Comparison of recorded rainfall with quantitative precipitation forecast in a rainfall-runoff simulation for the Langat River Basin, Malaysia

Abstract

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.

Coupling of Cellular Automata Urban Growth Model and HEC-HMS to Predict Future Flood Extents in the Upper Klang Ampang Catchment

“…modeling and simulation [6], flood forecasting [7], [8] and quantitative precipitation forecast [9], [10]. Flooding is aggravated by the increasing urbanization where natural flood plains are disturbed by human activities to fulfill the society needs [11], [12] and reduction of vegetation cover which directly impact surface runoff and increased rapid river flow in the event of heavy and high intensity rain leading to flash flooding in the urban area [13].…”

Mean Field Bias Correction to Radar QPE as Input to Flood Modeling for Malaysian River Basins

“…Many efforts have been made to mitigate the flood problem in Malaysia, including the construction of a SMART tunnel worth RM1.88 billion in the heart of Malaysia's capital, Kuala Lumpur and the development of the National Flood Forecasting and Warning System (NaFFWS). Numerous studies have also been conducted regarding floods in Malaysia, among others, flood modeling and simulation [2], [3], flood hazard mapping [4], rainfall forecast [5], [6] and flood forecasting [7], [8].…”

Analysis of spatial variability of near-surface soilmoisture to increase rainfall-runoff modellingaccuracy in SW Hungary

“…Spatially and temporally varying soil moisture has been increasingly used as an input parameter to hydrological and meteorological models as soil moisture plays a crucial role in the surface runoff of water originating from heavy rainfalls [1]. The spatial distribution of soil moisture varies as a function of the spatial distribution of evapotranspiration and precipitation, ultimately influ-enced by topography, soil texture, and land use type [5][6][7][8]. Alternatives to collecting sufficient knowledge on its spatial distribution include high resolution in situ measurement and monitoring [17,18] or satellite remote sensing applications [19,20].…”

“…One way to estimate the magnitude of runoff and peak discharge values is the use of rainfall-runoff numeric models [5,6]. However, such models require accurate spatial parameterization of antecedent catchment states [6,7].…”