Assessing the response lag and vulnerability of terrestrial vegetation to various compound climate events in mainland China under different vegetation types DOI Creative Commons
Tian Yao, Chuanhao Wu, Pat J.‐F. Yeh

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 11, 2024

Abstract In the context of climate warming, compound dry-hot (CDH), dry-cold (CDC), wet-hot (CWH), and wet-cold (CWC) events have become more frequent widespread in recent decades, causing severe but disproportionate impacts on terrestrial vegetation. However, understanding how vegetation vulnerability responds to these (CCEs) is still limited. Here, we developed a multivariate copula conditional probabilistic model integrating Standardized Precipitation Index (SPI), Temperature (STI), Normalized Difference Vegetation (NDVI) together quantify response each CDH, CDC, CWH CWC under diverse climates mainland China. Results show that CDC result largest probability loss relative other three CCEs, with NDVI below 40% percentile being 4.8%-13.0% (0.5%-2.6%) larger than individual dry (cold) events. contrast, leads lowest among all 5.6% ~ 6.9% (4.2% 5%) less wet (hot) The CCEs varies considerably ecosystems types. Loess Plateau northwestern Xinjiang (Inner Mongolia) highly susceptible (CDH) events, while northeastern southern China (eastern coastal southwestern regions) vulnerable (CWH) Shrubland, grassland cropland exhibit higher CDH deciduous (evergreen) forests are CWC(CWH) which may be related physiological characteristics, survival strategies, climatic adaptations. This study enhances our various types provides theoretical support for development measures mitigate hazards.

Language: Английский

Estimation of Spatial–Temporal Dynamic Evolution of Potential Afforestation Land and Its Carbon Sequestration Capacity in China DOI Creative Commons
Zhipeng Zhang, Wang Zong, Xiaoyuan Zhang

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(16), P. 3098 - 3098

Published: Aug. 22, 2024

Afforestation is an important way to effectively reduce carbon emissions from human activities and increase sinks in forest ecosystems. It also plays role climate change mitigation. Currently, few studies have examined the spatiotemporal dynamics of future afforestation areas, which are crucial for assessing sequestration In order obtain dynamic distribution potential land over time under scenarios China, we utilized random method this study calculate weights selected influencing factors on land, such as natural vegetation attributes environmental factors. The “weight hierarchy approach” was used quality index different regions 5-year intervals 2021 2060 extract high-quality lands each period. By dynamically analyzing 2060, can identify optimal sites period formulate a progressive plan. This approach allows more accurate application FCS model evaluate changes capacity newly afforested 2060. results indicate that average area will reach 75 Mha northern region, areas mainly distributed both sides “Hu Line”, while southern they primarily Yunnan–Guizhou Plateau some coastal provinces. calculated cumulative storage 11.68 Pg C, with peak rate during 2056–2060 0.166 C per year. Incorporating information succession, production potential, resilience quantifying factor enhance accuracy predictions lands. conclusions provide reference formulation plans assessment their capacity.

Language: Английский

Citations

3
Tracking mangrove condition changes using dense Landsat time series DOI Creative Commons
Xiucheng Yang, Zhe Zhu, Kevin D. Kroeger

et al.

Remote Sensing of Environment, Journal Year: 2024, Volume and Issue: 315, P. 114461 - 114461

Published: Oct. 11, 2024

Language: Английский

Citations

3
An adaptive spatiotemporal tensor reconstruction method for GIMMS-3g+ NDVI DOI

Mengyang Cai,

Yao Zhang, Xiaobin Guan

et al.

Remote Sensing of Environment, Journal Year: 2024, Volume and Issue: 316, P. 114511 - 114511

Published: Nov. 15, 2024

Language: Английский

Citations

3
Rainfall seasonality dominates critical precipitation threshold for the Amazon forest in the LPJmL vegetation model DOI Creative Commons
Da Nian, Sebastian Bathiany, Boris Sakschewski

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 947, P. 174378 - 174378

Published: July 1, 2024

Understanding the Amazon Rainforest's response to shifts in precipitation is paramount with regard its sensitivity climate change and deforestation. Studies using Dynamic Global Vegetation Models (DGVMs) typically only explore a range of socio-economically plausible pathways. In this study, we applied state-of-the-art DGVM LPJmL simulate forest's under idealized scenarios where linearly decreased subsequently increased between current levels zero. Our results indicate nonlinear but reversible relationship vegetation Above Ground Biomass (AGB) Mean Annual Precipitation (MAP), suggesting threshold at critical MAP value, below which biomass decline accelerates decreasing MAP. We find that approaching accompanied by slowing down, can hence be expected warn accelerating rainfall. The lowest northwestern Amazon, whereas eastern southern regions may already their thresholds. Overall, identify seasonality potential evapotranspiration (PET) as most important parameters determining value. While fires show little effect on pattern general, ability trees adapt water stress investing deep roots leads lower some areas PET are high. findings underscore risk forest degradation due changes cycle, imply currently characterized higher availability exhibit heightened vulnerability future drying.

