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In line with the United Nations Sustainable Development Goal (SDG) 6, the main goal of the Special Issue on "Remote sensing for water resources and environmental management" was to solicit papers from a diverse range of scientists around the world on the use of cutting-edge remote sensing technologies to assess and monitor freshwater quality, quantity, availability, and management to ensure water security. Special consideration was given to scientific manuscripts that covered, but were not limited to, the development of geospatial techniques and remote sensing applications for detecting, quantifying, and monitoring freshwater water resources, identifying potential threats to water resources and agriculture, as well as other themes related to water resources and environmental management at various spatial scales. The Special Issue attracted over thirteen peer-reviewed scientific articles, with the majority of manuscripts originating from China. Most of the studies made use of satellite datasets, ranging from coarse spatial resolution data, such as the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO), to medium spatial resolution data, such as the Landsat series, ERA5, Modern-Era Retrospective Analysis for Research and Application Land version 2 reanalysis product (MERRA2), CLSM and NOAH ET, and MODIS (Moderate Resolution Imaging Spectroradiometer). Google Earth Engine (GEE) data, together with big data processing techniques, such as the remote sensing-based energy balance model (ALEXI/DisALEXI approach) and the STARFM data fusion technique, were used for analyzing geospatial datasets. Overall, this Special Issue demonstrated significant knowledge gaps in various big data image processing techniques and improved computing processes in assessing and monitoring water resources and the environment at various spatial and temporal scales. [ABSTRACT FROM AUTHOR]

Due to global warming, the overheating risk in the severe cold region of China has attracted attention, but so far, no studies have examined summer overheating in this region. This paper aims to reveal the overheating risk in recent and future climates in the severe cold region of China. An 18-storey residential building in the severe cold region of China was monitored from May to September 2021 to validate the simulation data of the indoor temperature. Weather files of the typical meteorological year (TMY) from 2007 to 2020, observations in 2021, and forecasts for the climate in different carbon emission scenarios (2030, 2060) were used to simulate the indoor temperature and assess the overheating risk. The results revealed the severity of the overheating risk; the overheating hours in the south-facing bedroom were recorded as 884 h (24.07%) with the TMY weather data and 1043 h (28.40%) in 2030 and 1719 h (46.81%) in 2060 under the RCP8.5 carbon emission scenario. Thus, the low carbon emissions policy may significantly alleviate overheating. Moreover, to cope with climate change, it is suggested that the Chinese local design standards should consider the summer overheating risk and make the necessary adjustments. [ABSTRACT FROM AUTHOR]

Climate change has significantly affected agricultural production. As one of China's most important agricultural production regions, the North China Plain (NCP) is subject to climate change. This paper examines the influence of climate change on the wheat and maize yields at household and village levels, using the multilevel model based on a large panel survey dataset in the NCP. The results show that: (i) Extreme weather events (drought and flood) would significantly reduce the wheat and maize yields. So, the governments should establish and improve the emergency service system of disaster warning and encourage farmers to mitigate the adverse effects of disasters. (ii) Over the past three decades, the NCP has experienced climate change that affects its grain production. Therefore, it is imperative to build the farmers' adaptive capacity to climate change. (iii) Spatial variations in crop yield are significantly influenced by the household characteristics and the heterogeneity of village economic conditions. Therefore, in addition to promoting household production, it is necessary to strengthen and promote China's development of the rural collective economy, especially the construction of rural irrigation and drainage infrastructures.

