Search Results (61)

Identifying a water–nitrogen coupling strategy to achieve high efficiency, emission reduction, and optimal yield in summer maize under multi-objective conditions is crucial for enhancing nitrogen fertilizer utilization and promoting agricultural sustainability. This study conducted a field experiment on water–fertilizer coupling in summer maize, with three irrigation levels (60%θ_f, 70%θ_f, 80%θ_f, with θ_f representing field capacity) and four nitrogen application levels (0, 180, 270, 360 kg/ha). It analyzed variations in yield, partial factor productivity of nitrogen fertilizer (PFP_N), and the soil CO₂ emission flux across different water–nitrogen combinations, establishing a multi-vector optimization model. NSGA-III (non-dominated Sorting Genetic Algorithm III) was utilized to determine the most effective combination of water and nitrogen. The results indicated that maize yield initially increases and then declines as irrigation and nitrogen levels rise. PFP_N showed a decreasing trend, and its decline gradually decreased with increasing irrigation levels, suggesting that water can alleviate nitrogen stress to some extent. Soil carbon dioxide exhalation intensity increased with both irrigation and nitrogen levels. The NSGA-III optimization revealed that the optimal water–nitrogen ratio is 1086.28 m³/ha for irrigation and 265.79 kg/ha for nitrogen. Compared with the best water–nitrogen combination (W2N3) from the experiment, this optimized scheme showed no significant difference in irrigation volume, yield, or soil CO₂ emission flux while increasing PFP_N by 13.46% and saving 1.56% of nitrogen fertilizer. In summary, the optimized water–fertilizer coupling scheme provides a scientific basis for high-efficiency, high-yield, and low-emission maize production in Henan Province, supporting sustainable agricultural development. Full article

Water use efficiency (WUE) reflects the quantitative relationship between vegetation gross primary productivity (GPP) and surface evapotranspiration (ET), serving as a crucial indicator for assessing the coupling of carbon and water cycles in ecosystems. As a sensitive region to climate change, the Qinghai Tibetan Plateau’s WUE dynamics are of significant scientific interest for understanding carbon water interactions and forecasting future climate trends. However, due to the scarcity of observational data and the unique environmental conditions of the plateau, existing studies show substantial errors in GPP simulation accuracy and considerable discrepancies in ET outputs from different models, leading to uncertainties in current WUE estimates. This study addresses these gaps by first employing a machine learning approach (random forest) to integrate observed GPP flux data with multi-source environmental information, developing a predictive model capable of accurately simulating GPP in the Qinghai Tibetan Plateau (QTP). The accuracy of the random forest simulation results, RF_GPP (R² = 0.611, RMSE = 69.162 gC·m⁻²·month⁻¹), is higher than that of the multiple linear regression model, regGPP (R² = 0.429, RMSE = 86.578 gC·m⁻²·month⁻¹), and significantly better than the accuracy of the GLASS product, GLASS_GPP (R² = 0.360, RMSE = 91.764 gC·m⁻²·month⁻¹). Subsequently, based on observed ET flux data, we quantitatively evaluate ET products from various models and construct a multiple regression model that integrates these products. The accuracy of REG_ET, obtained by integrating five ET products using a multiple linear regression model (R² = 0.601, RMSE = 21.04 mm·month⁻¹), is higher than that of the product derived through mean processing, MEAN_ET (R² = 0.591, RMSE = 25.641 mm·month⁻¹). Finally, using the optimized GPP and ET data, we calculate the WUE during the growing season from 1982 to 2018 and analyze its spatiotemporal evolution. In this study, GPP and ET were optimized based on flux observation data, thereby enhancing the estimation accuracy of WUE. On this basis, the interannual variation of WUE was analyzed, providing a data foundation for studying carbon water coupling in QTP ecosystems and supporting the formulation of policies for ecological construction and water resource management in the future. Full article

