Minh Tue Vu

Statistical downscaling rainfall using artificial neural network: significantly wetter Bangkok?

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.

Assessment of CMIP5 historical simulations of rainfall over Southeast Asia

We present preliminary analyses of the historical (1986–2005) climate simulations of a ten-member subset of the Coupled Model Inter-comparison Project Phase 5 (CMIP5) global climate models over Southeast Asia. The objective of this study was to evaluate the general circulation models’ performance in simulating the mean state of climate over this less-studied climate vulnerable region, with a focus on precipitation. Results indicate that most of the models are unable to reproduce the observed state of climate over Southeast Asia. Though the multi-model ensemble mean is a better representation of the observations, the uncertainties in the individual models are far high. There is no particular model that performed well in simulating the historical climate of Southeast Asia. There seems to be no significant influence of the spatial resolutions of the models on the quality of simulation, despite the view that higher resolution models fare better. The study results emphasize on careful consideration of models for impact studies and the need to improve the next generation of models in their ability to simulate regional climates better.

Climate Change and Its Impacts on Streamflow: WRF and SCE-Optimized SWAT Models

It has been noted that global warming is likely to increase both the frequency and severity of weather events such as heat waves and heavy rainfall. These could lead to large scale effects such as melting of large ice sheets with major impacts on low-lying regions throughout the world (Intergovernmental Panel on Climate Change, IPCC 2007a). Since these projected climate changes will impact water resources, agriculture, bio-diversity and health, one of the key challenges of climate research is the application of climate models to quantify both future climate change and its impacts on the physical and biological environment. One of the widely studied impacts is on hydrology, right from large scale river basins, river deltas through to small scale urban reservoirs. In this context, this chapter discusses some hydrological impact studies and presents results of a study done over the Sesan catchment in Lower Mekong Basin (in Southeast Asia). Sensitivity analysis and an optimization calibration scheme, SCE-UA algorithm, are applied to the SWAT model.

Hydro-meteorological drought assessment under climate change impact over the Vu Gia–Thu Bon river basin, Vietnam

ABSTRACT Hydro-meteorological drought was assessed with respect to climate change over an estuary catchment Vu Gia-Thu Bon in Central Vietnam, which economy is dependent on agriculture. The fully-distributed hydrological model MIKE SHE was used to simulate river flow over the study region for the period 1991–2010. Drought were assessed using the Standardized Precipitation Index and the Standardized Runoff Index. The future climate was studied using the regional climate model Weather Research and Forecasting by downscaling an ensemble of three global climate models – CCSM3.0, ECHAM5 and MIROC-medium resolution over current (1961–1990) and future climates (2011–2040), under the A2 emissions scenario. The results suggest that, despite hotter and wetter future climate, the area is likely to suffer more from severe and extreme droughts, increasing about 100% in the median range for drought characteristics. Thus, there is a need for proper adaptation and planning for water resources management in this region.