Decomposition Model Research Articles

A flexible class of priors for orthonormal matrices with basis function-specific structure

Statistical modeling of high-dimensional matrix-valued data motivates the use of a low-rank representation that simultaneously summarizes key characteristics of the data and enables dimension reduction. Low-rank representations commonly factor the original data into the product of orthonormal basis functions and weights, where each basis function represents an independent feature of the data. However, the basis functions in these factorizations are typically computed using algorithmic methods that cannot quantify uncertainty or account for basis function correlation structure a priori. While there exist Bayesian methods that allow for a common correlation structure across basis functions, empirical examples motivate the need for basis function-specific dependence structure. We propose a prior distribution for orthonormal matrices that can explicitly model basis function-specific structure. The prior is used within a general probabilistic model for singular value decomposition to conduct posterior inference on the basis functions while accounting for measurement error and fixed effects. We discuss how the prior specification can be used for various scenarios and demonstrate favorable model properties through synthetic data examples. Finally, we apply our method to two-meter air temperature data from the Pacific Northwest, enhancing our understanding of the Earth system’s internal variability.

An optimised scattering power decomposition method oriented to ship detection in polarimetric synthetic aperture radar imagery

AbstractThis paper describes an optimised scattering power decomposition method suitable for ship detection in polarimetric synthetic aperture radar imagery. Based on the principal component analysis technique for dimension reduction and the scattering power decompositions with strong physical interpretation, a novel optimised scattering power decomposition model is proposed which comprises surface scattering, double‐bounce scattering, ±45° oriented dipole, and volume scattering components. Wherein, the oriented dipole component can be used to characterise the compounded scattering of the target which is composed of even‐bounce and odd‐bounce reflectors. Furthermore, pocket algorithm and support vector machine are adopted to solve linear non‐separable problems under complex experimental conditions in this study. Extensive experiments carried out on RADARSAT‐2 data and NASA/JPL AIRSAR data show that the oriented dipole component significantly contributes to the optimised four‐component decomposition method and can play a vital role in ship detection. Compared to other scattering power decompositions, the optimised decomposition method not only achieves better performance but is more in accordance with the real scattering mechanisms, which is more applicable to ship detection.

A novel bearing weak fault diagnosis method based on rank constrained low-rank and sparse decomposition

Abstract Addressing the challenge of diagnosing incipient bearing faults amidst significant noise, a novel diagnostic approach is introduced, leveraging a Rank Constrained Low-Rank and Sparse Decomposition (RCLRSD) model tailored for weak fault detection in bearings. Initially, we raised the Autocorrelation Function of the Square Envelope in Frequency Domain (AFSEFD) as an innovative method for the estimation of fault frequencies. Subsequently, we constructed a two-dimensional observation matrix, which is formulated independently of predefined assumptions. Then, we examine the configuration and distribution patterns of bearing fault signals, uncovering the low-rank nature of fault characteristics within a designated two-dimensional transform domain. Moreover, we found the noise signal to exhibit sparsity and an approximate Gaussian distribution. Based on this, a rank-constrained low-rank sparse decomposition model is established, and rank-constrained low-rank regular constraints for feature information and sparse regular constraints and Gaussian constraints for interference signals are constructed respectively. Ultimately, we employed the Gray Wolf Optimization (GWO) algorithm to refine the model parameters, and we deduced the model's solver via the Alternating Direction Method of Multipliers (ADMM). The proposed RCLRSD model decomposes bearing fault data into three constituents: low-rank, sparse, and Gaussian components, effectively addressing the challenge of extracting weak fault signatures from bearings. The weak feature extraction capability of the RCLRSD model is verified using a multi-interference simulation model and experimental data of bearing failures under strong noise conditions; the generalization of the model is verified by the classification effect of the Support Vector Machine (SVM) under two bearing failure datasets. Comparison with various algorithms confirms the superiority of the proposed algorithm.