Water use efficiency (WUE) plays a pivotal role in connecting the carbon and water cycles and represents the amount of water used by plants or ecosystems to achieve carbon sequestration. The response of WUE to climate warming and its underlying mechanisms remain unclear. Here, we examined the effects of varying levels of warming on carbon fluxes, water fluxes, and WUE in an alpine peatland, with Blysmus sinocompressus and Carex secbrirostris as dominant species. Open-top chambers were utilized to simulate two levels of warming: low-level warming (TL) and high-level warming (TH). The carbon dioxide and water fluxes were monitored over a growing season (June to September). Gradient warming significantly decreased both gross primary productivity (GPP) and net ecosystem carbon exchange (NEE); GPP was 10.05% and 13.31% lower and NEE was 21.00% and 30.00% lower in the TL and TH treatments, respectively, than in the control. Warming had no significant effect on soil evaporation, and plant transpiration and evapotranspiration were 36.98% and 23.71% higher in the TL treatment than in the control, respectively; this led to decreases of 31.38% and 28.17% in canopy water use efficiency (WUEc) and ecosystem water use efficiency (WUEe), respectively. Plant transpiration was the main factor affecting both WUEe and WUEc in response to warming. The findings underscore the essential function of water fluxes in regulating WUE and enhance our understanding of carbon–water coupling mechanisms under climate change. Full article

In terrestrial ecosystems, the quantification of carbon absorption is primarily represented by the gross primary productivity (GPP), which signifies the initial substances and energy acquired by the ecosystem. The GPP also serves as the foundation for the carbon cycle within the entire terrestrial ecosystem. The Biome-BGC model is a widely used biogeochemical process model for simulating the stocks and fluxes of water, carbon, and nitrogen between ecosystems and the atmosphere. However, it is the abundance of eco-physiological parameters that lead to challenges in calibrating the model. The parameter optimization method of coupling the differential evolution algorithm (DE) with the Biome-BGC model was used to calibrate and validate the eco-physiological parameters of the seven typical vegetation types in the Yellow River Basin (YRB). And then we used the calibrated parameters to simulate the GPP by way of grid-based simulation. Finally, we conducted model adaptability testing and spatiotemporal analysis of GPP variations in the YRB. The results of the validation (R², RMSE) were: temperate grasses (0.94, 24.33 g C m⁻²), alpine meadows (0.94, 18.13 g C m⁻²), shrubs (0.94, 29.20 g C m⁻²), evergreen needle leaf forests (0.96, 27.88 g C m⁻²), deciduous broad leaf forests (0.94, 32.09 g C m⁻²), one crop a year (0.96, 16.19 g C m⁻²), and two crops a year (0.90, 38.15 g C m⁻²). After adaptability testing, the average R² value between the simulated GPP values and the GPP product values in the YRB was 0.85, and the average RMSE value was as low as 50.92 g C m⁻². Overall, the model exhibited strong simulation accuracy. Therefore, after calibrating the model with the DE algorithm, the Biome-BGC model could effectively adapt to the ecologically complex YRB. Moreover, it was able to accurately estimate the GPP, which establishes a foundation for analyzing the spatiotemporal trends of the GPP in the YRB. This study provides a reference for optimizing Biome-BGC model parameters and simulating diverse vegetation types on a large scale. Full article

Carbon use efficiency (CUE) and water use efficiency (WUE) are key metrics for quantifying the coupling between terrestrial ecosystem carbon and water cycles. The impacts of intensifying climate change and human activities on carbon and water fluxes in Central Asian vegetation remain unclear. In this study, the CUE and WUE in Central Asia from 2001 to 2022 were accurately estimated with the help of the Google Earth Engine (GEE) data platform; the Theil–Sen median slope estimation combined with the Manna–Kendall significance test and partial derivative analysis were used to investigate the CUE and WUE trends and their responses to climate change and human activities. CUE and WUE show overall declining trends with significant spatial variability. Among meteorological factors, vapor pressure deficit and temperature show the strongest correlation with CUE, while precipitation and temperature are most correlated with WUE. Compared to human activities, climate change has a greater impact on CUE and WUE, mainly exerting a negative influence. Human activities are the main drivers in regions with developed agriculture, such as oases, farmlands, and areas near rivers and lakes. This study provides scientific references for the optimization of water and soil resources and the integrated regional environmental management in Central Asia. Full article

