimum value) to evaluate the spread of the return level If the stationarity hypothesis does not hold the or, even better, the probability distribution of the definition of return period/return level as “the return level to fully characterised the process.
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