Search Results (231)

Evapotranspiration (ET) is essential to the terrestrial water and energy cycle. Accurate evapotranspiration estimates are crucial for understanding global and regional climate change and effective water management. This research uses meteorological observations to provide insights into the spatial and temporal trend patterns of potential evapotranspiration (PET) and evapotranspiration in Xinjiang. A comparative analysis was conducted on six remote sensing-based, land surface model-based, and reanalysis-based products across multiple temporal scales (yearly, seasonally, and monthly) and point-to-point spatial dimensions and impacts of different land cover types was explored. The results show that: (1) The annual PET in Xinjiang showed a significant increasing trend, but showed a significant decreasing trend in summer and autumn. The actual evapotranspiration increased significantly in autumn. (2) The simulation of ET products in Xinjiang exhibits pronounced spatial heterogeneity and seasonal dependency. The datasets demonstrated a superior ability to simulate evapotranspiration in the northern part of Xinjiang compared to the southern part. Product performance varied extremely widely in desert areas but was stable in oasis areas. (3) Significant discrepancies exist across the multiple datasets, with the reanalysis-based products demonstrating superior comprehensive performance. This study offers critical insights for the suitable selection of evapotranspiration products and model optimization in the hydro-meteorological research of Xinjiang. Full article

In the context of global climate change, a comprehensive understanding of the spatiotemporal impacts of drought on vegetation productivity is essential for assessing terrestrial ecosystem stability. Utilizing outputs from six global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6), this study systematically assessed historical and projected drought probability, the drought vulnerability of Net Primary Productivity (NPP), and overall drought risk across the Mongolian Plateau under three Shared Socioeconomic Pathway scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5). Results revealed that the Standardized Precipitation Evapotranspiration Index (SPEI) exhibited a declining trend, whereas NPP showed an overall increasing trend. These changes were most pronounced under the SSP5-8.5 scenario, with the SPEI decreasing at a rate of −0.39/10a and NPP increasing at 25.8/10a. Drought severity exhibited strong spatial heterogeneity, intensifying from northeast to southwest, whereas NPP demonstrated an inverse spatial pattern. The spatial distribution of high-drought-risk zones varied markedly across scenarios: the southwestern region was most affected under SSP1-2.6, the northwestern region under SSP2-4.5, and the southeastern region under SSP5-8.5. Based on 12-month SPEI values and NPP derived from the Carnegie–Ames–Stanford Approach (CASA) model, SSP2-4.5 presented the highest overall drought risk, despite lower emissions. The annual mean NPP drought vulnerability ranked as follows: SSP2-4.5 (0.60 $\mathrm{g}\mathrm{C}{\mathrm{m}}^{-2}{\mathrm{y}\mathrm{r}}^{-1}$) > SSP1-2.6 (−1.03 $\mathrm{g}\mathrm{C}{\mathrm{m}}^{-2}{\mathrm{y}\mathrm{r}}^{-1}$) > SSP5-8.5 (−1.24 $\mathrm{g}\mathrm{C}{\mathrm{m}}^{-2}{\mathrm{y}\mathrm{r}}^{-1}$). Projections indicated a substantial increase in drought occurrence probability during the period 2061–2100, particularly under SSP2-4.5 and SSP5-8.5. Under higher emissions, the spatial extent of areas with negative drought vulnerability values was expected to expand 68%. Wind speed was the dominant factor influencing drought risk under SSP1-2.6 and SSP2-4.5, whereas precipitation became the primary driver (45.34%) under SSP5-8.5. These findings offer critical insights for early drought warning systems and for strengthening ecosystem resilience across the Mongolian Plateau. Full article

