Increased precipitation has not enhanced the carbon sequestration of afforestation in Northwest China

Increased precipitation has not enhanced the carbon sequestration of afforestation in Northwest China | Communications Earth & Environment

Abstract

Concerns have been raised about the sustainability of large-scale afforestation in semi-arid regions due to potential water constraints. This study investigated whether increased humidity in the semi-arid regions of northwest China could sustain the continued expansion of afforestation efforts. Using multi-source remote sensing data, we found that between 2012 to 2020, annual cumulative precipitation increased by 2.5 millimeters per year, while annual average carbon sequestration in afforested areas declined by 0.002 kilograms of carbon per square meter per year, indicating asynchronous trends. This disparity was primarily attributed to the trade-off between vegetation photosynthesis and transpiration in response to external water conditions, which led to a decline in the water use efficiency of afforested vegetation. The effect of water use efficiency on carbon sequestration was driven by gross primary productivity, rather than evapotranspiration. These findings underscore the importance of targeted afforestation in semi-arid regions, considering local water resource sustainability.

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Introduction

Large-scale afforestation efforts have been initiated globally in response to climate change and as part of sustainable development initiatives 1. Over the past two decades, global vegetation coverage has increased by an area comparable to the Amazon rainforest (550 million hectares) 2. China has contributed 25% to global greening, which makes it one of the countries with the most substantial growth in forest resources worldwide 3. By 2017, China had completed the afforestation of 7.36 million hectares of land, including 912,000 hectares of reforestation efforts in the northwest semi-arid regions. The current afforestation efforts in China’s northwest semi-arid regions appear to have exceeded a certain threshold of net primary productivity of 400 ± 5 g C m–2 yr–1 4, 5. Further expansion of afforestation efforts would inevitably exacerbate soil moisture depletion and water scarcity issues. Despite this, China is poised to implement even larger-scale afforestation programs in pursuit of its “dual carbon” goals, which aim to peak carbon emissions by 2030 and achieve carbon neutrality by 2060 6. Consequently, concerns have arisen about the sustainability of additional afforestation activities in semi-arid regions with seasonal water constraints 7. Notably, the northwest region of China has exhibited a pattern of warming and wetting conditions in recent years due to global climate change 8. However, these increases in precipitation often occur as extreme characterized by intense and unevenly distributed short-duration rainfall 9. Therefore, the question remains whether such precipitation patterns can support the continued expansion of large-scale afforestation and effectively enhance carbon sequestration in the semi-arid regions of northwestern China.

Carbon sequestration is widely recognized as an effective method to mitigate greenhouse gas emissions and curb global warming. Forests have a higher carbon sink capacity compared to other terrestrial ecosystems such as grasslands, wetlands, and farmlands 10. Previous studies indicate that global forest carbon sequestration decreased by 6 Pg from 1990 to 2020, whereas Chinese forests have continued to function as a carbon sink over the past few decades, primarily due to China’s extensive afforestation efforts 11, 12. Forested area in China has been expanded by an average of 3 million hectares annually since 2000 13. As of 2021, planted forests comprise approximately 33% of the total forested area, with young forests accounting for 41% and middle-aged forests for 30%, highlighting their significant carbon sequestration potential 14. Among China’s major afforestation efforts, the Grain to Green Program (GTGP) on the Loess Plateau (CLP), located in the hinterland of northwest China, is a prominent example 15. The program was initiated in 1999 to address fragile ecological conditions and carbon-water losses in the region 16. Research indicates that 80% of the CLP region experienced accelerated vegetation recovery after 2000, leading to a roughly 45% increase in carbon sequestration in artificial forests 17. By 2008, the Loess Plateau’s total carbon sequestration associated with the GTGP had reached 96.1Tg, accounting for over 4% of the global total during that period 18. Actual carbon density data from the CLP revealed that its forests acted as a carbon sink between 2001 and 2020, sequestering an average of 10.10 g C m–2 yr–1 19. Various forest types have varying capacities for carbon sequestration due to differences in photosynthesis, transpiration, and sensitivity to external environmental changes. Notably, broadleaf forests sequester more carbon annually than mixed and coniferous forests 20. Despite large-scale afforestation efforts on the CLP, which have significantly increased carbon sequestration compared to pre-1999 levels, arid conditions and water scarcity, coupled with high evapotranspiration rates, pose significant challenges to the growth and regeneration of young forests 21. Nevertheless, recent shifts in global climate patterns have led to a wetter climate in the CLP 22, which could provide an opportunity to enhance the carbon sequestration capacity of vegetation in this semi-arid region.

Net primary productivity (NPP) is a key indicator of the carbon sequestration capacity of vegetation in terrestrial ecosystems, and it is highly sensitive to the effects of climate change 23. Precipitation is the primary factor limiting carbon sequestration capacity of vegetation compared to temperature in arid and semi-arid regions 24. In water-limited ecosystems, a positive correlation exists between the interannual variation in precipitation and NPP 25. Consequently, favorable hydrothermal conditions can enhance carbon sequestration in semi-arid environments 26. Precipitation increases soil moisture availability, facilitating ecosystem carbon storage in semi-arid regions 27. With global climate warming, precipitation patterns in China have shown a shift from the Yangtze River Basin in the south to the Yellow River Basin in the north. For instance, the downstream Yellow River Basin has experienced unprecedented flooding, with maximum hourly rainfall reaching 201.9 mm 28. Meanwhile, the Yangtze River Basin endured its most severe drought in 61 years in 2022, characterized by an unusual return to dry conditions during the peak flood season 29.

