Category Archives: Publication

Suitability of ANN applied as a hydrological model coupled with statistical downscaling model: A case study in the northern area of Peninsular Malaysia.

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Abstract:

The increase in global surface temperature in response to the changing composition of the atmosphere will significantly impact upon local hydrological regimes and water resources. This situation will then lead to the need for an assessment of regional climate change impacts. The objectives of this study are to determine current and future climate change scenarios using statistical downscaling model (SDSM) and to assess climate change impact on river runoff using artificial neural network (ANN) and identification of unit hydrographs and component flows from rainfall, evaporation and streamflow data (IHACRES) models, respectively. This study investigates the potential of ANN to project future runoff influenced by large-scale atmospheric variables for selected watershed in Peninsular Malaysia. In this study, simulations of general circulation models from Hadley Centre 3rd generation with A2 and B2 scenarios have been used. According to the SDSM projection, daily rainfall and temperature during the 2080s will increase by up to 2.23 mm and 2.02 °C, respectively. Moreover, river runoff corresponding to downscaled future projections presented a maximum increase in daily river runoff of 52 m3/s. The result revealed that the ANN was able to capture the observed runoff, as well as the IHACRES. However, compared to the IHACRES model, the ANN model was unable to provide an identical trend for daily and annual runoff series.

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Cite as:

Hassan, Z., Shamsudin, S., Harun, S., Malek, M. A., and Hamidon, N. (2015). Suitability of ANN applied as a hydrological model coupled with statistical downscaling model: A case study in the northern area of Peninsular Malaysia. Environmental Earth Sciences, 74(1), 463-477. DOI: 10.1007/s12665-015-4054-y

Application of SDSM and LARS-WG for simulating and downscaling of rainfall and temperature

Abstract

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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http://link.springer.com/article/10.1007/s00704-013-0951-8

or https://www.researchgate.net/publication/242019465_Application_of_SDSM_and_LARS-WG_for_simulating_and_downscaling_of_rainfall_and_temperature?ev=prf_pub

Cite as:

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

Choosing the best fit distribution for rainfall event characteristics based on 6H-IETD within Peninsular Malaysia

Abstract:

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.

Abstrak:

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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http://www.jurnalteknologi.utm.my/index.php/jurnalteknologi/article/view/3058

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Cite as:

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