Analysis of Spatial and Temporal Patterns of Meteorological Drought Exposure and Its Impact on Economic Crops, Nakhon Ratchasima Province, Thailand

Abstract

Background and Objectives: Drought is one of the most complex influencing factors among all-natural disasters. It is a complex phenomenon because of the unpredictable start and end of its period, the length of the event,  as well as the nonspecific spatial extent or geography and uncertain frequency and intensity. Meanwhile, meteorological drought is usually defined based on the degree of dryness and the duration of the dry period. Nakhon Ratchasima province is a drought-prone area since the annual rainfall between 1975 and 2022 was mostly lower than the average annual rainfall in the same period, with a value of 1,223.59 mm for about 24 years. Therefore, this study aims to examine spatial and temporal patterns of meteorological drought exposure and its impact on economic crops in Nakhon Ratchasima province. The objectives of the study were (1) to classify and map meteorological drought frequency, intensity and exposure and (2) to analyze spatial and temporal patterns of meteorological drought exposure and its impact on economic crops. Herein

Methodology: The research methodology comprised four main steps after the Standardized Precipitation Index calculation in 4 periods, including 3m7 (May to July), 3m10 (August to October), 6m10 (May to October), and 12m (January to December): (1) meteorological drought frequency index extraction and classification, (2) meteorological drought intensity index extraction and classification, (3) meteorological drought exposure index extraction and classification, and (4) spatial and temporal patterns analysis of meteorological drought exposure and its impact on economic crops: rice, cassava, sugarcane and corn. Herein, three meteorological drought indices, meteorological drought frequency, meteorological drought intensity, and meteorological drought exposure, were calculated based on a long-term rainfall record (1975-2022) from 37 stations. In the meantime, spatial and temporal patterns of meteorological drought exposure at district and sub-district levels using zonal analysis with majority operation.

Main Results: The most dominant class of meteorological drought exposure classification of the 4 periods (3m7, 3m10, 6m10 and 12m) was a moderate, moderate, moderate, and low covered area of about 33.22%, 35.26%, 42.11% and 35.69%, respectively. The spatial distribution of the meteorological drought exposure classification of the 4 periods displayed a completely different pattern. Still, the meteorological drought exposure severity classification of the 4 periods showed a strong positive linear relationship among them. The correlation coefficient values varied from 0.8100 to 0.8966. These results imply the similarity of meteorological drought exposure patterns among 4 periods. Besides, the majority severity classification of the meteorological drought exposure in the 6m10 period exhibited the highest impacts at district and sub-district levels, with 16 districts and 138 sub-districts. Based on the spatial pattern changes of meteorological drought exposure severity levels among 3-periods (3m7, 3m10 and 6m10), covering the economic crop calendar, the severity classification of the meteorological drought exposure in the 6m10 period exposed the highest meteorological drought compared with other periods (3m7 and 3m10). In the meantime, the potential impact areas of meteorological drought exposure in the 6m10 period (May to October) at moderate, high, and very high severity levels on rice in 2023 was about 3,939.40 sq. km 64.65% of the total area of rice, cassava about 2,918.67 sq. km 75.74% of the total area of cassava, sugarcane, about 1,423.47 sq. km or 69.48% of the total area of sugarcane, and corn, about 441.33 sq. km or 56.38% of the total area of corn. Furthermore, based on Pearson bivariate correlation analysis, the most dominant meteorological drought exposure index that impacts crop yield is the meteorological drought exposure index in the 3m7 period (May to July). This index displayed a negative linear relationship with the average rice, cassava and corn yield between 2011 and 2022. On the contrary, the meteorological drought exposure index showed no linear relationship with sugarcane since a multi-cropping system of about three years is applied for sugarcane by farmers.

Conclusions: Spatial and temporal patterns analysis of meteorological drought exposure were successfully conducted based on a standardized precipitation index for quantifying the severity of drought and its impact on economic crops in different periods (3m7, 3m10, 6m10 and 12m). The presented research workflow can be used as a guideline for the relevant government agencies, such as the Department of Agricultural Extension and the Department of Disaster Prevention and Mitigation, to monitor meteorological drought for mitigation of the potential impact of drought on economic crops in the future. In addition, early warning systems of meteorological drought at the regional level are recommended to be implemented by the Thai Meteorological Department.