Diverse responses of gross primary production and leaf area index to drought on the Mongolian Plateau

Bai, Yu; Liu, Menghang; Guo, Qun; Wu, Genan; Wang, Weimin; Li, Shenggong

doi:10.1016/j.scitotenv.2023.166507

Cited by 10 publications

(8 citation statements)

References 104 publications

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“…6(g), it is discovered that the grid points with poor performance in June and August are concentrated in the forest area, which is the dark green area in the WRB's northeast hinterland. As is widely known, forests have more access to deeper soil moisture than farming land and grassland (Xu et al, 2018;Bai et al, 2023), resulting in forests having higher drought tolerance than other terrestrial vegetation types (Jiang et al, 2020;Chen et al, 2022). However, the soil moisture data used in this study is only 0 to 10cm of soil surface layer, which could explain why GTDI's drought diagnosis ability in the forest region is skewed.…”

Section: The Efficacy Verification In Identifying Droughtmentioning

confidence: 89%

GTDI: a gaming integrated drought index implying hazard causing and bearing impacts changing

Zhao,

Yang,

Zhang

et al. 2024

Preprint

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Abstract. Developing an effective and reliable integrated drought index is crucial for tracking and identifying droughts. The study employs game theory to create a spatially variable weight drought index (GTDI) by combining two single-type indices: the agricultural drought index (SSMI), which implies drought hazard-bearing conditions, and the meteorological drought index (SPEI), which implies drought hazard-causing conditions. Also, the entropy theory-based drought index (ETDI) is induced to incorporate a spatial comparison to the GTDI to illustrate the rationality of gaming weight integration. Leaf Area Index (LAI) data is employed to confirm the reliability of the GTDI in identifying drought by comparing it with the SPEI, SSMI, and ETDI. Furthermore, an assessment is conducted on the temporal trajectories and spatial evolution of the GTDI-identified drought to discuss the GTDI’s advancedness in monitoring changes in hazard-causing and bearing impacts. The results showed that the GTDI has a greatly high correlation with single-type drought indices (SPEI and SSMI), and its gaming weight integration is more logical and trustworthy than the ETDI. As a result, it outperforms ETDI, SPEI, and SSMI in recognizing drought spatiotemporally, and is projected to replace single-type drought indices to provide a more accurate picture of actual drought. Additionally, GTDI exhibits the gaming feature, indicating a distinct benefit in monitoring changes in hazard-causing and bearing impacts. The case studies show drought events in the Wei River Basin are dominated by a lack of precipitation. The hazard-causing index SPEI dominates the early stages of a drought event, whereas the hazard-bearing index SSMI dominates the later stages. This study surely serves as a helpful reference for the development of integrated drought indices as well as regional drought mitigation, prevention, and monitoring.

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Section: The Efficacy Verification In Identifying Droughtmentioning

confidence: 89%

GTDI: a gaming integrated drought index implying hazard causing and bearing impacts changing

Zhao,

Yang,

Zhang

et al. 2024

Preprint

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“…Previous studies also revealed that GPP was more sensitive to drought than LAI in China [61,62]. The changes in GPP reflect the changes in photosynthesis, which can be influenced by vegetation greenness, light use efficiency, and canopy conductance [2,62]. Climate change can affect the influencing factors of GPP, and greenness is only one of the influencing factors for GPP; therefore, GPP is more sensitive to climate change than LAI [61,62].…”

Section: The Significant Increase In Vegetation and Its Driving Forcesmentioning

confidence: 98%

“…Vegetation is a crucial driver in global water and energy cycles, carbon balance, and climate stability [1]. Vegetation indexes, such as the normalized difference vegetation index (NDVI), the leaf area index (LAI), and gross primary productivity (GPP), are the main indicators for remote sensing monitoring of vegetation [2,3]. In many parts of the global vegetated areas, vegetation coverage has dramatically risen since the 1980s [1].…”

