Evaluating the impact of different normalization strategies on the construction of drought condition indices

Zhang, Hongyan; Yin, Guoying; Zhang, Liangpei

doi:10.1016/j.agrformet.2022.109045

Cited by 13 publications

(2 citation statements)

References 75 publications

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“…Few studies have examined the effect of spatial and temporal normalisation that is widely deployed on constructed drought indices. The study of [47] concluded that rescaling of time-series, original data would lead to losing the ability to track the drought development, reduce the truthfulness of drought severity, obscure the drought signal and reduce the evidence content.…”

Section: Discussionmentioning

confidence: 99%

SPI and SPEI Drought Assessment and Prediction Using TBATS and ARIMA Models, Jordan

Hasan,

Dongkai,

Al-Shibli

2023

Water

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Drought is a complex threat where its propagation is not yet controllable, causing more environmental, social, and economic damage. This research assesses the effects of incessant warming and decrescent precipitation by calculating SPI and SPEI from 1985 to 2021 in the Amman –Zarqa Basin based on five grid points on time and space scales. The study applied the Pearson Correlation Coefficient (PCC) between each one-to-one index at different time scales and the Mann–Kendall test (MKT) to determine trends with different data sources to measure the inferential capturing of historical drought features. Machine learning algorithms are used to predict near-future droughts from 2022 to 2025. TBATS and ARIMA models run diverse input datasets, including observations, CSIC, and CMIP6-ssp126 datasets. The longest drought duration was 14 months. Drought severity and average intensity were found to be −24.64 and −1.76, −23.80 and −1.83, −23.57 and −1.96, and −23.44 and −2.0 where the corresponding drought categories were SPI-12 Sweileh, SPI-9 Sweileh, SPI-12 Wadi Dhullal, SPI-12 Amman Airport, respectively. The dominant drought event occurred between Oct 2020 and Dec 2021. CMIP6-ssp126 can capture the drought occurrence and severity by measuring SPI but did not capture the severity magnitude as the observations (SPI was −2.87 by observation and −1.77 by CMIP6). There are significant differences in drought dimensions between SPI and SPEI, where SPI was more sensitive to drought assessment than SPEI. Using CMIP6-ssp126, ARIMA was more accurate than TBATS, as well as using the observed historical SPEI and CSIC across all stations. The performance metrics ME, RMSE, MAE, and MASE implied significantly promising forecasting models with values of −0.0046, 0.278, 0.179, and 0.193, respectively, for ARIMA and −0.0181, 0.538, 0.416, and 0.466, respectively, for TBATS. The outcomes suggest an increased risk of drought incidents and, consequently, water deficits in the future. Hybrid modelling is suggested for more consistency and robustness of forecasting approaches.

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Section: Discussionmentioning

confidence: 99%

SPI and SPEI Drought Assessment and Prediction Using TBATS and ARIMA Models, Jordan

Hasan,

Dongkai,

Al-Shibli

2023

Water

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“…NDVI has been widely used in many studies of vegetation dynamics [32,33]. Variations in NDVI data are driven by weather and ecosystem components; therefore, a vegetation condition index was developed to estimate the impact of weather on vegetation [34].…”

Section: Ndvimentioning

confidence: 99%

Arctic Greening Trends: Change Points in Satellite-Derived Normalized Difference Vegetation Indexes and Their Correlation with Climate Variables over the Last Two Decades

Seo,

Kim

2024

Remote Sensing

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In this study, we utilized NDVI data from the moderate resolution imaging spectroradiometer (MODIS) alongside climatic variables obtained from a reanalyzed dataset to analyze Arctic greening during the summer months (June–September) of the last two decades. This investigation entailed a detailed analysis of these changes across various temporal scales. The data indicated a continuous trend of Arctic greening, evidenced by a 1.8% per decade increment in the NDVI. Notably, significant change points were identified in June 2012 and September 2013. A comparative assessment of NDVI pre- and post-these inflection points revealed an elongation of the Arctic greening trend. Furthermore, an anomalous increase in NDVI of 2% per decade was observed, suggesting an acceleration in greening. A comprehensive analysis was conducted to decipher the correlation between NDVI, temperature, and energy budget parameters to elucidate the underlying causes of these change points. Although the correlation between these variables was relatively low throughout the summer months, a distinct pattern emerged when these periods were dissected and examined in the context of the identified change points. Preceding the change point, a strong correlation (approximately 0.6) was observed between all variables; however, this correlation significantly diminished after the change point, dropping to less than half. This shift implies an introduction of additional external factors influencing the Arctic greening trend after the change point. Our findings provide foundational data for estimating the tipping point in Arctic terrestrial ecosystems. This is achieved by integrating the observed NDVI change points with their relationship with climatic variables, which are essential in comprehensively understanding the dynamics of Arctic climate change, particularly with alterations in tundra vegetation.

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Integration of SPEI and machine learning for assessing the characteristics of drought in the middle ganga plain, an agro-climatic region of India

Kundu,

Rana,

Kundu

et al. 2024

Environ Sci Pollut Res

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Evaluating the impact of different normalization strategies on the construction of drought condition indices

Cited by 13 publications

References 75 publications

SPI and SPEI Drought Assessment and Prediction Using TBATS and ARIMA Models, Jordan

SPI and SPEI Drought Assessment and Prediction Using TBATS and ARIMA Models, Jordan

Arctic Greening Trends: Change Points in Satellite-Derived Normalized Difference Vegetation Indexes and Their Correlation with Climate Variables over the Last Two Decades

Integration of SPEI and machine learning for assessing the characteristics of drought in the middle ganga plain, an agro-climatic region of India

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