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As a raw material for clean energy supply for the new generation, the soybean is conducive to the realization of global energy transition and sustainable development in the context of "carbon neutrality". However, global warming has been affecting soybean yields in recent years. How to clarify the correlation between meteorological factors and soybean yields, so as to ensure the security of soybean growth and development and the stability of renewable energy development, is a key concern of the government and academia. Based on the data of temperature, precipitation, sunshine duration and active accumulated temperature during the soybean growing season in Hulunbuir, Inner Mongolia Autonomous Region from 1951 to 2019, and soybean yield data of the city from 1985 to 2019, this paper adopted statistical methods such as the Trend Analysis Method, the Rescaled Range Analysis Method and so on to analyze the trends of yield changes, characteristics of abrupt changes and periodic patterns of climate factors and soybean yields in Hulunbuir. A Pearson Correlation Analysis and a Grey Relation Analysis were used to explore the correlation between climatic factors and soybean yields, followed by a comprehensive impact model of the combined effect of temperature and precipitation on soybean yields established by the Method of Integral Regression. The results showed that temperature and active accumulated temperature are the dominant factors affecting soybean yields in Hulunbuir, while the decrease in precipitation is unfavorable to the improvement of soybean yields. Meanwhile, temperature and precipitation have different effects on the growth and development of the soybean at different stages. The conclusion of this paper is of great practical significance for Hulunbuir to promote the sustainable development of clean energy. [ABSTRACT FROM AUTHOR]

In this paper, recent research on terrestrial ecosystem predictability using the conditional nonlinear optimal parameter perturbation (CNOP-P) method is summarized. The main findings include the impacts of uncertainties in climate change on uncertainties in simulated terrestrial ecosystems, the identification of key physical parameters that lead to large uncertainties in terrestrial ecosystem modeling and prediction, and the evaluation of the simulation ability and prediction skill of terrestrial ecosystems by reducing key physical parameter errors. The study areas included the Inner Mongolia region, north–south transect of eastern China, and Qinghai–Tibet Plateau region. The periods of the studies were from 1961 to 1970 for the impacts of uncertainties in climate change on uncertainties in simulated terrestrial ecosystems, and from 1951 to 2000 for the identification of the most sensitive combinations of physical parameters. Climatic Research Unit (CRU) data were employed. The numerical results indicate the important role of nonlinear changes in climate variability due to the occurrences of extreme events characterized by CNOP-P in the abrupt grassland ecosystem equilibrium state and formation of carbon sinks in China. Second, the most sensitive combinations of physical parameters to the uncertainties in simulations and predictions of terrestrial ecosystems identified by the CNOP-P method were more sensitive than those obtained by traditional methods (e.g., one-at-a-time (OAT) and stochastic methods). Furthermore, the improvement extent of the simulation ability and prediction skill of terrestrial ecosystems by reducing the errors of the sensitive physical parameter combinations identified by the CNOP-P method was higher than that by the traditional methods. [ABSTRACT FROM AUTHOR]

Background: Desertification is one of the main obstacles to global sustainable development. Monitoring, evaluating and mastering its driving factors are very important for the prevention and control of desertification. As one of the largest deserts in China, the development of desertification in Otindag Sandy Land (OSL) resulted in the reduction in land productivity and serious ecological/environmental consequences. Although many ecological restoration projects have been carried out, the vegetation restoration of OSL and the impact mechanism of climate and human activities on desertification remain unclear. Methods: Taking OSL as the research area, this paper constructs the desertification index by using the remote sensing images and meteorological and socio-economic data, between 1986 and 2016, and analyzes the spatio-temporal evolution process and driving factors of desertification by using trend analysis and spearman rank correlation. Results: The results showed that: (1) Desertification in the OSL has fluctuated greatly during the past 30 years. Desertification recovered between 1986 and 1990, expanded and increased between 1990 and 2000, reduced between 2000 and 2004, developed rapidly between 2004 and 2007, and recovered again between 2007 and 2016; (2) The desertification of OSL is dominated by a non-significant change trend, accounting for 73.27%. In the significant change trend, the area of desertification rising trend is 20.32%, which is mainly located in the north and east, and the area of declining trend is 6.41%, which is mainly located in the southwest; (3) Desertification is the result of the superposition of climate and human activities. Climate change is the main influencing factor, followed by human activities, and the superposition effects of the two are spatio-temporal differences. Conclusions: These results shed light on the development of desertification in OSL and the relative importance and complex interrelationship between human activities and climate in regulating the process of desertification. Based on this, we suggest continuing to implement the ecological restoration policy and avoid the destruction of vegetation by large-scale animal husbandry in order to improve the situation of desertification. [ABSTRACT FROM AUTHOR]

