Anomaly Variation of Vegetation and Its Influencing Factors in Mainland China During ENSO Period

Zhao, Qingzhi; Ma, Xiongwei; Yao, Wanqiang; Liu, Yang; Yao, Yibin

doi:10.1109/access.2019.2962787

Cited by 17 publications

(8 citation statements)

References 39 publications

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“…The negative (positive) SST values indicate high (low) rainfall associated with droughts and floods in the catchment. Generally, if the average Niño 3.4 index is above (below) 0.5°C (−0.5°C) this indicates El Niño (La Niña) and the values 0.5°C and −0.5°C represent the neutral phase (Archer et al, 2017;Barbieri et al, 2019;Zhao et al, 2020). Farmers are aware of the drastic changes in rainfall, as stated by Mkuhlani et al (2019a), but those in the LRC could experience challenges in accessing platforms that are used to disseminate weather forecasts (TV and smart phones).…”

Section: Influence Of Enso On Rainfall Variabilitymentioning

confidence: 99%

Assessing the frequency of drought/flood severity in the Luvuvhu River catchment, Limpopo Province, South Africa

Mazibuko

Mukwada

Moeletsi

2021

WSA

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The Luvuvhu River catchment experiences rainfall variability with a high frequency of extremely dry and wet conditions. Understanding the frequency of drought and floods in this catchment area is important to the agriculture sector for managing the negative impacts of these natural hazards. This study was undertaken to investigate the frequency and severity of drought/floods and linkages with the El Niño Southern Oscillation (ENSO) phenomenon. Poor and resource-limited small-scale farmers in the Luvuvhu River catchment area struggle to adjust due to decreasing crop yields and livestock mortality caused by drought and floods. Monthly rainfall data from 15 grid points (0.5° × 0.5°) was used to compute the Standardised Precipitation Index (SPI) for the period between 1979 and 2016. The 3-month SPI was calculated for the December–January–February (DJF) period. The second half of the agricultural season was selected because the influence of ENSO is high during the late summer season (DJF) in the catchment. The SPI results indicate that the agricultural seasons 1982/83, 1991/92 and 2015/16 were characterised by extreme drought. Conversely, the SPI values also show that the wettest seasons were recorded in 1998/99 and 1999/00. The catchment experiences a high frequency of moderate to severe drought in the north and north-eastern parts. Spatially, the occurrence of moderate to severe dry conditions covers large areas in the north and south-western parts. Severe to extreme wet conditions cover large areas in the north and south-eastern parts of the catchment. The SST index (Niño 3.4) shows a strong influence on rainfall variability in the catchment, resulting in either dry or wet conditions. Therefore, this study recommends further research focusing on more climatic modes that influence rainfall variability, as well as further development of drought and flood forecasting to improve farmers’ adaptations options and reliability of weather forecasts used as a tool to manage crop production.

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Section: Influence Of Enso On Rainfall Variabilitymentioning

confidence: 99%

Assessing the frequency of drought/flood severity in the Luvuvhu River catchment, Limpopo Province, South Africa

Mazibuko

Mukwada

Moeletsi

2021

WSA

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“…By moving the selected windows in the dataset analysis, the independent local correlation coefficient for each window can be calculated. Therefore, a smooth time series can be generated and the continuity of each time process can be analyzed [44,45]. In this study, MWCA is introduced to investigate the relationship between monthly PWV/ T and ENSO.…”