Damage detection for railway bridges using time‐frequency decomposition and conditional generative model

AbstractA novel damage detection model, which utilizes the spatiotemporal characteristics of the acceleration data, is proposed to assess the structural integrity of railway bridges. For this, the measured acceleration data are decomposed into several intrinsic mode functions (IMFs) using the sparse random mode decomposition model. The generated IMFs are subsequently integrated into the enhanced time series conditional generative adversarial network model to identify possible damage in bridges across various frequency bands. The influence of environmental and operational variables (EOVs), particularly temperature fluctuations, was also investigated. The proposed model was verified using both numerical and experimental data from a plate girder bridge. Further validation was conducted using the Z24 bridge dataset, and damage cases under the influence of EOVs were successfully predicted. Throughout the validation process, various anomaly metrics were introduced to establish a threshold value, and a covariance‐based time domain metric was proven to be the most effective in our cases.

Decomposing social groups differential in stunting among children under five in India using nationally representative sample data

Stunting among children is a reflection of the chronic malnutrition caused by a complex set of behavioural, demographic, and socioeconomic factors. This long-term detrimental exposure to chronic malnutrition is disproportionately higher among social and economically deprived groups, leading to significant differentials in the prevalence of stunting across various social strata. Therefore, this study investigates the inequality of social groups in terms of the prevalence of stunting across Schedule Caste (SC)-Scheduled Tribe (ST) and non-SC-ST. The study used 1,93,886 children’s data aged 0–59 months from the recent round of the National Family Health Survey. Descriptive statistics, multivariable logistic regression, F-test, t-test and chi-squared (χ²) test were applied to understand the prevalence, determinants, and associations, respectively. The Fairlie decomposition model was applied to quantify the factors contributing to the inequality of stunting across social groups. The results revealed that the prevalence of stunting was higher among SC-ST (39.60%) children compared to non-SC-ST (33.27%). In addition, children aged 15–30 months (AOR: 1.895, 95% CI: 1.843–1.949), and male (AOR: 1.074, 95% CI: 1.053–1.095), mothers had lower BMI (AOR: 1.543, 95% CI: 1.492–1.595), mothers who had no education (AOR: 1.595, 95% CI: 11.532–1.662), belongs to poorest wealth index (AOR: 1.857, 95% CI: 1.766–1.952), and the children belong to the household with unhygienic satiation practices (AOR: 1.097, 95% CI: 1.070–1.123) were more likely to be stunted. The decomposition results revealed that the variables included in the study could explain 68.9% of the stunting inequality between SC-ST and non-SC-ST groups. The household’s wealth index is found to be a leading factor, which contributed nearly 41.3% of total stunting inequality exists between these two groups, followed by mothers’ education (12.86%) and mothers’ BMI (11.02%), sanitation facilities (4.26%), children’s birth order (3.32%) and mother’s type of delivery (1.49%). These findings emphasize the importance of targeted interventions. Prioritizing policies that address household economic enhancement, women’s education and empowerment can be instrumental in reducing social group inequality and lowering the overall prevalence of stunting. Ensuring access to improved hygienic sanitation facilities in the household is equally important for achieving better health outcomes for the children.

Comparative Study on Forecast Performance from Decomposition, Winter’s and Sarima Models

This study investigates the forecasting accuracy of three univariate time series models—Decomposition, Winter’s method, and Seasonal Autoregressive Integrated Moving Average (SARIMA) to predict the Agricultural GDP of Nigeria. Quarterly data on Nigeria's Agricultural GDP from 2010 to the first quarter of 2023, obtained from the National Bureau of Statistics, were analyzed. The study applied Box-Jenkins SARIMA modeling, time series decomposition, and Winter’s method to compare their forecasting accuracy using Root Mean Square Error (RMSE) as the selection criterion. The results revealed that the SARIMA (0, 0, 2)(2, 1, 0) model outperformed the other methods, with the lowest RMSE, indicating its superior accuracy in forecasting Agricultural GDP. Winter’s method and the Decomposition method. The forecast from the SARIMA model indicated a positive trend in Nigeria’s Agricultural GDP over the study period, reinforcing the sector’s critical role in economic growth.