The coupled processes of ecosystem carbon and water cycles are usually evaluated using the water use efficiency (WUE), and improving WUE is crucial for maintaining the sustainability of ecosystems. However, it remains unclear whether the WUE in different ecosystem responds synchronously to the synergistic effect of the same climate factors at daily and monthly scales. Therefore, we employed a machine learning-driven factor analysis method and a geographic detector model, and we quantitatively evaluated the individual effects and the synergistic effect of climate factors on the daily mean water use efficiency (WUE_D) and monthly mean water use efficiency (WUE_M) in different ecosystems in China. Our results showed that (1) among the 10 carbon flux monitoring sites in China, WUE_D and WUE_M exhibited the highest positive correlations with the near-surface air humidity and the highest negative correlation with solar radiation. The correlation between WUE_M and climate factors was generally greater than that between WUE_D and climate factors. (2) There were significant differences in the order of importance and degree of impact of the same climate factors on WUE_D and WUE_M in the different ecosystems. Among the 10 carbon flux monitoring sites in China, the near-surface air humidity imposed the greatest influence on the WUE_D and WUE_M changes, followed by the near-surface water vapor pressure. (3) There were significant differences in the synergistic effects of the same climate factors on WUE_D and WUE_M in the different ecosystems. Among the 10 carbon flux monitoring sites in China, the WUE_D variability was most sensitive to the synergistic effect of solar radiation and photosynthetically active radiation, while the WUE_M variability was most sensitive to the synergistic effect of the near-surface air humidity and soil moisture. The research results indicated that synchronous responses of the WUE in very few ecosystems to the same climate factors and their synergistic effect occurred at daily and monthly scales. This finding enhances the understanding of sustainable water resource use and the impact of climate change on water use efficiency, providing crucial insights for improving climate-adaptive ecosystem management and sustainable water resource utilization across different ecosystems. Full article

Vegetation phenology is a biological factor that directly or indirectly affects the dynamic equilibrium between water and carbon fluxes in ecosystems. Quantitative evaluations of the regulatory mechanisms of vegetation phenology on water–carbon coupling are of great significance for carbon neutrality and sustainable development. In this study, the interannual variation and partial correlation between vegetation phenology (the start of growing season (SOS), the end of growing season (EOS), and the length of growing season (LOS)) and ET (evapotranspiration), GPP (gross primary productivity), WUE (water use efficiency; water–carbon coupling index) in the Huang-Huai-Hai and Yangtze River Basins in China from 2001 to 2019 were systematically quantified. The response patterns of spring (autumn) and growing season WUE to SOS, EOS, and LOS, as well as the interpretation rate of interannual changes, were evaluated. Further analysis was conducted on the differences in vegetation phenology in response to WUE across different river basins. The results showed that during the vegetation growth season, ET and GPP were greatly influenced by phenology. Due to the different increases in ET and GPP caused by extending LOS, WUE showed differences in different basins. For example, an extended LOS in the Huang-Huai-Hai basins reduced WUE, while in the Yangtze River Basin, it increased WUE. After extending the growing season for 1 day, ET and GPP increased by 3.01–4.79 mm and 4.22–6.07 gC/m², respectively, while WUE decreased by 0.002–0.008 gC/kgH₂O. Further analysis of WUE response patterns indicates that compared to ET, early SOS (longer LOS) in the Yellow River and Hai River basins led to a greater increase in vegetation GPP, therefore weakening WUE. This suggests that phenological changes may increase ineffective water use in arid, semi-arid, and semi-humid areas and may further exacerbate drought. For the humid areas dominated by the Yangtze River Basin, changes in phenology improved local water use efficiency. Full article