As one of the common mulberry tree species, Morus notabilis C. K. Schneid plays a significant role in various industries such as silkworm rearing, papermaking, and medicine due to its valuable mulberry leaves, fruits, and wood. This study utilizes the maximum entropy (MaxEnt) model to predict the potential distribution of M. notabilis in China under future environmental changes. By integrating the relative percentage contribution score of environmental factors with jackknife test analysis, important variables influencing the distribution of M. notabilis were identified along with their optimal values. The results indicate that Annual Precipitation (bio12), Precipitation of Driest Month (bio14), Min Temperature of Coldest Month (bio6), Temperature Annual Range (bio5–bio6) (bio7), Precipitation of Warmest Quarter (bio18), and Precipitation of Coldest Quarter (bio19) are the primary environmental variables affecting its potential distribution. Currently, M. notabilis exhibits high suitability over an area spanning 11,568 km2, while medium suitability covers 34,244 km2. Both current and future suitable areas for M. notabilis are predominantly concentrated in Sichuan, Yunnan, and Guizhou provinces, as well as Chongqing city in southwest China. Under the SSP5-8.5 scenario representing high greenhouse gas concentrations by 2050s and 2090s, there is an increase in high suitability area by 2952 km2 and 3440 km2, with growth rates reaching 25.52% and 29.74%, respectively. Notably, these two scenarios exhibit substantial expansion in suitable habitats for this species compared to others analyzed within this study period. [ABSTRACT FROM AUTHOR]

To understand the change in irrigation water requirement (IWR) in China under the changing climate conditions and grain planting structures, this paper focused on rice, wheat and maize and analyzed the IWR change in each county under the background of climate change and the adjustment of planting structure by means of the China Agro-Ecological Zones (China-AEZ) model. The results show that: (1) Climate change causes the increase of IWR. Compared with the 1961–1990 period, the average IWR of counties in China increased by 9.31 mm and 16.50% under the climate conditions of the 1981–2010 period. Regionally, the water consumption effect of climate change is stronger in water-poor areas than in water-rich areas. (2) Planting structure change causes the decrease of IWR. Compared with 1990, under the planting structure in 2014, the average IWR of counties in China decreased by 6.61 mm and 11.41%. Regionally, the water-saving effect of the change in planting structure in the north is stronger than that in the south. (3) With the change in climate conditions and planting structure, the average IWR of counties in China first increased and then decreased, with an overall increase of 2.06 mm and 3.56%. Regionally, the IWR in most parts of the country has shown a decreasing trend, while the IWR in northwestern and north China, where water resources are relatively scarce, has increased significantly. (4) Nationally, the change in IWR is dominated by climate change, and the overall trend is in the direction of water consumption. Therefore, adjusting the crop planting structure is a promising way to save irrigation water. To adapt to the pressures on water resources caused by climate change, the proportion of crops with water-saving advantages should be increased in the corresponding areas to achieve regional water savings. In northwest China, which mainly relies on irrigation water, many crops with low IWR should be planted, such as wheat and maize, while the planting proportion of rice should be reduced; furthermore, the planting proportion of wheat should be reduced, and the planting proportion of maize should be increased in north China. Finally, rice with high IWR should be more localized in central, southern and northeastern China. [ABSTRACT FROM AUTHOR]

Due to global warming and other climate changes, it is increasingly important to study the response of regional environmental changes and dynamic changes in vegetation to climate change. Based on meteorological data from the last 60 years, this paper calculates the humidity index of western China under a wide range of long time series in different regions and explores the cross-correlation effect between series by offering a comparison with NDVI data, to analyze the cross-correlation between wet and dry changes and changes in vegetation in western China on a spatial scale. The results show that the spatial distribution of the interdecadal humidity index is different between different regions in western China. For example, the semi-arid and the semi-humid zones of the Weihe River region exhibit significant changes, while the Xinjiang and Qinghai–Tibet regions show a trend of constant wetness, on the whole, and the Sichuan and Yunnan–Guizhou regions are relatively humid and the distribution of wetness and dryness is relatively stable. The distribution of high and low values of the humidity index is very obvious and consistent with that of the distribution of desert bare land and precipitation in western China. In common with the distribution in the humidity index, the maximum correlation number between the NDVI and the humidity index in the whole western region is also significantly different in spatial distribution. There is a positive correlation between the NDVI and the humidity index in 99% of the study area. However, the delay in response time of the NDVI to changes in the humidity index in each region is inconsistent. For example, changes in the NDVI lag changes in the humidity index in the Menggan region by generally either 2 months or 5 months, while in the Sichuan region the delay in response time is generally 3 months. The variation and trend in dry and wet areas are closely related to the geographical location, climate zone, and topographic terrain, which may be the reason for the differences in the distribution of vegetation types and the response time to dry and wet changes. There is significant interaction between the humidity index and the vegetation type or precipitation distribution in western China. The positive correlation between the NDVI and the humidity index means that the positive effect is more sensitive, and the response of grassland is the most sensitive in the ecosystem. [ABSTRACT FROM AUTHOR]