A partitioned functional-decomposition scheme for modelling wave-ship-sloshing interaction

A novel partitioned function decomposition method is proposed and validated for accurately and effectively solving the interaction between waves and ships with sloshing effect. This scheme employs a hybrid functional-decomposition method that utilizes both potential and viscous flows to accurately and efficiently solve the wave-ship interaction in the external domain, while employing the original viscous method for simulating sloshing in the internal domain. To further enhance computational efficiency, a single-phase Level-set method is employed. Firstly, the issue of non-conservation in the level-set model is addressed through the application of a mass correction method. Additionally, based on the principle of mass conservation, modifications are made to the boundary conditions for free surface motion. As a result, an improved single-phase Level-set method is developed, which combines jump condition correction and mass correction. Through simulations involving linear and nonlinear free surfaces, impulsive pressures, overall forces on the tank, as well as comparisons with experimental data, it is observed that the proposed Level-set method effectively solves the sloshing in the internal domain, a problem for which the traditional single-phase Level-set method often fails to tackle with. Subsequently, by adopting a multi-block grid technique to mark tank and non-tank grid blocks and integrating the improved single-phase Level-set method with the SWENSE model, a partitioned function decomposition method is established to handle ship motions with sloshing effect in waves. It is found that simultaneously solving the internal and external flow problems using traditional implicit and explicit motion-solving methods poses certain challenges. Therefore, an implicit-inner-iteration solution method is proposed. By combining the proposed motion-solving method with the partitioned function decomposition model, satisfactory results are achieved for the wave-ship-sloshing interaction.

Sustainable sentiment analysis on E-commerce platforms using a weighted parallel hybrid deep learning approach for smart cities applications

Sentiment analysis (SA) of several user evaluations on e-commerce platforms can be used to increase customer happiness. This method automatically extracts and identifies subjective data from product evaluations using natural language processing (NLP) and machine learning (ML) methods. These statistics may eventually reveal information on the favourable, neutral, or negative attitudes of the consumer base. Due to its capacity to grasp the complex links between words and phrases in reviews as well as the emotions they imply, deep learning (DL) is very useful for SA tasks. A unique approach termed Weighted Parallel Hybrid Deep Learning-based Sentiment Analysis on E-Commerce Product Reviews (WPHDL-SAEPR) is introduced by the proposed system. Accurately distinguishing between distinct sentiments found in online store reviews is the aim of the WPHDL-SAEPR technique. Additional data pre-processing processes are implemented within the WPHDL-SAEPR architecture to guarantee compatibility. Words are embedded into the paper using the word2vec model, while sentiment is classified using the WPHDL model. The Restricted Boltzmann Machine (RBM) and Singular Value Decomposition (SVD) models are combined in this model. The results of the WPHDL-SAEPR approach’s simulation were assessed using a consumer review database, with the results being emphasized at each stage.

Spatial and temporal analysis of decomposition models in China

This study presents a comprehensive evaluation of 15 decomposition models (12 empirical and 3 atmospheric transmittance models) for estimating diffuse horizontal irradiance at 17 radiation sites in China, using hourly radiation data from 2011 to 2020. Our results show distinct patterns in model performance across different geographical regions, seasons, and sky conditions. The Liu model demonstrates the best overall performance with an RMSE of 78.20 W/m2, while model accuracy shows significant geographical variation, performing best in South and Southeast China (RMSE<70 W/m2) and worst in the Qinghai-Tibet Plateau and Northwest China (RMSE>90 W/m2). Seasonal analysis reveals better performance in winter than in summer, with RMSE differences approaching 40 W/m2, mainly due to the higher proportion of solar elevation angles exceeding 30° in summer. Under different sky conditions (classified by clearness index: 0–0.35 for overcast, 0.35–0.65 for partly cloudy, 0.65–1 for clear skies), most models follow an RMSE pattern of partly cloudy > clear sky > overcast. However, the Reindl2, Boland, DIRINT, and DIRINDEX models deviate from this trend due to their formula structure and sensitivity to atmospheric parameters. To reduce these regional disparities, we propose a new region-specific model selection strategy: the DIRINDEX model for eastern regions, DIRINT for central areas, and Karatasou for western regions. This combined approach reduces the overall RMSE to 73.17 W/m2. This research deepens our understanding of the application of decomposition models in China's complex geographical and climatic conditions, offering valuable references for solar radiation modeling and renewable energy forecasting in diverse climatic regions.