The dynamic replacement of soil organic carbon represents a pivotal mechanism through which water erosion modulates soil–atmosphere CO₂ fluxes. However, the extent of this dynamic replacement of soil inorganic carbon within this process remains unclear. In our study, we focused on Yuanmou County, China, a prototypical region afflicted by water erosion, as our study area. We leveraged the WaTEM/SEDEM model to quantify the dynamic replacement of soil carbon, accounted for the average annual net change in soil carbon pools, and used isotope tracer techniques to track and measure the process of the coupled carbon–water cycling. This comprehensive approach enabled us to scrutinize the dynamic replacement of soil carbon under water erosion and delineate its ramifications for the carbon cycle. Our findings unveiled that the surface soil carbon reservoir in the Yuanmou area receives an annual replacement of 47,600 ± 12,600 tons following water erosion events. A substantial portion, amounting to 39,700 ± 10,500 tons, stems from the dynamic replacement of soil inorganic carbon facilitated by atmospheric carbon. These results underscore the critical role of the dynamic replacement of soil inorganic carbon in altering the soil–atmosphere CO₂ fluxes under water erosion, thereby influencing the carbon cycle dynamics. Consequently, we advocate for the integration of water erosion processes into regional carbon sink assessments to attain a more comprehensive understanding of regional carbon dynamics. Full article

In this manuscript, CH₄ emissions from sludge treatment centres are quantified using an unmanned aerial vehicle (UAV) framework, with particular focus on anaerobic digesters and digestate storage tanks. The outcomes are compared to those obtained using the carbon accounting workbook (CAW), which is the most commonly used industry tool by UK and Irish water companies to estimate the annual greenhouse gas emissions from their process operations. Path integrated concentrations are monitored with the use of an open-path tuneable diode laser absorption spectroscopy sensor embedded on a UAV. Measurements are interpolated using geostatistics (Kriging) and coupled with the mass balance approach to estimate emissions. The findings show that the CAW seems to underestimate emissions from digestate storage tanks by up to an order of magnitude. The results also show that CH₄ emissions are linked with the residence time in the tank and temperature of the digestate. This study highlights the limitations of assumptions made using current reporting methods based on the carbon accounting workbook. This study proves that the UAV framework, together with the mass balance approach, provides high spatial resolution data; it captures the dynamic nature of emissions compared to the CAW and can be a cost-effective solution to estimate CH₄ fluxes compared to other sensor-based systems. Full article

Studies on the spatiotemporal dynamics in ecosystem carbon and water exchanges are essential in predicting the effects of climate change on regional carbon and energy budgets. Using the eddy covariance technique, carbon and water fluxes were observed in a typical winter wheat ecosystem (WWE) and an agroforest ecosystem (AFE) in the southern Loess Plateau from 2004 to 2010. The seasonal and inter-annual variability in gross primary productivity (GPP), net ecosystem exchange (NEE), evapotranspiration (ET), and water use efficiency (WUE) were examined and the main influencing factors were identified using the Pearson correlation. The results indicate that the seasonal GPP and NEE showed a bimodal distribution in WWE, while this was unimodal in AFE. The sinusoidal function did well in the characterization of seasonal ET dynamics for both ecosystems, with the determination coefficients being 0.85 and 0.94, respectively. In WWE and AFE, the annual mean GPP were 724.33 and 723.08 g C m⁻² a⁻¹, respectively, and the corresponding ET were 392.22 and 410.02 mm a⁻¹. However, the difference in NEE between the two ecosystems was obvious, NEE were −446.28 and −549.08 g C m⁻² a⁻¹, respectively, showing a stronger carbon sink in AFE. There were strong coupling relationships between the GPP and ET of both ecosystems; the overall slopes were 1.71 and 1.69, respectively. The seasonal trend of WUE was bimodal in WWE, with peak values of 3.94 and 3.65 g C kg⁻¹ H₂O, occurring in November and April, respectively. However, the monthly WUE in AFE had one single peak of 4.07 g C kg⁻¹ H₂O in January. Photosynthetically active radiation (PAR) and soil temperature (T_s) were most positively correlated with GPP, net radiation (R_n) and T_s were the major factors influencing ET, while vapor pressure deficit (VPD) and soil water content (SWC) were the major influencing factors for WUE. These results provide observational support for regional carbon neutrality simulations. Full article

