Spatial and Temporal Variability of Drought Patterns over the Continental United States from Observations and Regional Climate Models

Aryal, Yog; Zhu, Jianting

doi:10.1007/s13351-021-0045-y

Cited by 6 publications

(5 citation statements)

References 90 publications

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“…Several studies have focused on identifying patterns and explanatory causes of droughts in North America (Aryal & Zhu, 2021; Barlow et al, 2001; Woodhouse et al, 2009). Yang et al (2020) attempted to determine how teleconnections in Canada could explain spatial and temporal variability of droughts.…”

Section: Resultsmentioning

confidence: 99%

“…As mentioned above, the period post-2000 has seen an increase in temperatures and conditions conducive to a rise in drought episodes, which seems to be transposed when analysing our results illustrated in Figure 3. Several studies have focused on identifying patterns and explanatory causes of droughts in North America (Aryal & Zhu, 2021;Barlow et al, 2001;Woodhouse et al, 2009). Yang et al (2020) attempted to determine how teleconnections in Canada could explain spatial and temporal variability of droughts.…”

Section: Temperature Analysismentioning

confidence: 99%

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Spatial and temporal characteristics of past droughts in New Brunswick (1971–2020)

Poirier,

Fortin,

Dubreuil

2023

Intl Journal of Climatology

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This study aimed to provide a historical picture from 1971 to 2020 of the state of droughts in New Brunswick (NB). More specifically, we wished to determine whether there were statistically significant trends using two drought indices: the Standardized Precipitation Index (SPI) and the Standardized Precipitation‐Evapotranspiration Index (SPEI). Monthly precipitation (rain and snow) and average maximum and minimum temperatures were obtained from 10 stations located in New Brunswick. Then, we applied Mann–Kendall and Sen's Slope's nonparametric tests to these two indices at a 1‐, 3‐, 6‐ and 12‐month timescale. The results have shown that the average value for the SPI and SPEI evolution throughout the study period indicated a decrease in indices values (at 95% confidence level). The temporal evolution of the SPI and SPEI were similar at various timescales regarding severities and intensities. However, there were still slight differences in the fluctuation values, especially during the last 20 years of the study period. At a 12‐month timescale, both indices indicated the driest years as being 2020, 2001 and 2004. The SPEI Miramichi, Saint John and Moncton stations had maximum negative MK values (−0.259, −0.221 and −0.215), respectively. As for spatial distribution, four stations located in the south stood out with the highest increase in dry conditions and significantly negative trends (meaning increased drought in this case). Stations in the northern part of the province (Bathurst and Charlo) show non‐significant trends. This study suggests a slight drought increase trend exists in New Brunswick, while an increase in annual precipitation is expected between 42 and 324 mm by 2100 according to climate scenario RCP8.5 of CMIP6.

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Section: Resultsmentioning

confidence: 99%

Section: Temperature Analysismentioning

confidence: 99%

Spatial and temporal characteristics of past droughts in New Brunswick (1971–2020)

Poirier,

Fortin,

Dubreuil

2023

Intl Journal of Climatology

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“…Currently, the widely used methodologies to discover spatial-temporal patterns include traditional matrix decomposition methods, such as Singular Value Decomposition (SVD) [10], Empirical Orthogonal Function (EOF) [11], Dynamic Mode Decomposition (DMD) [12], and Principal Component Analysis (PCA) [13,14]. However, the traditional matrix decomposition method is suitable for situations that do not have massive data and in which the analysis scenario is not complex [15].…”

Section: Related Workmentioning

confidence: 99%

Revealing Spatial–Temporal Patterns of Sea Surface Temperature in the South China Sea Based on Spatial–Temporal Co-Clustering