Section: Introductionmentioning

confidence: 99%

“…The combined use of LAI and GPP can reliably reflect vegetation growth. In addition, the responses of LAI and GPP to climate change may be different [2]. Therefore, comparative analyses of LAI and GPP can obtain more comprehensive results.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the interaction between various factors and the growth patterns of vegetation in different environments still needs to be further explored in the MHRB. The goals of this study are the following: (1) to clarify the spatial distribution and the spatiotemporal variation characteristics of LAI and GPP in the MHRB; (2) to analyze the driving forces and their interactive effects for the spatial heterogeneity of vegetation distribution; (3) to analyze the distribution of vegetation in different environments; and (4) to explore the driving forces for vegetation change trends.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Spatial Heterogeneity and the Increasing Trend of Vegetation and Their Driving Mechanisms in the Mountainous Area of Haihe River Basin

Cao,

Wang,

Zhang

et al. 2024

Remote Sensing

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In addition to serving as North China’s water supply and ecological barrier, the mountainous area of the Haihe River basin (MHRB) is a crucial location for the application of ecological engineering. Vegetation is an important component in the ecological conservation and eco-hydrological progress of the MHRB. A better understanding of regional vegetation growth can be achieved by a thorough investigation of vegetation indicators. In this research, the leaf area index (LAI) and gross primary productivity (GPP) were chosen as vegetation indicators. The characteristics and driving forces of the spatiotemporal variations of LAI and GPP in the MHRB were explored through Sen’s slope, the Mann–Kendall test, the optimal parameter-based geographical detector model, and correlation analysis. From 2001 to 2018, the annual LAI and GPP increased significantly on the regional scale. The areas with significantly increased vegetation accounted for more than 81% of the MHRB. Land use was the most influential element for the spatial heterogeneity of LAI and GPP, and the humidity index was the most crucial one among climate indicators. Non-linear enhancement or bivariate enhancement was discovered between any two factors, and the strongest interaction was from land use and humidity index. The lowest vegetation cover was found in dry regions with annual precipitation below 407 mm and the humidity index under 0.41; while in both forests and large undulating mountains, higher LAI and GPP were observed. About 87% of the significantly increased vegetation was found in areas with unaltered land use. The increase in vegetation in the MHRB from 2001 to 2018 was promoted by the increased precipitation and humidity index and the reduced vapor pressure deficit. The sensitivity of GPP to climate change was stronger than that of LAI. These findings can serve as a theoretical guide for the application of ecological engineering and ecological preservation in the MHRB.

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Spatial analysis of remote sensing and meteorological indices in a drought event in southwestern Spain

Quirós,

Fragoso-Campón

2024

Theor Appl Climatol

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The effects of global warming and climate change are being felt through more extreme and prolonged periods of drought. Multiple meteorological indices are used to measure drought, but they require hydrometeorological data; however, other indices measured by remote sensing and used to quantify vegetation vigor can be correlated with the former. This study investigated the correlation between both index types by vegetation type and season. The correlations were also spatially modeled in a drought event in southwestern Spain. In addition, three maps with different levels of detail in terms of vegetation categorization were compared. The results generally showed that grassland was the most well correlated category between the SPEI and the FAPAR, LAI, and NDVI. This correlation was more pronounced in autumn and spring, which is when most changes in vegetation senescence and growth occur. The spatiotemporal analysis indicated a very similar behavior for grasslands grouped in an area indicated by the climate change adaptation maps as having a high evapotranspiration forecast. Finally, in a forest-based forecast analysis, the indices that best explained the performance of the SPEI were again FAPAR, LAI, and NDVI, with a lag of up to 20 days. Therefore, the results showed that remotely sensed indices are good indicators of drought status and can be variably explanatory of traditional drought indicators. Moreover, complementing the study with spatiotemporal analysis made it possible to detect areas particularly vulnerable to climate change.

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Diverse responses of gross primary production and leaf area index to drought on the Mongolian Plateau

Cited by 10 publications

References 104 publications

GTDI: a gaming integrated drought index implying hazard causing and bearing impacts changing

GTDI: a gaming integrated drought index implying hazard causing and bearing impacts changing

Spatial Heterogeneity and the Increasing Trend of Vegetation and Their Driving Mechanisms in the Mountainous Area of Haihe River Basin

Spatial analysis of remote sensing and meteorological indices in a drought event in southwestern Spain

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