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Introduction
The basic cause of flooding in Malaysia is the incidence of heavy monsoon rainfall and the resultant large concentration of run-off, which exceeds river systems (Ho, 2002). Rapid urbanization within river catchments in recent years have also served to compound the problem with higher run-off and deteriorated river capacity that have resulted in increased flood frequency and magnitude. Various flood forecasting and warning system based on an advanced hydraulic model has been applied in Malaysia, but they proved inadequate for their inability to predict impending floods thus, they have had limited effect in reducing costs and damage to life and property due to flood.

Over the years various flood forecasting and warning system based on an advanced hydraulic model has been applied in Malaysia. However, the systems have been limited to the forecasting of water levels in the major rivers and thus have proved inadequate for flood early warning due to their inability to predict impending floods. Moreover the practical limitations of rain gauges for measuring mean rainfall over large areas and sometimes-inaccessible areas are becoming apparent. Hydrologist are thus increasingly turning to remote sensing as a possible means for quantifying the expected precipitation as input in to hydrological models, particularly in areas in of few surface gages.

Methods and Materials
The Langat river basin catchment area is about 1, 988 km2 (Fig1). The average annual rainfall depth is approximately 2,400 mm ranging from 1,800 to 3,000 mm. The highest rainfall occurs in the month of April and November with a mean of 280 mm. The lowest rainfall occurs in the month of June with a mean of 115 mm. The wet seasons occur in the transitional periods of the monsoons, from March to April and from October to November.

In this study near real-time NOAA-AVHRR data received at our local ground station are processed for quantitative precipitation estimates using cloud based modeling techniques. Precipitation estimates are then applied rainfall as input to a hydrological oriented GIS based on an integrated MIKE 11 hydrodynamic simulation model and ArcView GIS for pre flood “Nowcast”. The framework of the operational coupling of quantitative precipitation forecasting (QPF) with Mike 11 hydrodynamic oriented GIS in the bid to implement a fully automated simulation and early warning system is illustrated in figure 2.


Fig1. Langat river basin and Dekil simulation area



Fig 2. Frame -work of the flood early warning system

In the first part of the study (Fig 2) NOAA-AVHRR data are processed for QPF although it has moderately low spatial resolution of 1.1km but its repeated temporal resolution of about 6 hr daily, provides the near real-time data required for the precipitation estimate. NOAA-12 local area coverage (LAC) data is acquired for November 20, 2003 in monsoon season, as this period provides a better understanding into rain baring clouds. Bi-spectral techniques based on the relationship between cold and brightness temperature of clouds are used to evaluate precipitation probability.

Cloud model-based and cloud-indexing technique are used for the estimate of precipitation. Cloud model-base technique is a one dimensional cloud model that relates cloud temperature to rain-rate and rain area in convective stratiform technique (CST). Details this model can be found in (Gruber, (1973), Anagnostou et al., (1999), Reudenbach et al., (2001), Bendix, (1997, 2002)). The formulate for the model is where Slope S is calculated for each temperature minimum T min. Parameters defined as:

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