Language: Английский

Citations

2
Terrain or climate factor dominates vegetation resilience? Evidence from three national parks across different climatic zones in China DOI Creative Commons
Shuang Liu,

Lingxin Wu,

Shiyong Zhen

et al.

Forest Ecosystems, Journal Year: 2024, Volume and Issue: 11, P. 100212 - 100212

Published: Jan. 1, 2024

Vegetation resilience (VR), providing an objective measure of ecosystem health, has received considerable attention, however, there is still limited understanding whether the dominant factors differ across different climate zones. We took three national parks (Hainan Tropical Rainforest National Park, HTR; Wuyishan WYS; and Northeast Tiger Leopard NTL) China with less human interference as cases, which are distributed in climatic zones, including tropical, subtropical temperate monsoon climates, respectively. Then, we employed probabilistic decay method to explore spatio-temporal changes VR their natural driving patterns using Geographically Weighted Regression (GWR) model well. The results revealed that: (1) from 2000 2020, Normalized Difference Index (NDVI) fluctuated between 0.800 0.960, exhibiting overall upward trend, mean NDVI NTL (0.923) ​> ​HTR (0.899) ​WYS (0.823); (2) positive trend time vegetation exceeded that negative indicating gradual recovery since 2012; (3) HTR was primarily influenced by elevation, aspect, average ​annual temperature change (AATC), annual precipitation (AAPC); WYS' mainly affected (AAP), AAPC; while terrain (elevation slope) were main NTL; (4) among influencing changes, AAPC had highest proportion (66.7%), AAP occupied largest area WYS (80.4%). While NTL, elevation served factor for VR, encompassing 64.2% its area. Consequently, our findings indicated force parks, drove NTL. Our research promoted a deeper mechanism behind VR.

Language: Английский

Citations

1
Global forest resilience change from 2001 to 2022 DOI
Jing Guo, Zhiliang Zhu, Peng Gong

et al.

International Journal of Remote Sensing, Journal Year: 2024, Volume and Issue: 45(17), P. 5889 - 5900

Published: Aug. 2, 2024

Forest resilience, the ecosystem's capacity to withstand perturbations and retain primary functions characteristics, is an essential indicator in evaluating fate of ecosystem under rapid climate change. Detailed information about forest resilience change, such as number changes, direction (reduced vs increased), timing (start/end year), or duration critical but often not well demonstrated at global scale. Here, we applied lag-one autocorrelation (AC1) a on time series MODIS vegetation index images from 2001 2022 employed LandTrendr spectral-temporal segmentation algorithms track Our results showed that forests changed nearly 3 times average, over 50% had overall downward trend early 2000s up recently. However, current condition changing for better globally with approximately 53% currently showing increased during recent average patches reduced becoming smaller more scattered. Over half arid temperate domains still show decreased recently, which highlights need improved management practices.

Language: Английский

Citations

1
The Accelerating Loss of Resilience in Suburban Woodlands Can Largely Be Attributed to the Changes in Urban Precipitation Patterns DOI
Han Chen,

Yuhui Xiang

Global Change Biology, Journal Year: 2024, Volume and Issue: 30(10)

Published: Oct. 1, 2024

ABSTRACT Vegetation resilience holds significant importance for stabilizing ecosystem service functions in a changing climate. While global land surface vegetation changes have been extensively studied, the impact of urbanization on suburban woodlands remains inadequately understood. In this study, we utilized two critical slowing down (CSD) indicators, namely lag‐one autocorrelation (LOA) and variance (VA), to assess resilience, its long‐term trends, influencing factors across 1356 cities worldwide. The recovery rates estimated by LOA () VA showed close alignment with low forest coverage (SFC) areas (correlation coefficient ( r ) = 0.95). However, notable divergence was observed high SFC 0.73). Suburban typically exhibited lower rate estimates, thus indicating greater compared SFC. From 1986 2022, woodland over 83% demonstrated upward trend, an average 3.23 × 10 −3 year −1 both , signifying widespread decline resilience. accelerating pace led higher rising during 2010–2022 (5.11 1986–1999 (0.49 ). decrease forestland primarily attributed reduced precipitation urban suburbs, which can be explained urbanization‐induced heat island building barrier effects, causing shift center from suburbs central cities. summary, study revealed that diminishes altering patterns. These findings underscore necessity augmenting water availability restore these woodlands, thereby enhancing their value.

