Hua Zhu

Accurate modeling and partitioning of forest evapotranspiration (ET) are essential for understanding water cycle processes in forest ecosystems. This study develops an improved three-source ET model by integrating a physically based canopy interception evaporation (Ei) scheme into the Shuttleworth–Wallace–Hu (SWH) model. A Monte Carlo stochastic parameterization scheme was applied to optimize model parameters. The proposed framework disaggregates the total ET flux into three distinct components: vegetation transpiration, soil evaporation, and Ei, thereby reducing uncertainties associated with the original SWH model in humid forest regions. The new model’s performance was assessed using flux observations from two subtropical forest sites and compared to the SWH model. The verification results indicate that the three-source model provided reliable estimates of daily ET. At the QYZ station (2004–2007) and the DHS station (2005–2007), the fitting slopes for simulating daily ET were 0.97 and 1.01, respectively, with corresponding coefficients of determination of 0.92 and 0.81. The root mean square errors (RMSE) for the three-source model were 0.38 mm day⁻¹ and 0.52 mm day⁻¹, respectively, with a reduction of 4.33% and 3.10% in RMSE compared to the SWH model. Additionally, the new model simulated the annual T/ET ratio more accurately, with values closer to site-measured data than the SWH model’s estimates. At both sites, the T/ET ratios simulated by the new model were closer to the observed values than those simulated by the SWH model, indicating an improved representation of ecohydrological processes. Furthermore, environmental analysis revealed that vapor pressure deficit and precipitation primarily govern the T/ET ratio, exerting the strongest positive and negative effects, respectively. Importantly, it requires only one additional precipitation parameter compared to the SWH model, yet achieves higher simulation accuracy and a more realistic representation of hydrological processes. Overall, the three-source model provides an improved framework for estimating ET in humid forest ecosystems. Ultimately, these results offer deeper insights into the coupled water and energy fluxes within forest ecosystems, thereby facilitating more effective water management and guiding sustainable forestry under a shifting climate.

Carbon use efficiency (CUE) is a critical indicator of ecosystem carbon sink capacity. However, the nonlinear response of CUE to complex environmental drivers remains poorly understood in subtropical humid regions. This study analyzed the spatiotemporal dynamics of vegetation CUE in the Poyang Lake Basin (PYLB) from 2001 to 2020 and quantified the influence of natural and anthropogenic factors using XGBoost-SHAP and GeoDetector models. The results showed that: (1) The average annual CUE in the PYLB was 0.455, exhibiting a declining trend, with a linear rate of −0.001524 yr⁻¹. (2) SHAP analysis revealed that the association between LAI and CUE exhibited a non-monotonic transition at a threshold of approximately 1.80. Specifically, while lower LAI levels were positively correlated with CUE, this relationship shifted to a negative trend as LAI exceeded the threshold, demonstrating a phase-specific coupling pattern across the canopy density gradient. For hydrothermal drivers, CUE exhibited localized downward fluctuations when precipitation was between 1700 and 2700 mm and temperature ranged from 15.0 to 18.0 °C. (3) GeoDetector analysis indicated that LAI was the dominant individual factor controlling the spatial heterogeneity of CUE (q = 0.471), and its interaction with TEM exerted the strongest synergistic effect (q = 0.493). These results emphasize the necessity of considering nonlinear thresholds and factor interactions when evaluating ecosystem carbon budgets in changing climates.

Carbon storage (CS) is a critical ecosystem service for climate mitigation. CS in urbanizing areas is being squeezed by climate-driven capacity decline and human-induced stock loss. Focusing on the Yangtze River Delta Urban Agglomeration (YRDUA), we integrated the InVEST CS module with climate projections under SSP-RCP scenarios to quantify CS dynamics during 2000–2020 and project trajectories for 2030–2070, while attributing CS changes to land use change (LUC). The findings indicated that: (1) From 2000 to 2020, the share of cropland decreased from 54.72% to 49.60%, while the share of construction land increased from 6.05% to 12.55%. (2) Regional CS ranged from 2829.46 to 2941.96 Tg C and exhibited a persistent spatial gradient, decreasing from south to north. (3) CS is projected to increase under SSP5-8.5, to rise and then decline under SSP1-1.9, and to decrease overall under SSP2-4.5. (4) From 2000 to 2010, the conversion of cropland to forest made the largest positive contribution to CS changes, while the conversion of water to cropland dominated from 2010 to 2020. Conversely, cropland expansion into construction land was the primary driver of negative CS changes throughout the 2000–2020 period. For the future period (2030–2070), under all scenarios, the conversion of grassland to forest is expected to be the dominant driver of positive CS gains, whereas the conversion of grassland to cropland will consistently lead to the largest CS losses. These findings highlight the need for scenario-specific and spatially differentiated land-management strategies to sustain regional carbon sinks and enhance long-term climate resilience in agglomerations.

Managing agricultural water pollution (AWP) and agricultural carbon emissions (ACE) together is crucial for addressing the global water resources crisis and climate challenges. Traditional water quality indicators are limited in large-scale evaluations of AWP. The common trends of ACE and AWP, as well as the spatial heterogeneity of their common driving factors also remain unclear. This study introduces a novel framework for analyzing the synergistic reduction of AWP and ACE from the perspective of agricultural grey water footprint (AGWF) and examines disparities in common driving factors across areas with differing levels of economic development and pollution intensities in Zhejiang Province. The results indicate that ACE and AGWF in Zhejiang showed an upward trend from 2010 to 2012, followed by a significant decline from 2013 to 2020. A consistent synergistic reduction trend in grey water footprint and carbon emissions was identified in both the planting and livestock husbandry sectors across Zhejiang. Socio-economic factors jointly influenced the reductions in ACE and AGWF, with technological level and the labor-to-research-and-development (labor-R&D) ratio being the primary drivers, accounting for 79.41% and 78.38% of these reductions, respectively. The impact of agricultural R&D expenditure intensity on AGWF and ACE exhibited spatiotemporal heterogeneity and sectoral disparities. The key to promoting the synergistic reduction of AGWF and ACE lies in advancing the research, development, and application of green agricultural technologies especially in regions where such technologies are not yet fully developed. The results provide a theoretical framework and practical implementation for the integrated management of AWP and ACE, as well as sustainable agricultural policy formulation.

