Vegetation net primary productivity (NPP) is a crucial indicator for assessing the carbon balance in terrestrial ecosystems. Qualitative and comparative research on the NPP influenced by human activities, climate change, and their interactions remains insufficient. The Three-North Shelter Forest Program (TNSFP), initiated in 1978, provides a valuable reference for such investigations. This study employs an improved residual trend method to analyze the spatiotemporal patterns, trends, and driving factors of vegetation NPP during the second phase of the Three-North Shelter Forest Program (2001–2020), as well as TNSFP’s contribution to vegetation NPP. The results indicate that (1) from 2001 to 2020, overall vegetation NPP exhibited a significant fluctuating upward trend at a rate of 3.69 g C/m−2 annually; and (2) precipitation, accounting for 1.527 g C/m−2, had a more significant impact on vegetation net productivity compared to temperature (0.002 g C/m−2). Climate factors (76%) significantly influenced vegetation NPP in the Three-North Shelter Forest region more than human activities (24%). In the last decade (2011–2020), the climate contribution rate decreased to 67%, while the human activity contribution rate increased by seven percentage points compared to the previous decade (2001–2010); (3) during 2001–2020, TNSFP contributed 10.9% to the total human activity contribution to vegetation net primary productivity, approximately 2.6% of the overall contribution; (4) After the second phase of TNSFP was enacted, PM2.5 levels decreased by an average of −0.57 μg/m−3/a−1. Concurrently, soil conservation improved from 6.57 t/km2 in 2001 to 14.37 t/km2 in 2020.

Quantitative Assessment of the Impact of the Three-North Shelter Forest Program on Vegetation Net Primary Productivity over the Past Two Decades and Its Environmental Benefits in China

Climate extremes, such as heatwaves and droughts, significantly impact terrestrial ecosystems. This study investigates the influence of compound hot–dry (CHD) events on vegetation productivity in northern East Asia. Four of the most widespread CHD events occurring during the summer from 2003 to 2019 were selected as the focus of this research. We first verified the performance of the Community Land Model version 5 (CLM5) in the region and then conducted factor-controlled experiments using CLM5 to assess the effects of different climate factors on gross primary productivity (GPP) changes during CHD events. Our results show that vegetation productivity exhibits greater sensitivity to CHD events within the transitional climatic zone (TCZ) than in other affected areas. In grassland areas within the TCZ, precipitation deficit is the primary factor leading to the decrease in GPP (explaining 56%–90% of GPP anomalies), while high temperatures serve as a secondary detrimental factor (explaining 13%–32% of GPP anomalies). In high-latitude forests outside the TCZ, high temperature has a more significant impact on suppressing GPP, while the decrease in soil moisture has a synchronously negligible impact on GPP. There are differences in the effects of high solar radiation on grasslands and woodlands during CHD events. It was observed that high radiation benefits trees by increasing the maximum carboxylation rate (Vcmax) and maximum electron transport rate (Jmax), as well as enhancing photosynthesis, but has a negligible impact on grasses. Furthermore, this study highlights the potential for compound events to impact vegetation productivity more than expected from individual events due to confounding nonlinear effects between meteorological factors. More than 10% of the negative anomalies in GPP during two CHD events in 2017 and 2010 were attributed to these nonlinear effects. These research findings are significant for understanding ecosystem responses to climate extremes and their influence on carbon cycling in terrestrial ecosystems. They can also contribute to more precisely evaluating and predicting carbon dynamics in these regions.

Impacts of Compound Hot–Dry Events on Vegetation Productivity over Northern East Asia

The importance of integrating climate resilience into conservation efforts to safeguard the Western Ghats, one of the hottest hotspots in the world, is underscored by our findings. The research was designed to provide an integrated approach to help preserve the ecological productivity in a biodiversity sensitive area. The study investigates the dynamics of terrestrial productivity, examining its trends over time (2001-2023) and identifying the various factors that influence it, a topic that has been understudied in this biodiversity hotspot. In 2019, Net Primary Productivity (NPP) results experienced significant declines across all land cover types, with the decline starting in 2015 when forest NPP dropped by 0.24 and cropland by 0.1 kg C m[Formula: see text] year[Formula: see text]. The NPP steadily recovered the following year, especially in the croplands. The factor detector q value indicated the degree of influence of each factor as vegetation parameters ([Formula: see text]), climatic indicators ([Formula: see text]), anthropogenic factor ([Formula: see text]), and topographic condition ([Formula: see text]). Results from the innovative approach to assess spatial heterogeneity emphasize the influence of the combinations of Land Use Land Cover (LULC) with Fraction of Photosynthetically Active Radiation (FPAR), Leaf Area Index (LAI), Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), and Enhanced Vegetation Index (EVI), followed by LST interacting with FPAR, and NDVI with LAI, had the greatest impact on NPP. The study employs multiple regression to evaluate the effects of variables, the Mann-Kendall method for trend analysis, and Geo-detector for spatial variation. 

pdf/synergistic-analysis-of-climate-and-anthropogenic-impacts-on-orcw8psn4uhl.pdf

Synergistic analysis of climate and anthropogenic impacts on NPP dynamics in India’s Western Ghats using geodetector

