Response of Gross Primary Productivity to Drought Time‐Scales Across China

Sun, Shaobo; Du, Wen-Li; Zhang, Dongbo; Wu, Xiuchen; Chen, Baozhang; Wu, Yuntao

doi:10.1029/2020jg005953

Cited by 25 publications

(8 citation statements)

References 91 publications

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“…In general, soil moisture is positively correlated with stability, which is consistent with the results of a previous study of drylands (Kumar et al, 2020). Although a study found that in humid regions, higher soil moisture has a stronger ability to buffer the negative impact of short-term climate variability (Yao et al, 2022), and the corresponding ecosystem resistance is higher (Sun et al, 2021), in drylands, our results suggest that the ecosystem stability with respect to short-term climate variability is more dependent on the strong resilience that is caused by high soil moisture levels. Previous studies have also shown this phenomenon, under wet soil conditions, the vegetation resistance is low, which results in quick vegetation rehydration after the occurrence of rain pulses and exhibits higher resilience (Feldman et al, 2021).…”

Section: Environmental Factorssupporting

confidence: 89%

“…There was a significant positive but relatively weak spatial correlation between the total afforestation degree and ecosystem resistance ( p < 0.01), which was due to the higher resistance of forests when coping with climate anomalies compared with grasslands and farmlands (Kang et al, 2022; Sun et al, 2021). At the temporal scale, afforestation has a positive impact on stability (Figure 3f).…”

Section: Discussionmentioning

confidence: 99%

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Changed ecosystem stability in response to climate anomalies in the context of ecological restoration projects

Dang

et al. 2023

Land Degrad Dev

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Dryland ecosystems are fragile and under the context of climate change, the increased frequency of short‐term climate variability will put them under additional stress, so the ability of dryland ecosystems to stabilize against exposure to climate anomalies is becoming increasingly critical. To mitigate the degradation of dryland ecosystems in China, the Chinese Government has launched ambitious ecological restoration projects (ERPs) in drylands, and understanding the effects and mechanisms of these projects on dryland stability is particularly important for the future integration of stability enhancement into ecosystem management goals. However, few researchers have focused on such effects. Here, the agro‐pastoral ecotone in northern China, which is located in the northern drylands of China and contains several national ERPs, was used to analyze the above problem. Specifically, the stability of ecosystems in response to climate anomalies was evaluated from the perspectives of resilience and resistance, and we analyzed the impacts of ERPs and parallel policies on ecosystem stability combined with a social‐ecological systemic perspective. Our results showed that the ecosystem stability has improved in the last 20 years, and although environmental factors dominated the spatial heterogeneity and temporal variations in the stability, afforestation and socioeconomic processes associated with ERPs and parallel policies also showed significant effects. Therefore, guiding and establishing a solid and low‐ecological impact rural livelihoods is an important guarantee to achieving stability improvement through relevant policies. Our study decoupled the complex outcomes of ERPs from the perspective of ecosystem stability and contributes to the understanding of dryland stability from a social‐ecological perspective. Furthermore, new insights and theoretical foundations are provided for incorporating stability enhancement into future goals of dryland ecosystem management.

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Section: Environmental Factorssupporting

confidence: 89%

Section: Discussionmentioning

confidence: 99%

Changed ecosystem stability in response to climate anomalies in the context of ecological restoration projects

Dang

et al. 2023

Land Degrad Dev

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“…Hence, FDSI shows high predictability of VHI in grasslands/ savannah ecosystems with a lag of 0–1 week (area‐average median AC of −0.5 [‐] with a maximum of −0.93 [‐] respectively). Vegetation in arid and semi‐arid shrublands displays a higher drought tolerance due to characteristic physiology and rooting patterns (Breshears et al., 2016; Marchin et al., 2020), with a longer response time to drought (Sun et al., 2021). Hence, we observe a longer time‐lag in the response of shrubland vegetation to FDSI (2 or more weeks for about 50% of shrubland pixels) with areal median and maximum AC of −0.43 [‐] and −0.89 [‐] respectively.…”