The implementation of differentiated governance for agricultural water pollution (AWP) plays a significant role in alleviating the pressure on agricultural water resources. However, research that comprehensively assesses AWP and its influencing factors from a multidimensional perspective remains relatively limited. This study utilized the grey water footprint (GWF) model to quantify the agricultural grey water footprint (AGWF), agricultural grey water footprint efficiency (AGWFE), agricultural grey water footprint intensity (AGWFI), and agricultural water pollution level (AWPL) in Zhejiang from 2010 to 2020. Subsequently, we applied the standard deviational ellipse (SDE), the kernel density estimation (KDE), and the Dagum Gini coefficient to delve into the dynamic evolution and regional disparities of these indicators. Ultimately, we leveraged both the random forest model and the panel regression model to identify and examine the key factors shaping AGWF-related indicators. The results show that: (1) From 2010 to 2020, in Zhejiang, both AGWF and AGWFI exhibit a trend of first increasing and then decreasing, peaking in 2012. In contrast, AGWFE has consistently increased over the years, reaching an increase of 54.56 CNY/m³ by 2020. Meanwhile, despite fluctuations, AWPL in Zhejiang shows an overall gradual decline. (2) The centroids of relevant indicators for AWP in Zhejiang are primarily located in Jinhua (for AGWF and AGWFI), Shaoxing (for AWPL), and in the area where AGWFE converge. (3) Compared to 2010, the regional disparities in AGWF and AWPL have shrunk significantly in 2020, whereas the regional differences in AGWFE and AGWFI have increased to some extent. In most years, the regional disparities in AGWF, AGWFI, and AWPL are more pronounced in Northeastern Zhejiang compared to the southwestern part. (4) The influencing factors of AGWF, AGWFE, and AGWFI exhibit significant regional heterogeneity. In Northeastern Zhejiang, the primary factors influencing them are technological innovation, resource endowment, and crop-cultivation methods. Conversely, in the southwestern region, the primary factors exerting the same influence are the application intensities of fertilizers, pesticides, and agricultural film application. The primary drivers of AWPL in Zhejiang are grain yield, water resource availability, and crop-planting structure. Notably, these factors do not exhibit regional heterogeneity. The paper proposes AWP control policies from both a comprehensive and multi-dimensional perspective.

Developing a comprehensive research framework that integrates the water–energy–carbon (WEC) system with economic development is crucial to fostering sustainable development. However, common evaluation indicators for sustainable development fail to cover the most up-to-date climate objectives and policies comprehensively and also lack a decoupling analysis between various subsystems and economic development. By incorporating the Tapio model and the coupling coordination degree model (CCDM), we introduce a novel water–energy–carbon–economy (WECE) framework to evaluate the sustainability of regional resources and the environment. Taking the Yangtze River Economic Belt (YREB) as an example, we have constructed a comprehensive water–energy–carbon (WEC) indicator system that aligns with China’s sustainable development objectives and its most recent carbon emission reduction strategies. Employing the indicator system, we conducted an assessment of the sustainable development within the YREB from 2010 to 2019. The results reveal that the YREB has yet to achieve full decoupling between water use, energy consumption, carbon emissions, and economic development, with a prevailing trend towards weak decoupling (WD). The WEC system within the YREB exhibited coordination from 2010 to 2019. Notably, only the WEC system in Sichuan attained good coordination in 2019, indicating the imperative for more extensive initiatives in resource and environmental development to realize sustainable objectives. Finally, we delve into the driving mechanism of the coupling coordination degree (CCD) of the WEC system. Our findings suggest that, from the perspective of system collaborative management, the integrated approach of the WEC system offers superior benefits compared to individual management components. Consequently, it is imperative to bolster collaboration and institute a comprehensive set of policies to ensure sustainable development within the region.

Understanding the relationship between the agricultural water footprint (AWF) and agricultural economic growth (AEG) is of great significance for promoting sustainable agriculture and regional economic development. In this study, we used agricultural statistics data from Hangzhou from 2010 to 2021 to calculate the AWF, predicted the decoupling relationship between the AWF and AEG, and explored the influencing factors of the decoupling relationship between the AWF and AEG. The results showed the following: (1) The AWF in Hangzhou exhibited a decreasing trend, with a reduction from 58.88 × 10⁸ m³ in 2010 to 37.80 × 10⁸ m³ in 2021; this was mainly related to the decline in the water footprints of grain, pork, and egg production. (2) The strong decoupling accounted for 63.64% of the decoupling between the AWF and AEG in Hangzhou during the study period. It was found that an agricultural structure adjustment was the main factor for achieving decoupling between the AWF and AEG. Under the guidance of policy, the decoupling between them could be changed by regulating the output of agricultural products with different water footprint contents per unit. (3) From 2022 to 2026, the AWF in Hangzhou is expected to decrease to 28.21 × 10⁸ m³, while the agricultural economy is projected to increase to CNY 40.008 billion. There will continue to be a strong decoupling status between the AWF and AEG in Hangzhou.