Surface coal development activities include mining and ecological restoration, which significantly impact regional carbon sinks. Quantifying the dynamic impacts on carbon sequestration in vegetation (VCS) during coal development activities has been challenging. Here, we provided a novel approach to assess the dynamics of VCS affected by large-scale surface coal mining and subsequent restoration. This approach effectively overcomes the limitations imposed by the lack of finer scale and long-time series data through scale transformation. We found that mining activities directly decreased VCS by 384.63 Gg CO 

pdf/dynamics-of-carbon-sequestration-in-vegetation-affected-by-2z9d94ugwp.pdf

Dynamics of carbon sequestration in vegetation affected by large-scale surface coal mining and subsequent restoration

Abstract Vegetation productivity in India varies at intraseasonal to interannual time scales, influenced by meteorological factors sensitive to large‐scale climate teleconnections. While the impact of global climate variability on Indian monsoon and its extremes is well known, their effects on Indian vegetation productivity are relatively less understood. This study addresses this gap by decomposing dominant modes of spatio‐temporal variability of gross primary productivity (GPP) over India and examining their dependence on climate teleconnections. We found that El‐Niño Southern Oscillation (ENSO) and Pacific Meridional Mode (PMM) significantly impact GPP, especially in western and southern peninsular India during the monsoon and post‐monsoon seasons (correlation coefficient = ∼0.5). However, there is an east‐west asymmetry in the PMM‐GPP correlation. The western region and southern peninsula are negatively correlated, while northeast India positively correlates with PMM. Using wavelet decomposition, we show that more than half of temporal variability in the GPP comprises low‐frequency components. These low‐frequency signals primarily drive the relationship between GPP and climate teleconnections. Next, we identify the dominant spatial modes of low‐frequency signals of GPP. We tested the predictability of the principal components of GPP using teleconnections and hydrometeorological variables. While most of the predictive skill of GPP comes from its past (memory up to 5 months, R 2 score of up to 0.5), adding teleconnection indices as predictors improves the prediction skill at lead times (with an increase of 30%–50% in R 2 values, and up to 10%–15% reduction in RMSE). Our results underscore the utility of using hydrometeorological and distant climate teleconnection in GPP prediction for longer lead times. 

Vegetation Productivity in India Is Modulated by Climate Teleconnections From the Pacific Ocean

India is the second-highest contributor to the post-2000 global greening. With satellite data, here we show that this 18.51% increase in Leaf Area Index (LAI) during 2001-2019 fails to translate into increased carbon uptake due to warming constraints. Our analysis further shows 6.19% decrease in Net Primary Productivity (NPP) during 2001-2019 over the temporally consistent forests in India despite 6.75% increase in LAI. We identify hotspots of statistically significant decreasing trends in NPP over the key forested regions of Northeast India, Peninsular India, and the Western Ghats. Together, these areas contribute to 31% of the NPP of India (1274.8 TgC.year -1). These regions are the warming hotspots in India. Decreasing photosynthesis and stable respiration, above a threshold temperature, are the key reasons behind the declining NPP. Warming has already started affecting carbon uptake in Indian forests and calls for improved climate resilient forest management practices in a warming world.

pdf/warming-inhibits-increases-in-vegetation-net-primary-2mo79sil.pdf

Warming inhibits Increases in Vegetation Net Primary Productivity despite Greening in India

Causal and attribution studies are essential for earth scientific discoveries and critical for informing climate, ecology, and water policies. However, the current generation of methods needs to keep pace with the complexity of scientific and stakeholder challenges and data availability combined with the adequacy of data-driven methods. Unless carefully informed by physics, they run the risk of conflating correlation with causation or getting overwhelmed by estimation inaccuracies. Given that natural experiments, controlled trials, interventions, and counterfactual examinations are often impractical, information-theoretic methods have been developed and are being continually refined in the earth sciences. Here we show that transfer entropy-based causal graphs, which have recently become popular in the earth sciences with high-profile discoveries, can be spurious even when augmented with statistical significance. We develop a subsample-based ensemble approach for robust causality analysis. Simulated data, and observations in climate and ecohydrology, suggest the robustness and consistency of this approach.

pdf/robust-causality-and-false-attribution-in-data-driven-earth-2te14d0k.pdf

Robust Causality and False Attribution in Data-Driven Earth Science Discoveries

: Limited surface observations of turbulent heat fluxes result in incomplete knowledge about the surface energy balance that drives the climate system. Here, we developed a novel, purely physics-based analytical method grounded on the thermodynamic principle of maximum power. The approach derives the turbulent heat flux only from the four inputs of incoming and outgoing radiations at the land surface. The proposed approach does not use any parameterization, unlike the existing surface energy balance models, and hence does not suffer from uncertainty due to the same.We validated our methodology with 102 eddy covariance observation stations around the globe with different land use land covers. Using the satellite observations from CERES at a spatial resolution of 1 0 , we have obtained spatially distributed global analytical estimates of Sensible (𝐻) , latent heat ( 𝐿𝐸 ), and land surface heat storage ( ∆𝑄 (cid:3046) ) fluxes for the first time. For a global observed land Net radiation ( 𝑄 ∗ ) of 84 Wm -2 from the satellite, we found 𝐻 , 𝐿𝐸 and ∆𝑄 (cid:3046) to be 42 Wm -2 , 40 Wm -2 and 2 Wm -2 , respectively. The theoretical and precise estimates of all surface energy balance components will improve our understanding of surface warming for different land use land covers across the globe.

Global Estimates of Spatially Distributed Surface Energy Fluxes using Thermodynamic Principles

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Warming inhibits Increases in Vegetation Net Primary Productivity despite Greening in India

Robust Causality and False Attribution in Data-Driven Earth Science Discoveries

Global Estimates of Spatially Distributed Surface Energy Fluxes using Thermodynamic Principles