Thailand plays a central economic and policy-making role in Southeast Asia. Although climate change adaptation is being mainstreamed in Thailand, a well-organized overview of the impacts of climate change and potential adaptation measures has been unavailable to date. Here we present a comprehensive review of climate-change impact studies that focused on the Thai water sector, based on a literature review of six sub-sectors: riverine hydrology, sediment erosion, coastal erosion, forest hydrology, agricultural hydrology, and urban hydrology. Our review examined the long-term availability of observational data, historical changes, projected changes in key variables, and the availability of economic assessments and their implications for adaptation actions. Although some basic hydrometeorological variables have been well monitored, specific historical changes due to climate change have seldom been detected. Furthermore, although numerous future projections have been proposed, the likely changes due to climate change remain unclear due to a general lack of systematic multi-model and multi-scenario assessments and limited spatiotemporal coverage of the study area. Several gaps in the research were identified, and ten research recommendations are presented. While the information contained herein contributes to state-of-the-art knowledge on the impact of climate change on the water sector in Thailand, it will also benefit other countries on the Indochina Peninsula with a similar climate.
Abstract:GSMaP_NRT (Near Real Time) is a viable tool to provide satellite-based precipitation data for further analysis. Its usefulness can be evident in the areas where continuous precipitation data is vital. This is why GSMaP_NRT performance has been evaluated globally. In this study, we evaluate its performance in terms of 1) rainfall detection capability based on Probability of Detection (POD) and False Alarm Ratio (FAR) and 2) estimation capability based on correlation coefficient and Root Mean Square Error (RMSE) over the Chaophraya River basin during 2009-2010. A non-realtime GSMaP_MVK (Moving Vector with Kalman filter) is also evaluated. Our results show that, at daily scale, both GSMaP_NRT/GSMaP_MVK performs well in rainy season (POD and FAR can reach 0.75/0.94 and 0.45/0.49, respectively) with acceptable RMSE of 14.64/ 14.23 mm. GSMaP_NRT tends to under-estimate whereas GSMaP_MVK slightly over-estimates the rain rates with correlation coefficient of 0.70 and 0.75. We conclude that GSMaP_NRT is considered good but not sufficient for nearrealtime rainfall monitoring applications; whereas GSMaP_MVK is suitable for climate change studies.
The wormhole adaptive recovery-based routing via pre-emption(WARRP) core optoelectronic chip, which integrates coredeadlock-handling circuitry for a fully adaptive deadlock-freemultiprocessor network router, is presented. This chip demonstratesprimarily the integration of complex deadlock-recovery circuitry andfree-space optoelectronic input-output on a monolithicGaAs-based chip. The design and implementation of thefirst-generation, bit-serial, torus-connected chip that uses 1400transistors and six light-emitting diode-photodetector pairs is presented.
Rainfall estimation by geostationary meteorological satellite data provides good spatial and temporal resolutions. This is advantageous for real time flood monitoring and warning systems. However, a rainfall estimation algorithm developed in one region needs to be adjusted for another climatic region. This work proposes computationally-efficient rainfall estimation algorithms based on an Infrared threshold rainfall (ITR) method calibrated with regional ground truth. Hourly rain gauge data collected from 70 stations around the Chao-Phraya river basin were used for calibration and validation of the algorithms. The algorithm inputs were derived from FY-2E satellite observations consisting of infrared and water vapour imagery. The results were compared with the Global Satellite Mapping of Precipitation (GSMaP) near real time product (GSMaP_NRT) using the probability of detection (POD), root mean square error (RMSE) and linear correlation coefficient (CC) as performance indices. Comparison with the GSMaP_NRT product for real time monitoring purpose shows that hourly rain estimates from the proposed algorithm with the error adjustment technique (ITR_EA) offers higher POD and approximately the same RMSE and CC with less data latency.
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