Ecological drought in terrestrial systems is a vegetation-functional degradation phenomenon triggered by the long-term imbalance between ecosystem water supply and demand. This process involves nonlinear coupling of multiple climatic factors, ultimately forming a compound ecological stress mechanism characterized by spatiotemporal heterogeneity. Based on meteorological and remote sensing datasets from 1982 to 2022, this study identified the spatial distribution and temporal variability of ecological drought in China, elucidated the dynamic evolution and return periods of typical drought events, unveiled the scale-dependent effects of climatic factors under both univariate dominance and multivariate coupling, as well as deciphered the response mechanisms of ecological drought to meteorological drought. The results demonstrated that (1) terrestrial ecological drought in China exhibited a pronounced intensification trend during the study period, with the standardized ecological water deficit index (SEWDI) reaching its minimum value of −1.21 in February 2020. Notably, the Alpine Vegetation Region (AVR) displayed the most significant deterioration in ecological drought severity (−0.032/10a). (2) A seasonal abrupt change in SEWDI was detected in January 2003 (probability: 99.42%), while the trend component revealed two mutation points in January 2003 (probability: 96.35%) and November 2017 (probability: 43.67%). (3) The drought event with the maximum severity (6.28) occurred from September 2019 to April 2020, exhibiting a return period exceeding the 10-year return level. (4) The mean values of gridded trend eigenvalues ranged from −1.06 in winter to 0.19 in summer; 87.01% of the area exhibited aggravated ecological drought in winter, with the peak period (88.51%) occurring in January. (5) Evapotranspiration (ET) was identified as the dominant univariate driver, contributing a percentage of significant power (POSP) of 18.75%. Under multivariate driving factors, the synergistic effects of ET, soil moisture (SM), and air humidity (AH) exhibited the strongest explanatory power (POSP = 19.21%). (6) The response of ecological drought to meteorological drought exhibited regional asynchrony, with the maximum correlation coefficient averaging 0.48 and lag times spanning 1–6 months. Through systematic analysis of ecological drought dynamics and driving mechanisms, a dynamic assessment framework was constructed. These outcomes strengthen the scientific basis for regional drought risk early-warning systems and spatially tailored adaptive management strategies. Full article

Drought disturbances are becoming more frequent with global warming. Accurately assessing the regulatory effect of drought on vegetation phenology is key to understanding terrestrial ecosystem response mechanisms in the context of climate change. Previous studies on cumulative and lagged effects of drought on vegetation growth have mostly focused on a single vegetation type or the overall vegetation NDVI, overlooking the possible influence of different adaptation strategies of different vegetation types and differences in drought effects on different phenological nodes. This study investigates the cumulative and lagged effects of drought on vegetation phenology across a region of East Asia from 2001 to 2020 using NDVI data and the Standardized Precipitation Evapotranspiration Index (SPEI). We analyzed the start of growing season (SOS) and end of growing season (EOS) responses to drought across four vegetation types: deciduous needleleaf forests (DNFs), deciduous broadleaf forests (DBFs), shrublands, and grasslands. Results reveal contrasting phenological responses: drought delayed SOS in grasslands through a “drought escape” strategy but advanced SOS in forests and shrublands. All vegetation types showed earlier EOS under drought stress. Cumulative drought effects were strongest on DNFs, SOS, and shrubland SOS, while lagged effects dominated DBFs and grassland SOS. Drought impacts varied with moisture conditions: they were stronger in dry regions for SOS but more pronounced in humid areas for EOS. By confirming that drought effects vary by vegetation type and phenology node, these findings enhance our understanding of vegetation adaptation strategies and ecosystem responses to climate stress. Full article

Arid and semi-arid areas serve a critical regulatory function within the global carbon cycle. Understanding the response mechanisms of vegetation productivity to variations in moisture availability represents a fundamental scientific challenge in elucidating terrestrial carbon dynamics. This study systematically disentangled the respective influences of summer surface soil moisture (RSM) and precipitation (PRE) on gross primary productivity (GPP) across arid and semi-arid regions of China from 2000 to 2022. Utilizing GPP datasets alongside correlation analysis, ridge regression, and data binning techniques, the investigation yielded several key findings: (1) Both GPP and RSM exhibited significant upward trends within the study area, whereas precipitation showed no statistically significant trend; notably, GPP demonstrated the highest rate of increase at 0.455 Cg m⁻² a⁻¹. (2) Decoupling analysis indicated a coupled relationship between RSM and PRE; however, their individual effects on GPP were not merely a consequence of this coupling. Controlling for evapotranspiration and root-zone soil moisture interference, the analysis revealed that under conditions of elevated RSM, the average increase in summer–autumn GPP (SA_GPP) was 0.249, significantly surpassing the increase observed under high-PRE conditions (−0.088). Areas dominated by RSM accounted for 62.13% of the total study region. Furthermore, examination of the aridity gradient demonstrated that the predominance of RSM intensified with increasing aridity, reaching its peak influence in extremely arid zones. This research provides a quantitative assessment of the differential impacts of RSM and PRE on vegetation productivity in China’s arid and semi-arid areas, thereby offering a vital theoretical foundation for improving predictions of terrestrial carbon sink dynamics under future climate change scenarios. Full article