Section: Resultsmentioning

confidence: 99%

Global Flash Drought Monitoring Using Surface Soil Moisture

Sehgal

Mohanty

2021

Water Resources Research

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Abrupt onset and swift intensification characterize flash droughts. Global surface soil moisture (θRS) from NASA's Soil Moisture Active Passive (SMAP) satellite can facilitate a near‐real‐time assessment of emerging flash droughts at a 36‐km footprint. However, a robust flash drought monitoring using θRS must account for the (a) short observation record of SMAP, (b) nonlinear geophysical controls over θRS dynamics, and (c) emergent meteorological drivers of flash droughts. We propose a new method for near‐real‐time characterization of droughts using Soil Moisture Stress (SMS, drought stress) and Relative Rate of Drydown (RRD, drought stress intensification rate)—developed using SMAP θRS (March 2015–May 2021), footprint‐scale seasonal soil water retention parameters and land‐atmospheric coupling strength. SMS and RRD are nonlinearly combined to develop Flash Drought Stress Index (FDSI) to characterize emerging flash droughts (FDSI ≥ 0.71 for moderate to high RRD and SMS). Globally, FDSI shows a high correlation with concurrent meteorological anomalies. A mechanistic evaluation of flash droughts is presented for the Northern Great Plains, Central South Africa, and Eastern Australia using FDSI, SMS, and RRD. About 5.6% of the earth's landmass experienced flash droughts of varying intensity and duration during 2015–2021 (FDSI ≥ 0.71 for >30 consecutive days), majorly in global drylands. FDSI shows high skill in forecasting vegetation health with a lead of 0–2 weeks, with exceptions in irrigated croplands and mixed forests. With readily available parameters, low data latency, and no dependence on model simulations, we provide a robust tool for global near‐real‐time flash drought monitoring using SMAP.

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“…The advantages and uncertainties of the flux product have been widely studied and verified around the globe (Piao et al ., 2013; Mystakidis et al ., 2016; Chen et al ., 2017; Gentine and Alemohammad, 2018; Kenea et al ., 2020). Although it may not be perfect, it is widely applied as an observational reference to various model results in China (Zhu et al ., 2014; Zhang et al ., 2019; Du et al ., 2020; Sun et al ., 2021). In this study, the product using both remote sensing and meteorological data (RS + METEO) is chosen as the reference because it outperforms the RS‐only product (Jung et al ., 2020).…”

Section: Methodsmentioning

confidence: 99%

How the CMIP6 climate models project the historical terrestrial GPP in China

Zhang

Jie

et al. 2022

Intl Journal of Climatology

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Gross primary production (GPP) is an important indicator that measures the carbon uptake by vegetation through photosynthesis. How the latest climate models project GPP is critical for climate change evaluation and ecosystem prediction. This study compares the historical runs of seven climate models joining CMIP6 with an observation-based dataset from 1980 to 2013 in China. It is found that BCC-CSM2-MR and MPI-ESM1-2-HR from Beijing ClimateCenter and the Max Planck Institute give the best estimation of climatological GPP at both regional and national scales. MPI-ESM1-2-HR performs much better than others in characterizing the spatial structure in regions other than the temperate continental, while CMCC-CM2-SR5 from Italy performs the best in the temperate monsoonal. No climate model can capture well the GPP interannual variation even over one climate zone. BCC-CSM2-MR is a not-too-bad choice as it provides the most positively and significantly correlated GPP grids with observations. Further analyses reveal that BCC-CSM2-MR and CMCC-CM2-SR5 can well capture ecosystem response to climate over regions except for the Tibetan Plateau. With the response parameters and the observational climate, the two climate models can simply rebuild the GPP variabilities as the observational. Over the Tibetan Plateau, all climate models produce spuriously too large precipitation, which turns precipitation from the most confining into no longer significantly influential to the ecosystem. It highlights the urgency to improve the modelling of the Plateau climate and the corresponding ecosystem-climate feedbacks.

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Response of Gross Primary Productivity to Drought Time‐Scales Across China

Cited by 25 publications

References 91 publications

Changed ecosystem stability in response to climate anomalies in the context of ecological restoration projects

Changed ecosystem stability in response to climate anomalies in the context of ecological restoration projects

Global Flash Drought Monitoring Using Surface Soil Moisture

How the CMIP6 climate models project the historical terrestrial GPP in China

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