A spatiotemporal analysis of precipitation anomalies using rainfall Gini index between 1980 and 2022

Sahbeni, Ghada; Pleynet, Jean Baptiste; Jarocki, Konrad

doi:10.1002/asl.1161

Cited by 3 publications

(3 citation statements)

References 58 publications

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“…Although there are many more drought indices that we could have calculated to evaluate the hydrological consistency of the satellite observations (e.g., [77][78][79]), we selected a subset of either widely used ones (e.g., SPI) or ones that are important for forest resources (e.g., drydown rates). Two exceptions of some widely used indices that we did not focus on in this study were the leaf area index (LAI) and normalized difference vegetation index (NDVI).…”

Section: Discussionmentioning

confidence: 99%

Hydrologic Consistency of Multi-Sensor Drought Observations in Forested Environments

Andreadis,

Meason,

Corbett-Lad

et al. 2024

Remote Sensing

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Drought can have significant impacts on forests, with long periods of water scarcity leading to water stress in trees and possible damages to their eco-physiological functions. Satellite-based remote sensing offers a valuable tool for monitoring and assessing drought conditions over large and remote forested regions. The objective of this study is to evaluate the hydrological consistency in the context of drought of precipitation, soil moisture, evapotranspiration, and land surface temperature observations against in situ measurements in a number of well-monitored sites in New Zealand. Results showed that drought indicators were better captured from soil moisture observations compared to precipitation satellite observations. Nevertheless, we found statistically significant causality relationships between the multi-sensor satellite observations (median p-values ranging from 0.001 to 0.019), with spatial resolution appearing to be an important aspect for the adequate estimation of drought characteristics. Understanding the limitations and capabilities of satellite observations is crucial for improving the accuracy of forest drought monitoring, which, in turn, will aid in sustainable forest management and the development of mitigation and adaptation strategies in the face of changing climate conditions.

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Section: Discussionmentioning

confidence: 99%

Hydrologic Consistency of Multi-Sensor Drought Observations in Forested Environments

Andreadis,

Meason,

Corbett-Lad

et al. 2024

Remote Sensing

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“…Arrefian et al ( 2021) [101] established that MODIS data is a very fundamental tool in the determination and detection of intensive droughts on natural vegetation as demonstrated by the findings in this study by use of Sentinel-3 data. Sahbeni et al (2023) [110] successfully used ERA5 data to examine drought episodes by spatiotemporal analysis of precipitation anomalies and demonstrated the efficiency of the rainfall Gini Index as a drought indicator.…”

Section: Fractional Vegetation Cover (Fvc)mentioning

confidence: 99%

Analysis of Short-Term Drought Episodes Using Sentinel-3 SLSTR Data under a Semi-Arid Climate in Lower Eastern Kenya

et al. 2023

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This study uses Sentinel-3 SLSTR data to analyze short-term drought events between 2019 and 2021. It investigates the crucial role of vegetation cover, land surface temperature, and water vapor amount associated with drought over Kenya’s lower eastern counties. Therefore, three essential climate variables (ECVs) of interest were derived, namely Land Surface Temperature (LST), Fractional Vegetation Cover (FVC), and Total Column Water Vapor (TCWV). These features were analyzed for four counties between the wettest and driest episodes in 2019 and 2021. The study showed that Makueni and Taita Taveta counties had the highest density of FVC values (60–80%) in April 2019 and 2021. Machakos and Kitui counties had the lowest FVC estimates of 0% to 20% in September for both periods and between 40% and 60% during wet seasons. As FVC is a crucial land parameter for sequestering carbon and detecting soil moisture and vegetation density losses, its variation is strongly related to drought magnitude. The land surface temperature has drastically changed over time, with Kitui and Taita Taveta counties having the highest estimates above 20 °C in 2019. A significant spatial variation of TCWV was observed across different counties, with values less than 26 mm in Machakos county during the dry season of 2019, while Kitui and Taita Taveta counties had the highest estimates, greater than 36 mm during the wet season in 2021. Land surface temperature variation is negatively proportional to vegetation density and soil moisture content, as non-vegetated areas are expected to have lower moisture content. Overall, Sentinel-3 SLSTR products provide an efficient and promising data source for short-term drought monitoring, especially in cases where in situ measurement data are scarce. ECVs-produced maps will assist decision-makers with a better understanding of short-term drought events as well as soil moisture loss episodes that influence agriculture under arid and semi-arid climates. Furthermore, Sentinel-3 data can be used to interpret hydrological, ecological, and environmental changes and their implications under different environmental conditions.

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“…Then, the data were split based on the Gini index to ensure optimal partitioning attributes. The Gini index is calculated by subtracting the sum of the squared probabilities of each class from one [44,45]. We calculated the normalized total reduction value of the Gini index for each feature.…”

Section: Selection Of Feature Input Variablesmentioning

confidence: 99%

Predict Seasonal Maximum Freezing Depth Changes Using Machine Learning in China over the Last 50 Years

Wang,

Sheng,

Ran

et al. 2023

Remote Sensing

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Seasonal freezing depth change is important in many environmental science and engineering applications. However, such changes are rare at region scales, especially over China, in the long time series. In this study, we evaluated the annual changes in seasonal maximum freezing depth (MFD) over China from 1971 to 2020 using an ensemble-modeling method based on support vector machine regression (SVMR) with 600 repetitions. Remote sensing data and climate data were input variables used as predictors. The models were trained using a large amount of annual measurement data from 600 meteorological stations. The main reason for using SVMR here was because it has been shown to perform better than random forests (RF), k-nearest neighbors (KNN), and generalized linear regression (GLR) in these cases. The prediction results were generally consistent with the observed MFD values. Cross validation showed that the model performed well on training data and had a better spatial generalization ability. The results show that the freezing depth of seasonally frozen ground in China decreased year by year. The average MFD was reduced by 3.64 cm, 7.59 cm, 5.54 cm, and 5.58 cm, in the 1980s, 1990s, 2000s, and 2010s, respectively, compared with the decade before. In the last 50 years, the area occupied by the freezing depth ranges of 0–40 cm, 40–60 cm, 60–80 cm, 80–100 cm, and 120–140 cm increased by 99,300 square kilometers, 146,200 square kilometers, 130,300 square kilometers, 115,600 square kilometers, and 83,800 square kilometers, respectively. In addition to the slight decrease in freezing depth range of 100–120 cm, the reduced area was 29,500 square kilometers. Freezing depth ranges greater than 140 cm showed a decreasing trend. The freezing depth range of 140–160 cm, which was the lowest range, decreased by 89,700 square kilometers. The 160–180 cm range decreased by 120,500 square kilometers, and the 180–200 cm range decreased by 161,500 square kilometers. The freezing depth range greater than 200 cm, which was the highest reduction range, decreased by 174,000 square kilometers. Considering the lack of data on the change in MFD of seasonally frozen ground in China in recent decades, machine learning provides an effective method for studying meteorological data and reanalysis data in order to predict the changes in MFD.

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A spatiotemporal analysis of precipitation anomalies using rainfall Gini index between 1980 and 2022

Cited by 3 publications

References 58 publications

Hydrologic Consistency of Multi-Sensor Drought Observations in Forested Environments

Hydrologic Consistency of Multi-Sensor Drought Observations in Forested Environments

Analysis of Short-Term Drought Episodes Using Sentinel-3 SLSTR Data under a Semi-Arid Climate in Lower Eastern Kenya

Predict Seasonal Maximum Freezing Depth Changes Using Machine Learning in China over the Last 50 Years

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