Rainfall analysis in the northern region of Peninsular Malaysia

Modeling of rainfall is important for assessing the possible impacts of climate change. To achieve accurate projections of rainfall events, availability of sufficient hydrological station data is critical. Precipitation is one of the most important meteorological variables for hydrological modeling. In cases where long series of observed precipitation are not available, they can be stochastically generated by weather generators. Advanced Weather Generator (AWE-GEN) has been proven to generate precipitation data at the temperate climate regions with Gamma distribution being incorporated in the model to represent rainfall intensity. However, in a tropical climate such as Malaysia, some studies disputed the incorporation of Gamma distribution. As such, in this study, Weibull a heavy tail distribution is proposed to be used. The AWE-GEN has well performed in the wetter region such as the eastern of the peninsular. However, rainfall distribution within Peninsular Malaysia is highly variable temporally and spatially. The northern region is drier especially during the southwest monsoon season. This region receives minimal rain during the northeast monsoon due to the presence of the Titiwangsa Range which obstructs the region from getting rain by the north easterly winds. Therefore, the objectives of the study are two-fold. First, this study compares the performance of Gamma and Weibull that are incorporated in the AWE-GEN in simulating rainfall series for the northern region of the peninsular. Second, the monthly rainfall and the extreme rainfall series are simulated using the better distribution. The performances of Gamma and Weibull distributions are compared using the goodness of fit test, Root Mean Square Error (RMSE). Results showed that Gamma is the better distribution in simulating rainfall at rainfall stations located at the outer parts of the northern coast whereas Weibull is the better distribution for stations located in the interior parts of the northern coast. Hourly and daily extreme rainfalls seem to be well captured at all stations. Similarly, wet spell length is well simulated while in contrast, dry spell length is slightly underestimated at all stations. Overall, Gamma and Weibull produce commendable results in simulating extreme rainfall as well as wet spell length throughout the northern region of the peninsular. (c) 2017 The Authors. Published by IASE

Anthropogenic intensification of short-duration rainfall extremes

Short- duration (1-3 h) rainfall extremes can cause serious damage to societies through rapidly developing (flash) flooding and are determined by complex, multifaceted processes that are altering as Earth's climate warms. In this Review, we examine evidence from observational, theoretical and modelling studies for the intensification of these rainfall extremes, the drivers and the impact on flash flooding. Both short- duration and long- duration (\textgreater1 day) rainfall extremes are intensifying with warming at a rate consistent with the increase in atmospheric moisture (~7% K-1), while in some regions, increases in short- duration extreme rainfall intensities are stronger than expected from moisture increases alone. These stronger local increases are related to feedbacks in convective clouds, but their exact role is uncertain because of the very small scales involved. Future extreme rainfall intensification is also modulated by changes to temperature stratification and large- scale atmospheric circulation. The latter remains a major source of uncertainty. Intensification of short- duration extremes has likely increased the incidence of flash flooding at local scales and this can further compound with an increase in storm spatial footprint to considerably increase total event rainfall. These findings call for urgent climate change adaptation measures to manage increasing flood risks

Stochastic generation of hourly rainfall series in the Western Region of Peninsular Malaysis

Comprehensive analysis and modeling of rainfall distribution is essential in capturing the characteristics of high intense rainfall. The western region of Peninsular Malaysia which is more urbanized and densely populated is prone to flash flood occurrences due to the high intense rainfall brought by a convective rainfall during the inter-monsoon season. Convective rain is usually short live and intense. Therefore, knowledge pertaining to the distribution of rainfall intensity at short time scale is crucial in planning and decision making prior to, during and after a flood event, thereby minimizing the potentially catastrophic impact of flooding. The selection of appropriate probability distribution to represent rainfall intensity is highly critical to get a better indication of seasonal contribution to the annual rainfall. This study aimed to determine the better distribution of rainfall intensity to represent extreme rainfall events in the western region using Advanced Weather Generator (AWE-GEN). Model development consists of using hourly rainfall data and other meteorological data from three stations located within the studied region. Two probability distributions incorporated in the AWE-GEN model, namely, Weibull and Gamma were fitted to the historical data. Numerical evaluation using Root Mean Square Error goodness-of-fit test was used to compare the performance of the distributions. Results showed that AWE-GEN model is capable of simulating the monthly rainfall series at the west coast region with Weibull being the better distribution representing intensity. It was found that high values in model parameters and contribute to the higher intense rainfall within the studied region. The AWE-GEN model also performs quite well in reproducing the hourly and 24 hour extremes rainfall as well as generating the extreme wet spell; however the model slightly underestimates the extreme dry spell. Results can be beneficial, particularly, for a better rainfall forecasting at watersheds and urban areas

Future projections of extreme precipitation using Advanced Weather Generator (AWE-GEN) over Peninsular Malaysia

A stochastic downscaling methodology known as the Advanced Weather Generator, AWE-GEN, has been tested at four stations in Peninsular Malaysia using observations available from 1975 to 2005. The methodology involves a stochastic downscaling procedure based on a Bayesian approach. Climate statistics from a multi-model ensemble of General Circulation Model (GCM) outputs were calculated and factors of change were derived to produce the probability distribution functions (PDF). New parameters were obtained to project future climate time series. A multi-model ensemble was used in this study. The projections of extreme precipitation were based on the RCP 6.0 scenario (2081–2100). The model was able to simulate both hourly and 24-h extreme precipitation, as well as wet spell durations quite well for almost all regions. However, the performance of GCM models varies significantly in all regions showing high variability of monthly precipitation for both observed and future periods. The extreme precipitation for both hourly and 24-h seems to increase in future, while extreme of wet spells remain unchanged, up to the return periods of 10–40 years

