Spatiotemporal Variations of Meteorological Droughts and the Assessments of Agricultural Drought Risk in a Typical Agricultural Province of China

Guo, Youguang; Li, Qing; Wang, Hong; Long, Mark D.; Bai,

doi:10.3390/atmos10090542

Cited by 12 publications

(5 citation statements)

References 40 publications

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“…The SPEI at timescales from 1 to 48 months represents the cumulative water deficits and surpluses over the preceding 1–48 months, which can provide robust information on different types of drought events (Vicente‐Serrano et al., 2013). In this study, the SPEI at the 3‐month timescale (SPEI‐3) was used given that it has been proven to be more strongly correlated with soil moisture contents than other timescales (Guo et al., 2019; Labudová et al., 2017).…”

Section: Methodsmentioning

confidence: 99%

“…In this study, the SPEI at the 3-month timescale (SPEI-3) was used given that it has been proven to be more strongly correlated with soil moisture contents than other timescales (Guo et al, 2019;Labudová et al, 2017). Specifically, a 1-year extreme drought event (i.e., 1-year drought) was defined as meeting the following two criteria: (1) the annual mean value of SPEI-3 < −0.5, which indicates a state of drought stress throughout the whole year; and (2) there is at least 1 month with monthly mean SPEI-3 < −1.5 during the growing season (defined consistently as from May to October in the study region), representing an extreme drought event that occurred during the growing season.…”

Section: Identification Of Extreme Droughts With Different Durationsmentioning

confidence: 99%

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Enhanced growth resistance but no decline in growth resilience under long‐term extreme droughts

Wang,

Xu,

et al. 2023

Global Change Biology

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The frequency, intensity, and duration of extreme droughts, with devastating impacts on tree growth and survival, have increased with climate change over the past decades. Assessing growth resistance and resilience to drought is a crucial prerequisite for understanding the responses of forest functioning to drought events. However, the responses of growth resistance and resilience to extreme droughts with different durations across different climatic zones remain unclear. Here, we investigated the spatiotemporal patterns in growth resistance and resilience in response to extreme droughts with different durations during 1901–2015, relying on tree‐ring chronologies from 2389 forest stands over the mid‐ and high‐latitudinal Northern Hemisphere, species‐specific plant functional traits, and diverse climatic factors. The findings revealed that growth resistance and resilience under 1‐year droughts were higher in humid regions than in arid regions. Significant higher growth resistance was observed under 2‐year droughts than under 1‐year droughts in both arid and humid regions, while growth resilience did not show a significant difference. Temporally, tree growth became less resistant and resilient to 1‐year droughts in 1980–2015 than in 1901–1979 in both arid and humid regions. As drought duration lengthened, the predominant impacts of climatic factors on growth resistance and resilience weakened and instead foliar economic traits, plant hydraulic traits, and soil properties became much more important in both climatic regions; in addition, such trends were also observed temporally. Finally, we found that most of the Earth system models (ESMs) used in this study overestimated growth resistance and underestimated growth resilience under both 1‐year and 2‐year droughts. A comprehensive ecophysiological understanding of tree growth responses to longer and intensified drought events is urgently needed, and a specific emphasis should be placed on improving the performance of ESMs.

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Section: Methodsmentioning

confidence: 99%

Section: Identification Of Extreme Droughts With Different Durationsmentioning

confidence: 99%

Enhanced growth resistance but no decline in growth resilience under long‐term extreme droughts

Wang,

Xu,

et al. 2023

Global Change Biology

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“…The causes of droughts on agricultural land can be meteorological, agricultural, hydrological, and socioeconomic droughts (Wilhite, 2005). However, in recent years meteorological drought has been dominant in hampering agriculture practices (Bageshree et al, 2022;Guo et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Agricultural Droughts Monitoring of Aceh Besar Regency Rice Production Center, Aceh, Indonesia – Application Vegetation Conditions Index Using Sentinel-2 Image Data

