Mountain altitudinal belts are the miniature of horizontal differentiation and succession of climatic and vegetational zonation. However, altitudinal belts' vertical range, transition model, inner structure and combining pattern vary from place to place. In Mt. Taibai of the central section of China's north-south transitional zone, we have found an altitudinal belt with the largest range in the world, namely, the montane deciduous broad-leaved forest, which extends continuously from the mountain base to about 2800 m, including basal oak belt, typical oak belt of two sub-belts and cold-tolerant pioneer birch belt of two sub-belts, which could otherwise develop independently. Characterized by a "three layers and five sub-belts" structure, this "super altitudinal belt" is much vertically broader than the threshold of 1000 m for normal altitudinal belts. Its formation is closely related with its transitional geographic location, integral spectrum of altitudinal belts in central Qinling Mountains, rich and diverse species of deciduous woody plants, and their strong competitiveness. The finding of the super altitudinal belt has multiple significance: Its existence is another significant physio-geographic feature of China's north-south transitional zone; it shows that an altitudinal belt may have rather complex inner structure and broad vertical range in some special mountain environment. This broadens our understanding of altitudinal belt structures and their mechanisms, and is of great significance for developing structural theory for montane altitudinal belts. This finding also demonstrates that there are many big questions for us to explore and study in the north-south transitional zone, and it is expected that our finding could trigger in-depth study of local climate and biodiversity responsible for the formation of this super belt, and of the complex structure and ecological effect of China's north-south transitional zone.

Understanding the conceptual connotation related to urban heat island (UHI) effect and clarifying its quantitative characterization methods are important foundations for effective UHI effect research. The global urbanization has made the UHI effect more and more common, which leads to a rapid increase in the number of related studies. In this context, different concepts related to UHI effect have emerged, and particularly, various quantitative characterization methods have been developed for surface urban heat island (SUHI) which has the advantage of spatial heterogeneity characterization. However, there is still a lack of systematic review of quantitative characterization methods of SUHI. Therefore, this study distinguished and analyzed the confusing concepts such as urban heat island, urban heat island effect, surface temperature and urban thermal environment. Then, the typical spatial locations and scale ranges of various UHIs were summarized. In the quantitative characterization of SUHI effect, this study categorized the SUHI range identification methods into four categories: methods based on urban and rural temperature threshold, temperature grade threshold, Gaussian fitting parameter, and temperature attenuation mutation. The current SUHI range identification research focuses on the cognition of the SUHI impact scale. This study also sorted out the SUHI intensity indicators corresponding to various range identification methods. Understanding the essential connotation of each indicator is the prerequisite for grasping the potential differences between the indicators. Future research should integrate multi-source SUHI monitoring methods, develop large-scale SUHI quantitative characterization methods, and cognize connected SUHI spatial morphology.

Quantification of the surface temperature difference (ΔT_s, lake minus land) between lakes and their surrounding lands is an important step for understanding lake climate effects. In this study, we investigated the spatial and temporal patterns of ΔT_s of global lakes and elucidated biophysical mechanisms that underly these patterns. Results are based on outputs from a fully coupled simulation with the Community Earth System Model (CESM) for the period from 1991 to 2010. We found that ∆T_s showed large seasonal variations, with the strongest warming (mean ΔT_s = 4.37 K) in September and the strongest cooling (-0.99 K) in April in the northern hemisphere. There is no significant interannual variation in ∆T_s in individual climate zones or on the global scale. Spatially, only lakes in the arid climate showed a cooling effect (annual mean ΔT_s = -1.19 K). Lakes in the other four climate zones (tropical, temperate, cold, and polar) exhibited warming effects (annual mean ΔT_s from 0.92 K in cold climate to 2.78 K in tropical climate). The dominant biophysical drivers of ∆T_s differed across climate zones. In arid climate zone, the lake cooling effect was mainly caused by lake evaporation stronger than land evaporation. In tropical and temperate climate zones, low lake energy dissipation efficiency was the dominant contributor to lake warming. In cold and polar climate zones, the lake warming was caused by large albedo contrasts between the lake and the snow-covered land, with additional contribution from energy consumed by lake ice-melting. On the global scale, the reduced lake energy dissipation efficiency increased ∆T_s by 3.77±0.13 K, while enhanced evaporation decreased ∆T_s by -2.01±0.1 K.