China's economy has made significant strides in the past three decades. As a direct result of China's "one belt, one road" (OBOR) initiative, the country's rate of industrialization and urbanization is currently the fastest in the entire world. This rapid development is largely dependent on the enormous amounts of energy currently being consumed and forms the foundation of the world's high levels of carbon emissions. It is generally agreed that the production of greenhouse gases, particularly carbon dioxide, is the primary contributor to the current state of climate change. In this paper, a CO2 emission prediction model based on Bi-LSTM is constructed. In order to conduct empirical tests on the model, this study uses data from South Asian countries and China from 2001 to 2020. China's CO2 emissions from 2022 to 2030 were predicted along with those of other countries in order to study the combined effects of the scientific and technological progress, industrial structures, and energy structure factors affecting CO2 emissions. When compared with the LSTM and GRU methods, the Bi-LSTM model's results produced lower MAE, MSE, and MAPE values, indicating that it performs better. According to the findings, carbon emissions represent a significant problem that will become much worse in the future due to China and India's high emissions, particularly in the next 10 years, if the government does not implement policies that help reduce those emissions. [ABSTRACT FROM AUTHOR]

The vast forests of Northeast China are under great pressure from climate change. Understanding the effects of changing climate conditions on spring phenology is of great significance to assessing the stability of regional terrestrial ecosystems. Using Normalized Difference Vegetation Index data from 1982 to 2013, this paper investigated the changes in the start date of the vegetation growing season (SOS) of two main forest types in Northeast China, analyzing the changes in temporal and spatial patterns of forest spring phenology before and during the recent short-term warming slowdown, and exploring the effects of day and night temperatures and precipitation on the start of the growing season. The results showed that, during the rapid warming period (1982–1998), the SOS of deciduous needleleaf forests (DNF) was significantly advanced (−0.428 days/a, p < 0.05), while the rate of advance of SOS of deciduous broadleaf forests (DBF) was nonsignificant (−0.313 days/a, p > 0.10). However, during the short-term slowdown (1998–2013), the SOS of DBF was strongly delayed (0.491 days/a, p < 0.10), while the change in SOS of DNF was not significant (0.169 days/a, p > 0.10). The SOS was sensitive to spring maximum temperature for both forest types during the analysis period. Increases in winter precipitation influenced the SOS during the rapid warming period for DNF; this combined with the increase in the spring maximum temperature contributed to the advance in SOS. The decrease in the spring maximum temperature during the short-term slowdown, combined with a decrease in the previous summer maximum temperature, contributed to the rapid delay of SOS for DBF. DBF SOS was also more influenced by lagged effects of prior conditions, such as previous autumn to spring precipitation during the rapid warming period and previous summer maximum temperature during the short-term slowdown. In general, SOS was mainly determined by changes in daytime thermal conditions; DNF is more sensitive to temperature increases and DBF is more sensitive to decreases. Different regional climate conditions lead to differences in the distribution of DNF and DBF, as well as in the response of SOS to climate change during the rapid warming and short-term slowdown periods. [ABSTRACT FROM AUTHOR]

Vegetation degeneration has become a serious ecological problem for karst regions in the Anthropocene. According to the deficiency of long serial and high-resolution analysis of karst vegetation, this paper reconstructed the variation of vegetation landscape changes from 1987 to 2020 in a typical karst region of China. Using Landsat time series data, the dynamic changes and driving factors of natural karst vegetation were identified at the landscape scale. On the premise of considering the time-lag effect, the main climatic factors that influence vegetation growth were presented at the interannual timescale. Then, the approach of residual analysis was adopted to distinguish the dominant factors affecting vegetation growth. Results of trend analysis revealed that 21.5% of the forestland showed an overall significant decline in vegetation growth, while only 1.5% showed an increase in vegetation growth during the study period. Precipitation and radiation were the dominant meteorological factors influencing vegetation at the interannual timescale, as opposed to temperature. More than 70% of the natural vegetation growth was dominated by climatic factors. The area percentage of negative human impact has increased gradually since 2009 and reached 18.5% in 2020, indicating the currently serious situation of vegetation protection; fortunately, in recent years, human disturbances on vegetation have been mitigated in karst areas with the promotion of ecological conservation and restoration projects. [ABSTRACT FROM AUTHOR]