Language: Английский

Citations

1
Stand age diversity and climate change affects forests’ resilience and stability, although unevenly DOI Creative Commons
Elia Vangi, Daniela Dalmonech, Elisa Cioccolo

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: July 13, 2023

Abstract Stand age significantly influences the functioning of forest ecosystems by shaping structural and physiological plant traits, affecting water carbon budgets. Forest distribution is determined interplay tree mortality regeneration, influenced both natural anthropogenic disturbances. Thus, human-driven alteration presents an underexplored avenue for enhancing stability resilience. In our study, we investigated how impacts resilience budget under current future climate conditions. We employed a biogeochemical model on three historically managed stands, projecting their as undisturbed systems, i.e., left at evolution with no management interventions. The model, driven data from five Earth System Models four representative scenarios one baseline scenario, spanned 11 classes each stand. Our findings indicate that Net Primary Production (NPP) peaks in young middle-aged (16- to 50-year-old), aligning ecological theories, regardless scenario. Under change, beech exhibited increase NPP maintained across all classes, while remained constant rising atmospheric CO 2 temperatures. However, declined change Norway spruce Scots pine sites. these coniferous forests, were more influenced. These results underscore necessity accounting species-specific reactions evaluating We, therefore, advocate customized strategies enhance adaptability forests changing climatic conditions, taking into account diverse responses different species groups climate.

Language: Английский

Citations

3
Spatial Optimization Based on the Trade-Off between Ecosystem Conservation and Opportunity Cost of Tarim National Park in Xinjiang, China DOI Creative Commons
Xinyuan Zhang, Lu Zhang, Zhiming Zhang

et al.

Land, Journal Year: 2024, Volume and Issue: 13(1), P. 121 - 121

Published: Jan. 22, 2024

National parks (NPs) are the flagship protected areas in China’s conservation network and play a key role ecological protection of core objects important natural landscapes. However, shortage spatial optimization methods based on quantitative indicators has limited spatially explicit identification national parks. Therefore, this study, we selected main area Tarim River (MTR) Xinjiang as an example to optimize boundary park. We constructed evaluation system representativeness, importance, foundation existing area. Subsequently, comprehensively employed species distribution model simulate habitat primary targets. Additionally, optimized region using integer linear model, considering multiple scenarios. The results study show that fewer than 30% MTR protect objects. Using different goals, for eight scenarios most effective park establishment scenario covers total 15,009.3 km2, which is 8157.5 km2 more already place would include Populus Nature Reserve Luntai Forest Park. opportunity cost be paid according approximately USD 115.14 million. ratios each object expanded 50%, 27.7% higher effect produced by area, so recommend Park set up basis boundary.

Language: Английский

Citations

0
Unveiling the landscape predictors of resilient vegetation in coastal wetlands to inform conservation in the face of climate extremes DOI
Fangyan Cheng, Jialin Liu, Junlin Ren

et al.

Global Change Biology, Journal Year: 2024, Volume and Issue: 30(5)

Published: May 1, 2024

Abstract Unveiling spatial variation in vegetation resilience to climate extremes can inform effective conservation planning under change. Although many efforts are implemented on landscape scales, they often remain blind resilience. We explored the distribution of drought‐resilient (i.e., that could withstand and quickly recover from drought) its predictors across a heterogeneous coastal long‐term wetland conversion, through series high‐resolution satellite image interpretations, analyses, nonlinear modelling. found varied greatly drought be predicted with distances coastline tidal channel. Specifically, exhibited nearly bimodal had seaward optimum at ~2 km (corresponding an inundation frequency ~30%), pattern particularly pronounced areas further away channels. Furthermore, we were more likely eliminated by conversion. Even protected where conversion was slowed, increasingly lost landward combination rapid plant invasions optimum. Our study highlights using features but without incorporating this predictive understanding, may risk failing face extremes.

Language: Английский

Citations

0