A day-ahead wind speed correction method: Enhancing wind speed forecasting accuracy using a strategy combining dynamic feature weighting with multi-source information and dynamic matching with improved similarity function

Forecasting error of day-ahead wind speed (WS) seriously affects wind power integration and power system security and stability. In this regard, this paper fully considers the spatiotemporal correlation of wind farms (WFs) in different geographical locations, and proposes a day-ahead WS combined correction method that integrates multi-source station dynamic information weighting. Different from the previous WS correction methods, this paper fully considers the dynamic correlation of WS between the WFs, introduces an improved weighted similarity function to screen and dynamically weight the information of WFs with dynamic correlation, and introduces the dynamic weighting feature into the WS correction process. A combined decomposition mechanism is proposed, which combines sequential variational mode decomposition (SVMD) and feature mode decomposition (FMD) models to extract the most relevant trend components and non-stationary components of forecasted and measured WS. A combined correction model is introduced, and a combined architecture of Non-stationary Transformer combined with bidirectional long short-term memory network (Ns-Transformer-BILSTM) is used to correct the stationary WS component. A dynamic matching mechanism of fluctuation components considering improved similarity is proposed for the correction of non-stationary components. The proposed method is applied to several regional WFs in China. The experimental results show that the average correction of NRMSE, NMAE and R can reach 2.4 % ∼ 3.7 %, 2.0 % ∼ 3.0 % and 3.3 % ∼ 9.7 %, respectively. The NRMSE and NMAE corresponding to the corrected WS of certain individual WFs can be reduced by 10 % and 9 %, respectively, and R can be increased by 33 %.

Geometric decomposition and algorithmic model of main gate muqarnas of Ankara Cenabi Ahmet Pasha Mosque

Cenabi Ahmet Pasha Mosque (Cenab-ı Ahmet Paşa Mosque) is known to be the only work that resembles the style of Architect Sinan in Ankara. In this study, it is aimed to examine the two-dimensional muqarnas scheme of the mosque's main gate and to determine through algorithmic modeling the combination of muqarnas cells in the third dimension. By the photogrammetry method, the grid and star forms used in the muqarnas plan scheme are revealed. The geometric characteristic of the muqarnas in two dimensions is determined. Cell types of the muqarnas and their geometric configurations are obtained. It is demonstrated that there is a radial grid in the plan of the Cenabi Ahmet Pasha Mosque main gate muqarnas, and that the three-dimensional structure of the muqarnas consisting of six layers includes almond cell, triangle cell, deltoid cell, intermediate triangle cell, intermediate deltoid cell, biped cell and triped cell. Additionally, an algorithmic model based on the creation of cell unit system is proposed for the creation of the muqarnas geometry in 3D.

A two-echelon multi-trip vehicle routing problem with synchronization for an integrated water- and land-based transportation system

This study focuses on two-echelon synchronized logistics problems in the context of integrated water- and land-based transportation (IWLT) systems. The aim is to meet the increasing demand in city logistics as a result of the growth in transport activities, including parcel delivery, food delivery, and waste collection. We propose two models, a novel mixed integer linear joint model, and a logic-based Benders’ decomposition (LBBD) model, for a two-echelon problem under realistic settings such as multi-trips, time windows, and synchronization at the satellites with no storage and limited resource capacities. The objective is to optimize transfers and satellite assignments, thereby reducing overall logistics costs for street vehicles and vessels. Computational experiments demonstrate that the LBBD model is more robust in terms of solution quality and solution time on average while the added value of the LBBD is more evident when solving large-scale instances with 100 customers, reducing the overall costs by 10.6% on average and significantly reducing the fleet costs on both networks. Furthermore, we assess the effect of changing cost parameters and satellite locations in the proposed IWLT system–analyzing system behavior and suggesting potential improvements–and evaluate several system alternatives in city logistics–consisting of different transportation network designs (single- and two-echelon), vehicle types, and operational constraints. On average, the proposed two-echelon IWLT system reduces the number of kilometers traveled by vehicles at street level by ranging from 20% to 30% compared to a typical single-echelon service design that relies solely on trucks.