He,

Xu,

Song

et al. 2024

Applied Sciences

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To discover the spatial–temporal patterns of sea surface temperature (SST) in the South China Sea (SCS), this paper proposes a spatial–temporal co-clustering algorithm optimized by information divergence. This method allows for the clustering of SST data simultaneously across temporal and spatial dimensions and is adaptable to large volumes of data and anomalous data situations. First, the SST data are initially clustered using the co-clustering algorithm. Second, we use information divergence as the loss function to refine the clustering results iteratively. During the iterative optimization of spatial clustering results, we treat the temporal dimension as a constraint; similarly, during the iterative optimization of temporal clustering, we treat the spatial dimension as a constraint. This is to ensure better robustness of the algorithm. Finally, this paper conducts experiments in the SCS to verify our algorithm. According to the analysis of the experimental results, we have drawn the following conclusions. First, the use of the spatial–temporal co-clustering algorithm reveals that the SST in the SCS exhibits strong seasonal patterns in the temporal clustering results. The spatial distribution of SST varies significantly in different seasons. There is a slight difference in SST between the northern and southern regions of the SCS in winter, but the largest difference is in summer. Second, during ocean anomalies, our proposed algorithm can identify the corresponding abnormal patterns. When ENSO occurs, the seasonal distribution pattern of SST in the SCS is destroyed and replaced by an abnormal temporal pattern. The results indicate that during ENSO events, the SST in specific months in the SCS exhibits a correlation with the SST observed 4–5 months afterward.

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“…It can use a few linearly uncorrelated principal components to explain most of the total variance in the original data according to the calculation results of the covariance matrix and corresponding eigenvalues and eigenvectors (Raziei et al, 2009) without causing extreme loss of information (Zhao et al, 2012;Polong et al, 2019). Therefore, according to drought indices such as the SPI or SPEI, a large number of scholars have used PCA to study the temporal and spatial variations in drought (Gocic and Trajkovic, 2014;Guo et al, 2018;Aryal and Zhu, 2021). As a result, PCA is also used to analyze the temporal and spatial variation characteristics of drought in this paper.…”

Section: Pcamentioning

confidence: 99%

Zonal Patterns of Meteorological Drought on the Yunnan-Guizhou Plateau, China

Wang

Yang³

2021

Front. Environ. Sci.

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Drought is a natural phenomenon caused by a shortage of water resources and has a great impact on agriculture, the economy, and the environment. To study the Spatio-temporal distribution and explore the zonal patterns of drought, this paper took the Yunnan-Guizhou Plateau (YGP) as the research region, selected the air temperature, relative humidity, sunshine duration, wind speed, and precipitation data from 47 meteorological stations on the YGP. First, the standardized precipitation evapotranspiration index (SPEI) was calculated, and then the Spatio-temporal distribution of YGP drought was analyzed with the SPEI, Mann–Kendall test, and principal component analysis (PCA). Finally, the correlations between the average SPEI, drought characteristics extracted from the SPEI, and longitude/elevation/latitude were analyzed with the linear regression method, and then the zonal patterns of the YGP drought were obtained. The results revealed that the annual and seasonal SPEI values mainly decreased. Because the first component of the SPEI (the largest eigenvalue makes it the most important component) in annual and winter had a poor relationship with longitude/elevation, its correlation was weak, while the average SPEI values in other seasons were significantly correlated with longitude/elevation (α = 0.001), and the absolute value of the correlation coefficient was between 0.6879–0.9453. Except for PC1 in annual and winter, PC1 and PC2 were significantly correlated with longitude/elevation (α = 0.001), and the absolute value of the correlation coefficient was between 0.5087–0.9501. The duration, severity, intensity, frequency of drought were significantly correlated with longitude/elevation (α = 0.001) in most situations. The average SPEI values and drought characteristics showed a good multivariate linear correlation with longitude, latitude, and elevation, indicating that drought exhibited strong zonal patterns. This study will provide new ideas for drought research and technical support for regional industrial layouts, planting structure adjustments, and drought and disaster reduction.

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Spatial and Temporal Variability of Drought Patterns over the Continental United States from Observations and Regional Climate Models

Cited by 6 publications

References 90 publications

Spatial and temporal characteristics of past droughts in New Brunswick (1971–2020)

Spatial and temporal characteristics of past droughts in New Brunswick (1971–2020)

Revealing Spatial–Temporal Patterns of Sea Surface Temperature in the South China Sea Based on Spatial–Temporal Co-Clustering

Zonal Patterns of Meteorological Drought on the Yunnan-Guizhou Plateau, China

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