Development of Rainfall Model using Meteorological Data for Hydrological Use (original) (raw)
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Rainfall analysis in the northern region of Peninsular Malaysia
International Journal of ADVANCED AND APPLIED SCIENCES
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 twofold. 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.
Analyses of Rainfall Rate During Malaysian 2014 Flood Event
International Journal of Multimedia and Ubiquitous Engineering, 2016
An analysis based on rainfall rate characteristics has been carried out to estimate flood occurance. In this paper, we analyzed the rain gauge data for 5 different rain gauge stations. 14 days acquired data covering events before, during and after the flood tragedy in Malaysia. The analysis of the rain gauge data was processed on precipitation phenomena observed in year 2014 in Kota Bharu, Kelantan (Malaysia) from 13 December until 26 December. The data was acquired from the Malaysian Drainage and Irrigation Department (DID). The objective of the research is to derive the tropical flood estimation model using rain gauge data in Malaysia. Among the preliminary result shows that the average rainfall rate at kota bharu is 204.5 mm/hr during the flood tragedy.
2019
General Circulation Models (GCMs) are used to modelling the responses of the climate system to different scenarios of greenhouse gas and aerosol. However, the model needs to downscale into a fine resolution daily rainfall series appropriate for local scale hydrological impact studies. In this study, Statistical Down-Scaling Model (SDSM) is used to downscale the GCMs simulations from Hadley Centre 3rd generation (HadCM3) with A2 and B2 scenarios for future rainfall over the area of Perlis, Malaysia. The SDSM model is able to simulate satisfactorily the daily rainfall series by giving the average coefficient of correlation (R2) and standard error (SE) during the validation period are 0.11 and 9.88mm/day respectively. The study area is apparently will gain an increasing trend for annual mean rainfall on the 2020s and show the decreasing trend for annual mean rainfall for period 2050s and 2080s for both scenario emissions.
Theoretical and Applied Climatology, 2014
The interpretations of trend behaviour for dry and wet events are analysed in order to verify the dryness and wetness episodes. The fitting distribution of rainfall is computed to classify the dry and wet events by applying the standardised precipitation index (SPI). The rainfall amount for each station is categorised into seven categories, namely extremely wet, severely wet, moderately wet, near normal, moderately dry, severely dry and extremely dry. The computation of the SPI is based on the monsoon periods, which include the northeast monsoon, southwest monsoon and inter-monsoon. The trends of the dry and wet periods were then detected using the Mann-Kendall trend test and the results indicate that the major parts of Peninsular Malaysia are characterised by increasing droughts rather than wet events. The annual trends of drought and wet events of the randomly selected stations from each region also yield similar results. Hence, the northwest and southwest regions are predicted to have a higher probability of drought occurrence during a dry event and not much rain during the wet event. The east and west regions, on the other hand, are going through a significant upward trend that implies lower rainfall during the drought episodes and heavy rainfall during the wet events.
Distribution of rainfall events in northern region of Peninsular Malaysia
IOP Conference Series: Earth and Environmental Science , 2020
Understanding of rainfall characteristics is important in designing Best Management Practices (BMPs) facilities. Because of intermittent rainfall, many researches on hydrology employ the concept of rainfall events. The selection of appropriate rainfall events for treatment design is essential to ensure the effectiveness of BMPs systems. Thus, the objective of this study is to identify the distribution of rainfall event using 6-hour Minimum Inter Event Time (MIT) and to identify the extreme rainfall thresholds over the study area. It shows that the rainfall data series consist of a large number of small events and rainfall depth of less than 2.5 mm (Type 1) contributes the highest percentage toward the overall record. About 63% of the rainfall record consists of Type 2 rainfall depth between 2.6 mm to 80 mm and only 1.3% recorded for rainfall event Type 3 with a depth exceeding 80 mm. It was found that the extreme rainfall threshold in Northern region of Peninsular Malaysia vary from 45 mm to 80 mm for R95 indices and 72 mm to 175 mm for R99 indices. These findings could be used as reference for better BMPs facilities design with extreme rainfall adaptation strategies.