Ecosystem water use efficiency (WUE) is defined as the ratio of the total gross primary productivity (GPP) to evapotranspiration (ET). It plays a critical role in characterizing the coupling of carbon and water cycles and serves as a key indicator for assessing ecosystem sensitivity to climate change 30, 31. ET includes canopy interception (\({{{{\rm{E}}}}}_{{{{\rm{i}}}}}\)), soil evaporation (\({{{{\rm{E}}}}}_{{{{\rm{s}}}}}\)), and vegetation transpiration (\({{{{\rm{E}}}}}_{{{{\rm{c}}}}}\)). Process-based models have demonstrated that seasonal variations in WUE are primarily driven by seasonal climatic factors rather than by changes in atmospheric CO2 concentration 32. Vapor pressure deficit (VPD) is recognized as a crucial environmental factor regulating photosynthesis in forest ecosystems 33, 34, 35. Soil moisture (SM) primarily influences WUE by altering the transpiration-to-evaporation ratio in vegetation 36, 37, 38. Research indicates that moderate water stress can enhance WUE by regulating stomatal function to reduce transpiration, thereby increasing WUE without reducing carbon sequestration capacity 39. Substantial evidence suggests that high VPD induces plant water deficits by increasing ET demand, which constrains photosynthesis 40, 41. Under low SM conditions, vegetation closes its stomata to reduce transpiration and maintain physiological balance, leading to limitations in photosynthesis 42. VPD and SM are thus key environmental variables that regulate WUE by influencing both photosynthesis and transpiration. While many studies have focused on the direct response of WUE to climate variables in the Chinese Loess Plateau (CLP), such as precipitation, temperature, sunshine duration, humidity, radiation, atmospheric VPD, and SM, the primary mechanisms driving WUE responses to environmental changes remain unclear 31, 43, 44, 45. It is therefore essential to further investigate the dominant mechanisms underlying WUE changes, particularly from the perspective of the trade–off between photosynthesis and transpiration.

To assess whether increased precipitation supports large-scale afforestation on the CLP region to enhance carbon sequestration, we conducted several analyses. First, we examined whether the spatiotemporal patterns of carbon sequestration capacity in afforested areas of the CLP correspond to the linear increasing trend in precipitation. Second, we analyzed how carbon-water coupling affects vegetation carbon sequestration capacity in response to changes in external water conditions (e.g., VPD and SM) as precipitation increases. This study quantified and compared the carbon sequestration capacity in afforested areas of the CLP with the interannual variation in annual cumulative precipitation, controlling for land use and cover changes. Additionally, we analyzed the direct impact of WUE on NPP by investigating how extreme rainfall-induced changes in the spatiotemporal patterns of VPD and SM influence this relationship. Finally, we explored the primary mechanisms driving WUE variation in afforested areas by examining the trade-off between environmental factors such as VPD and SM.

Results

Asynchronous trends in afforestation carbon sequestration capacity and increasing precipitation

Precipitation conditions in semi-arid regions are the key climatic factors determining vegetation growth. We found that annual cumulative precipitation (ACP) and annual average carbon sequestration (\(\overline{{{{\rm{NPP}}}}}\)) in the afforestation areas (including both Grain to Green, GTG and Non–Grain to Green, NGTG) (Supplementary Fig. 1) on the CLP showed asynchronous trend between 2001 and 2020. Specifically, ACP demonstrated a linear increasing trend (2.5 mm yr–1), while \(\overline{{{{\rm{NPP}}}}}\) followed a non-monotonic trend of first increasing (0.01 kg C m–2 yr–1) and then decreasing (–0.002 kg C m–2 yr–1) (Fig. 1a). The reliability of the precipitation trends was verified using three meteorological datasets (ERA5–Land, CHIRPS, and CPM–L3) in conjunction with Measurement site data (MS, representing the average ACP from 86 meteorological stations on the CLP) (Supplementary Fig. 1b).

a The red curve represents the interannual trend of \(\overline{{{{\rm{NPP}}}}}\) from 2001 to 2020, with the red shading showing the standard error of the \(\overline{{{{\rm{NPP}}}}}\). The purple and orange lines indicate the fitting curves of the \(\overline{{{{\rm{NPP}}}}}\) during the IP and LP periods, respectively. The blue bar chart and dashed blue line represent the interannual trend of ACP and its fitting curve, respectively. b Interannual variation trends of ACP across different data products. c1–c3 depict the spatial trends of ACP on the CLP during the IP, LP, and Δ, respectively. d1–d3 represent the spatial trends of the \(\overline{{{{\rm{NPP}}}}}\) on the CLP during the IP, LP, and Δ, respectively. P and N in the upper left corner of the raster maps indicate the percentages of pixels with positive and negative trends, respectively.

The interannual variation trends of ACP and \(\overline{{{{\rm{NPP}}}}}\) during the Initial period (IP, 2001–2011) and the Later period (LP, 2012–2020), as well as the difference between LP and IP (Δ=LP–IP), exhibited spatial heterogeneity. During the IP, 94.2% of the region showed an increasing precipitation trend, which was spatially homogeneous. In the LP and Δ, regions with increasing precipitation was primarily concentrated in the southeastern CLP (Fig. 1c1–c3). We found that the precipitation trend in the LP was greater than that in the IP, although the proportion of areas with positive trends was smaller in the LP than in the IP. By combining the R20mm, RX1day, and R99P extreme indices, we observed an increasing trend in extreme precipitation events in afforestation areas during the LP (Supplementary Fig. 2). Compared to the IP, precipitation in the LP was more extreme and abnormal. The spatial distribution of \(\overline{{{{\rm{NPP}}}}}\) trends was relatively uniform during the IP, while the LP exhibited a clear gradient in \(\overline{{{{\rm{NPP}}}}}\) trends, decreasing from the center of afforestation towards the periphery (Fig. 1d1–d2). The proportion of areas with negative differences between the IP and the LP reached 87%, suggesting a decline of afforestation carbon sequestration capacity after 2012 (Fig. 1d3).