Semi-arid coastal aquifers face critical challenges characterized by lower rainfall, higher evaporation rates, and looming risk of over-exploitation. These conditions, coupled with climate change, are conducive to seawater intrusion and promote mechanisms associated with it. The understanding of metal behavior in such environments is limited, and hence, an attempt is made through this review to bridge the knowledge gap. A study on the behavior of trace metals within a specific context of semi-arid coastal aquifers was carried out, and 11 aquifers from 6 different countries were included. The review observed that trace metals within semi-arid coastal aquifers exhibit distinctive behaviors influenced by their surrounding environment. The prevalence of evaporation and continuous seawater intrusion played a pivotal role in shaping trace metal dynamics by curtailing groundwater flux. The findings suggest that the formation of stable Cl and organic ligands under increased alkaline conditions (pH > 8) has higher control over Zn, Pb, and Cd toxicity in a highly ionic reactive condition. In addition, dominant control of Fe/Mn-hydroxide association with Pb and high organic affinity of Zn played a pivotal role in controlling its bioavailability in aquifers such as WFB, Saudi Arabia NW-C and India. On the contrary, under prevailing acidic conditions (pH < 6), carbonate and SO₄-ligands become more dominant, controlling the bioavailability/desorption of Cu irrespective of its origin. The behavior of Ni is found to be controlled by stable organic ligands increasing salinity. An increase in salinity in the considered aquifers shows an increase in bioavailability of Ni, except UmC, South Africa, where organic ligands act as a sink for the metal, even at low pH conditions (pH < 5.5). This study indicates that factors such as mineral saturation, carbonate complexes, pH variations (pH > 8), and chloride complexes govern the distribution of trace metals further enhanced by prolonged water residence time. Nonetheless, specific conditions, such as a reducing and acidic environment, could potentially elevate the solubility of highly toxic Cr (VI) released from anthropogenic sources. Full article

Elevated temperature and frequent drought events under global climate change may seriously affect soil respiration. However, the underlying mechanism of the effects of warming and drought on soil respiration is not fully understood in the context of the Loess Plateau. This study examined the response of soil respiration (Rs) to multiple factors, including warming (W), drought (P), and their interaction (WP), in the semi-arid grassland of the Loess Plateau in Northwest China. The research period was from May to November 2022, with an open-top heating box used for warming and a rain shelter used for drought. The results showed the following: (1) Rs ranged from 1.67 μmol m⁻² s⁻¹ to 4.77 μmol m⁻² s⁻¹, with an average of 3.36 ± 0.07 μmol m⁻² s⁻¹. The cumulative soil carbon flux ranged from 500.97 g C·m⁻² to 566.97 g C·m⁻², and the average cumulative soil respiration was 535.28 ± 35.44 g C·m⁻². (2) Warming increased Rs by 5.04 ± 3.11%, but drought inhibited Rs by 3.40 ± 3.14%, and the interaction between warming and drought significantly reduced soil respiration by 11.27 ± 3.89%. (3) The content of particulate organic carbon (POC), dissolved organic carbon (DOC), soil organic carbon (SOC), and readily oxidized carbon (ROC) decreased with the increased soil depth. ROC after W and WP treatments was significantly higher than that of the control, and POC after P treatment was significantly higher than CK (p < 0.05). (4) The seasonal variation of soil respiration was positively correlated with soil temperature, soil water content, plant height, and leaf area index (p < 0.05), but the response rules differed during different regeneration periods. Soil water content; soil water content and leaf area index; and soil water content, soil temperature, and leaf area index were the factors that regulated the variation in soil respiration in the first, second, and third regeneration periods, respectively. These results clearly showed the limiting effect of drought stress on the coupling between temperature and soil respiration, especially in semi-arid regions. Collectively, the variations in soil respiration under warming, drought, and their interactions were further regulated by different biotic and abiotic factors. Considering future warming, when coupled with increased drought, our findings indicate the importance of considering the interactive effects of climate change on soil respiration and its components in arid and semi-arid regions over the next decade. Full article

