Analysis of Rainfall in the Eastern Thailand

Nounmusig, Wonlee

doi:10.21660/2018.46.7282

Cited by 10 publications

(4 citation statements)

References 10 publications

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“…The area has a tropical monsoon climate. The monsoon season occurs from mid-May to mid-October with an average rainfall of 1400 mm, while the area during dry season receives about 400 mm of rainfall [38,39]. The region has nine national parks (NP) and wildlife sanctuaries (WS) hosting elephants [5].…”

Section: Study Areamentioning

confidence: 99%

Spatiotemporal Distribution of Human–Elephant Conflict in Eastern Thailand: A Model-Based Assessment Using News Reports and Remotely Sensed Data

Kitratporn

Takeuchi

2019

Remote Sensing

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In Thailand, crop depredation by wild elephants intensified, impacting the quality of life of local communities and long-term conservation of wild elephant populations. Yet, fewer studies explore the landscape-scale spatiotemporal distribution of human–elephant conflict (HEC). In this study, we modeled the potential HEC distribution in ten provinces adjacent to protected areas in Eastern Thailand from 2009 to 2018. We applied the time-calibrated maximum entropy method and modeled the relative probability of HEC in varying scenarios of resource suitability and direct human pressure in wet and dry seasons. The environmental dynamic over the 10-year period was represented by remotely sensed vegetation, meteorological drought, topographical, and human-pressure data. Results were categorized in HEC zones using the proposed two-dimensional conflict matrix. Logistic regression was applied to determine the relevant contribution of each scenario. The results showed that although HEC probability varied across seasons, overall HEC-prone areas expanded in all provinces from 2009 to 2018. The largest HEC areas were estimated during dry seasons with Chantaburi, Chonburi, Nakhon Ratchasima, and Rayong provinces being the HEC hotspots.However, the HEC potential was reduced during severe and prolonged droughts caused by El Nino events. Direct human pressure caused a more gradual increase of HEC probability around protected areas. On the other hand, resource suitability showed large variation across seasons. We recommend zone-dependent management actions towards a fine-balance between human development and the conservation of wild elephants.

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Section: Study Areamentioning

confidence: 99%

Spatiotemporal Distribution of Human–Elephant Conflict in Eastern Thailand: A Model-Based Assessment Using News Reports and Remotely Sensed Data

Kitratporn

Takeuchi

2019

Remote Sensing

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“…April is the hottest month for all regions of Thailand [61]. The analysis on Thailand's regions produced one dominant mode with an SCF value of 98%.…”

Section: Thailandmentioning

confidence: 96%

Joint Pattern Analysis of Forest Fire and Drought Indicators in Southeast Asia Associated with ENSO and IOD

2022

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Land and forest fires in Southeast Asia often coincide with severe dry seasons in the specific region caused by the warm phase of an El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). This research aims to identify and quantify the ENSO–IOD effect on a joint pattern between the dry season and land and forest fires in Southeast Asia. This research provides a quantitative result which represents the ENSO–IOD’s impact in Southeast Asia for hotspots, dry spells, and precipitation anomalies. The methods used in this research are singular value decomposition, probability density skill score, and coherence analysis. Cambodia, Myanmar, and Thailand gave a similar result with less than a 25% increasing severity in the hotspots from normal to either El Niño, positive IOD, or El Niño–positive IOD years. The maximum increase in hotspot severity in North Sumatra was 13.06% and happened during a weak El Niño and positive IOD. Meanwhile, South Sumatra had a maximum accumulation of more than 89% and Kalimantan had more than a 72% increase during the strong El Niño in 2015. Even though the relationship between the ENSO and IOD was inconsistent, the occurrence of both phenomena in the same year can lead to fires and need to be considered.

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“…Figure 3a essentially shows that higher rainfall intensity occurs in eastern coastal strips of the upper sub-region (about 2.0 mm day −1 ). This area is notably located near the Gulf of Thailand with high convective activity [65], unlike other parts of the upper part of Thailand [18,66]. Moreover, abundant rainfall is more profound during the winter season in southern Thailand (i.e., >1.0 mm day −1 ), with the highest intensity (>5.0 mm day −1 ) on the east coast along the South China Sea.…”

Section: Spatial Distribution Of Summer and Winter Rainfall Climatologymentioning

confidence: 99%

Evaluation of Spatial-Temporal Characteristics of Rainfall Variations over Thailand Inferred from Different Gridded Datasets

Torsri

Lin

Dike

et al. 2022

Water

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The fidelity of gridded rainfall datasets is important for the characterization of rainfall features across the globe. This study investigates the climatology, interannual variability, and spatial-temporal variations of seasonal rainfall over Thailand during the 1970–2007 period using station data obtained from the Thai Meteorological Department (TMDstn). In addition, the performance of three gridded rainfall datasets, namely APHRODITE, CRU, and GPCC, in reproducing these seasonal rainfall features were intercompared and further validated with the results derived from the TMDstn. Results show that the gridded datasets can reproduce the spatial distribution of the TMDstn’s summer mean rainfall. However, large systematic underestimation is seen in APHRODITE, while GPCC shows better agreement with TMDstn as compared to others. In the winter, the spatial distribution of the seasonal mean of rainfall is well captured by all gridded data, especially in the upper part of Thailand, while they failed to capture high rainfall intensity in the south and the eastern parts of Thailand. Meanwhile, all the gridded datasets underestimated the interannual variability of summer and winter season rainfall. Using EOF analysis, we demonstrate that all the gridded datasets captured the first two dominant modes of summer rainfall, while they underestimated the explained variance of EOF-1. In the winter season, a good agreement is found between the first two modes of the TMDstn and the gridded datasets for both the spatial pattern and temporal variation. Overall, the GPCC data show relatively better performance in reproducing the spatial distribution of rainfall climatology and their year-to-year variation over Thailand. Furthermore, the performance of the gridded datasets over Thailand is largely dependent on the season and the complexity of the topography. However, this study indicates the existence of systematic bias in the gridded rainfall datasets when compared with TMDstn. Therefore, this indicates the need for users to pay attention to the reliability of gridded rainfall datasets when trying to identify possible mechanisms responsible for the interannual variability of seasonal rainfall over Thailand.

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Analysis of Rainfall in the Eastern Thailand

Cited by 10 publications

References 10 publications

Spatiotemporal Distribution of Human–Elephant Conflict in Eastern Thailand: A Model-Based Assessment Using News Reports and Remotely Sensed Data

Spatiotemporal Distribution of Human–Elephant Conflict in Eastern Thailand: A Model-Based Assessment Using News Reports and Remotely Sensed Data

Joint Pattern Analysis of Forest Fire and Drought Indicators in Southeast Asia Associated with ENSO and IOD

Evaluation of Spatial-Temporal Characteristics of Rainfall Variations over Thailand Inferred from Different Gridded Datasets

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