Spatial and temporal responses of environmental variables and WUE to Increasing precipitation

The increase in precipitation directly alters the spatiotemporal pattern of the CLP hydrological cycle. Within afforestation areas, the spatial trends of annual average VPD (\(\overline{{{{\rm{VPD}}}}}\)) mainly showed an initial decrease during the IP followed by an increase during the LP, with area proportions of 98% and 51.5%, respectively. Areas experiencing decreased \(\overline{{{{\rm{VPD}}}}}\) in the IP and increased \(\overline{{{{\rm{VPD}}}}}\) in the LP spatially coincided, primarily located on the left side of the region. Consequently, these left–sided forested areas exhibited larger discrepancies compared to other forested regions (Fig. 2a1–a3). Compared to the IP, the trend of annual average SM (\(\overline{{{{\rm{SM}}}}}\)) variation in the LP exhibited spatial heterogeneity. During LP, \(\overline{{{{\rm{SM}}}}}\) changes were dominated by an increase (63.8%), mainly concentrated in the southern region (Fig. 2b1–b3), with a certain degree of spatial overlap with areas of increased precipitation. \(\overline{{{{\rm{VPD}}}}}\) and \(\overline{{{{\rm{SM}}}}}\) exhibited interannual trends of initially decreasing and subsequently increasing in the afforestation area. (Fig. 2a4, b4). Specifically, the trends of \(\overline{{{{\rm{VPD}}}}}\) and \(\overline{{{{\rm{SM}}}}}\) in the LP (\({\overline{{{{\rm{VPD}}}}}}_{{{{\rm{T}}}}{{{\rm{rend}}}}}^{{{{\rm{LP}}}}}\) and \({\overline{{{{\rm{SM}}}}}}_{{{{\rm{T}}}}{{{\rm{rend}}}}}^{{{{\rm{LP}}}}}\)) of GTG were –0.04 hPa yr–1 and 0.0003 m3 m–3 yr–1, respectively, while for NGTG, \({\overline{{{{\rm{VPD}}}}}}_{{{{\rm{T}}}}{{{\rm{rend}}}}}^{{{{\rm{LP}}}}}\) and \({\overline{{{{\rm{SM}}}}}}_{{{{\rm{T}}}}{{{\rm{rend}}}}}^{{{{\rm{LP}}}}}\) were 0.06 hPa yr–1 and 0.0005 m3 m–3 yr–1.

a1–a3 represent the spatial trends of \(\overline{{{{\rm{VPD}}}}}\) during the IP, LP, and Δ, respectively. a4 illustrates the interannual linear trends of \(\overline{{{{\rm{VPD}}}}}\) for different afforestation types (GTG and NGTG). b1–b3 represent the spatial trends of \(\overline{{{{\rm{SM}}}}}\) during the IP, LP, and Δ, respectively. b4 represents the interannual linear trends of \(\overline{{{{\rm{SM}}}}}\) for GTG and NGTG. P and N in the upper left corner of the raster maps indicate the percentages of pixels with positive and negative trends, respectively.

Previous research indicated that VPD and SM are the primary environmental factors influencing WUE. At the spatial scale, regions of decreased annual average \(\overline{{{{\rm{WUE}}}}}\) coincide with areas of high \(\overline{{{{\rm{VPD}}}}}\) and \(\overline{{{{\rm{SM}}}}}\) (Fig. 2a1–a3, Supplementary Fig. 3a1–a3). \(\overline{{{{\rm{WUE}}}}}\), \(\overline{{{{\rm{GPP}}}}}\), and \(\overline{{{{\rm{ET}}}}}\) all initially increased before subsequently decreasing. Specifically, 60.8% of forested areas experienced a decline in \(\overline{{{{\rm{WUE}}}}}\), with proportions of decreased \(\overline{{{{\rm{GPP}}}}}\) and \(\overline{{{{\rm{ET}}}}}\) at 55.2% and 89.4%, respectively (Supplementary Fig. 3b1–b3, c1–c3). We found that the reduction in \(\overline{{{{\rm{WUE}}}}}\) during the LP (\({\overline{{{{\rm{WUE}}}}}}_{{{{\rm{T}}}}{{{\rm{rend}}}}}^{{{{\rm{LP}}}}}\,\)= –0.012 g C kg–1 H2O yr–1) in afforestation was primarily driven by reductions in NGTG, with trends of decreased \(\overline{{{{\rm{GPP}}}}}\) and \(\overline{{{{\rm{ET}}}}}\) at –0.03 g C m–2 d–1 yr–1 and –0.01 mm d–1 yr–1, respectively. Conversely, the increase of \(\overline{{{{\rm{WUE}}}}}\) in GTG was mainly attributed to a greater reduction in transpiration (–0.013 mm d–1 yr–1) compared to GPP (0.004 g C m–2 d–1 yr–1).

Weakened carbon sequestration capacity due to reduced carbon-water coupling

This study first analyzed the Granger causality relationships among ACP, environmental variables (VPD and SM), WUE (GPP and ET), and NPP. We found that during the IP and LP, the causality relationships and their significance among variables exhibited consistency in the lower triangle of the heat map (Fig. 3a–b). Specifically, there was no causality between ACP and VPD (p > 0.05) during the LP, while the causality between ACP and SM was 0.61 (p < 0.01). The above findings might be attributed to the greater influence of temperature increase on VPD than precipitation 33. For semi-arid regions, seasonal precipitation is an essential supplement to SM 46. The causality relationships between VPD and WUE, and between SM and WUE, were 0.54 (p < 0.01) and 0.44 (p < 0.05), respectively. The causality between VPD and GPP was 0.7, while no significant causality was found between SM and GPP (p > 0.05). The significance of the causality between SM and ET (p < 0.01) was greater than that between VPD and ET. We observed that there were significant causal relationships (p < 0.05) between NPP and both WUE and GPP, whereas the relationship with ET was not significant (p > 0.05). It is important to note that causality relationships only demonstrate the existence of causal connections between two variables, and the direction and extent of the influence among variables ultimately need to be quantified through partial correlation methods.