China is one of the countries that are most severely affected by floods worldwide. Due to the geographical and climatic environment, floods frequently occur in China. Rapid socioeconomic growth and urban sprawl in the past decades have significantly changed both exposure and vulnerability dimensions of flood risk in China. In response to high risks of flood, the Chinese government has adopted a series of effective measures, such as the "Spongy City" Program and building many large dams and reservoirs, and some measures have achieved significant results. However, there is still a lack of studies with an integrated view on analyzing the causes, socioeconomic impact trends, and disaster risk reduction (DRR) measures of flood risk in China in the past decades. Accordingly, this paper aims to fill in the gap and provides some new insights into China's contributions in DRR over the period of 1950–2019. Our results show that annual flood-induced fatalities and socioeconomic vulnerability to floods have significantly decreased in China, owing to a range of structural and non-structural measures. Nevertheless, China still faces the complex coupling effects of climate change and urbanization, and thus threats from extreme floods. In addition, China needs to further improve its flood risk management system. [ABSTRACT FROM AUTHOR]

In recent years, extreme weather events caused by global climate change have occurred frequently, intensifying the frequency of flood disasters. For flood hazard analysis, high-quality data and a reasonable weight assignment of the relevant factors are critical. This study conducts four rainfall fusion methods, to fuse the Tropical Rainfall Measuring Mission (TRMM) 3B42 and the observations in Dazhou City, China. Then, the random forest was applied to obtain the weights of various factors to facilitate a comprehensive flood hazard analysis under four rainfall durations. The results show that (1) the linear regression performs best out of the four fusion methods, with a correlation coefficient of 0.56; (2) the Digital Elevation Model (DEM) is the most impact factor with a weight of more than 0.2; and (3) the proposed flood analysis system performs well, as 70% of historical flood points are distributed in high and sub-high hazard areas and more than 93% of historical flood points are distributed in medium hazard areas. This study identified the flood hazard grade and distribution in Dazhou City, which could provide a valuable methodology to contribute to flood hazard analysis and disaster management with satellite rainfall. Furthermore, the results of this paper are profound for future work on the high-resolution flood risk assessment and management in Dazhou City. [ABSTRACT FROM AUTHOR]

The Loess Plateau (LP) of China, which is the pilot region of the "Grain to Green Project" (GGP), has received worldwide attention due to its significant changes in the natural and social environment. Investigation of vegetation variations in response to climate change and human activities is vital for providing support for further ecological restoration planning. This paper aimed to monitor vegetation dynamics of the LP with trend comparisons of various vegetation types, disentangle the effects of climate variations and ecological programs on vegetation variations, and detect the consistency of vegetation variations. More specifically, vegetation dynamics during 1982–2015 were analyzed using the Global Inventory Modelling and Mapping System third-generation Normalized Difference Vegetation Index (GIMMS NDVI3g) data with the application of Breaks for Additive Season and Trend (BFAST) and Hurst Exponent. The results showed that: (1) Vegetation manifested a significant greening trend (0.013 decade−1p < 0.01) in the LP during 1982–2015, and a breakpoint (BP) was detected in 1999, which was the beginning of the GGP. Interannual NDVI after the BP (ABP) showed more than 3.5 times greening rates compared to the NDVI before the BP (BBP). (2) Human activities dominated the vegetation variation (accounted for 59.46% of vegetation variation), among which reforestation and land-use change with steep slopes (i.e., ≥15°) lead to the greening after the GGP implementation. (3) Future trends should be noticed in the Forest Zone and Forest-Grass Zone, where the greening trends tend to slow down or even reverse in the southern LP. The long-term GIMMS NDVI3g time series and multiple geospatial analyses of this study might facilitate a better understanding of the mechanisms of vegetation variations for the assessment of the large restoration programs in fragile ecosystems. [ABSTRACT FROM AUTHOR]