Evapotranspiration (ET) crucially regulates water storage dynamics and is an essential component of the terrestrial water cycle. Understanding ET dynamics is fundamental for sustainable water resource management, particularly in regions facing increasing drought risks under climate change. In regions like southwestern China, where extreme drought events are prevalent due to complex terrain and climate warming, ET becomes a key factor in understanding water availability and drought dynamics. Using the SWAT model, this study investigates ET dynamics and influencing factors in the Jizi Basin, Yunnan Province, a small basin with over 71% forest coverage. The model calibration and validation results demonstrated a high degree of consistency with observed discharge data and ERA5, confirming its reliability. The results show that the annual average ET in the Jizi Basin is 573.96 mm, with significant seasonal variations. ET in summer typically ranges from 70 to 100 mm/month, while in winter, it drops to around 20 mm/month. Spring ET exhibits the highest variability, coinciding with the occurrence of extreme hydrological events such as droughts. The monthly anomalies of ET effectively reproduce the spring and early summer 2019 drought event. Notably, ET variation exhibits significant uncertainty under scenarios of +1 °C temperature and −20% precipitation. Furthermore, although land use changes had relatively small effects on overall ET, they played crucial roles in promoting groundwater recharge through enhanced percolation, especially forest cover. The study highlights that, in addition to climate and land use, soil moisture and groundwater conditions are vital in modulating ET and drought occurrence. The findings offer insights into the hydrological processes of small forested basins in southwestern China and provide important support for sustainable water resource management and effective climate adaptation strategies, particularly in the context of increasing drought vulnerability. Full article

The introduction of the ”dual carbon” target has increased the need for products that can accurately measure carbon dioxide levels, reflecting the rising demand. Due to challenges in achieving the required spatiotemporal resolution, accuracy, and spatial continuity with current carbon dioxide concentration products, it is essential to explore methods for obtaining carbon dioxide concentration products with completeness in space and time. Based on the 2018 OCO-2 carbon dioxide products and environmental variables such as vegetation coverage (FVC, LAI), net primary productivity (NPP), relative humidity (RH), evapotranspiration (ET), temperature (T) and wind (U, V), this study constructed a multiple regression model to obtain the spatial continuous carbon dioxide concentration products in the provinces of Huai River Basin. Using indicators such as correlation coefficient, root mean square error (RMSE), local variance, and percentage of valid pixels, the performance of model was validated. The validation results are shown as follows: (1) Among the selected environmental variables, the primary factors affecting the spatiotemporal distribution of carbon dioxide concentration are ET, LAI, FVC, NPP, T, U, and RH. (2) Compared with the OCO-2 carbon dioxide products, the percentage of valid pixels of the reconstructed carbon dioxide concentration data increased from less than 1% to over 90%. (3) The local variance in reconstructed data was significantly larger than that of original OCO-2 ${\mathrm{C}\mathrm{O}}_2$ products. (4) The average monthly RMSE is 2.69. Therefore, according to the model developed in this study, we can obtain a carbon dioxide concentration dataset that is spatially complete, meets precision requirements, and is rich in local detail information, which can better reflect the spatial pattern of carbon dioxide concentration and can be used to examine the carbon cycle between the terrestrial environment, biosphere, and atmosphere. Full article