The vertical axis (Cause Parameter) represents the independent variable, while the horizontal axis (Effect Parameter) represents the dependent variable. The lower triangle of the matrix displays Granger causality from the Cause Parameter to the Effect Parameter. The color of each grid cell indicates the strength of causality between the two variables. a and b correspond to IP and LP, respectively. ** indicates p < 0.01; * indicates p < 0.05.

The weakening of afforestation carbon sequestration capacity may be influenced by decoupling from carbon-water cycle. Spatially, the influence of WUE, GPP, and ET on NPP exhibited a gradient increasing from the center to the periphery of afforestation areas (Fig. 4a–b). A positive correlation was observed between WUE and NPP (Fig. 4a1, b1). The proportion of positive correlation reached over 90% of both GTG and NGTG during different periods (Fig. 4c1). During the IP, the spatial trend of GPP and ET explained 72.8% and 45% of the variation in NPP, respectively (Fig. 4a2–a3). In the LP, the weakening of WUE explained a reduction of 51% in afforestation NPP (Fig. 4b1). Notably, GPP exhibited a significant positive correlation with NPP (R2 = 0.65, p < 0.01), but ET showed a non-significant positive correlation with NPP (R2 = 0.27, p > 0.05). This indicates that, at this time, the positive impact of WUE on NPP was primarily driven by the decreasing trend in GPP rather than ET (Fig. 4b2–b3). We observed no difference in the proportion of positive or negative correlation between NPP and WUE (or GPP) for GTG and NGTG. However, there was a difference in the proportion of positive or negative correlation between NPP and ET for GTG and NGTG (Fig. 4c1–c3). These findings may be attributed to the intersecting spatial distribution of GTG and NGTG, where lateral energy transfer resulted in minimal differences in water and heat conditions between the two forest types. Additionally, compared to GTG, older NGTG forests had a higher Leaf Area Index (LAI) (Supplementary Figs. 4– 6), which reduced the proportion of solar radiation reaching the soil surface in NGTG areas, leading to decreased bare soil evaporation and consequently a larger difference in ET between GTG and NGTG forests 47.

a1–a3 represent the partial correlation coefficients between WUE and NPP, GPP and NPP, and ET and NPP during the IP period. b1–b3 display these coefficients for the LP. c1–c3 depict the proportions of positive and negative correlations between WUE and NPP, GPP and NPP, and ET and NPP for GTG and NGTG. The white bar charts represent the overall proportions, while the red and blue bar charts indicate significant positive (p < 0.05) and negative correlations (p < 0.05), respectively.

Discussion

Mechanistic explanation for low carbon-water coupling

VPD and SM are known to be important environmental factors affecting WUE, which influence the rate of photosynthetic carbon fixation and water loss by transpiration, mainly by driving water movement between the soil and atmosphere 33, 34, 48(Fig. 5). Both field measurements and model simulations have shown that high VPD is a major factor leading to limited plant photosynthesis (GPP) and reduced transpiration (\({{{\rm{Ec}}}}\)) in semi-arid regions 49. This finding is consistent with the negative correlation observed between VPD and GPP in our remote sensing data but contradicts the positive correlation between VPD and \({{{\rm{Ec}}}}\) we obtained 50(Supplementary Fig. 7a–d), suggesting that increased VPD primarily affects GPP. Furthermore, in situations of insufficient SM, plants maintain physiological balance by reducing transpiration to prevent water loss, often by closing stomata to restrict CO2 absorption, thereby inhibiting photosynthesis 39, 51. This conclusion contradicts our results, which showed photosynthetic limitations with increasing SM (R2 = 0.2, Supplementary Fig. 7d2). However, it is consistent with the positive correlation between SM and \({{{\rm{Ec}}}}\) (R2 = 0.3, Supplementary Fig. 7d4), indicating that while increasing SM does not primarily cause changes in GPP, it does promote vegetation transpiration in afforested areas.

\({\overline{{{{\rm{H}}}}}}_{{{{\rm{Trend}}}}}^{{{{\rm{LP}}}}}\) represents the annual average trend of each parameter during the LP period. H denotes NPP, GPP, ET, \({{{\rm{Ec}}}}\), \({{{\rm{Es}}}}\), WUE, LAI, respectively. Symbols “–” and “+” indicate decreasing and increasing trends, respectively.

The magnitude of WUE is primarily determined by the trade-off between photosynthesis and transpiration. This study emphasized the combined effect of atmospheric VPD and SM on WUE by coupling the processes of photosynthetic carbon gain and water movement 52, 53. We found that the increasing trend in VPD mainly inhibited GPP, while the rising trend in SM promoted the increase in Ec. However, the increase in annual average \({{{\rm{Ec}}}}\) during LP (\({\overline{{{{\rm{Ec}}}}}}_{{{{\rm{Trend}}}}}^{{{{\rm{LP}}}}}\) = 0.0005 mm d–1 yr–1) was not sufficient to fully offset the decrease in annual average soil evaporation during LP (\({\overline{{{{\rm{Es}}}}}}_{{{{\rm{Trend}}}}}^{{{{\rm{LP}}}}}\) = –0.024 mm d–1 yr–1), resulting in a reduction in ET (Supplementary Fig. 5a and Supplementary Fig. 8). Consequently, the decrease in average WUE during the LP in afforestation areas was primarily driven by a greater reduction in GPP compared to the decline in ET. Given the uneven spatial and temporal distribution of rainfall in the region, the increase in precipitation was insufficient to offset the additional water consumption across all afforestation areas, thereby indirectly leading to a weakening of carbon sequestration capacity in these areas.

