nonparametric statistical temporal downscaling of daily precipitation to hourly precipitation and...
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Article history:Received 23 April 2013Received in revised form 9 September 2013Accepted 15 December 2013Available online 21 December 2013
Hydro-meteorological time series on ner temporal scales, such as hourly, are essential for assessing the
the hydrological effects of climate change on medium- or small-sized watersheds, time series of hydro-meteorological variablesof interest such as precipitation (or rainfall) are not alwaysavailable at the desired time interval (Aleri et al., 2012).Therefore, hydro-meteorological data with time scales on the orderof 1 h or less are urgently required (Krajewski et al., 1991).
ations with dailyby averag(Eagleson,poral reso
the creation of hourly time series from daily time seriesdisaggregation rules such as preserving the diurnal cycthe additive condition is recommended (Jones and Ha2009).
Several approaches for statistical temporal downscaling ofprecipitation time series have been suggested in the literature,including methods based on a point-process model (Rodriquez-Iturbe et al., 1988; Glasbey et al., 1995; Koutsoyiannis and Onof,2001; Marani and Zanetti, 2007). Koutsoyiannis and Onof (2001)used the Bartlett-Lewis model to simulate rainfall time series by
Corresponding author. Tel.: +82 2 950 7567; fax: +82 2 950 7579.E-mail addresses: firstname.lastname@example.org (T. Lee), email@example.com (C. Jeong).
Journal of Hydrology 510 (2014) 182196
Contents lists availab
Journal of H
els1 Tel.: +82 55 772 1797; fax: +82 55 772 1799.employ the outputs of these models to characterize the interactionbetween the land surface and the atmosphere and to assess thehydrological effects of climate change (Prudhomme and NickReynard, 2002). While time series data are essential for assessing
2012). However, physical rainfall-runoff simulrainfall underestimate the magnitude of runoffthe characteristics of short and intense rainfallTo obtain precipitation time series with ner tem0022-1694/$ - see front matter 2013 Elsevier B.V. All rights reserved.http://dx.doi.org/10.1016/j.jhydrol.2013.12.027ing out1978).lution,using
le andrpham,1. Introduction
General climate models (GCMs) and their spatially downscaledversions, regional climate models (RCMs), provide superb arealcoverage (Orlowsky et al., 2008; Kendon et al., 2010). Hydrologists
Weather generators, one of the popular statistical downscalingmethods, can provide daily outputs. In the literature, weathergenerators for statistical downscaling are used to simulate thetime series of daily hydro-meteorological variables (Soltani andHoogenboom, 2003; Mason, 2004; Boe et al., 2007; Lee et al.,This manuscript was handled by AndrasBardossy, Editor-in-Chief, with theassistance of Uwe Haberlandt, AssociateEditor
Keywords:Climate changeDaily precipitationExtreme eventsHourly precipitationRCPTemporal downscalinghydrological effects of land use or climate change on medium and small watersheds. However, these timeseries are, in general, available at no ner than daily time intervals. An alternative method of obtainingner time series is temporal downscaling of daily time series to hourly time series. In the current study, atemporal downscaling model that combines a nonparametric stochastic simulation approach with agenetic algorithm is proposed. The proposed model was applied to Jinju station in South Korea for a his-torical time period to validate the model performance. The results revealed that the proposed model pre-serves the key statistics (i.e., the mean, standard deviation, skewness, lag-1 correlation, and maximum) ofthe historical hourly precipitation data. In addition, the occurrence and transition probabilities are wellpreserved in the downscaled hourly precipitation data. Furthermore, the RCP 4.5 and RCP 8.5 climate sce-narios for the Jinju station were also analyzed, revealing that the mean and the wet-hour probability (P1)signicantly increased and the standard deviation and maximum slightly increased in these scenarios.The magnitude of the increase was greater in RCP 8.5 than RCP 4.5. Extreme events of different durationswere also tested. The downscaled hourly precipitation adequately reproduced the statistical behavior ofthe extremes of the historical hourly precipitation data for all durations considered. However, theinter-daily relation between the 1st hour of the present day and the last hour of the previous day wasnot preserved. Overall, the results demonstrated that the proposed temporal downscaling model is a goodalternative method for downscaling simulated daily precipitation data from weather generators or RCMoutputs.