Accurately assessing the sensitivity of runoff to climate change is critical for water resource management. In recent years, multi-source meteorological and hydrological datasets have been widely used in the analysis of runoff sensitivity to climate change, but no studies has evaluated different datasets from the perspective of runoff sensitivity. Based on the observed meteorological and hydrological data of six watersheds in China with different climatic conditions, the sensitivity of runoff to changes in precipitation and potential evapotranspiration were estimated. The sensitivity index was then used as the benchmark to evaluate the simulation capability of runoff sensitivity of 45 datasets in GLDAS (Global Land Data Assimilation System), ISIMIP2a (Inter-Sectoral Impact Model Intercomparison Project Phase 2a), ISIMIP2b, CMIP6 (Coupled Model Intercomparison Project Phase 6). The results showed that the simulation accuracy of the GLDAS dataset was low, and the simulation accuracies of CMIP6, ISIMIP2a, and ISIMIP2b datasets had little difference. For specific datasets, CLM4.0 in ISIMIP2a, UKESM1-0-LL and MIROC6 in CMIP6 had good runoff sensitivity simulation results in the six watersheds, and they can be applied to runoff sensitivity simulations under different climatic conditions. The results can provide reference for the predictions of runoff and water resources changes in watersheds with limited observation data under the influence of climate change.

Studying the impact of climate change on the tourism and leisure functions provided by plant ecosystems is an important field of tourism geography. Obtaining plant phenological timing data and analyzing its change characteristics and mechanism not only provides evidence of the impact of climate change on tourism but also supports the practical work of tourism responding to climate change. Based on the logistic curve fitting method, this paper developed and optimized a method to extract the timing for cherry blossom-viewing tourism, filtered 587891 valid microblogs from 2879033 Sina blogs related to cherry blossoms, and then extracted and reconstructed a dataset of cherry blossom-viewing tourism activities in China from 2010 to 2019, which has been verified by observation site records and the relationship between temperature factors. On this basis, the spatiotemporal pattern of the start timing, end timing, and duration of cherry blossom-viewing activities in the past 10 years were analyzed, and the time trend of cherry blossom-viewing activities from 2020 to 2050 were simulated and predicted under the climatic scenarios of SSP2-4.5 and SSP5-8.5. The results revealed that: (1) In the past 10 years, the start time of cherry blossom-viewing activities in most cities (61.9%) across the country was earlier, so was the end time (76.2%), and the duration was shortened (52.4%). For every 1° increase in latitude, the start time and end time were delayed by 0.286 days (P < 0.01) and 0.394 days (P < 0.01), respectively, and the duration was shortened by 0.286 days (P > 0.05). (2) Under future climate scenarios, the start time and end time of cherry blossom-viewing activities in most cities will be advanced and the duration will be extended. The SSP5-8.5 scenario has a more obvious impact on the cherry blossom-viewing activities than the SSP2-4.5 scenario. The framework and method in this paper can inspire researchers to focus more on the impact of climate change on specific tourism activities using big data methods.

Soil moisture is an important variable that affects water-heat exchange between land and air, indicating the evolutionary characteristics of drought. In order to reveal the characteristics of soil moisture in dry cropland across China and its meteorological driving factors, this paper uses site data to study the spatial and temporal characteristics of soil moisture change trends in dry cropland across the country from 1992 to 2018, and uses correlation and sensitivity analysis methods to explore the driving mechanism of soil moisture. The results showed that the soil moisture in dry cropland across China tended to change from decreasing to increasing in 2001, which was dominated by soil moisture in summer and autumn. In addition, there is spatial heterogeneity in the change of soil moisture. Since 2001, the soil moisture in the northeast region has increased significantly, while that of the southwest region has decreased significantly. The spatiotemporal variation of precipitation and evapotranspiration is the main reason for the spatiotemporal variation of soil moisture, and the two factors have a synergistic effect. The northern region is dominated by precipitation, while the southern region is dominated by evapotranspiration.