As an extreme event driven by global climate change, drought poses a severe threat to terrestrial ecosystems. The Yangtze River Basin (YZRB) and Yellow River Basin (YRB) are key ecological barriers and economic zones in China, holding strategic importance for exploring the evolution of drought patterns and their ecological impacts. Using meteorological station data and Climatic Research Unit Gridded Time Series (CRU TS) data, this study analyzed the spatiotemporal characteristics of drought evolution in the YZRB and YRB from 1961 to 2021 using the standardized precipitation evapotranspiration index (SPEI) and run theory. Additionally, this study examined drought effects on ecosystem carbon sequestration (CS) at the city, county, and pixel scales. The results revealed the following: (1) the CRU data effectively captured precipitation (annual r = 0.94) and temperature (annual r = 0.95) trends in both basins, despite significantly underestimating winter temperatures, with the optimal SPEI calculation accuracy found at the monthly scale; (2) both basins experienced frequent autumn–winter droughts, with the YRB facing stronger droughts, including nine events which exceeded 10 months (the longest lasting 25 months), while the mild droughts increased in frequency and extreme intensity; and (3) the drought impacts on CS demonstrated a significant threshold effect, where the intensified drought unexpectedly enhanced CS in western regions, such as the Garzê Autonomous Prefecture in Sichuan Province and Changdu City in the Xizang Autonomous Region, but suppressed CS in the midstream and downstream plains. The CS responded positively under weak drought conditions but declined once the drought intensity surpassed the threshold. This study revealed a nonlinear relationship between drought and CS across climatic zones, thereby providing a scientific foundation for enhancing ecological resilience. Full article

Climate change intensifies hydrological cycles, leading to an increased variability in terrestrial water storage anomalies (TWSAs) and a heightened drought risk. Understanding the spatiotemporal dynamics of TWSAs and their driving factors is crucial for sustainable water management. While previous studies have primarily attributed TWSAs to regional factors, this study employs wavelet coherence, partial correlation analysis, and multiple linear regression to comprehensively analyze TWSA dynamics and their drivers in the Hengduan Mountains (HDM) region from 2003 to 2022, incorporating both regional and global influences. Additionally, dry–wet variations were quantified using the GRACE-based Drought Severity Index (GRACE-DSI). Key findings include the following: The annual mean TWSA showed a non-significant decreasing trend (−2.83 mm/y, p > 0.05), accompanied by increased interannual variability. Notably, approximately 36.22% of the pixels in the western HDM region exhibited a significantly decreasing trend. The Nujiang River Basin (NRB) (−17.17 mm/y, p < 0.01) and the Lancang (−17.17 mm/y, p < 0.01) River Basin experienced the most pronounced declines. Regional factors—particularly precipitation (PRE)—drove TWSA in 59% of the HDM region, followed by potential evapotranspiration (PET, 28%) and vegetation dynamics (13%). Among global factors, the North Atlantic Oscillation showed a weak correlation with TWSAs (r = −0.19), indirectly affecting it via winter PET (r = −0.56, p < 0.05). The decline in TWSAs corresponds to an elevated drought risk, notably in the NRB, which recorded the largest GRACE-DSI decline (slope = −0.011, p < 0.05). This study links TWSAs to climate drivers and drought risk, offering a framework for improving water resource management and drought preparedness in climate-sensitive mountain regions. Full article

Accurate terrestrial evapotranspiration (ET) estimation is crucial for understanding land–atmosphere interactions, evaluating ecosystem functions, and supporting water resource management, particularly across climatically diverse regions. To address the limitations of traditional ET models, we propose a simple yet robust Sigmoid-RH model that characterizes the nonlinear relationship between relative humidity and ET. Unlike conventional approaches such as the Penman–Monteith or Priestley–Taylor models, the Sigmoid-RH model requires fewer inputs and is better suited for large-scale applications where data availability is limited. In this study, we applied the Sigmoid-RH model to estimate ET over mainland China from 2001 to 2018 by using satellite remote sensing and meteorological reanalysis data. Key driving inputs included air temperature (Ta), net radiation (Rn), relative humidity (RH), and the normalized difference vegetation index (NDVI), all of which are readily available from public datasets. Validation at 20 flux tower sites showed strong performance, with R-square (R²) ranging from 0.26 to 0.93, Root Mean Squard Error (RMSE) from 0.5 to 1.3 mm/day, and Kling-Gupta efficiency (KGE) from 0.16 to 0.91. The model performed best in mixed forests (KGE = 0.90) and weakest in shrublands (KGE = 0.27). Spatially, ET shows a clear increasing trend from northwest to southeast, closely aligned with climatic zones, with national mean annual ET of 560 mm/yr, ranging from less than 200 mm/yr in arid zones to over 1100 mm/yr in the humid south. Seasonally, ET peaked in summer due to monsoonal rainfall and vegetation growth, and was lowest in winter. Temporally, ET declined from 2001 to 2009 but increased from 2009 to 2018, influenced by changes in precipitation and NDVI. These findings confirm the applicability of the Sigmoid-RH model and highlight the importance of hydrothermal conditions and vegetation dynamics in regulating ET. By improving the accuracy and scalability of ET estimation, this model can provide practical implications for drought early warning systems, forest ecosystem management, and agricultural irrigation planning under changing climate conditions. Full article