Study on runoff forecasting and error correction driven by atmosphere–ocean-land dataset

Accurate runoff forecasting results can not only provide an important basis for flood control scheduling, but also provide scientific support for water resources optimization, which promotes the maximization of the overall benefits of the basin. To further explore the inherent mechanisms of the atmosphere–ocean-land factors driving runoff changes, this study proposes the factors dimension reduction and interpretation framework based on Pearson, eXtreme Gradient Boosting and SHapley Additive exPlanations (P-XGBoost-SHAP). Base on this, the Gaussian Process Regression (GPR), Long Short-Term Memory neural network (LSTM) and Support Vector Machine (SVM) models are used to construct the atmospheric-ocean-land data-driven runoff prediction model. Meanwhile, for the runoff prediction residuals, this paper proposes an error multi-step correction framework based on ensemble empirical mode decomposition and autoregressive model (EEMD-AR). The case study of Lianghekou hydrological station shows that the factor dimension reduction and interpretation framework can greatly reduce the input dimension of the model, and explain the factors globally and locally by using SHAP Value. Compared with the traditional Random Forest (RF) dimension reduction method, it shows higher prediction accuracy. The Nash-Sutcliffe efficiency coefficient (NSE) can be increased to about 0.93, which is 4.91 % and 1.97 % higher than the series–parallel coupling (AR-Parallel) and empirical mode decomposition-autoregressive (EMD-AR) correction methods, respectively. The accuracy of the runoff forecasting prediction is improved while reducing the input dimension of the model.

Short-term inbound passenger flow prediction at high-speed railway stations considering the departure passenger arrival pattern

Accurate prediction of short-term inbound passenger flow at high-speed railway stations is of great significance for the refined operation of stations, the formulation of emergency plans, and the provision of intelligent services. The arrival of passengers traveling on the same train at the same station shows a similar pattern, which is called the departure passenger arrival pattern (DPAP). The short-term inbound passenger flow at the station is composed of the short-term inbound passenger flow of all waiting trains within the same period. Inspired by this, this paper develops an ensemble prediction model based on the time series decomposition modeling strategy to introduce the DPAP to the short-term inbound passenger flow prediction at stations. Firstly, we propose a new framework for studying the DPAP to calculate the fitted station short-term inbound passenger flow, which is only affected by the DPAP. During this process, we find that 7minutes is the optimal time granularity. Secondly, based on the singular spectrum analysis, we prove that the DPAP is the determining factor affecting the station short-term inbound passenger flow. Finally, we propose an ensemble prediction model that considers the DPAP to achieve short-term inbound passenger flow prediction at stations. The model consists of two parts: the deterministic and stochastic components prediction, where the former is predicted by the fitted station short-term inbound passenger flow, and the latter is achieved by the combination of historical stochastic components and weather type with the help of the Seq2Seq model based on time attention mechanism. Using real inbound passenger flow data, we compare the proposed model with 13 benchmark models and the results show that under different training and prediction steps, our model achieves optimal prediction performance, whether in all-day period and the busiest period of the station. Through further ablation experiments, it has been proven that the introduction of the DPAP effectively improves the prediction accuracy. Our model can provide scientific support for the intelligent operation of stations and the refined management of passenger flow.

Chained Spatial Beam Adomian Decomposition Model: A Novel Model of Flexible Slender Beams for Large Spatial Deflections

Abstract The main element of compliant mechanisms and continuum robots is flexible slender beams. However, the modeling of beams can be complicated due to the geometric nonlinearity becoming significant at large elastic deflections. This paper presents an explicit nonlinear model called the spatial beam Adomian decomposition model (SBADM) for intermediate spatial deflections of a slender beam with uniform, bisymmetric sections subjected to general end-loading. Specifically, the elongation, bending, torsion, and shear deformations of the beams are modeled based on Timoshenko's assumptions and Cosserat rod theory. Then, the quaternion transformation and Adomian decomposition are used to solve the nonlinear governing differential equations for the beam by truncating the higher-order terms, yielding an explicit expression for spatially deflected beams within intermediate deflection ranges. Simulations demonstrate the accuracy and time-wise efficiency of the SBADM, as well as its advantages over the state-of-the-art. In addition, this paper also introduces a discretization-based scheme called the chained SBADM (CSBADM) for large spatial deflections of flexible beams. Real-world experiments with two different configurations have also been performed to validate the effectiveness of the CSBADM. The results indicate that the CSBADM can accurately calculate the load-displacement relations for large deformed beams.

