Analysis of the spatiotemporal characteristics of drought disasters in North China during the Ming and Qing dynasties

Bi, Shengjie; Chen, Changchun; Lü, Ying; Nkunzimana, Athanase; Li, Yanping

doi:10.1080/19475705.2020.1848929

Cited by 4 publications

(4 citation statements)

References 21 publications

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“…Thus, the first eigenvectors of the directional components in summer can well represent the spatiotemporal evolution type of the direction angles of the HRTs in China between 2008 and 2019. The decomposition of the time coefficients with ensemble empirical mode decomposition (EEMD) (Bi et al ., 2020) was utilized to obtain the variation form of the main directions (Figure 15).…”

Section: Resultsmentioning

confidence: 99%

Spatiotemporal patterns and evolution of heavy rainfall trajectories in China

Chen

Zhu

2022

Intl Journal of Climatology

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A heavy rainfall trajectory (HRT) is defined as the trajectory formed by the movement of the rainstorm centre of a heavy rainfall event over time. Based on this definition, the dispersion degree (DD) of HRTs is applied based on the Euclidean distance and an empirical orthogonal function (EOF) to quantify the spatiotemporal distribution and evolution characteristics of HRTs in China. The results show that (a) the spatial distribution and length of HRTs in China have distinct geographical and seasonal characteristics, which are strongly influenced by the East Asian summer monsoon. (b) From southeast China to northwest China, the DDs increased to 8 and the length of HRTs decreased by 53,925 m. (c) According to EOF analysis, the direction angle change of northeast-oriented HRTs shows a significant negative trend across the country, while those of southeast-, southwest-and northwest-oriented HRTs show a significant positive trend. From 2008 to 2019, HRTs flattened in the northeast and southwest directions, while HRTs steepened in the southeast and northwest directions. This research provides certain scientific guidance for the prevention and mitigation of regional disasters and provides necessary theoretical support for the changing patterns of the climate system under the impact of climate change.

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Section: Resultsmentioning

confidence: 99%

Spatiotemporal patterns and evolution of heavy rainfall trajectories in China

Chen

Zhu

2022

Intl Journal of Climatology

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“…The intrinsic mode function (IMF) calculated by multiple iterations of empirical mode decomposition (EMD) is generally averaged to offset the added white noise to effectively suppress the appearance of mode aliasing. In recent years, the EEMD method has been widely used by many researchers (Bi et al, 2018; Huang & Shen, 2005; Wu & Huang, 2009; Zhang et al, 2021). Its decomposition steps are as follows: Set the overall average frequency

M

. Add white noise

n_{i} (t)

with standard normal distribution to the original signal

x (t)

to generate a new signal:

x_{i} (t) = x (t) + n_{i} (t),

where

n_{i} (t)

represents the i th additive white noise sequence and

x_{i} (t)

is the additional noise signal in the i th experiment,

i = 1, 2, \dots M

. This noisy signal

x_{i} (t)

is decomposed by EMD respectively, so as to calculate the sum of each IMF:

x_{i} (t) = \sum_{j}^{J} c_{i, j} (t) + r_{i, j} (t),

where

c_{i, j} (t)

is the j th IMF decomposed after adding white noise for the i th time,

r_{i, j} (t)

, as a residual function, represents the average trend of signals, and J is the current IMF iteration....…”

Section: Methodsmentioning

confidence: 99%

“…The intrinsic mode function (IMF) calculated by multiple iterations of empirical mode decomposition (EMD) is generally averaged to offset the added white noise to effectively suppress the appearance of mode aliasing. In recent years, the EEMD method has been widely used by many researchers (Bi et al, 2018;Huang & Shen, 2005;Wu & Huang, 2009;Zhang et al, 2021). Its decomposition steps are as follows:…”

Section: Ensemble Empirical Mode Decompositionmentioning

confidence: 99%

Characteristics of warming and humidification in the Yellow River's upper reaches and their impact on surface water resources

