Summer monsoon rains are a critical factor in Thailand's water resources and agricultural planning and management. In fact, they have a significant impact on the country's economic health. Consequently, understanding the variability of the summer monsoon rains over Thailand is important for instituting effective mitigating strategies against extreme rainfall fluctuations. To this end, the authors systematically investigated the relationships between summer monsoon precipitation from the central and northern regions of Thailand and large-scale climate features. It was found that Pacific sea surface temperatures (SSTs), in particular, El Niño-Southern Oscillation (ENSO), have a negative relationship with the summer monsoon rainfall over Thailand in recent decades. However, the relationship between summer rainfall and ENSO was weak prior to 1980. It is hypothesized that the ENSO teleconnection depends on the SST configuration in the tropical Pacific Ocean, that is, an eastern Pacific-based El Niño pattern, such as is the case in most of the post-1980 El Niño events, tends to place the descending limb of the Walker circulation over the Thailand-Indonesian region, thereby significantly reducing convection and consequently, rainfall over Thailand. It is believed that this recent shift in the Walker circulation is instrumental for the nonstationarity in ENSO-monsoon relationships in Thailand. El Niños of 1997 and 2002 corroborate this hypothesis. This has implications for monsoon rainfall forecasting and, consequently, for resources planning and management.
This paper describes the development of a statistical forecasting method for summer monsoon rainfall over Thailand. Predictors of Thailand summer (August-October) monsoon rainfall are identified from the large-scale ocean-atmospheric circulation variables (i.e. sea-surface temperature and sea-level pressure) in the Indo-Pacific region. The predictors identified are part of the broader El Niño southern oscillation (ENSO) phenomenon. The predictors exhibit a significant relationship with the summer rainfall only during the post-1980 period, when the Thailand summer rainfall also shows a relationship with ENSO. Two methods for generating ensemble forecasts are adapted. The first is the traditional linear regression, and the second is a local polynomial-based nonparametric method. The associated predictive standard errors are used for generating ensembles. Both the methods exhibit significant comparable skills in a cross-validated mode. However, the nonparametric method shows improved skill during extreme years (i.e. wet and dry years). Furthermore, the models provide useful skill at 1-3 month lead time that can have a strong impact on resources planning and management.
The development of statistical relationships between local hydroclimates and large-scale atmospheric variables enhances the understanding of hydroclimate variability. The rainfall in the study basin (the Upper Chao Phraya River Basin, Thailand) is influenced by the Indian Ocean and tropical Pacific Ocean atmospheric circulation. Using correlation analysis and cross-validated multiple regression, the large-scale atmospheric variables, such as temperature, pressure and wind, over given regions are identified. The forecasting models using atmospheric predictors show the capability of long-lead forecasting. The modified k-nearest neighbour (k-nn) model, which is developed using the identified predictors to forecast rainfall, and evaluated by likelihood function, shows a long-lead forecast of monsoon rainfall at 7-9 months. The decreasing performance in forecasting dry-season rainfall is found for both short and long lead times. The developed model also presents better performance in forecasting pre-monsoon season rainfall in dry years compared to wet years, and vice versa for monsoon season rainfall.Key words rainfall; hydroclimate variability; ENSO; large-scale atmospheric variables; long-lead forecasting; statistical approach; modified k-nn model; cross-validated multiple regression; Chao Phraya River Basin; Ping River Basin; Thailand Variabilité hydroclimatique et prévision à long terme des précipitations en Thaïlande à l'aide de variables atmosphériques de grande échelle Résumé L'établissement de relations statistiques entre variables pluviométriques locales et variables atmosphériques de grande échelle permet une meilleure compréhension de la variabilité hydroclimatique. Les précipitations dans le bassin d'étude (le bassin supérieur de la Rivière Chao Phraya, Thaïlande) sont influencées par la circulation atmosphérique dans l'Océan Indien et dans l'Océan Pacifique tropical. Par l'analyse de corréla-tions et régressions multiples en validation croisée, les prédicteurs de variables atmosphériques de grande échelle, à savoir température, pression et vent, sont identifiés sur des régions cibles, et montrent une bonne capacité de prévision à long terme. Le modèle modifié des k-plus proches voisins (k-nn), développé par les prédicteurs identifiés pour prévoir les pluies, et évalué par fonction de probabilité, montre une prévisibilité à long terme (7-9 mois) des pluies de mousson. La moindre performance dans la prévision des pluies de saison sèche est prouvée pour la prévision à court et à long terme. Le modèle développé présente également une meilleure performance pour les pluies de pré-mousson en année sèche, par rapport à une année humide, et inversement pour les pluies de mousson.Mots clefs précipitations; variabilité hydroclimatique; ENSO; variables atmosphériques de grande échelle; prévision à long terme; approche statistique; modèle k-nn modifié; régression multiple à validation croisée; bassin du Fleuve Chao Phraya; bassin du Fleuve Ping; T haïlande