How does family support mitigate the impact of childhood adversity on adolescent mental health?

Abstract Background Childhood adversity is associated with increased risk of developing mental health problems over the life course. We investigated how family emotional support in adolescence may mitigate the impact of childhood adversity on adolescent mental health. Methods We used longitudinal data from the UK Millennium Cohort Study on 9,269 children followed to age 17 years. Individuals were assigned to exposure trajectories characterised using group-based trajectory models that included measures of poverty and family adversities (parental mental ill health, parental alcohol misuse and domestic violence) up to age 14 years. Using counterfactual causal mediation analysis and four-way decomposition modelling approach, we evaluated how perceived family emotional support at age 14 (measured using the three-item Short Social Provisions Scale) influences the association of childhood adversity trajectories on mental health at ages 14 and 17, assessing the relative contributions of mediation and interaction simultaneously. Results Compared with children experiencing low adversity and poverty, those exposed to childhood adversity were almost three times more likely to experience poor mental health (RR 2·99, 95% CI 2 ·41 to 3·57) at age 14 and age 17 (RR 2·58, 95% CI 2·09 to 3·06). Perceived emotional support mitigates up to 18% (95% CI: 9% to 26%) of this effect at age 14, and 13% (95% CI: 5% to 22%) at age 17, mainly due to interactive mechanisms. Conclusions Childhood adversity has a strong effect on mental health, which is partially mitigated by perceived family support in adolescence. Policies that support positive family functioning may be particularly beneficial for children who have experienced adversity. Key messages • Socio-economic – associated mental health problems in adolescence differed based on levels of family support. • Approximately 20% of the social inequality in mental health outcomes in adolescence may be mitigated or reduced by improving levels of family support.

Enhancing streamflow prediction in the Wujiang River basin: a two-stage decomposition approach with deep learning integration

ABSTRACT Global climate change and human activities have profoundly impacted the geological and hydrological processes in watersheds, increasing the challenges in streamflow prediction. In this study, we propose a streamflow prediction model based on deep learning and dual-mode decomposition specifically tailored for the Wujiang River basin, a significant tributary on the southern bank of the upper Yangtze River. This model effectively addresses the prediction error issue caused by high-frequency components through dual-mode decomposition of time-series data. The results demonstrate that employing the coupled complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)–variational mode decomposition (VMD)–temporal convolutional network (TCN)–long short-term memory network (LSTM) model reduces prediction errors by at least 35% compared to single decomposition models. Furthermore, compared to individual TCN or LSTM models, the TCN–LSTM coupled model exhibits greater stability and higher prediction accuracy during training, with reductions in mean absolute error and root mean square error by 43.13 and 24.57%, respectively. This model holds promising prospects for application and can provide crucial insights for water resource management.

A New Algorithm for Predicting Dam Deformation Using Grey Wolf-Optimized Variational Mode Long Short-Term Neural Network

To solve the problems of difficult-to-model parameter selections, useful-signal extraction and improper-signal decomposition in nonlinear, non-stationary dam displacement time series prediction methods, we propose a new predictive model for grey wolf optimization and variational mode decomposition and long short-term memory (GVLSTM). Firstly, we used the grey wolf optimization (GWO) algorithm to optimize the parameters of variable mode decomposition (VMD), obtaining the optimal parameter combination. Secondly, we used multiscale permutation entropy (MPE) as a standard to select signal screening, determining and recon-structing the effective modal components. Finally, the long short-term memory neural network (LSTM) was used to learn the dam deformation characteristics. The result shows that the GVLSTM model can effectively reduce the estimation deviation of the prediction model. Compared with VMDGRU and VMDANN, the average RMSE and MAE value of each station is increased by 19.11%~28.58% and 27.66%~29.63%, respectively. We used determination (R2) coefficient to judge the performance of the prediction model, and the value of R2 was 0.95~0.97, indicating that our method has good performance in predicting dam deformation. The proposed method has outstanding advantages of high accuracy, reliability, and stability for dam deformation prediction.