Discussion on the rational selection of afforestation species and associated management policies

Afforestation can mitigate global warming through carbon sequestration 54. However, research indicates that large-scale afforestation projects in water-scarce regions of China are approaching their limits 5. This limitation arises due to competing demands for water to support both local ecosystems and human populations 55. Our study reveals that the CLP, a semi-arid region, is experiencing a trend of increasing warmth and precipitation. Despite this, expanding afforestation and reforestation efforts in the area is not ideal. On the one hand, although the forested area of the CLP has expanded, this growth has not resulted in a corresponding increase in carbon sequestration capacity (Fig. 1a) and thus has failed to yield major ecological advantages. On the other hand, the increase in water resources in this region is primarily attributed to extreme precipitation events. However, this extreme precipitation is not effectively utilized and instead leads to excessive consumption of soil and groundwater resources to sustain the continuous growth of young forests 56, 57. Additionally, with the dual impacts of global climate change and human activities, hydrometeorological conditions exhibit notable non-stationary characteristics, evidenced by frequent extreme climate events. Consequently, adapting afforestation efforts to future climate change is becoming increasingly challenging, particularly in dry and semi-arid regions 58.

Afforestation on the CLP primarily involves species such as locust trees, poplars, Chinese pines, and honeysuckles, which exhibit ecological and physiological characteristics of drought resistance, strong adaptability, and rapid growth. However, due to the extensive root systems of these vegetation types, excessive SM consumption occurs, leading to a decrease in soil water content 59. Consequently, to minimize the negative impact of afforestation on groundwater resources to the greatest extent possible, the selection of reasonable tree species and scientific implementation of planting patterns emerge as critical factors, particularly toward the goals of carbon peaking and carbon neutrality 60.According to previous research, the heavily planted deciduous broadleaf trees of this area (i.e., black locust and poplar) are C3 species. C3 species generally have lower carbon sequestration capacity compared to C4 plants but consume more water 61. Furthermore, C3 plants have limited adaptability to environmental conditions like light, temperature, and moisture, thriving under low light intensity, cool temperatures, and abundant water availability—conditions that are difficult to achieve in the arid northwest region of China 62. Therefore, selecting C4 plants with strong adaptability to future conditions is one avenue to achieving the dual objectives of reduced water consumption and increased carbon sequestration. Moreover, in the wake of large-scale afforestation efforts on the CLP, some tree species have failed to thrive in certain areas due to inadequate scientific guidance. Subsequent interventions involving the replanting of non-surviving seedlings have been reported. Previous research similarly demonstrates that while afforestation cover on the CLP has substantially expanded, issues such as degradation (e.g., stunted tree growth) and diminished functionality have gradually affected the restored vegetation 63.

Adoption of scientifically informed planting methods to improve afforestation survival rates in the future is essential. For example, planting various types of forests simultaneously can increase ecosystem diversity and stability 64, providing ideal growth conditions for wooded areas. The planting density of each species should also be considered 65. Excessive planting density can lead to inefficient use of sunlight and soil nutrients, slowing tree development 66. Conversely, insufficient density, while potentially hastening the attainment of desired trunk diameters, may reduce lateral exchange of air and moisture within the afforestation, leading to suboptimal overall canopy development 67. Hence, meticulous evaluation of disparities among planting approaches aimed at maximizing carbon sequestration benefits will be a critical step toward achieving harmony between humans and nature and facilitating sustainable development. Given the existence of a threshold for forest carbon sequestration capacity, maintaining high carbon sequestration in artificial forests over the long term necessitates context–specific afforestation and reforestation efforts based on factors such as water availability, soil conditions, and tree species as well as the integration of sound scientific management practices. For example, appropriately managing the age structure of forests and optimizing their spatial–temporal layout can extend the duration of their carbon sequestration services 68. Furthermore, considering China’s limited arable land resources, the simplistic and disorganized implementation of large–scale afforestation and reforestation projects may conflict with human food security 69. Therefore, rational cultivation of forested areas within constrained land resources can have great carbon sequestration benefits and remains essential.

Limitation and Future Direction

Our study revealed that increased precipitation did not enhance the carbon sequestration capacity of afforestation in the semi-arid CLP. We offered insight into enhancing the sustainable development of afforestation in the region by carefully selecting tree species and implementing scientifically sound planting methods. However, our research has some limitations that should be addressed in future studies. Firstly, the robustness of the data was validated by comparing the consistency among different datasets for each parameter (ACP, ET, GPP, NPP, VPD, and SM). The correlation among different precipitation datasets ranged from 0.77 to 0.9, while correlations among different ET datasets were concentrated around 0.7. The root mean square difference (RMSD) of GPP and NPP datasets were almost zero. The consistency among different datasets for VPD and SM is not high (Supplementary Fig. 9a–f). These discrepancies among datasets may stem from differences in data collection methods, processing techniques, satellite sensors, and spatial resolutions. In future research, we aim to improve data stability by integrating products from various sources. Second, this study primarily used the 30 m CLCD dataset to capture spatial variations in afforestation on the CLP. The afforestation processes initiated in 1999 were complex and diverse, including direct afforestation after land abandonment, afforestation after land abandonment and tillage, no afforestation following land abandonment, simultaneous land abandonment and afforestation, and repeated afforestation (replanting due to tree mortality). As a result, afforestation areas were spatially dispersed and exhibited irregular parcel shapes. Furthermore, the underlying surface was fragmented and prone to spectral confusion from native forests, crops, grasslands, and other vegetation types 70, 71, 72. Therefore, the 30 m CLCD dataset was not fully capable of accurately capturing afforestation on small and dispersed parcels. In future work, sub-pixel scale land cover datasets should be generated and combined with key time nodes of afforestation to extract complete spatial information on afforestation on the CLP since 1999, enabling precise estimation of its carbon sequestration capacity.