Stochastic Modeling of Rainfall Series in Kelantan Using an Advanced Weather Generator

Weather generator is a numerical tool that uses existing meteorological records to generate series of synthetic weather data. The AWE-GEN (Advanced Weather Generator) model has been successful in producing a broad range of temporal scale weather variables, ranging from the high-frequency hourly values to the low-frequency inter-annual variability. In Malaysia, AWE-GEN has produced reliable projections of extreme rainfall events for some parts of Peninsular Malaysia. This study focuses on the use of AWE-GEN model to assess rainfall distribution in Kelantan. Kelantan is situated on the north east of the Peninsular, a region which is highly susceptible to flood. Embedded within the AWE-GEN model is the Neyman Scott process which employs parameters to represent physical rainfall characteristics. The use of correct probability distributions to represent the parameters is imperative to allow reliable results to be produced. This study compares the performance of two probability distributions, Weibull and Gamma to represent rainfall intensity and the better distribution found was used subsequently to simulate hourly scaled rainfall series. Thirty years of hourly scaled meteorological data from two stations in Kelantan were used in model construction. Results indicate that both probability distributions are capable of replicating the rainfall series at both stations very well, however numerical evaluations suggested that Gamma performs better. Despite Gamma not being a heavy tailed distribution, it is able to replicate the key characteristics of rainfall series and particularly extreme values. The overall simulation results showed that the AWE-GEN model is capable of generating tropical rainfall series which could be beneficial in flood preparedness studies in areas vulnerable to flood

Historical trend of hourly extreme rainfall in Peninsular Malaysia

Hourly rainfall data between the years 1975 and 2010 across the Peninsular Malaysia were analyzed for trends in hourly extreme rainfall events. The analyses were conducted on rainfall occurrences during the northeast monsoon (November–February) known as NEM, the southwest monsoon (May–August) known as SWM, and the two inter-monsoon seasons, i.e., March–April (MA) and September–October (SO). Several extreme rainfall indices were calculated at the station level. The extreme rainfall events in Peninsular Malaysia showed an increasing trend between the years 1975 and 2010. The trend analysis was conducted using linear regression; no serial correlation was detected from the Durbin-Watson test. Ordinary kriging was used to determine the spatial patterns of trends in seasonal extremes. The total amount of rainfall received during NEM is higher compared to rainfall received during inter-monsoon seasons. However, intense rainfall is observed during the inter-monsoon season with higher hourly total amount of rainfall. The eastern part of peninsular was most affected by stratiform rains, while convective rain contributes more precipitation to areas in the western part of the peninsular. From the distribution of spatial pattern of trend, the extreme frequency index (Freq >20) gives significant contribution to the positive extreme rainfall trend during the monsoon seasons. Meanwhile, both extreme frequency and extreme intensity (24-Hr Max, Freq >95th, Tot >95th, Tot >99th, and Hr Max) indices give significant contribution to the positive extreme rainfall trend during the inter-monsoon seasons. Most of the significant extreme indices showed the positive sign of trends. However, a negative trend of extreme rainfall was found in the northwest coast due to the existence of Titiwangsa Range. The extreme intensity, extreme frequency, and extreme cumulative indices showed increasing trends during the NEM and MA while extreme intensity and extreme frequency had similar trends during the SWM and SO throughout Peninsular Malaysia. Overall, the hourly extreme rainfall events in Peninsular Malaysia showed an increasing trend between the year 1975 and 2010 with notable increasing trends in short temporal rainfall during inter-monsoon season. The result also proves that convective rain during this period contributes higher intensity rains which can only be captured using short duration rainfall serie

Probability distributions comparative analysis in assessing rainfall process in time and space

The need for a reliable rainfall model to produce accurate simulation of rainfall series is imperative in water resources planning. Simulated series are used when there are shortages of observed series at location of interest. This study focuses on modelling of rainfall series with a range of probability distributions representing rainfall intensity of the Space-Time Neyman Scott (ST-NS) model. Theoretically, the ST-NS model is constructed by having parameters to represent the physical attributes of rainfall process. Therefore having appropriate distributions to describe the parameters are critical so that credible rainfall series could be generated. In this study, the performance of four probability distributions namely Mixed-Exponential, Gamma, Weibull and Generalized Pareto in representing rainfall intensity are assessed and compared. Model construction of the ST-NS model involved the merging of rainfall data from sixteen stations located all over Peninsular Malaysia. Simulations of hourly rainfall series for each distribution are carried at out of sample site. Performance assessments between the distributions are conducted using Root Mean Square Error, Akaike Information Criterion, Bayesian Information Criterion, Kolmogrov-Smirnov Test and Anderson-Darling Test. Results revealed that mixture type distributions tend to perform better. The performance of both Mixed-Exponential and Generalized Pareto are very similar and both are equally good at representing rain intensity in Peninsular Malaysia. The adopted method and the results could also be extended to other tropical regions