Forests are a key terrestrial carbon sink, storing carbon in biomass, the forest floor, and the mineral soil (SOC). Since Pinus radiata D. Don is the most widely planted forest species in Chile, it is important to understand how environmental and soil factors influence these carbon pools. Our objective was to evaluate the effects of climate and site variables on carbon stocks in adult radiata pine plantations across contrasting water and nutrient conditions. Three 1000 m² plots were installed at 20 sites with sandy, granitic, recent ash, and metamorphic soils, which were selected along a productivity gradient. Biomass carbon stocks were estimated using allometric equations, and carbon stocks in the forest floor and mineral soil (up to 1 m deep) were assessed. SOC varied significantly, from 139.9 Mg ha⁻¹ in sandy soils to 382.4 Mg ha⁻¹ in metamorphic soils. Total carbon stocks (TCS) per site ranged from 331.0 Mg ha⁻¹ in sandy soils to 552.9 Mg ha⁻¹ in metamorphic soils. Across all soil types, the forest floor held the lowest carbon stock. Correlation analyses and linear models revealed that variables related to soil water availability, nitrogen content, precipitation, and stand productivity positively increased SOC and TCS stocks. In contrast, temperature, evapotranspiration, and sand content had a negative effect. The developed models will allow more accurate estimation estimates of C stocks at SOC and in the total stand. Full article

Accurate estimation of soil organic carbon (SOC) content is crucial for assessing terrestrial ecosystem carbon stocks. Although traditional methods offer relatively high estimation accuracy, they are limited by poor timeliness and high costs. Combining measured data, remote sensing technology, and machine learning (ML) algorithms enables rapid, efficient, and accurate large-scale prediction. However, single ML models often face issues like high feature variable redundancy and weak generalization ability. Integrated models can effectively overcome these problems. This study focuses on the Weigan–Kuqa River oasis (Wei-Ku Oasis), a typical arid oasis in northwest China. It integrates Sentinel-2A multispectral imagery, a digital elevation model, ERA5 meteorological reanalysis data, soil attribute, and land use (LU) data to estimate SOC. The Boruta algorithm, Lasso regression, and its combination methods were used to screen feature variables, constructing a multidimensional feature space. Ensemble models like Random Forest (RF), Gradient Boosting Machine (GBM), and the Stacking model are built. Results show that the Stacking model, constructed by combining the screened variable sets, exhibited optimal prediction accuracy (test set R² = 0.61, RMSE = 2.17 g∙kg⁻¹, RPD = 1.61), which reduced the prediction error by 9% compared to single model prediction. Difference Vegetation Index (DVI), Bare Soil Evapotranspiration (BSE), and type of land use (TLU) have a substantial multidimensional synergistic influence on the spatial differentiation pattern of the SOC. The implementation of TLU has been demonstrated to exert a substantial influence on the model’s estimation performance, as evidenced by an augmentation of 24% in the R² of the test set. The integration of Boruta–Lasso combination screening and Stacking has been shown to facilitate the construction of a high-precision SOC content estimation model. This model has the capacity to provide technical support for precision fertilization in oasis regions in arid zones and the management of regional carbon sinks. Full article