The PML–V2 model determines land cover types based on input data from MCD12Q1. Although MCD12Q1 is updated annually and includes categories such as cropland, forest, and grassland, it has limitations in capturing small-scale seasonal conversions from cropland to forest.

These limitations may result in underestimating differences in GPP, ET, and WUE between forests and croplands. This poses a major challenge for studies focusing on fine-scale land type conversions using MODIS products 73. In the future, integrating land cover data with higher temporal and spatial resolutions, such as those from Landsat or Sentinel–2, could supplement and refine MCD12Q1 data, enhancing the accuracy of land cover change detection.

Conclusions

We explored whether increased humidity in the semi-arid region of the CLP could enhance the carbon sequestration capacity of afforested vegetation, primarily through the use of remote sensing data and the application of methods such as Granger causality and partial correlation analysis. The results showed that although the CLP experienced increased humidity since 2012, the annual average NPP of afforested vegetation did not increase correspondingly. Our findings suggest that the increase in VPD suppressed the photosynthesis (GPP) of afforested vegetation, while the rise in SM stimulated vegetation transpiration. The trade-off between photosynthesis and transpiration ultimately determined the WUE, with a decrease in WUE accounting for 51% of the reduction in NPP. Specifically, the positive correlation between NPP and GPP (R² = 0.65) was greater than the negative correlation with ET (R² = 0.27). These findings highlight that, despite increased precipitation, the expansion of afforestation in semi-arid areas has not achieved the anticipated ecological benefits. Therefore, it is imperative to adopt scientifically grounded and reasonable afforestation strategies to mitigate the negative impacts on groundwater resources, which is particularly important for achieving the goals of carbon peaking and carbon neutrality.

Methods

Precipitation Products

Precipitation data were sourced from ERA5–Land, CHIRPS, and GPM–L3 datasets. ERA5–Land combines global model data with actual observations to provide a long-time series of spatially consistent data for with 50 climate variables 74. The Climate Hazards Group Infrared Precipitation Stations dataset consists of daily global precipitation data for over 30 years. CHIRPS merges 0.05° resolution satellite imagery with ground station data to create gridded precipitation time series, facilitating trend analysis at spatial scales 75.The Global Precipitation Measurement datasets, at a 0.1° resolution, provides hourly global measurements of rain and snow 76. This dataset is generated using the Integrated Multi-satellite Retrievals for GPM (IMERG) algorithm, which calibrates, merges, and interpolates microwave precipitation estimates from multiple satellites while integrating calibrated infrared satellite measurements and gauge-based analyses. This approach results in a more comprehensive global precipitation dataset.

NPP Products

NPP data were sourced from the yearly 500 m resolution MOD17A3HGF product for the period 2001–2020. Annual NPP was calculated by summing 8-day net photosynthesis data across the year 77. To ensure high data quality, we extracted only high-quality annual NPP values by examining the quality assessment bit flags in the quality control layers provided with the MOD17A3HGF product. Additionally, this study used a monthly 1 km resolution NPP dataset of terrestrial ecosystems north of 18°N in China to validate the robustness of the MODIS dataset 78.

ET and GPP Products

ET and GPP were sourced from the 8–day, 500 m resolution Penman–Monteith–Leuning Evapotranspiration V2 (PML_V2) products for the period 2001–2020. ET is divided into three components: vegetation transpiration, soil evaporation, and canopy interception. The primary distinctions between PML_V2 and conventional diagnostic models, which estimate ET and GPP separately, are (a) the consideration of atmospheric CO2 concentrations in carbon assimilation and canopy conductance and (b) the estimation of canopy conductance from GPP, using VPD as a constraint. This makes the product suitable for assessing the coupling between carbon and water cycles. PML_V2 is well calibrated against 8–day measurements at 95 widely distributed flux towers covering 10 plant functional types, as demonstrated by root mean square error and bias values of 0.69 mm d–1 and –1.8% for ET and 1.99 g C m–2 d–1 and 4.2% for GPP, respectively 79. Additionally, we utilized other ET datasets from MOD16A2 80, ERA5–Land 74, and MERRA–2, as well as other GPP datasets, including EC–LUE 33, \({{{\rm{NIRv}}}}\) 81.

We calculated WUE at the ecosystem scale, representing the amount of CO2 fixed or dry matter produced per unit of water lost in forest ecosystems. WUE primarily reflects the relationship between the carbon sequestration process of photosynthesis by vegetation and the water consumption characteristics of evapotranspiration. This relationship is expressed as \({{{\rm{WUE}}}}={{{\rm{GPP}}}}/({{{{\rm{E}}}}}_{{{{\rm{c}}}}}+{{{{\rm{E}}}}}_{{{{\rm{s}}}}}+{{{{\rm{E}}}}}_{{{{\rm{i}}}}})\), where WUE is influenced by both the trend in GPP and the combined trends in \({{{{\rm{E}}}}}_{{{{\rm{c}}}}}\), \({{{{\rm{E}}}}}_{{{{\rm{s}}}}}\), and \({{{{\rm{E}}}}}_{{{{\rm{i}}}}}\) 82.

VPD Products

VPD was derived from the HiMIC–Monthly dataset, which provides 1 km resolution atmospheric humidity index data for China from 2003 to 2020 with a monthly temporal resolution 83. The VPD index demonstrates strong accuracy, with an R² exceeding 0.96, and both the root mean square error and mean absolute error falling within acceptable ranges. To convert the VPD data to hPa, we applied a scaling factor of 0.01. Additionally, we utilized the monthly VPD data from TerraClimate 84.