COMPARATIVE STUDY OF RAINFALL FORECASTING MODELS
Newyork Science Journal, 2011
The weekly average of seven weather variables viz. rainfall, maximum and maximum temperature, relative humidity at 7.00 am and 2.00 PM., bright sunshine hours and pan evaporation of 39 years for the month of June were collected from the IMD approved Metrological Observatory situated at
SOLA
The Non-Hydrostatic Regional Climate Model (NHRCM) was used in simulating the present and future rainfall climate over Malaysia under the RCP8.5 scenario in this study. Simulation and projection from 1979 to 2002 for present day and 2070 to 2100 for the end of century were conducted over the Malaysia. The 20 km resolution MRI-AGCM3.2 model simulation from Meteorological Research Institute (MRI) was used as boundary conditions. The objective of this study was to estimate the extreme rainfall projections in Malaysia at 5 km of resolution during the November to February period, representing the northeast monsoon season. Overall, the model was capable to simulate the historical rainfall climatology and distribution, but model tended to underestimate high rainfall frequency and mean rainfall intensity in Malaysia. However, compared with simulations at 25 km, added values have been shown at 5 km resolution. Based on the NHRCM05 simulations, a number of hotspots have been identified with significant projected increases up to 80% for the extreme rainfall indices (R20mm, RX1day, R95pTOT and R99pTOT), 30% increases in mean rainfall intensity (SDII) and 20% for consecutive dry days indices (CDD).
… European Journal of …, 2011
Observed rainfall is used for runoff modeling in flood forecasting where possible, however in cases where the response time of the watershed is too short for flood warning activities, a deterministic quantitative precipitation forecast (QPF) can be used. This is based on a limited-area meteorological model and can provide a forecasting horizon in the order of six hours or less. This study applies the results of a previously developed QPF based on a 1D cloud model using hourly NOAA-AVHRR (Advanced Very High Resolution Radiometer) and GMS (Geostationary Meteorological Satellite) datasets. Rainfall intensity values in the range of 3-12 mm/hr were extracted from these datasets based on the relation between cloud top temperature (CTT), cloud reflectance (CTR) and cloud height (CTH) using defined thresholds. The QPF, prepared for the rainstorm event of 27 September to 8 October 2000 was tested for rainfall runoff on the Langat River Basin, Malaysia, using a suitable NAM rainfall-runoff model. The response of the basin both to the rainfall-runoff simulation using the QPF estimate and the recorded observed rainfall is compared here, based on their corresponding discharge hydrographs. The comparison of the QPF and recorded rainfall showed R 2 = 0.9028 for the entire basin. The runoff hydrograph for the recorded rainfall in the Kajang sub-catchment showed R 2 = 0.9263 between the observed and the simulated, while that of the QPF rainfall was R 2 = 0.819. This similarity in runoff suggests there is a high level of accuracy shown in the improved QPF, and that significant improvement of flood forecasting can be achieved through 'Nowcasting', thus increasing the response time for flood early warnings.
A Review of Rainfall Modelling for Rainfall Occurrence and Amount
Rainfall modelling is very important in water management and is frequently used especially in the field of Agriculture, hydrology, climatology and industries. The lack of proper water management will results in negative consequences. There are two different types of rainfall modelling on any given time scales in which are based on the rainfall occurrence and rainfall amount. However, many researchers believe that using separate models for rainfall occurrence and rainfall amount will results in losing certain information of the rainfall features. Therefore, many researchers have tried to solve these weaknesses by consolidating the models of rainfall occurrence and rainfall amount simultaneously. Thus, this paper will review various types of rainfall models which have been used in modelling rainfall occurrence, rainfall amount or combination of both rainfall occurrence and rainfall amount.