Global trend analysis of the MODIS drought severity index

Orvos, Péter; Homonnai, Viktória; Várai, A.; Bozóki, Zoltán; Jánosi, Imre M.

doi:10.5194/gi-4-189-2015

Cited by 5 publications

(3 citation statements)

References 41 publications

(55 reference statements)

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“…To overcome this limitation, the research uses MODIS data as the basis for drought research. MODIS data has a better temporal and spatial resolution [36] and is less affected by weather, making it widely used for calculating drought indices [37,38]. The research results indicate that CWSI is more suitable for drought monitoring in the study area, followed by VSWI.…”

Section: Discussionmentioning

confidence: 88%

Drought in Shanxi Province Based on Remote Sensing Drought Index Analysis of Spatial and Temporal Variation Characteristics

Chen

Yang

et al. 2023

Atmosphere

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Drought is a natural disaster with long duration and which causes great harm. Studying the characteristics of drought evolution in Shanxi Province can grasp the regularity of drought occurrence and provide a basis for drought prevention and resistance. This study utilizes MODIS products to analyze and quantify the extent of drought in a specific area. The study calculates several indices, including the Crop Water Stress Index (CWSI), Vegetation Supply Water Index (VSWI), and Temperature Vegetation Dryness Index (TVDI), using variables such as the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), Evapotranspiration (ET), and Potential Evapotranspiration (PET). Additionally, three drought indices are analyzed for correlation with the self-calibrated Palmer Drought Severity Index (sc-PDSI), and the most suitable drought index is selected through validation with typical drought events. Finally, the selected indices are used to investigate the spatiotemporal characteristics of drought in the study area from 2001 to 2020. The results show: (1) CWSI and sc-PDSI have a strong correlation both in terms of time and spatial analysis. Furthermore, CWSI has been shown to be more effective in monitoring significant drought events. (2) The multi-year mean values of CWSI range from 0.71 to 0.85, with a significant degree of spatial heterogeneity. In the study area, the percentage of the area affected by different levels of drought is in the following order: moderate drought > severe drought > mild drought > no drought. (3) The trend of CWSI changes shows that the drought situation in Shanxi Province has been alleviated from 2001 to 2020, and the overall spatial distribution indicates that the degree of drought alleviation in the southern region is greater than that in the northern region. The turning point from drought to wetness in the study area was in 2011, showing the overall characteristic of “dry in the north and wet in the south”.

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Section: Discussionmentioning

confidence: 88%

Drought in Shanxi Province Based on Remote Sensing Drought Index Analysis of Spatial and Temporal Variation Characteristics

Chen

Yang

et al. 2023

Atmosphere

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“…Based on the MODIS drought severity index (DSI), Orvos et al (2015) reported 17% of the land area exhibited significant trend of either drying or wetting, and most of such locations were joined to large, geographically correlated areas. Our study found significant reductions of rainfall and soil moisture in drought period, while higher than normal air temperatures were detected during drought period in most of the land covers.…”

Section: Direct Concurrent Impacts Of Droughtsmentioning

confidence: 99%

“…Among all of these metrics, the PDSI is perhaps the best well known and commonly used drought index which is determined by monthly water supply, water outputs, and preceding soil water status. Nonetheless, the PDSI has weaknesses of lag of emerging droughts and ineffectiveness for mountainous regions or in spring (Orvos et al, 2015;Mu et al, 2013). indicator of the recovery potential, should be investigated to examine the large scale ecological collapse.…”

Section: Introductionmentioning

confidence: 99%

Responses and adaptation strategies of terrestrial ecosystems to climate change

Yu¹

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Responses and adaptation strategies of regional terrestrial ecosystems to climate change Zhen Yu Terrestrial ecosystems are likely to be affected by climate change, as climate changeinduced shift of water and heat stresses patterns will have significant impacts on species composition, habitat distribution, and ecosystem functions, and thereby weaken the terrestrial carbon (C) sink and threaten global food security and biofuel production. This thesis investigates the responses of terrestrial ecosystems to climate change and is structured in four main chapters. The first chapter of the thesis is directed towards the impacts of snow variation on ecosystem phenology. Variations in seasonal snowfall regulate regional and global climatic systems and vegetation growth by changing energy budgets of the lower atmosphere and land surface. We investigated the effects of snow on the start of growing season (SGS) of temperate vegetation in China. Across the entire temperate region in China, the winter snow depth increased at a rate of 0.15 cm•yr-1 (p=0.07) during the period 1982-1998, and decreased at a rate of 0.36 cm•yr-1 (p=0.09) during the period 1998-2005. Correspondingly, the SGS advanced at a rate of 0.68 d•yr-1 (p<0.01) during 1982 to 1998, and delayed at a rate of 2.13 d•yr-1 (p=0.07) during 1998 to 2005, against a warming trend throughout the entire study period of 1982-2005. Spring air temperature strongly regulated the SGS of both deciduous broad-leaf and coniferous forests; whilst the winter snow had a greater impact on the SGS of grassland and shrubs. Snow depth variation combined with air temperature contributed to the variability in the SGS of grassland and shrubs, as snow acted as an insulator and modulated the underground thermal conditions. Additionally, differences were seen between the impacts of winter snow depth and spring snow depth on the SGS; as snow depths increased, the effect associated went from delaying SGS to advancing SGS. The observed thresholds for these effects were snow depths of 6.8 cm (winter) and 4.0 cm (spring). The results of this study suggest that the response of the vegetation's SGS to seasonal snow change may be attributed to the coupling effects of air temperature and snow depth associated with the soil thermal conditions. The second chapter further addresses snow impacts on terrestrial ecosystem with focus on regional carbon exchange between atmosphere and biosphere. Winter snow has been suggested to regulate terrestrial carbon (C) cycling by modifying micro-climate, but the impacts of snow cover change on the annual C budget at the large scale are poorly understood. Our aim is to quantify the C balance under changing snow depth. Here, we used site-based eddy covariance flux data to investigate the relationship between snow cover depth and ecosystem respiration (Reco) during winter. We then used the Biome-BGC model to I would never have been able to survive during this long journey of dissertation without the guidance of my committee members, help from friends, and support from ...

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