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Vu M.T.,National University of Singapore | Raghavan S.V.,National University of Singapore | Liong S.-Y.,National University of Singapore | Liong S.-Y.,Willis Re Inc.
Journal of Hydrologic Engineering | Year: 2016

The sharing of water resources across transboundary regions between countries has long been a political problem. Sharing of data among countries is also a significant impediment for both planning and research purposes. In the context of climate change, it is necessary to know about possible future climatic changes and their potential impacts that would be especially crucial to water resources management. This requires data for hydrological modeling and data management, as in the case of river basins. This paper presents a study over the course of the Da River, which flows from China (upstream) to Vietnam (downstream), and it is assumed that rainfall data from China are not available. To overcome data limitation, regional climate model outputs are used as proxy data for this upstream region to study changes over the downstream Da River. The Weather Research and Forecasting (WRF) model was used as the regional climate model, driven by the European reanalysis data for the baseline period of 1961-1987 for a domain covering the transboundary areas, at a resolution of 25 km. Precipitation outputs from this model were used as inputs to the hydrological model and soil and water assessment tools (SWAT) to calibrate/validate the model at data-available gauging station sites. The initial results of this study imply that the regional climate model (RCM) data proxies serve as a good alternative to assess water resources over transboundary regions and are useful tools for assessing future climate changes and their impacts at subregional and local scales. © ASCE. Source

Liew S.C.,National University of Singapore | Raghavan S.V.,National University of Singapore | Liong S.-Y.,National University of Singapore | Liong S.-Y.,Willis Re Inc.
Hydrological Processes | Year: 2014

Optimal designs of stormwater systems rely very much on the rainfall Intensity-Duration-Frequency (IDF) curves. As climate has shown significant changes in rainfall characteristics in many regions, the adequacy of the existing IDF curves is called for particularly when the rainfall are much more intense. For data sparse sites/regions, developing IDF curves for the future climate is even challenging. The current practice for such regions is, for example, to 'borrow' or 'interpolate' data from regions of climatologically similar characteristics. A novel (3-step) Downscaling-Comparison-Derivation (DCD) approach was presented in the earlier study to derive IDF curves for present climate using the extracted Dynamically Downscaled data an ungauged site, Darmaga Station in Java Island, Indonesia and the approach works extremely well. In this study, a well validated (3-step) DCD approach was applied to develop present-day IDF curves at stations with short or no rainfall record. This paper presents a new approach in which data are extracted from a high spatial resolution Regional Climate Model (RCM; 30×30km over the study domain) driven by Reanalysis data. A site in Java, Indonesia, is selected to demonstrate the application of this approach. Extremes from projected rainfall (6-hourly results; ERA40 Reanalysis) are first used to derive IDF curves for three sites (meteorological stations) where IDF curves exist; biases observed resulting from these sites are captured and serve as very useful information in the derivation of present-day IDF curves for sites with short or no rainfall record. The final product of the present-day climate-derived IDF curves fall within a specific range, +38% to +45%. This range allows designers to decide on a value within the lower and upper bounds, normally subjected to engineering, economic, social and environmental concerns. Deriving future IDF curves for Stations with existing IDF curves and ungauged sites with simulation data from RCM driven by global climate model (GCM ECHAM5) (6-hourly results; A2 emission scenario) have also been presented. The proposed approach can be extended to other emission scenarios so that a bandwidth of uncertainties can be assessed to create appropriate and effective adaptation strategies/measures to address climate change and its impacts. © 2013 John Wiley & Sons, Ltd. Source

Sun Y.,National University of Singapore | Wendi D.,National University of Singapore | Kim D.E.,National University of Singapore | Liong S.-Y.,National University of Singapore | And 2 more authors.
Hydrology and Earth System Sciences | Year: 2016

Accurate prediction of groundwater table is important for the efficient management of groundwater resources. Despite being the most widely used tools for depicting the hydrological regime, numerical models suffer from formidable constraints, such as extensive data demanding, high computational cost, and inevitable parameter uncertainty. Artificial neural networks (ANNs), in contrast, can make predictions on the basis of more easily accessible variables, rather than requiring explicit characterization of the physical systems and prior knowledge of the physical parameters. This study applies ANN to predict the groundwater table in a freshwater swamp forest of Singapore. The inputs to the network are solely the surrounding reservoir levels and rainfall. The results reveal that ANN is able to produce an accurate forecast with a leading time of 1 day, whereas the performance decreases when leading time increases to 3 and 7 days. © 2016 Author(s). Source

Vu M.T.,National University of Singapore | Aribarg T.,Rangsit University | Supratid S.,Rangsit University | Raghavan S.V.,National University of Singapore | And 2 more authors.
Theoretical and Applied Climatology | Year: 2015

Artificial neural network (ANN) is an established technique with a flexible mathematical structure that is capable of identifying complex nonlinear relationships between input and output data. The present study utilizes ANN as a method of statistically downscaling global climate models (GCMs) during the rainy season at meteorological site locations in Bangkok, Thailand. The study illustrates the applications of the feed forward back propagation using large-scale predictor variables derived from both the ERA-Interim reanalyses data and present day/future GCM data. The predictors are first selected over different grid boxes surrounding Bangkok region and then screened by using principal component analysis (PCA) to filter the best correlated predictors for ANN training. The reanalyses downscaled results of the present day climate show good agreement against station precipitation with a correlation coefficient of 0.8 and a Nash-Sutcliffe efficiency of 0.65. The final downscaled results for four GCMs show an increasing trend of precipitation for rainy season over Bangkok by the end of the twenty-first century. The extreme values of precipitation determined using statistical indices show strong increases of wetness. These findings will be useful for policy makers in pondering adaptation measures due to flooding such as whether the current drainage network system is sufficient to meet the changing climate and to plan for a range of related adaptation/mitigation measures. © 2015 Springer-Verlag Wien Source

Sun Y.,National University of Singapore | Doan C.D.,National University of Singapore | Doan C.D.,Willis Re Inc. | Dao A.T.,National University of Singapore | And 3 more authors.
Journal of Hydrology | Year: 2014

The classic Kalman filter implementation uses the measurements up to the time of forecast to update the initial conditions of the numerical model, with the updating effect limited to a prediction horizon when the improved initial conditions are washed out. To further enhance the prediction capability, this study proposes a new hybrid data assimilation scheme, which adopts chaos theory to predict the measurements into the forecast phase, and then assimilates the predicted measurements into the numerical model using the ensemble Kalman filter.The hybrid data assimilation scheme is applied in a simulated real-time forecast of the Ciliwung river model. It is revealed that the hybrid scheme can further improve the modelling accuracy up to a prediction horizon of 4. days as compared to the update based solely on the ensemble Kalman filter. © 2014 Elsevier B.V. Source

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