This study quantitatively evaluates the effects of human activities (HAs) and climate change (CC) on the terrestrial ecosystem carbon cycle, providing a scientific basis for ecosystem management and the formulation of sustainable development policies in urban agglomerations located in arid and ecotone regions. Using the LanXi urban agglomeration in China as a case study, we simulated the spatiotemporal variation of vegetation net primary productivity (NPP) from 2000 to 2023 based on MODIS remote sensing data and the CASA model. Trend analysis and the Hurst index were employed to identify the dynamic trends and persistence of NPP. Furthermore, the Geographical Detector model with optimized parameters, along with nonlinear residual analysis, was employed to investigate the driving mechanisms and relative contributions of HAs and CC to NPP variation. The results indicate that NPP in the LanXi urban agglomeration exhibited a fluctuating upward trend, with an average annual increase of 4.26 gC/m² per year. Spatially, this trend followed a pattern of “higher in the center, lower in the east and west,” with more than 95% of the region showing an increase in NPP. Precipitation, mean annual temperature, evapotranspiration, and land use types were identified as the primary driving factors of NPP change. The interaction among these factors demonstrated a stronger explanatory power through factor coupling. Compared with linear residual analysis, the nonlinear model showed clear advantages, indicating that vegetation NPP in the LanXi urban agglomeration was jointly influenced by HAs and CC. These findings can further act as a basis for resource and environmental research in similar ecotone regions globally, such as Central Asia, the Mediterranean Basin, the southwestern United States, and North Africa. Full article

Sustained reductions in terrestrial water storage (TWS) have been observed globally using Gravity Recovery and Climate Experiment (GRACE) satellite data since 2002. However, the underlying mechanisms remain incompletely understood due to limited record lengths and data discontinuity. Recently, explainable artificial intelligence (XAI) has provided robust tools for unveiling dynamics in complex Earth systems. In this study, we employed a deep learning technique (Long Short-Term Memory network, LSTM) to reconstruct global TWS dynamics, filling gaps in the GRACE record. We then utilized the Local Interpretable Model-agnostic Explanations (LIME) method to uncover the underlying mechanisms driving observed TWS reductions. Our results reveal a consistent decline in the global mean TWS over the past 22 years (2002–2024), primarily influenced by precipitation (17.7%), temperature (16.0%), and evapotranspiration (10.8%). Seasonally, the global average of TWS peaks in April and reaches a minimum in October, mirroring the pattern of snow water equivalent with approximately a one-month lag. Furthermore, TWS variations exhibit significant differences across latitudes and are driven by distinct factors. The largest declines in TWS occur predominantly in high latitudes, driven by rising temperatures and significant snow/ice variability. Mid-latitude regions have experienced considerable TWS losses, influenced by a combination of precipitation, temperature, air pressure, and runoff. In contrast, most low-latitude regions show an increase in TWS, which the model attributes mainly to increased precipitation. Notably, TWS losses are concentrated in coastal areas, snow- and ice-covered regions, and areas experiencing rapid temperature increases, highlighting climate change impacts. This study offers a comprehensive framework for exploring TWS variations using XAI and provides valuable insights into the mechanisms driving TWS changes on a global scale. Full article

Ecosystems modulate Earth’s climate through the exchange of carbon and water fluxes. However, long-term trends in these terrestrial fluxes remain unclear due to the lack of continuous measurements on the global scale. This study combined flux data from 197 eddy covariance sites with satellite-retrieved solar-induced chlorophyll fluorescence (SIF) to investigate spatiotemporal variations in gross primary productivity (GPP), evapotranspiration (ET), and their coupling via water use efficiency (WUE) from 2001 to 2020. We developed six global GPP and ET products at 0.05° spatial and 8-day temporal resolution, using two machine learning models and three SIF products, which integrate vegetation physiological parameters with data-driven approaches. These datasets provided mean estimates of 128 ± 2.3 Pg C yr⁻¹ for GPP, 522 ± 58.2 mm yr⁻¹ for ET, and 1.8 ± 0.21 g C kg⁻¹ H₂O yr⁻¹ for WUE, with upward trends of 0.22 ± 0.04 Pg C yr⁻² in GPP, 0.64 ± 0.14 mm yr⁻² in ET, and 0.0019 ± 0.0005 g C kg⁻¹ H₂O yr⁻² in WUE over the past two decades. These high-resolution datasets are valuable for exploring terrestrial carbon and water responses to climate change, as well as for benchmarking terrestrial biosphere models. Full article