Yang,

Zhang,

Yue

et al. 2023

Intl Journal of Climatology

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Based on multi‐source data, including observation, analysis, model calculation, satellite remote sensing, and scenario estimation data, this study has systematically analysed the warming and dry–wet change characteristics of the Yellow River's upper reaches in the last 60 years. The causes and features of the current warming and humidification and their impact on surface water resources are also discussed. This study found that since 1961, the average temperature in the Yellow River's upper reaches has increased at a rate of 0.37°C/10a, which is more significant than the increases observed for the world, China, and even northwest China in the same period. At the beginning of this century, the rate of change in precipitation in the Yellow River's upper reaches reversed direction from decreasing to increasing, with a significant increase (35 mm/10a) since 2000. Therefore, climate change has shown a significant warming and humidification trend in the upper reaches of the Yellow River. From the dynamic change perspective, the Yellow River's upper reaches were warming and drying in each climatic period from 1961 to 2010; however, the tendency towards dryness slowed period by period. In contrast, the region showed consistent warming and humidification from 1991 to 2020. From the perspective of multiple time scales, the change in average temperature is mainly dominated by the multi‐decadal scale. In contrast, precipitation is dominated by interannual change. The increase in the multi‐decadal component in average temperature and precipitation explains the current warming and humidification. Precipitation changes in the upper reaches of the Yellow River Basin are mainly affected by the westerly circulation, the East Asian summer monsoon, and the South Asian summer monsoon circulation systems. However, precipitation was affected by different circulation factors on different time scales. On an interannual scale, it was mainly affected by the South Asian summer monsoon circulation. On an interdecadal scale, it was mainly affected by the westerly wind and the East Asian summer monsoon circulation. On a multi‐decadal scale, it was jointly influenced by the westerly wind, the East Asian summer monsoon, and the South Asian summer monsoon circulation. Under the control of multi‐decadal synergistic enhancement of the westerly, the East Asian summer monsoon, and the South Asian summer monsoon circulations, the vertical field shows an abnormal upward movement. There has been a significant abnormal easterly and southerly airflow that transports water vapour, and cold air is transported along the northwest airflow at the back of the Mongolian cyclone, which makes the climate in the upper reaches of the Yellow River more humid. The warming and humidifying of the upper reaches of the Yellow River has significantly increased runoff and lake area and significantly degraded the frozen soil. The surface water budget in the Yellow River's upper reaches has significantly responded to the current warming and humidification. The actual surface evapotranspiration and surface runoff increased significantly, but the net surface water budget still showed a decreasing trend. The increase in precipitation did not change the surface water deficit.

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“…Despite improvements in the measures taken to cope with natural disasters, drought is still a major meteorological event that can cause severe damage to both livelihoods and socio‐ecological systems. China has struggled with drought for thousands of years and several major historical drought events have directly influenced the downfall of ancient dynasties (Bi et al., 2020). At the peak of drought in 2022, 52 million people in China were affected and 7.6 million people needed financial assistance.…”

Section: Introductionmentioning

confidence: 99%

Effective Deep Learning Seasonal Prediction Model for Summer Drought Over China

Liu,

Huang,

Wang

2024

Earth's Future

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Drought is an important meteorological event in China and can cause severe damage to both livelihoods and socio‐ecological systems, but current seasonal prediction skill for drought is far from successful. This study used convolutional neural network (CNN) to build an effective seasonal forecast model for the summer consecutive dry days (CDD) over China. The principal components (PC) of the six leading empirical orthogonal function modes of CDD anomaly were predicted by CNN, using the previous winter precipitation, 2‐m temperature and 500 hPa geopotential height as predictors. These predicted PCs were then projected onto the observed spatial patterns to obtain the final predicted summer CDD anomaly over China. In the independent hindcast period of 2007–2018, the interannual variabilities of first six PCs were significantly predicted by CNN. The spatial patterns of CDD were then skillfully predicted with anomaly correlation coefficient skills generally higher than 0.2. The interannual variability of summer CDD over the middle and lower Yangtze River valley, northwestern China and northern China were significant predicted by our CNN model three months in advance. CNN identified that the previous winter precipitation was the important predictor for PC1–PC3, whereas the previous winter 2‐m temperature and 500 hPa geopotential height were important for the prediction of PC4–PC6. This research provides a new and effective method for the seasonal prediction of summer drought.

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Analysis of the spatiotemporal characteristics of drought disasters in North China during the Ming and Qing dynasties

Cited by 4 publications

References 21 publications

Spatiotemporal patterns and evolution of heavy rainfall trajectories in China

Spatiotemporal patterns and evolution of heavy rainfall trajectories in China

Characteristics of warming and humidification in the Yellow River's upper reaches and their impact on surface water resources

Effective Deep Learning Seasonal Prediction Model for Summer Drought Over China

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