Crop production structure and layout is a traditional research content of agricultural geography, and the research on the evolution characteristics and mechanism of crop production pattern can provide some reference for the optimal allocation of regional agricultural production elements. In this paper, we use the location Gini coefficient, gravity center movement model and comparative advantage index to reveal the evolution characteristics of the production pattern of four major crops, i.e., grain, sugar, fruits and vegetables, in Guangxi from 1995-2019 and their formation mechanism, and accordingly put forward relevant optimization suggestions. The results showed that grain crops "retreating", and sugar, fruits and vegetables "advancing" constitute the basic situation of the major crops competition. In terms of spatial distribution, the grain and vegetables are relatively scattered, the sugar production has an obvious trend of geographical agglomeration, and the fruit production has moved from relative concentration to dispersion. The scale comparative advantage of major crops dominates the aggregated comparative advantage, and the weak position of grain production is gradually strengthened. Sugar production has the absolute scale and aggregated comparative advantages. Fruits and vegetables have a prominent scale comparative advantage, but the efficiency comparative advantage needs improvement. The combination of forces such as the constraints of resource endowments, the dominance of market supply and demand and comparative efficiency, the inducement of changes of rural social development factors, the guidance or correction of institutional environment, and the circular accumulation of agglomeration economy, all together drive the evolution of crop production patterns. The unique regional situation determines that Guangxi's crop production should coordinate economic, social and ecological functions, and promote the transformation of crop production from scale expansion to comprehensive benefit enhancement by adjusting production structure, optimizing the spatial layout and strengthening industrial agglomeration.

Frequent occurrence of extreme drought events has great impacts on cotton production in Central Asia. In this study, data from field experiments designed for different sowing densities and irrigation levels at the Zangiata experimental station in the Tashkent of Uzbekistan were adopted for calibration and validation. Using the CMIP6 climate change datasets for three shared socioeconomic pathways (SSP1-2.6, SSP3-7.0 and SSP5-8.5), we analyzed the variations in the standardized precipitation evapotranspiration index in three-month steps (SPEI-3) within the growing seasons from May to August during the period from 2021 to 2090 and simulated the changes in cotton yields under meteorological drought conditions using the APSIM-Cotton model considering CO₂ fertilization. The results showed that the APSIM-Cotton model accurately simulated the cotton yield in Tashkent, and the relative errors between the simulated and measured yields were lower than 10.4% in both the calibration and validation years. Both temperature and drought frequency are expected to increase in the future during the cotton growing period in Tashkent. For the SSP3-7.0 and SSP5-8.5 scenarios, the percentages of severe drought are 36.7% and 40.0% from 2021 to 2050, respectively, and the percentages of extreme drought are 66.7% and 86.7% from 2061 to 2090, respectively. Severe meteorological droughts under three emission scenarios (SSP1-2.6, SSP3-7.0, and SSP5-8.5) would cause reductions of 28.0%, 29.6%, and 32.1% in cotton yields from 2021 to 2050, respectively, compared with the period 1961-1990. Severe meteorological droughts under the SSP1-2.6 scenario would result in a reduction of 31.5% in cotton yield from 2061-2090. Under the SSP3-7.0 and SSP5-8.5 scenarios, severe meteorological droughts would result in reductions of 33.1% and 35.7% in cotton yield, respectively, and extreme meteorological droughts would cause reductions of 41.3% and 54.2% in cotton yields, respectively. CO₂ has a certain fertilization effect. The rise in CO₂ concentration would increase the cotton yield by 14.9% to 25.0%; however, the increase in yields will not be sustained at concentrations above 750 µmol/mol. The results will provide scientific data for planting strategies for cotton in Central Asia that account for climate change and a reference for the future cooperation mode of the cotton industry between China and Central Asian countries.