Spatio-temporal variability of meteorological drought over India with footprints on agricultural production

Dar, Junaid; Dar, Abdul Qayoom

doi:10.1007/s11356-021-14866-7

Cited by 14 publications

(4 citation statements)

References 42 publications

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“…The utilization of meteorological station data or satellite products is failed to project climate changes (Dar and Dar, 2021). However, the Coupled Model Intercomparison Project (CMIP) has provided a great number of GCMs (General Circulation Models) for researchers to catch future climate changes.…”

Section: Introductionmentioning

confidence: 99%

Machine-learning ensembled CMIP6 projection reveals socio-economic pathways will aggravate global warming and precipitation extreme

Zhang

Chen

2022

Preprint

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Abstract. The climate change plays a key role in ecosystem evolution and has been proved to be affected by comprehensive factors including anthropogenic activities. The application of GCMs (General Circulation Models) launched by CMIP6 (Coupled Model Intercomparison Project Phase 6) has become a primary implement to catch future climate characteristics under different future socio-economic pathways. However, quantitative future climate change records with high credibility generated by robust GCMs merged dataset from CMIP6 is scare. The majority of former conclusions depend on traditional GCMs ensemble datasets (e.g., single, mean and medium) which have proved to be highly instable. In this study, 3 machine learning methods (Ordinary Least Squares regression, Decision Tree, and Deep Neural Networks) were applied to ensemble temperature and precipitation from 16 CMIP6 GCMs simultaneously. Monthly optimal estimation of precipitation and temperature from the three datasets were selected to generate a new ensemble dataset under three Socio-Economic Pathways (SSP1-2.6, SSP2-4.5 and SSP5-8.5). The new precipitation (temperature) ensemble dataset with the R=0.81 (0.99) is more accurate than all the single GCM. High credible analyses demonstrate that Europe and North America contribute more to global warming than Oceania, Africa and South America. The global continent break through 1.5 °C, 2 °C and 3 °C rising threshold in 2024, 2031 and 2048 under SSP5-8.5 scenarios, of which the driving capacity for global warming ranks first. Most precipitation aggregates in July and August, while dry months fall in April and September to next February till the end of 21st century. Global precipitation will be accelerated polarization with the decreasing trends of Africa and Asia (p < 0.05) under the scenario of SSP5-8.5. The proposed analysis provides credible opportunities and quantitative fundamental to understand future climate characteristics for ecology and meteorology.

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Section: Introductionmentioning

confidence: 99%

Machine-learning ensembled CMIP6 projection reveals socio-economic pathways will aggravate global warming and precipitation extreme

Zhang

Chen

2022

Preprint

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“…e agricultural drought manifests itself as a deficiency in precipitation, a deficit in soil moisture condition, crop failure, etc. [34,35]. Further, the prolonged period without rainfall becomes the root of the hydrological drought [36,37].…”

Section: Introductionmentioning

confidence: 99%

Characterization of Meteorological Drought Using Monte Carlo Feature Selection and Steady‐State Probabilities

et al. 2022

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Drought is a creeping phenomenon that slowly holds an area over time and can be continued for many years. The impacts of drought occurrences can affect communities and environments worldwide in several ways. Thus, assessment and monitoring of drought occurrences in a region are crucial for reducing its vulnerability to the negative impacts of drought. Therefore, comprehensive drought assessment techniques and methods are required to develop adaptive strategies that a region can undertake to reduce its vulnerability to drought substantially. For this purpose, this study proposes a new method known as a regional comprehensive assessment of meteorological drought (RCAMD). The Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), and Standardized Precipitation and Temperature Index (SPTI) are jointly used for the development of the RCAMD. Further, the RCAMD employs Monte Carlo feature selection (MCFS) and steady-state probabilities (SSPs) to comprehensively collect information from various stations and drought indices. Moreover, the RCAMD is validated on the six selected stations in the northern areas of Pakistan. The outcomes associated with the RCAMD provide a comprehensive regional assessment of meteorological drought and become the initial source for bringing more considerations to drought monitoring and early warning systems.

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“…Pramudya & Onishi (2018) applied SPI to analyze the extent and duration of drought in Tegal City, Indonesia, in 2015. Dar & Dar (2021) applied SPI-3 to study the temporal and spatial variability of drought in India and its impact on agriculture.…”

Section: Introductionmentioning

confidence: 99%

The dynamic identification mechanism research of drought based on process description

Xie

Yang

et al. 2022

Water Supply

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Drought is one of the most common natural disasters, which can cause heavy losses on a global scale. Strengthening the research on drought identification mechanism has an important guiding role in drought disaster prevention and mitigation. The current drought identification is mostly static in the process of drought, and there is a problem that the historical evaluation information and the drought prediction information are not closely combined, with limited application value. Based on the physical cumulative and recessional effects of drought events, this paper further uses the Standardized Precipitation Index (SPI) to divide the whole drought process into four stages: accumulation → outbreak → reaction → restoration. In addition, this study proposes a new dynamic identification mechanism based on drought process, and develops a drought prediction model combining singular spectrum analysis and BP neural network (SSA-BPNN), filling the gap between scientific research and practical application. Using three drought events in the Yulin region of China as examples for simulation studies, the results show that the use of the new mechanism can not only improve the application value of the SSA-BPNN model, but also effectively advance the drought preparation time and resistance level.

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Spatio-temporal variability of meteorological drought over India with footprints on agricultural production

Cited by 14 publications

References 42 publications

Machine-learning ensembled CMIP6 projection reveals socio-economic pathways will aggravate global warming and precipitation extreme

Machine-learning ensembled CMIP6 projection reveals socio-economic pathways will aggravate global warming and precipitation extreme

Characterization of Meteorological Drought Using Monte Carlo Feature Selection and Steady‐State Probabilities

The dynamic identification mechanism research of drought based on process description

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