Section: Empirical Orthogonal Functionmentioning

confidence: 99%

A Novel ENSO Monitoring Method using Precipitable Water Vapor and Temperature in Southeast China

et al. 2020

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Southeast China, a non-core region influenced by the El Niño–Southern Oscillation (ENSO), has been seldom investigated before. However, the occurrence of ENSO will affect the redistribution of precipitation and the temperature (T) spatial pattern on a global scale. This condition will further lead to flood or drought disasters in Southeast China. Therefore, the method of monitoring the occurrence of ENSO is important and is the focus of this paper. The spatiotemporal characteristics of precipitable water vapor (PWV) and T are first analyzed during ENSO using the empirical orthogonal function (EOF). The results showed that a high correlation spatiotemporal consistency exist between PWV and T. The response thresholds of PWV and T to ENSO are determined by moving the window correlation analysis (MWCA). If the sea surface temperature anomaly (SSTA) at the Niño 3.4 region exceeded the ranges of (−1.17°C, 1.04°C) and (−1.15°C, 1.09°C), it could cause the anomalous change of PWV and T in Southeast China. Multichannel singular spectral analysis (MSSA) is introduced to analyze the multi-type signals (tendency, period, and anomaly) of PWV and T over the period of 1979–2017. The results showed that the annual abnormal signal and envelope line fluctuation of PWV and T agreed well in most cases with the change in SSTA. Therefore, a standard PWV and T index (SPTI) is proposed on the basis of the results to monitor ENSO events. The PWV and T data derived from the grid-based European Center for Medium-Range Weather Forecasting (ECMWF) reanalysis products and GNSS/RS stations in Southeast China were used to validate the performance of the proposed SPTI. Experimental results revealed that the time series of average SPTI calculated in Southeast China corresponded well to that of SSTA with a correlation coefficient of 0.66 over the period of 1979–2017. The PWV values derived from the Global Navigation Satellite System (GNSS) and radiosonde data at two specific stations (WUHN and 45004) were also used to calculate the SPTI. The results showed that the correlation coefficients between SPTI and SSTA were 0.73 and 0.71, respectively. Such results indicate the capacity of the proposed SPTI to monitor the ENSO in Southeast China.

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“…Precipitable water vapor (PWV) is an important component of the atmosphere and one of the most active parameters in the atmospheric composition [21], with its content in the atmosphere varying dramatically over time and space [22][23][24]. Atmospheric precipitation also affects PWV [25].…”

Section: Introductionmentioning

confidence: 99%

Monitoring 2019 Forest Fires in Southeastern Australia with GNSS Technique

et al. 2021

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From late 2019 to early 2020, forest fires in southeastern Australia caused huge economic losses and huge environmental pollution. Monitoring forest fires has become increasingly important. A new method of fire detection using the difference between global navigation satellite system (GNSS)-derived precipitable water vapor and radiosonde-derived precipitable water vapor (ΔPWV) is proposed. To study the feasibility of the new method, the relationship is studied between particulate matter 10 (PM10) (2.5 to 10 microns particulate matter) and ΔPWV based on Global Positioning System (GPS) data, radiosonde data, and PM10 data from 1 June 2019 to 1 June 2020 in southeastern Australia. The results show that before the forest fire, ΔPWV and PM10 were smaller and less fluctuating. When the forest fire happened, ΔPWV and PM10 were increasing. Then after the forest fire, PM10 became small with relatively smooth fluctuations, but ΔPWV was larger and more fluctuating. Correlation between the 15-day moving standard deviation (STD) time series of ΔPWV and PM10 after the fire was significantly higher than that before the fire. This study shows that ΔPWV is effective in monitoring forest fires based on GNSS technique before and during forest fires in climates with more uniform precipitation, and using ΔPWV to detect forest fires based on GNSS needs to be further investigated in climates with more precipitation and severe climate change.

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Anomaly Variation of Vegetation and Its Influencing Factors in Mainland China During ENSO Period

Cited by 17 publications

References 39 publications

Assessing the frequency of drought/flood severity in the Luvuvhu River catchment, Limpopo Province, South Africa

Assessing the frequency of drought/flood severity in the Luvuvhu River catchment, Limpopo Province, South Africa

A Novel ENSO Monitoring Method using Precipitable Water Vapor and Temperature in Southeast China

Monitoring 2019 Forest Fires in Southeastern Australia with GNSS Technique

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