The practice of straw returning to agricultural fields (SRF) affects crop yields and greenhouse gas (GHG) emissions. However, the responses of crop yields and GHG emissions vary significantly due to diverse climatic conditions, soil conditions, and field management practices. In this study, we conducted a meta-analysis to assess the effects of SRF on the crop yield and GHG emissions from staple crops in China. Our results indicate that the average increment in the yield of three staple crops is 13.00% with SRF. Moreover, SRF decreased the N₂O emissions compared to those without straw returning in regions with 800–1200 mm of MAP, SOC > 20 g kg^–1, 0.9–1.5 g kg^–1 TN, pHs of 6.5–7.5, and a SRF duration < 3 years, in rice cultivation systems, and with partial SRF. However, irrespective of the climatic conditions, soil properties, or field management practices, SRF increased the CO₂ emissions compared to those without straw returning. Additionally, while SRF significantly increased the CH₄ emissions in paddy fields, it had no discernible effect on the CH₄ uptake in upland fields compared to that without straw returning. These findings offer valuable insights for optimizing straw management practices and reducing GHG emissions in farmland ecosystems.

Climate change refers to a statistically significant change in the average state of the climate or a climate alteration that lasts for a long period of time. Runoff (R) is as a measure of the interaction between climate change and human activities and plays an important role in the hydrological cycle, as it is directly related to the development of agricultural water management. Therefore, it is a requirement to correctly simulate R and have the ability to separate the impacts due to climate change and human activities. In this paper, five single-type simulation models (Back Propagation Neural Network (BP), Non-Autoregressive (NAR), Radial Basis Function (RBF), Support Vector Machine (SVM) and TOPMODEL Hydrological Model (TOPMODEL)) were adopted to simulate the R to analyze the simulating quality by comparing the evaluation indexes like relative error (RE), relative mean squared error (RMSE) and Nash–Sutcliff Efficiency (NSE) with the combined hierarchical structure hydrological (CHSH) simulation model. In traditional studies, only the relative contribution of the impacts of human activities and climate change on R are considered; however, in this study, the relative contribution of each meteorological factor affecting R is included. To quantitatively analyze the impact of human activities and climate change on R, we used a CHSH simulation model to calculate runoff values for the Lancang River of China for a period of nine years (2005–2013). Our objective was to use this type of model to improve both the accuracy and stability of calculated values of R. For example, the RE, RMSE and NSE of simulated monthly R calculated with the CHSH model were 6.41%, 6.67 × 10⁸ m³ and 0.94, respectively. These values substantiate the improved accuracy and stability of calculated values of R obtained with single-type simulation models (the SVM model, for instance, widely used in runoff simulations, and the RE, RMSE and NSE were 14.1%, 12.19 × 10⁸ m³ and 0.87, respectively). The total contribution of human activities and climate change to R, respectively, accounted for 34% and 66% for the nine-year period based on the CHSH model. Furthermore, we adopted a vector autoregressive (VAR) model to analyze the impacts of the meteorological factors on R. The results from this analysis showed that R has a strong fluctuation response to the changes in precipitation (P) and potential water evaporation (E_p). The contribution rates of E_p, P and air temperature (T_a) to R were 15%, 14% and 2%, respectively. Based on the total climate change contribution, the corresponding contribution rates of E_p, T_a and P in the Lancang River of China were 32%, 30% and 5%, respectively. The values of R calculated with the CHSH model are more accurate and stable compared to values obtained with single-type simulation model. Further, they have the advantage of avoiding drawbacks associated when using a single-type simulation model. Moreover, moving away from the traditional method of separating the impact of meteorological factors on R, the vector autoregressive model proposed in this paper can describe the contribution of different meteorological factors on R in more detail and with precision.

Socioeconomic drought is one of the most frequent natural disasters in the world and is closely related to human life. The main cause of socioeconomic drought is the contradiction between water supply and demand; hence, as local reservoirs play a major role in improving water supply and coping with extreme climate, it is reasonable to estimate socioeconomic drought based on reservoir operations. The multivariate standardized reliability and resilience index (MSRRI) is utilized to evaluate socioeconomic drought, considering the characteristics of reservoir management and storage water resources. Therefore, with the MSRRI, this study takes the Heihe River Basin in northwestern China, which is controlled by two reservoirs dominating the upstream and downstream regions, as a case study to reveal the evolution characteristics of socioeconomic drought in the basin and the external impacts of climate variability. The results showed that (1) the drought intensity in the up-midstream region is stronger than that in the downstream region; in view of the hysteresis in the downstream region, the occurrence of drought in the up-midstream region could be regarded as an early warning to implement preventive measures in the downstream region; (2) an increasing trend in socioeconomic drought throughout the basin exists on both monthly and annual scales, which indicates that the increasing possibility of drought should be effectively addressed; (3) cross wavelet analysis indicated that the large-scale climate indices contribute to the variations in the socioeconomic droughts throughout the basin, indicating that climate variability may provide a reference for managers to deal with socioeconomic drought in the HRB.

Rapid urbanization enhances the necessity of exploring sustainable development paths to achieve ecological and carbon storage protection. This study takes Zhengzhou, one of the national central cities in China, as a case to investigate the dynamic correlation between urbanization (UR) and carbon storage (CS). The PLUS and InVEST models were employed to simulate land use and carbon storage dynamics under natural development, cultivated land protection, and ecological protection scenarios for 2030 and 2040. This was also complemented by elasticity analysis of UR, construction land expansion (CEL), and CS. The results show that from 2000 to 2020, cultivated land declined by 15.33%, while construction land expanded by 13.31%. By 2030, construction land growth is expected to be 7.34%, 2.87%, and 4.96% across scenarios, with cultivated land of −6.96%, −2.36%, and −4.78%, respectively. Carbon storage in 2030 decreases under all scenarios (5.181 × 10⁷ t, 5.235 × 10⁷ t, 5.209 × 10⁷ t) but stabilizes by 2040, with ecological protection enhancing forest/water bodies and mitigating losses. Elasticity analysis reveals that unconstrained elasticity coefficient significantly exacerbates carbon losses, while policy interventions reduce losses through expansion control and carbon sequestration. Integrating land use policies to balance farmland protection, ecological restoration, and low-carbon urban expansion is critical for sustainable megacity development and a scalable framework for carbon governance.

The Bidirectional polarization distribution function (BPDF) of land surfaces is important for studies of land surfaces and aerosol. With the availability of a huge number of polarization measurements, several semi-empirical BPDF models have been proposed. However, these models do not pay much attention to canopy structure, which is fundamental for generation of polarization. In this article, we propose a new BPDF model using canopy structure information, which is parameterized by visible-sunlit areas. It is evaluated over grassland using POLDER BPDF and MODIS leaf area index data sets. Experiments suggest that compared to Nadal–Bréon and Litvinov models, the new BPDF model reduces root-mean-square error by 7% and 10%, respectively. The new BPDF model also provides better performance when it is fitted using observations clustered by sun zenith angle. The new BPDF model thus provides an effective tool for the study of land surface polarization.