Future Scenarios of Forest Carbon Sink in a Typical Subtropical County

In the context of achieving global carbon neutrality, forests play a pivotal role in sequestering atmospheric CO2, particularly in China, where forest management is central to national climate strategies. This study evaluates the forest carbon sink capacity in Zixi County, a subtropical region, under varying climate scenarios (SSP2-4.5 and SSP5-8.5). Using the Forest-DNDC (Denitrification–Decomposition) model, combined with high-precision climate data and a random forest model, we simulate forest carbon density and forest carbon sink under different management strategies. The results indicate that under the baseline scenario, forest carbon density in Zixi County increases by 31% over 42 years under the SSP2-4.5 climate scenario and by 28.6% under SSP5-8.5. In the enhancing economic scenario, carbon density increases by 8.5% under SSP2-4.5 and by 7.2% under SSP5-8.5. For the natural development scenario, a significant increase of 130% is observed under SSP2-4.5, while SSP5-8.5 shows an increase of 120%. Spatially, forest carbon sinks in Zixi County total 843,152 T C in 2020, 542,852 T C in 2030, and 877,802 T C in 2060 under the baseline SSP2-4.5 scenario; under SSP5-8.5, these values are 841,321 T C in 2020, 531,301 T C in 2030, and 1,016,402 T C in 2060. In the enhancing economic scenario, the total carbon sink is 34,650 T C in both 2020 and 2030, increasing to 427,351 T C in 2060 under SSP2-4.5, while under SSP5-8.5, it is 46,200 T C in 2020, 34,650 T C in 2030, and 415,801 T C in 2060. The natural development scenario shows the total carbon sink under SSP2-4.5 as 11,157,332 T C in 2020, 3,441,910 T C in 2030, and 1,409,104 T C in 2060, and under SSP5-8.5, it is 10,903,231 T C in 2020, 3,337,960 T C in 2030, and 1,131,903 T C in 2060. Spatial analysis reveals that elevation and forest type significantly affect carbon density, with high-altitude areas and forests dominated by Chinese fir and broadleaf species showing higher carbon accumulation. The findings highlight the importance of targeted forest management, prioritizing species with higher carbon sequestration potential and considering spatial heterogeneity. These strategies, applied locally, can contribute to broader national and global carbon neutrality efforts.

Assessing the Impact of Climate Change on Methane Emissions from Rice Production Systems in Southern India

The impact of climate change on methane (CH4) emissions from rice production systems in the Coimbatore region (Tamil Nadu, India) was studied by leveraging field experiments across two main treatments and four sub-treatments in a split-plot design. Utilizing the closed-chamber method for gas collection and gas chromatography analysis, this study identified significant differences in CH4 emissions between conventional cultivation methods and the system of rice intensification (henceforth SRI). Over two growing seasons, conventional cultivation methods reported higher CH4 emissions (range: from 36.9 to 59.3 kg CH4 ha−1 season−1) compared with SRI (range: from 2.2 to 12.8 kg CH4 ha−1 season−1). Experimental data were subsequently used to guide parametrization and validation of the DeNitrification–DeComposition (DNDC) model. The validation of the model showed good agreement between the measured and modeled data, as denoted by the statistical tests performed, which included CRM (0.09), D-index (0.99), RMSE (7.16), EF (0.96), and R2 (0.92). The validated model was then used to develop future CH4 emissions projections under various shared socio-economic pathways (henceforth SSPs) for the mid- (2021–2050) and late (2051–2080) century. The analysis revealed a potential increase in CH4 emissions for the simulated scenarios, which was dependent on specific soil and irrigation management practices. Conventional cultivation produced the highest CH4 emissions, but it was shown that they could be reduced if the current practice was replaced by minimal flooding or through irrigation with alternating wetting and drying cycles. Emissions were predicted to rise until SSP 370, with a marginal increase in SSP 585 thereafter. The findings of this work underscored an urgency to develop climate-smart location-specific mitigation strategies focused on simultaneously improving current water and nutrient management practices. The use of methanotrophs to reduce CH4 production from rice systems should be considered in future work. This research also highlighted the critical interaction that exists between agricultural practices and climate change, and emphasized the need to implement adaptive crop management strategies that can sustain productivity and mitigate the environmental impacts of rice-based systems in southern India.