Lakes are sensitive indicators of climatic change and are important resources for regional economic development. In recent years, there have been many studies related to the changes in lake area and lake volume. However, further studies are still required to understand the responses of lakes to climatic change and human activities. This paper utilizes lakes in the Qaidam Basin, an inland arid region of China, as the object of study and investigates the characteristics of variability in lake changes and its driving forces by combining multi-source remote sensing, model simulations and historical data. We first analyzed the spatiotemporal pattern of climatic change in the basin under the background of global warming. The response of lake water volume to climatic change and human activities is then discussed. Finally, the main factors that affect the change in lake water volume in different regions of the basin are delineated. The water volume of lakes in the Qaidam Basin increased by 3.81 km3 from 1990 to 2020. Particularly since the 21st century, the water volume of lakes has increased rapidly, and an increasingly abrupt change appeared around 2015. The increases in precipitation and vegetation area are the main and secondary factors that led to the increase in total lake water volume in the basin, respectively. However, the main influencing factors still vary in different regions. The impact of air temperature, evaporation, and changes in the cropland area on the change in lake water volume is generally not obvious. Human activities, such as the development of salt lakes and damming, have led to substantial changes in the spatial pattern of lakes in the middle of the basin and are associated with the replacement, genesis, and disappearance of Yiliping Lake, Yahu Lake and West Taijinar Lake, respectively. This study reveals the changing characteristics of climate and lake water volume in inland arid basins in China, which are highly important to understand the responses of lakes to climatic change and human activities, and provides a scientific basis for the rational development and utilization of lake resources in arid basins. [ABSTRACT FROM AUTHOR]

Proper agricultural water management in arid regions is the key to tackling climatic risks. However, an effective assessment of the current response to climate change in agricultural water use is the precondition for a group adaptation strategy. The paper, taking the Tarim River basin (TRB) as an example, aims to examine the agricultural water use sustainability of water resource increase caused by climatic variability. In order to describe the response result, groundwater change has been estimated based on the Gravity Recovery and Climate Experiment (GRACE) and the Global Land Data Assimilation System (GLDAS)-Noah land surface model (NOAH) data. In order to better understand the relationship between water resource increase and agricultural water consumption, an agricultural water stress index has been established. Agricultural water stress has been in a severe state during the whole period, although it alleviated somewhat in the mid-late period. This paper illustrates that an increase in water supply could not satisfy agricultural production expansion. Thus, seasonal groundwater loss and a regional water shortage occurred. Particularly in 2008 and 2009, the sharp shortage of water supply in the Tarim River basin directly led to a serious groundwater drop by nearly 20 mm from the end of 2009 to early 2010. At the same time, a regional water shortage led to water scarcity for the whole basin, because the water consumption, which was mainly distributed around Source Rivers, resulted in break-off discharge in the mainstream. Therefore, current agricultural development in the Tarim River basin is unsustainable in the context of water supply under climatic risks. Under the control of irrigation, spatial and temporal water allocation optimization is the key to the sustainable management of the basin. [ABSTRACT FROM AUTHOR]

As an important part of the Earth's environmental system, sandy soils are particularly sensitive to changes in the climatic environment. As one of the four major desert regions in China, the MuUs desert has transformed from a desert to an oasis after more than half a century of ecological management. In this paper, we analyzed the spatial and temporal patterns of surface albedo, evapotranspiration, and fraction vegetation cover in the MuUs desert based on the Global Land Surface Satellite (GLASS) product with high spatial and temporal resolution and assessed the relationships between their variability and snow cover, air temperature, and precipitation. It is of great significance to understand the effect of desertification control and climate change after the conversion of land surface types in the MuUs region. The results show that the desertification control in the MuUs area has achieved remarkable results since 1982. The fraction vegetation coverage of the MuUs desert showed a significant increasing trend, with an interannual change rate of 1.32% each decade−1. The surface albedo of MuUs desert decreased significantly. Affected by vegetation and snow cover, it was lower in summer and higher in winter. The evapotranspiration showed a significant upward trend, higher in summer and lower in winter, which is significantly correlated with the changes in surface albedo, air temperature, and vegetation. In addition, the local-scale biophysical effects caused by vegetation change have influenced the climate of the MuUs region, manifested as the increase in precipitation and air temperature. In general, with the support of relevant policies and human construction projects, the overall ecological environment in the MuUs desert is developing in a good way. [ABSTRACT FROM AUTHOR]