SM Products

SM was derived from the Global daily surface soil moisture dataset at 1 km resolution for the period 2000 to 2020. This dataset offers spatial and temporal resolutions of 1 km and daily/monthly intervals, respectively. Through a combination of machine learning techniques and multisource data fusion, the dataset not only enhances temporal resolution by filling gaps in the ESA CCI product but also improves spatial resolution by incorporating observations from the International Soil Moisture Network (ISMN) and optical remote sensing data. The dataset demonstrates high accuracy, with a correlation coefficient (R) of 0.89 and a low unbiased root mean square error (ubRMSE) of 0.045 m3 m−3 85. The data are stored as integers, with a scaling factor of 0.001. We also used SM datasets from ERA5–Land 74, ESA CCI, and SMOS 86.

LAI Product

Leaf Area Index (LAI) was derived from the MCD15A3H dataset 4–day 500 m resolution for 2002–2020. The dataset primarily selects the best pixels available from all acquisitions of NASA’s Terra and Aqua satellites within four days 87.

In–situ data

Accumulated precipitation data were collected from 86 surface stations across the CLP, provided by the National Meteorological Information Centre ( http://data.cma.cn). To ensure data accuracy and reliability, the meteorological data underwent quality control, homogenization, and correction processes. Using this data, we established interannual extreme precipitation datasets for the CLP, covering the period from 2001 to 2020.

Land use datasets

The spatial extent of afforestation was based on 30 m resolution annual China Land Cover Dataset (CLCD) from 2001 to 2020 52. These CLCDs were generated from 335709 scenes of Landsat TM/ETM+ or Landsat OLI images using the Google Earth Engine. All images were preprocessed for atmospheric correction and georectification. The overall accuracy of the CLCDs is consistently high, ranging from 76.45% to 82.51%. The accuracy for forest areas specifically averages 85.49% ± 1.30%.

Identification of pure afforestation pixels from 2001 to 2020

Initially, cropland from 1997 to 1999 and forestland from 2018 to 2020 were extracted. The overlapping areas between regions exclusively cropland from 1997 to 1999 and exclusively forestland from 2018 to 2020 were defined as Grain to Green (GTG). Forestland that existed before 1999 and remained unchanged in land cover through 2020 was classified as Non-Grain to Green (NGTG). Collectively, GTG and NGTG defined as afforestation. The predominant tree afforestation species in the region include locust, poplar, pine, and honeysuckle, all of which are characterized by drought resistance, strong adaptability, and rapid growth 88. Additionally, the mismatch in spatial resolution between the land use/cover datasets and the PML_V2 datasets may result in mixed pixels, potentially increasing the uncertainty of parameter calculations. To achieve relatively pure pixels, we aggregated the 30 m CLCD data to the resolution of the PML_V2 datasets and calculated the area percentages of GTG and NGTG within 0.5 × 0.5 km grids. Finally, we selected grids wherein the area ratios of GTG and NGTG exceeded 90% for further analysis.

Establishment of the extreme precipitation index dataset

The Expert Team on Climate Change Detection and Indices has established 27 extreme climate indices to quantify extreme climate based on daily precipitation and temperature data, including 16 extreme temperature and 11 extreme precipitation indices. Characterized by their low noise, high significance, and moderate extremity, these indices provide a comprehensive assessment of changes in the frequency, duration, and intensity of extreme climate events from multiple perspectives. These climate indices have been extensively used by scholars in the United States and globally to analyze variation in regional and global climate extremes.

We selected the maximum 1–day precipitation amount (RX1day, unit: mm), extreme wet-day precipitation (R99P, unit: mm), and the number of very heavy precipitation days (R20mm, unit: d) 89 to effectively quantify the intensity, frequency, and duration of extreme precipitation events on the CLP from 2001 to 2020. RX1day, an intensity index, measures the maximum 1–day precipitation per month in millimeters. R99P is a relative indicator that quantifies annual daily precipitation at or above the 99% quantile. R20mm is an absolute index that counts the number of days with at least 20 mm of precipitation per year. Daily precipitation data from 86 meteorological stations in the research area were acquired from 2001 to 2020 (Supplementary Table 1). The RClimDex program was used to determine extreme precipitation indices for each station. These indices were then spatially interpolated using the inverse distance weighted method to produce gridded data with a 500 m resolution. Finally, a dataset of annual extreme precipitation indicators was generated for the CLP from 2001 to 2020.

Time segmentation criteria and trend variations from 2001 to 2020

This study applied a segmented approach using a cubic polynomial curve-fitting to the time series data from 2001 to 2020 90. The steps were as follows: Firstly, a cubic function model was established to describe the trend of annual cumulative precipitation over time. Secondly, the tangent point of the cubic function model representing the interannual variation trend of precipitation was determined. Typically, the tangent point corresponds to the time points where the derivative of the cubic function equals zero. The cubic function model yielded the equation for the annual cumulative precipitation between 2001 and 2020: \(y=-0.0896{x}^{3}+0.76167{x}^{2}-19.995x+479.45\), where \(y\) represented the annual cumulative precipitation and \(x\) represented the year. Finally, we identified 2012 as the segmentation year for precipitation between 2001 and 2020.

At the spatiotemporal scale, we calculated the trends of ACP and other parameters before and after 2012 and their differences. The period before 2012 mainly represented the time span from 2001 to 2011, defined as the Initial period (IP), while the period after 2012 mainly represented the time span from 2012 to 2020, defined as the Later period (LP). The trends and their differences were defined as \({\overline{{{{\rm{H}}}}}}_{{{{\rm{Trend}}}}}^{{{{\rm{i}}}}}\) and \(\Delta {\overline{{{{\rm{H}}}}}}_{{{{\rm{Trend}}}}}\), respectively. \(\overline{{{{\rm{H}}}}}\) denoted the annual average H. The difference in the trend of\(\,{\overline{{{{\rm{H}}}}}}_{{{{\rm{Trend}}}}}\) before and after 2012 was \(\Delta {\overline{{{{\rm{H}}}}}}_{{{{\rm{T}}}}{{{\rm{rend}}}}}\) = \({\overline{{{{\rm{H}}}}}}_{{{{\rm{Trend}}}}}^{{{{\rm{LP}}}}}{{{\rm{\hbox{-}}}}}{\overline{{{{\rm{H}}}}}}_{{{{\rm{Trend}}}}}^{{{{\rm{IP}}}}}\). \(\overline{{{{\rm{H}}}}}\) stood for \(\overline{{{{\rm{NPP}}}}}\), \(\overline{{{{\rm{WUE}}}}}\), \(\overline{{{{\rm{GPP}}}}}\), \(\overline{{{{\rm{ET}}}}}\), \(\overline{{{{\rm{VPD}}}}}\), \(\overline{{{{\rm{SM}}}}}\), \({\overline{{{{\rm{E}}}}}}_{{{{\rm{C}}}}}\), \({\overline{{{{\rm{E}}}}}}_{{{{\rm{S}}}}}\).