Sugianto¹,

Rusdi²,

Budi³

et al. 2023

J. Ecol. Eng.

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Monitoring the agricultural drought of paddy rice fields is a crucial aspect of preparing for proper action in maintaining food security in Indonesia. The Aceh Province is one of Indonesia's national rice production centers, especially Aceh Besar Regency; it includes three central districts; Indrapuri, Kuta Cot Glie, and Seulimeum. Satellite-Sentinel 2A data have been tested to monitor the drought levels of around 2,803 Ha in the three districts in this study. This study aimed to determine the drought level in Indrapuri, Kuta Cot Glie, and Seulimeum districts, Aceh Besar Regency's paddy rice fields using Sentinel-2A data imagery. The vegetation conditions index (VCI) of Sentinel-2 data was utilized to identify a vegetative drought level in the area for the 2018, 2019, 2020, 2021, and 2022 growing seasons. The vegetation inertia index is derived from the Normalized Difference Vegetation Index (NDVI). The results show that the VCI looked volatile, but the trendline increased by four percent, from 92.56 in July 2019 to 96.08 in July 2021. Most areas on the dates investigated found that the no drought category was still dominant. The designated data analyzed found that the June 2022 data tend to be distributed to the drought in extreme, severe, moderate, and mild increases compared to the previous data investigated. This figure shows an increasing drought in the study area, and the average drought index is in the category of mild drought. In addition, there has been a trendline decline in the value of NDVI in recent years, causing agricultural land for paddy rice fields to be slightly vulnerable to drought.

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“…In this sense, the ratio of ET to precipitation directly determines the regional water availability on the mean annual scale. Therefore, accurately quantifying ET is of great importance for hydrological practices such as water resources management, crop yield prediction, and drought forecasting [3][4][5][6]. However, ET is a complex natural process, regulated by multiple interacting factors such as climate, topography, soil, and vegetation, and often has great variability in space and time [7,8].…”

Section: Introductionmentioning

confidence: 99%

Different Vegetation Information Inputs Significantly Affect the Evapotranspiration Simulations of the PT-JPL Model

et al. 2022

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Evapotranspiration (ET) is an essential part of the global water cycle, and accurate quantification of ET is of great significance for hydrological research and practice. The Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) model is a commonly used remotely sensed (RS) ET model. The original PT-JPL model includes multiple vegetation variables but only requires the Normalized Difference Vegetation Index (NDVI) as the vegetation input. Other vegetation inputs (e.g., Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)) are estimated by the NDVI-based empirical methods. Here we investigate whether introducing more RS vegetation variables beyond NDVI can improve the PT-JPL model’s performance. We combine the vegetation variables derived from RS and empirical methods into four vegetation input schemes for the PT-JPL model. The model performance under four schemes is evaluated at the site scale with the eddy covariance (EC)-based ET measurements and at the basin scale with the water balance-based ET estimates. The results show that the vegetation variables derived by RS and empirical methods are quite different. The ecophysiological constraints of the PT-JPL model constructed by the former are more reasonable in spatial distribution than those constructed by the latter. However, as vegetation input of the PT-JPL model, the scheme derived from empirical methods performs best among the four schemes. In other words, introducing more remotely sensed vegetation variables beyond NDVI into the PT-JPL model degrades the model performance to varying degrees. One possible reason for this is the unrealistic ET partitioning. It is necessary to re-parameterize the biophysical constraints of the PT-JPL model to ensure that the model obtains reasonable internal process simulations, that is, “getting the right results for right reasons.”

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Spatiotemporal Variations of Meteorological Droughts and the Assessments of Agricultural Drought Risk in a Typical Agricultural Province of China

Cited by 12 publications

References 40 publications

Enhanced growth resistance but no decline in growth resilience under long‐term extreme droughts

Enhanced growth resistance but no decline in growth resilience under long‐term extreme droughts

Agricultural Droughts Monitoring of Aceh Besar Regency Rice Production Center, Aceh, Indonesia – Application Vegetation Conditions Index Using Sentinel-2 Image Data

Different Vegetation Information Inputs Significantly Affect the Evapotranspiration Simulations of the PT-JPL Model

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