Transitions in animal exploitation patterns are caused by topographical and climatic variations on both macro and micro scales. This paper presents temporally and spatially contextualized faunal profiles from 27 sites in the Guanzhong (关中) region of Shaanxi province (陕西省), PRC which date from the Early Neolithic to the Bronze Age (ca. 6000–1000 BCE). Climatic and environmental data was cross-referenced with archaeological, archaeobotanical and (where appropriate) historical sources to examine the reasons for the clear micro-transitions observed. Faunal profiles from sites in the Wei River plain (渭河盆地), loess plateau, and the transitional zone between them were analyzed. Animal utilization was found to vary substantially between different zones during the period under analysis. The transition in praxis between the Wei River valley and the loess plateau was not gradual. The hilly transition zone was found to have its own distinct animal exploitation pattern. These spatio-temporal differences in animal exploitation were caused by changes in both the local microclimates and the topography of the landscape in which the communities were living. Some regions apparently reverted to 'earlier' animal exploitation patterns in response to climatic changes. These environmental factors were also augmented by internal social developments and interactions with neighboring communities. [ABSTRACT FROM AUTHOR]

Highlights: The topography and climate background determine that the hazard happens periodically, and these factors are fundamental for the weak vulnerability in the Sedongpu basin; The dynamic glacier simulations with the Elmer/Ice model showed that the glacier surface velocity can reach 19 cm/d on the Dongpu glacier; Heavy rain and an earthquake were triggering factors of the ice avalanche that led to the glacial debris flow. In this paper, the glacial debris flow blockage event, on 17 October 2018, in the Sedongpu basin of the Yarlung Zangbo River is taken as an example to analyse the occurrence and development of glacier hazards in this region. Multi-sources including remote sensing products, DEM, earthquake records and meteorological data were used to analyse the characteristics and mechanism of glacier hazards. The Elmer/Ice dynamic model was chosen to simulate the glacial surface velocity. It was found that topography and climate background determine that the hazard happens periodically. Based on the meteorological records of the Linzhi station, the warming rate was greater than 0.40 °C/10a during the period 1960–2017. The short-term heavy rainfall with daily values of 9.3 mm before the blockage event was also regarded as a factor. Both heavy rain and earthquake were triggering factors of the ice avalanche that led to the glacial debris flow. The glacier surface velocity of the Dongpu glacier simulated by Elmer/Ice model can reach 19 cm/d. This study has extensive applicability significance in glacier hazard mitigation under a changing climate. [ABSTRACT FROM AUTHOR]

In recent years, the frequent occurrence of extreme climate disasters has seriously affected agriculture in Northeast China. Based on precipitation data derived from 83 geographical stations in the study area for 60 years (1960–2019), we chose the reliable statistical methods of the Mann–Kendall test, Sen's slope, and the Standardized Precipitation Index, and regarded drought and flood as a whole in this paper, to identify the temporal and spatial variation characteristics of precipitation and extreme climate events in Northeast China. The results show that an increasing trend of disasters was detected at the 95% confidence level (Z = 2.3). According to the timescale analysis, abrupt climate changes started in 2006. Temporal and spatial distribution of extreme climate disasters, mainly drought and flood disasters, showed a significant upward trend from 2006 to 2019. According to the spatial analysis, the precipitation in Northeast China decreased from south to north and fluctuated less from east to west. Moreover, stations with extreme climate trends (trend of climatic anomaly with confidence level > 90%) followed the same spatial pattern as those with a high frequency of extreme climate disasters (more than 17.87 times/decade). The severity and frequency of extreme climate have increasingly threatened Northeast China in the past decade. In particular, the Northeast Plain experienced the most severe and extreme climate events that seriously threatened the study area in 2007, 2009, 2010, and 2019. Our results highlight the urgent need for the development of monitoring and early warning of droughts and flood disasters to reduce economic losses. [ABSTRACT FROM AUTHOR]

