Sometimes a hydrologist may need to know what the chances are over a given time period that a flood will reach or exceed a specific magnitude. This is called the probability of occurrence or the exceedance probability.
Let’s say the value “p” is the exceedance probability, in any given year. The exceedance probability may be formulated simply as the inverse of the return period. For example, for a two-year return period the exceedance probability in any given year is one divided by two = 0.5, or 50 percent. Continue reading
Climate change is believed to have significant impacts on the water basin and region, such as in a runoff and hydrological system. However, impact studies on the water basin and region are difficult, since general circulation models (GCMs), which are widely used to simulate future climate scenarios, do not provide reliable hours of daily series rainfall and temperature for hydrological modeling. There is a technique named as “downscaling techniques”, which can derive reliable hour of daily series rainfall and temperature due to climate scenarios from the GCMs output. In this study, statistical downscaling models are used to generate the possible future values of local meteorological variables such as rainfall and temperature in the selected stations in Peninsular of Malaysia. The models are: (1) statistical downscaling model (SDSM) that utilized the regression models and stochastic weather generators and (2) Long Ashton research station weather generator (LARS-WG) that only utilized the stochastic weather generators. The LARS-WG and SDSM models obviously are feasible methods to be used as tools in quantifying effects of climate change condition in a local scale. SDSM yields a better performance compared to LARS-WG, except SDSM is slightly underestimated for the wet and dry spell lengths. Although both models do not provide identical results, the time series generated by both methods indicate a general increasing trend in the mean daily temperature values. Meanwhile, the trend of the daily rainfall is not similar to each other, with SDSM giving a relatively higher change of annual rainfall compared to LARS-WG.
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Hassan, Z., Shamsudin, S., and Harun, S. (2014). Application of SDSM and LARS-WG for simulating and downscaling of rainfall and temperature. Theoretical and Applied Climatology, 116(1-2), 243–257. DOI:10.1007/s00704-013-0951-8
In selecting the best-fit distribution model for the rainfall event characteristics based on the inter-event time definition (IETD) of 6 hours for the selected rainfall in the Peninsular of Malaysia, seven distributions were utilized namely the beta (B4), exponential (EX1), gamma (G2), generalized extreme value (GEV), generalized Pareto (GP), Log-Pearson 3 (LP3), and Wakeby (WKB). Maximum likelihood estimation (MLE) was applied to estimate the parameters of each distribution. Based on the results, GP, WKB and GEV were found to be the most suitable distribution for describing the rainfall event characteristics in the studied regions.
Dalam permilihan model taburan yang bersesuaian dengan sifat musim hujan berdasarkan definasi masa antara kejadian untuk 6 jam bagi kawasan Semenanjung Malaysia, 7 jenis taburan telah digunakan iaitu the beta (B4), exponential (EX1), gamma (G2), generalized extreme value (GEV), generalized Pareto (GP), Log-Pearson 3 (LP3), dan Wakeby (WKB). Penganggar kebolehjadian maksimum (MLE) telah digunakan untuk menganggarkan parameter untuk setiap taburan. Bedasarkan keputusan analisa, WKB, GP dan GEV menjadi taburan yang bersesuaian untuk memperihal sifat musim hujan bagi kawasan kajian. Kata kunci: Musim hujan, model taburan, definasi antara kejadian, Semenanjung Malaysia.
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Hassan, Z., Shamsudin, S., and Harun, S. Choosing the best fit distribution for rainfall event characteristics based on 6H-IETD with Peninsular Malayisa. Jurnal Teknologi (Sciences and Engineering), 75 (1), 145–157. DOI: http://dx.doi.org/10.11113/jt.v75.3058