In situ field investigations coupled with coral culture experiments were carried out in the coral reef waters of the eastern coast of Shenzhen, Da’ao Bay (DAB), Dalu Bay (DLB), and Yangmeikeng Sea Area (YMKSA) to study the dynamics of the carbon dioxide (CO₂) system in seawater and its controlling factors. The results indicated that the CO₂ parameters were highly variable over a range of spatiotemporal scales, forced by various physical and biochemical processes. Comprehensively, DAB acted as a sink for atmospheric CO₂ with exchange flux of –1.51 ± 0.31 to 0.27 ± 0.50 mmol C m⁻² d⁻¹, while DLB and YMKSA acted as a CO₂ source with exchange fluxes of –0.42 ± 0.36 to 1.69 ± 0.74 mmol C m⁻² d⁻¹ and –0.58 ± 0.48 to 1.69 ± 0.41 mmol C m⁻² d⁻¹, respectively. The biological process and mixing effect could be the most important factor for the seasonal variation in total alkalinity (TA). In terms of dissolved inorganic carbon (DIC), in addition to biological process and mixing, its seasonal variation was affected by air–sea exchange and coral metabolism to some extent. Different from the former, the other CO₂ parameters, total scale pH (pH_T), partial pressure of CO₂ (pCO₂), and aragonite saturation state (Ω_A), were mainly controlled by a combination of the temperature change, biochemical processes, air–sea exchange, and coral metabolism, while water mixing has little effect on them. In addition, our results indicated that coral communities could significantly increase the DIC/TA ratio by reducing the TA concentration and increasing the DIC in the reef waters, which may promote the acidification of local seawater and need attention. Full article

Soil erosion is a key factor in soil quality degradation and carbon balance in arid ecosystems. However, many models ignore the soil erosion process in arid regions, which may lead to limits in our understanding of ecosystem processes in arid regions. In this study, we added the soil erosion process according to field observed data of soil hydrothermal regimes and carbon flux. We validated this coupling version of IBIS (Integrated Biosphere Simulator) and RUSLE (RU–IBIS) by examining four different vegetation types and the carbon budget in the arid region on the Loess Plateau (LP). Our results indicated that the coupling model (RU–IBIS) produced more reliable simulations of the soil water content (with the r from 0.23–0.90 to 0.71–0.97) and evaporation (ET) (the average r was 0.76) and significantly improved the simulation of the leaf area index (LAI) (the average r was 0.95) and net primary production (NPP) (the average r was 0.95). We also conducted sensitivity experiments to determine how soil texture and aerodynamic roughness (Z_0m) affect the soil water content. Moreover, it was revealed that specific leaf area (SLA) plays a key role in the simulation of NPP and NEE. Our study suggests that the coupled soil erosion process and parameterization can effectively improve the performance of IBIS in arid regions. These results need to be considered in future Earth system models. Full article

Terrestrial carbon fluxes are crucial to the global carbon cycle. Quantification of terrestrial carbon fluxes over the Tibetan Plateau (TP) has considerable uncertainties due to the unique ecosystem and climate and scarce flux observations. This study evaluated our recent improvement of terrestrial flux parameterization in the weather research and forecasting model coupled with the vegetation photosynthesis and respiration model (WRF-VPRM) in terms of reproducing observed net ecosystem exchange (NEE), gross ecosystem exchange (GEE), and ecosystem respiration (ER) over the TP. The improvement of VPRM relative to the officially released version considers the impact of water stress on terrestrial fluxes, making it superior to the officially released model due to its reductions in bias, root mean square error (RMSE), and ratio of standard deviation (RSD) of NEE to 0.850 μmol·m⁻²·s⁻¹, 0.315 μmol·m⁻²·s⁻¹, and 0.001, respectively. The improved VPRM also affects GEE simulation, increasing its RSD to 0.467 and decreasing its bias and RMSE by 1.175 and 0.324 μmol·m⁻²·s⁻¹, respectively. Furthermore, bias and RMSE for ER were lowered to −0.417 and 0.954 μmol·m⁻²·s⁻¹, with a corresponding increase in RSD by 0.6. The improved WRF-VPRM simulation indicates that eastward winds drive the transfer of lower CO₂ concentrations from the eastern to the central and western TP and the influx of low-concentration CO₂ inhibits biospheric CO₂ uptake. The use of an improved WRF-VPRM in this study helps to reduce errors, improve our understanding of the role of carbon flux cycle over the TP, and ultimately reduce uncertainty in the carbon flux budget. Full article