High Mountain Asia (HMA), with its high altitude, widely distributed snow and frozen soil, influences the climate of the northern hemisphere and even the world through thermal balance and the water vapor cycle and is also an indicator of global climate change. The influence of HMA snow cover on its surrounding areas has always been a research hotspot. Taking the Yangtze River Basin (YRB) of China as an example, this paper analyzes the relationship between winter snow depth in HMA and drought and flood in spring and summer in the YRB in the recent 40 years by using Singular Value Decomposition (SVD). The results show that the influence of snow cover on drought and flood in spring is inversely different between eastern and western parts of HMA, while the effect in summer is consistent. When the snow depth is larger (smaller) in the east and smaller (larger) in the west in winter, the YRB is drier (wetter) in spring. When the overall snow depth in HMA is larger (smaller) in winter, the northern part of the middle and lower reaches of the YRB is drier (wetter) in summer. The results provide support for understanding the impact of HMA snow cover on the surrounding climate and some important indicators for drought and flood prediction in the YRB. [ABSTRACT FROM AUTHOR]

Research on precipitation regularity in the past 120 years is an important link in analyzing the precipitation characteristics of watersheds. This paper systematically analyzes the characteristic changes of centennial precipitation data in the Haihe River basin with the help of CRU data, PCI, SPI, and the Pearson type III curve. The results show that the spatial and temporal distribution of precipitation in the Haihe River basin has a more obvious inconsistency. The temporal distribution shows the characteristics of relatively stable in the early period and increasing fluctuation in the later period, the concentration of precipitation gradually decreases, and the overall drought level decreases. The spatial distribution shows a general pattern of gradually decreasing from southwest to northeast, the overall trend of summer precipitation changes from stable to north–south extremes, and the distribution probability of extreme precipitation events in the basin decreases from southeast to northwest, while the drought-prone area transitions from the northeast to the west and southwest of the basin. Under the influence of both climate change and human activities, the seasonal distribution of precipitation tends to be average, the area affected by extreme precipitation rises, and the arid area shifts to the inland area. [ABSTRACT FROM AUTHOR]

This paper looks at regional water security in eastern China in the context of global climate change. The response of runoff to climate change in the Qinhuai River Basin, a typical river in eastern China, was quantitatively investigated by using the Soil and Water Assessment Tool (SWAT) model and the ensemble projection of multiple general circulation models (GCMs) under three different shared socioeconomic pathways (SSPs) emission scenarios. The results show that the calibrated SWAT model is applicable to the Qinhuai River Basin and can accurately characterize the runoff process at daily and monthly scales with the Nash–Sutcliffe efficiency coefficients (NSE), correlation coefficients (R), and the Kling–Gupta efficiency (KGE) in calibration and validation periods being above 0.75 and relative errors (RE) are ±3.5%. In comparison to the baseline of 1980–2015, the mean annual precipitation in the future period (2025–2060) under the three emission scenarios of SSP1-2.6, SSP2-4.5, and SSP5-8.5 will probably increase by 5.64%, 2.60%, and 6.68% respectively. Correspondingly, the multiple-year average of daily maximum and minimum air temperatures are projected to rise by 1.6–2.1 °C and 1.4–2.0 °C, respectively, in 2025–2060. As a result of climate change, the average annual runoff will increase by 16.24%, 8.84%, and 17.96%, respectively, in the period of 2025–2060 under the three SSPs scenarios. The increase in runoff in the future will provide sufficient water supply to support socioeconomic development. However, increases in both rainfall and runoff also imply an increased risk of flooding due to climate change. Therefore, the impact of climate change on flooding in the Qinhuai River Basin should be fully considered in the planning of flood control and the basin's development. [ABSTRACT FROM AUTHOR]

