The Fractal Nature of Drought: Power Laws and Fractal Complexity of Arizona Drought

Azizi, Tahmineh; Azizi, Sepideh

doi:10.28924/ada/ma.2.17

Cited by 6 publications

(5 citation statements)

References 31 publications

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“…With increasing temperature around the world, photosynthesis and microbial and plant respiration and as a result releasing carbon dioxide to the atmosphere also increase [26]. During recent decades, climate changes and its long-term impacts such as accelerating ice melt in poles, warming oceans, and rising sea level and many other aspects of life entire earth has been widely recognized by many different organizations and researchers [5,18]. According to the most recent Intergovernmental Panel on Climate Change (IPCC) report (IPCC, 2014 [38]) greenhouse gases (GHG) are at the highest level which caused the most significant changes in earth temperature in history [18].…”

Section: Introductionmentioning

confidence: 99%

Non-Integer Dimension of Seasonal Land Surface Temperature (LST)

Azizi¹,

Azizi²

2023

Preprint

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During few last years, climate change including global warming which is attributed to human activities and also its long-term adverse effects on the planet’s functions have been identified as the most challenging discussion topics which have arisen many concerns and efforts to find the possible solutions. Since the warmth arising from Earth’s landscapes affects the world’s weather and climate patterns, we decided to study the changes in the Land Surface Temperature (LST) patterns in different seasons through non-linear methods. Here, we particularly want to estimate the non-integer dimension and fractal structure of the land surface temperature. For this study, the (LST) data has been obtained during the daytime by the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Terra satellite. Depending on what time of the year data has been collected, temperatures change in different ranges. Since equatorial regions remain warm, and Antarctica and Greenland remain cold, and also because altitude affects temperature, we selected Riley County in the U.S. state of Kansas, which does not belong to any of this type locations and we are interested to observe the seasonal changes in temperature in this county. The results of the present study show that the Land Surface Temperature (LST) belongs to the class of fractal process with non-integer dimension.

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Section: Introductionmentioning

confidence: 99%

Non-Integer Dimension of Seasonal Land Surface Temperature (LST)

Azizi¹,

Azizi²

2023

Preprint

Self Cite

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show abstract

“…With increasing temperatures around the world, photosynthesis and microbial and plant respiration and, as a result, the release of carbon dioxide to the atmosphere also increase [1]. During recent decades, climate change and its long-term impacts, such as accelerating ice melt in poles, warming oceans, and the rising sea level, as well as influences on many other aspects of life throughout the Earth, have been widely recognized by many different organizations and researchers [2,3]. According to the most recent Intergovernmental Panel on Climate Change (IPCC) report (IPCC, 2014 [4]), greenhouse gases (GHG) are at the highest level, causing the most significant changes in the Earth's temperature in history [3].…”

Section: Introductionmentioning

confidence: 99%

“…In studying real-world time series data, we may deal with databases that exhibit the power law or self-similarity over a wide range of size and time scales. For this type of phenomenon, we need to apply nonlinear techniques to characterize the complexity and self-similarity of data, because the traditional time series techniques fail to classify databases with a variety range of scaling features [2,[22][23][24][25][26]. In this study, for the first time, we aim to determine whether the Land Surface Temperature (LST) may also exemplify some general principles of systems with self-organization patterns.…”

Section: Introductionmentioning

confidence: 99%

Noninteger Dimension of Seasonal Land Surface Temperature (LST)

Azizi

2023

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During the few last years, climate change, including global warming, which is attributed to human activities, and its long-term adverse effects on the planet’s functions have been identified as the most challenging discussion topics and have provoked significant concern and effort to find possible solutions. Since the warmth arising from the Earth’s landscapes affects the world’s weather and climate patterns, we decided to study the changes in Land Surface Temperature (LST) patterns in different seasons through nonlinear methods. Here, we particularly wanted to estimate the noninteger dimension and fractal structure of the Land Surface Temperature. For this study, the LST data were obtained during the daytime by a Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Terra satellite. Depending on the time of the year data were collected, temperatures changed in different ranges. Since equatorial regions remain warm, and Antarctica and Greenland remain cold, and also because altitude affects temperature, we selected Riley County in the US state of Kansas, which does not belong to any of these location types, and we observed the seasonal changes in temperature in this county. According to our fractal analysis, the fractal dimension may provide a complexity index to characterize different LST datasets. The multifractal analysis confirmed that the LST data may define a self-organizing system that produces fractal patterns in the structure of data. Thus, the LST data may not only have a wide range of fractal dimensions, but also they are fractal. The results of the present study show that the Land Surface Temperature (LST) belongs to the class of fractal processes with a noninteger dimension. Moreover, self-organized behavior governing the structure of LST data may provide an underlying principle that might be a general outcome of human activities and may shape the Earth’s surface temperature. We explicitly acknowledge the important role of fractal geometry when analyzing and tracing settlement patterns and urbanization dynamics at various scales toward purposeful planning in the development of human settlement patterns.

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“…

Drought, a natural segment of climate variability, is characterized by prolonged periods of reduced precipitation, affecting various aspects of life and natural resources, including water supplies, businesses, economic stability, social well-being, and the environment [1][2][3][4][5][6]. The escalating trend of global warming has led to a more frequent occurrence of drought, presenting significant challenges to both humanity and society.

…”

mentioning

confidence: 99%

“…We have also demonstrated the table of reported impacts during past droughts in Arizona for each level of drought on the US Drought Monitor in Fig. 3 When studying real-world time series, one often encounters databases exhibiting nonlinear power-law properties, indicative of self-similar or fractal-like patterns across various scales [6,[29][30][31][32][33][34][35]. ].…”

mentioning

confidence: 99%

Classification of drought severity in contiguous USA during the past 21 years using fractal geometry

Azizi,

Azizi

2024

EURASIP J. Adv. Signal Process.

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Drought is characterized by a moisture deficit that can adversely impact the environment, economy, and society. In North America, like many regions worldwide, predicting the timing of drought events is challenging. However, our novel study in climate research explores whether the Drought Monitor database exhibits fractal characteristics, represented by a single scaling exponent. This database categorizes drought areas by intensity, ranging from D0 (abnormally dry) to D4 (exceptional drought). Through vibration analysis using power spectral densities (PSD), we investigate the presence of power-law scaling in various statistical moments across different scales within the database. Our multi-fractal analysis estimates the multi-fractal spectrum for each category, and the Higuchi algorithm assesses the fractal complexity, revealing that D4 follows a multi-fractal pattern with a wide range of exponents, while D0 to D3 exhibit a mono-fractal nature with a narrower range of exponents.

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The Fractal Nature of Drought: Power Laws and Fractal Complexity of Arizona Drought

Cited by 6 publications

References 31 publications

Non-Integer Dimension of Seasonal Land Surface Temperature (LST)

Non-Integer Dimension of Seasonal Land Surface Temperature (LST)

Noninteger Dimension of Seasonal Land Surface Temperature (LST)

Classification of drought severity in contiguous USA during the past 21 years using fractal geometry

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