2013 Elsevier B.V. All rights reserved.a r t i c l e i n f o s u m m a r yNonparametric statistical temporal downto hourly precipitation and implications
Taesam Lee a,1, Changsam Jeong b,aDepartment of Civil Engineering, ERI, Gyeongsang National University, 501 Jinju-daerobDepartment of Civil & Environmental Engineering, Induk University, Seoul, Republic of
journal homepage: www.aling of daily precipitationr climate change scenarios
u, Gyeongnam 660-701, Republic of Koreaea
le at ScienceDirect
evier .com/locate / jhydrol
Fig. 1. Selection of crossover element with respect to the rule of gradual variation in a precipitation event. Note that for both (a and b) cases, it is supposed to substitute x2into x2 since x
2 more likely follows the rule of precipitation gradual variation.
Fig. 2. Key statistics of the historical data () and downscaled data (boxplots) with the proposed model without considering the inter-daily connection for month 8 of Jinjustation. The employed values of the GA mixture parameters, Pc and Pm are 0.3 and 0.1, respectively.
T. Lee, C. Jeong / Journal of Hydrology 510 (2014) 182196 183
Fig. 3. Occurrence (P1) & transition probabilities (P01 and P11) of historical () and downscaled data (boxplots) with the same model as in Fig. 2 for month 8 of Jinjustation.
Fig. 4. Lag-1 correlation and transition probabilities of historical () and downscaled data (boxplots) with the same model as in Fig. 2 for month 8 of Jinju station exceptthat the tuned Pc(=0.1) and Pm(=0.01) are used.
184 T. Lee, C. Jeong / Journal of Hydrology 510 (2014) 182196
ydrT. Lee, C. Jeong / Journal of Hadjusting simulated data to meet the additive condition. Maraniand Zanetti (2007) presented a temporal downscaling methodbased on a point-process model that employs theoretically basedestimates of rainfall variability on an hourly scale derived fromdaily statistics to produce approximately unbiased estimates ofrainfall variance on an hourly time scale.
Machine learning techniques (e.g., neural networks) have alsobeen applied to temporally downscale precipitation data (Coulibalyet al., 2005; Kumara et al., 2012). Kumara et al. (2012) downscaledmultiple meteorological variables from monthly to sub-daily whileretaining consistent correlations between variables.
In addition, some nave methods have been introduced (Kondoand Xu, 1997; Wang and Liu, 2006; Yang et al., 2009; Chen et al.,2012). Kondo and Xu (1997) suggested that the hourly rainfallamount follows a sinusoidal distribution in which the peak densityvalues and the last time of rainfall are proportional to the dailyrainfall amounts. Wang and Liu (2006) employed a random distri-bution method, but this method overestimates the amount of rain-fall when the rainfall intensity is high. Chen et al. (2011) suggesteda proportional method in which the daily rainfall amount isdivided into four periods and distributed proportionally. Thesemethods are direct and easy to use. However, they cannot repro-duce key hourly statistics (e.g., the hourly mean, standard devia-tion, and skewness) and the intermittency of rainfall events.
One of the most important characteristics of hourly precipita-tion is the diurnal cycle of the local and global weather andclimate. For example, summertime rainfall may evaporate morequickly in the afternoon hours but can easily permeate into the soiland be stored overnight. Specically, Dai et al. (1999) argued thatover the Rocky Mountains, precipitation is twice as likely to occur
Fig. 5. Key statistics of the historical data () and downscaled data (boxplot) with thology 510 (2014) 182196 185in approximately the 1518th hours of the day (i.e., 3 PM6 PM)than at any other times. The diurnal cycle can strongly inuencethe hydrological cycle and agriculture (Yin et al., 2009; He andZhang, 2010) as well as the surface air temperature range (Dai,1999). Reproduction of the diurnal cycle and the key statistics ofhourly precipitation data are crucial for exploring future climatescenarios and assessing the impacts of climate change for water re-source management and ood control.
However, these existing models do not consider the diurnal cy-cle and the specic details of key hourly statistics such as themean, standard deviation, skewness, and lag-1 correlation fordownscaled hourly precipitation data. Lee et al. (2010) proposeda nonparametric disaggregation model and applied it to the Colo-rado River streamow to disaggregate the yearly streamow tomonthly streamow. They adapted a meta-heuristic optimizationtechnique, a genetic algorithm (GA) (Goldberg, 1989), and k-near-est neighbor resampling (KNNR) for nonparametric disaggregation.KNNR is a nonparametric resampling technique that has beenemployed for stochastic simulations, particularly for hydro-meteo-rological variables (Lee et al., 2007; Salas and Lee, 2010). GAs are aclass of adaptive stochastic optimization algorithms and a pro-gramming technique that mimics biological evolution as a prob-lem-solving strategy; these algorithms are commonly used insearch and optimization. The GA process contains three operators,reproduction, crossover and mutation. These th