We determined the effects of climate change on pre-monsoon (May-June-July: MJJ) and monsoon (August-September-October: ASO) season rainfall in the Upper Chao Phraya River Basin, Thailand, by downscaling surface rainfall from large-scale atmospheric variables, i.e. surface air temperature (SAT), sea level pressure (SLP), and zonal and meridional wind (u and v, respectively). The data were obtained from the Geophysical Fluid Dynamics Laboratory (GFDL) model and used as predictors in a modified k-nearest neighbor (k-nn) model. Under climate change scenarios A2 and B2, during 2011-2100, the increasing trends of annual SAT over northern Thailand and the South China Sea vary from 1.65 to 3.47°C century -1. By the end of the 21st century, the annual SAT anomalies range from + 2 to +10°C. The increasing trends of annual SLP over the Gulf of Thailand and northern Thailand range from 0.40 to 0.83 mb century -1. Depending upon the regions and scenarios, increasing and decreasing trends of annual u and v were observed. From the modified k-nn model, the effects of climate change on MJJ and ASO rainfall indicate decreasing trends during 2011-2100 with a maximum decrease by 6.16 mm yr -1 , corresponding to the ASO rainfall under Scenario B2. In terms of effects on the frequency of extreme events, dry (wet) conditions during 2011-2100 showed a greater (lesser) chance of occurrence than the climatology, with the exception of ASO rainfall under the Scenario A2, which showed a greater chance of being both dry and wet. With a probability > 70%, dry MJJ and ASO conditions will be observed more often than wet, especially the dry ASO under Scenario B2, which was predicted for the 55 yr from 2046 -2100. KEY WORDS: Climate change · Thailand rainfall · Upper Chao Phraya River Basin · Ping River BasinResale or republication not permitted without written consent of the publisher Clim Res 49: 155-168, 2011 terms of intensity and frequency of extreme events. Increasing greenhouse gas concentrations, in particular CO 2 , greatly influence global warming (Mitchell 1989, Maslin 2007, NIC 2009, NASA 2010. Consequently, climate change is expected to occur due to increasing global surface temperatures (UNEP 2003, Trenberth 2008. Since rainfall in Thailand shows significant links to large-scale atmospheric variables (LSAVs), e.g. temperature and pressure (Chen & Yoon 2000, Singhrattna et al. 2005b, the changing climate affects rainfall via atmospheric-oceanic circulations. The objective of this study was to determine the effects of climate change on summer monsoon season rainfall in the Upper Chao Phraya River Basin (Thailand). Rainfall simulation by a stochastic statistical model is a tool for long-term planning in water resources and an adaptation strategy to deal with future climate change. METHODSBased on the significant relationships between local hydroclimates (e.g. precipitation) and LSAVs (e.g. sea level pressure), LSAVs have been used as predictors in multiple regressions to forecast hydroclimates (Hamlet et al. 2002, McCa...
Abstract. The local hydroclimates get impacts from the large-scale atmospheric variables via atmospheric circulation. The developing of their relationships could enhance the understanding of hydroclimate variability. This study focuses on the Upper Chao Phraya River Basin in Thailand in which rainfall is influenced by the Indian Ocean and tropical Pacific Ocean atmospheric circulation. The Southwest monsoon from the Indian Ocean to Thailand is strengthened by the temperature gradient between land and ocean. Thus, the anomalous sea surface temperature (SST) is systematically correlated with the monthly rainfall and identified as the best predictor based on the significant relationships revealed by cross-correlation analysis. It is found that rainfall, especially during the monsoon season in the different zones of study basin, corresponds to the different SST indices. This suggests that the region over the ocean which develops the temperature gradient plays a role in strengthening the monsoon. The enhanced gradient with the SST over the South China Sea is related to rainfall in High Rainfall Zone (HRZ); however, the anomalous SST over the Indian Ocean and the equatorial Pacific Ocean are associated with rainfall in Normal and Low Rainfall Zone (NRZ and LRZ) in the study area. Moreover, the identified predictors are related to the rainfall with lead periods of 1–4 months for the pre-monsoon rainfall and 6–12 months for the monsoon and dry season rainfall. The study results are very useful in developing rainfall forecasting models and consequently in the management of water resources and extreme events.
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