Analysis of the Variability Characteristics and Applicability of SPEI in Mainland China from 1985 to 2018

Shi, Xiaoliang; Yang, Yuanqi; Ding, Hao; Chen, Fei; Shi, Mengqi

doi:10.3390/atmos14050790

Cited by 10 publications

(6 citation statements)

References 59 publications

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“…Regional drought analysis using SPEI has been performed in the Gediz Basin, Turkey, with different distributions found to be the best fit for different reference periods (Öney & Anli, 2023). In China, the SPEI has been used to accurately monitor drought events, with spatiotemporal distribution and trends analyzed in various climatic sub-regions (Shi et al, 2023). In Malaysia, the SPEI has been used to determine drought indices for the Pahang River Basin, with the aim of mitigating the impact on water supply and economic development (Azman et al, 2022a).…”

Section: Methods For Calculating Drought Indexmentioning

confidence: 99%

Application of Machine Learning to Forecast Drought Index for the Mekong Delta

Duc

2024

Preprint

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Droughts have a substantial effect on water resources, agriculture, and ecosystems on a worldwide scale. In the Mekong Delta of Vietnam, droughts exacerbated by climate change are significantly endangering the region's agricultural sustainability and output. Conventional forecasting techniques frequently do not capture the intricate dynamics of meteorological phenomena associated to drought effectively, prompting the exploration of more advanced methodologies. This work utilises artificial intelligence, particularly machine learning methods like Gradient Boosting and Extreme Gradient Boosting (XGBoost), to enhance drought prediction in the Mekong Delta. The study utilises data from 11 meteorological stations spanning from 1990 to 2022 to create and evaluate Machine Learning models based on several climatic factors. We utilise Gradient Boosting and XGBoost algorithms to estimate the Standardised Precipitation-Evapotranspiration Index (SPEI) and evaluate their effectiveness in comparison to conventional forecasting techniques. The results show that Machine Learning, particularly XGBoost, surpasses traditional approaches in predicting SPEI accuracy at various time scales. XGBoost demonstrates skill in understanding the complex relationships between climatic factors, with R² values falling between 0.90 and 0.94 for 1-month forecasts. The progress highlights the potential of Machine Learning in improving drought management and adaptation tactics, proposing the incorporation of Machine Learning forecasting models into decision-making processes to enhance drought resistance in susceptible areas.

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Section: Methods For Calculating Drought Indexmentioning

confidence: 99%

Application of Machine Learning to Forecast Drought Index for the Mekong Delta

Duc

2024

Preprint

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“…Finally, the linear relationship between ET 0a and ET 0 was analyzed. The ET 0 of the current station was interpolated by Equation (8).…”

Section: Standardized Precipitation Evapotranspiration Index (Spei)mentioning

confidence: 99%

“…A large number of domestic and foreign scholars have conducted quantitative research on drought using the drought indexes of standardized precipitation index (SPI) [5], palmer drought severity index (PDSI) [6], and standardized precipitation evapotranspiration index Atmosphere 2023, 14, 1188 2 of 14 (climate inclination rate) [7,8]. Among them, SPI is a drought index based on precipitation, but it does not consider the correlation between other meteorological variables (e.g., temperature, wind, and relative humidity) and drought, and there are certain limitations in evaluating drought [5].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Characteristics of Drought in Northwest China Based on SPEI Analysis

Peng

Tao²,

2023

Atmosphere

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Drought has a direct impact on regional agricultural production, ecological environment, and economic development. The northwest region of China is an important agricultural production area, but it is also one of the most serious areas of water shortage due to drought and little rain. It is of great significance to make full use of agricultural resources to clarify the temporal and spatial distribution characteristics of the drought regime in Northwest China. Based on the Standardized Precipitation Evapotranspiration Index (SPEI), this paper used the methods of Mann–Kendall non-parameter trend, mutation test, and Morlet wavelet analysis to explore the drought characteristics in Northwest China from 1961 to 2017. The results showed that the spatial distribution of SPEI on annual and seasonal scales differed slightly in different regions, but from northwest to southeast, the distribution was generally wetter to drier. The drought intensity (Sij) had a step-like distribution with a range of 1.14–1.98. Based on Sij analysis, the frequency of drought in Northwest China was moderate, followed by extreme drought, severe drought, and light drought. The inter-annual drought station proportion (Pj) ranged from 7.4% to 84.1%. A total of 25, 18, 7, and 5 years of pan-regional drought, regional drought, partial region drought, and local drought occurred, respectively, based on Pj analysis. Moreover, from the whole study period, the regional drought changes tended to cause humidification to different degrees. The results of Morlet wavelet analysis showed that there were multiple time scales of 33–52, 11–19, and 4–7 years of SPEI in the entire time domain, and dry and wet trends occurred. The results of the present research can provide a reference for the efficient utilization of water resources, drought monitoring and early warning, drought prevention, and drought relief in Northwest China.

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“…Regional drought analysis using SPEI has been performed in the Gediz Basin, Turkey, with different distributions found to be the best fit for different reference periods [32]. In China, the SPEI has been used to accurately monitor drought events, with spatiotemporal distribution and trends analyzed in various climatic sub-regions [37]. In Malaysia, the SPEI has been used to determine drought indices for the Pahang River Basin, with the aim of mitigating the impact on water supply and economic development [38].…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning to Forecast Drought Index for the Mekong Delta

Duong,

Phong,

et al. 2024

Preprint

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Droughts have a substantial effect on water resources, agriculture, and ecosystems on a worldwide scale. In the Mekong Delta of Vietnam, droughts exacerbated by climate change are significantly endangering the region's agricultural sustainability and output. Conventional forecasting techniques frequently do not capture the intricate dynamics of meteorological phenomena associated to drought effectively, prompting the exploration of more advanced methodologies. This work utilises artificial intelligence, par-ticularly machine learning methods like Gradient Boosting and Extreme Gradient Boosting (XGBoost), to enhance drought pre-diction in the Mekong Delta. The study utilises data from 11 meteorological stations spanning from 1990 to 2022 to create and evaluate Machine Learning models based on several climatic factors. We utilise Gradient Boosting and XGBoost algorithms to estimate the Standardised Precipitation-Evapotranspiration Index (SPEI) and evaluate their effectiveness in comparison to con-ventional forecasting techniques. The results show that Machine Learning, particularly XGBoost, surpasses traditional approaches in predicting SPEI accuracy at various time scales. XGBoost demonstrates skill in understanding the complex relationships between climatic factors, with R² values falling between 0.90 and 0.94 for 1-month forecasts. The progress highlights the potential of Machine Learning in improving drought management and adaptation tactics, proposing the incorporation of Machine Learning forecasting models into decision-making processes to enhance drought resistance in susceptible areas.

show abstract

Analysis of the Variability Characteristics and Applicability of SPEI in Mainland China from 1985 to 2018

Cited by 10 publications

References 59 publications

Application of Machine Learning to Forecast Drought Index for the Mekong Delta

Application of Machine Learning to Forecast Drought Index for the Mekong Delta

Spatiotemporal Characteristics of Drought in Northwest China Based on SPEI Analysis

Application of Machine Learning to Forecast Drought Index for the Mekong Delta

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