Granger causality analysis

The Granger Causality Analysis (GCA) test method primarily analyses the causal relationship between two stationary time series data 91. Firstly, the Augmented Dickey–Fuller (ADF) method was employed to conduct a pseudo regression on the time series data X and Y. The calculation formula was as follows:

t represented the time variable; α was the constant term; β was the trend term; εt denoted the residual term; i was the lag order; and m was the maximum lag order. For time series, assuming the null hypothesis H0: δ = 0, and the alternative hypothesis H1: δ ≠ 0. If the variables did not pass the unit root test, Engle–Granger (E–G) was further applied for the cointegration test. Firstly, we established the regression equation for the time series and obtained the residual sequence βt; then, we conducted an ADF test on the residual sequence βt. If the result rejected H0, indicating no unit root in the residual sequence βt, thus βt was a stationary time series, allowing for the Granger causality test.

Subsequently, a PVAR model was constructed, and the optimal lag order was determined based on minimizing the Akaike Information Criterion, Bayesian Information Criterion, and Hannan–Quinn Information Criterion. Granger causality tests were conducted, and the regression model was as follows:

Where \({\alpha }_{i}\)、\({\beta }_{j}\) represented the trend terms of the time series; p and q denote the maximum lag order; C was the constant term; \({u}_{t}\) was the residual term. Assuming the null hypothesis H0: X does not Granger cause Y, alternative hypothesis H1: X Granger causes Y. Without considering X, the regression model’s residual sum of squares \({RS}{S}_{r}\) was established. When considering X, the regression model’s residual sum of squares \({{RSS}}_{{ur}}\) was established. Construct the F statistic:

\({RS}{S}_{r}\)、 \({RS}{S}_{{ur}}\) were the residual sum of model squares under unrestricted and restricted conditions, respectively. This statistic followed an F (q, T–p–q–1) distribution under the condition that the null hypothesis held. Given a significance level, when the value of the statistic exceeded the critical value, it indicated rejection of the null hypothesis, meaning that X was a Granger cause of Y; conversely, accepting the null hypothesis implied that X was not a Granger cause of Y.

Partial correlation analysis

Partial correlation analysis is a statistical method used to study the relationship between two variables, which measures the degree of association between two variables after excluding the effects of other relevant variables. The formula for the first–order partial correlation coefficient was:

\({r}_{{ij}}\), \({r}_{{ih}}\), and \({r}_{{jh}}\) were the simple correlation coefficient between \({x}_{i}\) and \({x}_{j}\), \({x}_{i}\) and \({x}_{h}\), along with \({x}_{j}\) and \({x}_{h}\) respectively. In this study, partial correlation analysis was primarily employed to analyze the relationship between NPP and WUE, GPP, and ET. And environmental factors (VPD, SM) and WUE, GPP, and ET, during different time periods (2001–2011, 2012–2020).

Data availability

All data used in the analysis are publicly accessible. Precipitation datasets for different products are available at https://doi.org/10.24381/cds.68d2bb30, https://www.chc.ucsb.edu/data/chirps, and https://urs.earthdata.nasa.gov/. Net primary product datasets are available at https://lpdaac.usgs.gov/products/mod17a3hgfv006/, and http://www.geodoi.ac.cn. Evapotranspiration datasets are available at https://earthengine.google.com/, https://lpdaac.usgs.gov/products/mod16a2v006/, https://doi.org/10.24381/cds.68d2bb30, and https://gmao.gsfc.nasa.gov/reanalysis/MERRA-Land/. Gross Primary products are available at https://developers.google.com/earth, https://glass-product.bnu.edu.cn/dload.html, and https://doi.org/10.6084/m9.figshare.12981977.v2. The Vapor pressure deficit datasets are available at https://zenodo.org/records/8070140, and https://www.climatologylab.org/terraclimate.html. The Soil moisture datasets are available at https://data.tpdc.ac.cn/en/data/30131436–88d1–4be3–8e3d–14905a29d6d6, https://doi.org/10.24381/cds.68d2bb30, https://catalogue.ceda.ac.uk/uuid/ff890589c21f4033803aa550f52c980c, and https://earth.esa.int/eogateway. The Leaf Area Index is available at https://lpdaac.usgs.gov/products/mcd15a3hv061/. The Climate dataset is available at http://data.cma.cn. The China Land use and land cover dataset is available at https://doi.org/10.5281/zenodo.4417810.

Code availability

The Python and Matlab scripts used for processing these datasets and generating some of the figures are available at https://zenodo.org/records/13335688.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (U2340221) and the “Science for a Better Development of Inner Mongolia” Program of the Bureau of Science and Technology of the Inner Mongolia Autonomous Region (2022EEDSKJXM003).

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Li, X., Xia, K., Wu, T. et al. Increased precipitation has not enhanced the carbon sequestration of afforestation in Northwest China. Commun Earth Environ 5, 619 (2024). https://doi.org/10.1038/s43247-024-01733-9

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Received:
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Accepted:
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Published:
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DOI: https://doi.org/10.1038/s43247-024-01733-9