• Designing an integrated regional water environment modeling framework • Concurrent analysis of seasonal water and its driving forces across space and time • Differentiating how different blocks of driving factors interact with water scarcity • Driving factor impacts on water scarcity increase substantially over time • Local resource endowment and utilization critically impact spatial heterogeneity Increased grazing and agricultural production, industrialization, population growth, and consequent land use land cover (LULC) changes considerably increase water consumption. Global climate change exaggerates the uncertainty of water sources and supplies. Unfortunately, most current examinations are either confined within disciplinary silos or not integrated for considering wide-ranging socioenvironmental, management, and policy factors. The paper develops an integrated regional water environment modeling framework, examining how climate, LULC, socioenvironmental, and policy factors interact with the water environment. It also adopts a block-based econometric panel data analysis to quantify this framework. The paper extracts seasonal water area and LULC data through image processing from 2000 to 2014 in the Hulun-Buir watershed, Inner Mongolia of China. The paper quantitatively analyzed the interactions between seasonal water changes and major driving factors, such as climatic, land-use, socioeconomic, policy, space, and time. Many of these driving factors were interacting with the seasonal water environment and showing long-term causal relationships. The socioeconomic variables explained 71% of the variance of seasonal water change, the environmental and climatic factors about 9%, the regional disparities around 13%, and the yearly differences about 4%. The findings confirm that it is critical to carry out a time-series examination of causal relationships between seasonal water change and its manifold driving factors at the scale of regional watershed studies. This integrated watershed modeling framework is suitable for adaptation in other geographic areas or for integrated studies of other socio-environmental systems. Graphical Abstract [Display omitted]. [ABSTRACT FROM AUTHOR]

The paper investigates how climate changes and socioeconomic transformations affected grassland productivity from 2000 to 2017 in counties in Inner Mongolia of China. The grassland productivity and climatic change variables included 162 readings (9 periods in the growing season × 18 years), while ten socioeconomic variables contained 18 yearly readings. The mismatch between the environmental observations and socioeconomic data compounds quantitative analysis of causal relations between them. We applied several time-series processing methods to generate five datasets. We fit them with conventional, dependent-lagged, and dynamic-lagged panel regression models to investigate which combinations augment causal relationships between grassland productivity, climate, and socioeconomic changes. The results confirmed that the conventional model with the original data failed to reveal causal relationships between these factors. The panel regression models with the original data tended to intensify the causal effects of climate factors on grassland productivity. The models with the transformed datasets disclosed more socioeconomic variables showing causal relationships with grassland productivity. The dependent-lagged model with the empirical mode decomposition transformed data produced the highest R-squared value and lowest prediction error. GDP, state fixed asset investment, highway construction, grain plantation, and livestock density were primary factors affecting grassland productivity. In brief, the integration of time-series data mining and time-lagged panel analysis effectively unveiled the causes of grassland deterioration under climate and socioeconomic changes. [Display omitted] • Augment causality hidden in env-socio-economic systems with data assimilation. • Assimilate temporal environmental and socioeconomic data into compatible time-series. • Experiment with four groups of computational data mining methods on data assimilation. • Compare combinations of data assimilation methods and lagged panel regression models. [ABSTRACT FROM AUTHOR]

• A hydrological modeling approach was proposed to distinguish the relative contribution of climate changes and human activities on Daihai Lake's recession. • In the absence of natural runoff and human-induced water withdrawal observations, the natural state's lake water storage changes were reconstructed. • Without considering the high water years, the anthropogenic factor took the responsibility for the lake shrinkage about 58%, while the climatic factor took roughly about 42% in period 1974–2018. • This investigation serves as a useful example of applying a distributed model to the assessment of lake hydrology, especially for the lake catchments with limited observations. In recent decades, water levels in many closed inland lakes over north-western China experienced severe declines, among which the Daihai Lake in Inner Mongolia, China was particularly significant. Remotely sensed investigations indicated that the Daihai Lake had shrunk approximately 64% in an area with the dramatic decline of water level approximately 9 m from the 1980 s to 2010 s, which has caused serious concern on the sustainable development of the surrounding region. Consequently, several previous studies have concentrated on the possible causes of lake shrinkage. However, predicting climate variability and anthropogenic activities' relative contributions to lake water decline is challenging due to the lack of direct water consumption upstream and watershed-scale natural runoff observations. In this paper, a hydrological modeling approach was presented to quantitatively evaluate and separate the relative contributions of climate variations and intensifying anthropogenic activities. The simulation accuracy was evaluated through comparisons with observed data and cross-validation with previous studies. Based on a baseline period, the natural streamflow draining into the lake was reconstructed using the model. The results revealed that the water loss of the Daihai Lake was caused predominately by human activities rather than climate variability. Specifically, for wet conditions, the climate factors posed a negative effect on the water level decline of the lake and delayed the shrinking process of the lake area. The anthropogenic factors took a dominant role (roughly 58%) of lake shrinkage at the annual average scale for normal and dry conditions, while the climate factors accounted for approximately 42%. Hence, the rational policy for water resource management, ecological restoration, and dam and reservoir constructions/operations should be the essential precautions for sustainable development of the Daihai Lake and its surroundings. Additionally, the hydrological modeling approach adopted in this investigation serves as a useful example of the quantitative assessment of climate variations and human activities, especially for the ungauged catchments. [ABSTRACT FROM AUTHOR]