Artificial and natural water bodies, such as reservoirs, ponds, rivers and lakes, are important components of water-related ecosystems; they are also important indicators of the impact of human activities and climate change on surface water resources. However, due to the global and regional lack of artificial and natural water bodies data sets, understanding of the changes in water-related ecosystems under the dual impact of human activities and climate change is limited and scientific and effective protection and restoration actions are restricted. In this paper, artificial and natural water bodies data sets for China are developed for the years 2000, 2005, 2010, 2015 and 2020 based on satellite remote sensing surface water and artificial water body location sample data sets. The characteristics and causes of the temporal and spatial distributions of the artificial and natural water bodies are also analyzed. The results revealed that the area of artificial and natural water bodies in China shows an overall increasing trend, with obvious differences in spatial distribution during the last 20 years, and that the fluctuation range of artificial water bodies is smaller than that of natural water bodies. This research is critical for understanding the composition and long-term changes in China's surface water system and for supporting and formulating scientific and rational strategies for water-related ecosystem protection and restoration. [ABSTRACT FROM AUTHOR]

As the most concentrated area of human activities, cities consume many natural resources and discharge a large amount of waste into the natural environment, which has a huge environmental impact. Most of the ecological and environmental problems, such as environmental pollution, global climate change, and loss of biodiversity, are related to urban systems. How to coordinate urban development with the urban ecological carrying capacity is related to the destiny of the city itself, and also to whether its surrounding areas can successfully achieve the goal of high environmental quality and sustainable development. At present, the theory and methods of urban ecological carrying capacity research are relatively new, which has caused problems for policy makers in practical applications. This paper proposes a theoretical framework for urban ecological carrying capacity assessment based on the analysis of ecosystem services supply and demand. Combined with multi-source spatial data and spatial model methods, the supply and demand of ecosystem services were spatially quantified. The capital city of China, Beijing, was the case study area for this research. The spatial differentiation of the supply–demand relationship of ecosystem services is formed. The priority areas for ecological carrying capacity improvement at pixel scale and at the administrative level are obtained, respectively. The results show that the first priority area is concentrated in the center of the urban area, accounting for 31.11% of the total area of Beijing. According to the secondary zone and the specific ecosystem service type, the ecological carrying capacity improvement strategy of different zones is proposed. This study provides a new perspective for investigating urban ecological carrying capacity and for identifying the priority areas for ecological carrying capacity improvement, and helps the policy-makers to design tailored policy actions. [ABSTRACT FROM AUTHOR]

Currently, the world is moving rapidly toward carbon neutrality, and renewable energy technology is very important in carbon neutrality. Among various renewable energy technologies, wind power is attracting much attention due to its sustainability, resource abundance, and high applicability. This study analyzed wind power patents from 2010 to 2021 to derive current global originality and convergence information. For IP5 countries, the growth stages of technology and the patent convergence networks were inferred by time series analysis and an association rule mining algorithm, respectively. The results showed that, during the analysis period, about 5000 patents were applied on annual average, and it was analyzed that China held the most patents at 48%, followed by the US, Europe, Korea, and Japan. According to the technology convergence network of patent codes from all the IP5 countries, patents related to 'wind turbines with rotation axes in the wind direction' occupied a very central position, and 'power conversion electric or electronic aspects' and 'integration of patents related to renewable energy sources in buildings with wind power' were found to show high connection strength. By country, it was analyzed that the US and China showed high patent competitiveness onshore and possessed many 'independent power conversion cluster' technologies. The research hypothesis was that technology development trends can be analyzed and the characteristics of each country can be understood through patent analysis in the wind power field. This hypothesis was analyzed through various patent analysis techniques, and this paper has novelty in that it presents the global megatrend in the wind power field through patent analysis and quantitatively presents the current status of technology development in five major countries. [ABSTRACT FROM AUTHOR]

Natural regeneration is crucial for the development of sustainable forestry practices in light of the current global climate changes. In this paper, we compared the size distributions of Quercus acutissima seedlings in the understory of Q. acutissima forest plantations in Mount Tai in 2010 and 2017, studied the physiological and morphological responses of seedlings to the microenvironment, and explored the maintenance mechanisms of the seedling bank. The results showed that the density of understory seedlings in 2017 was only 61.63% of that in 2010, especially in the 20–40 cm height class. Between 2011 and 2016, the precipitation and soil water content were the highest in 2011, followed by 2013. The 2–4-year seedlings (height < 40 cm) were not significantly different in seedling biomass, biomass allocation, and root morphology (root total surface area, root volume, and root average diameter), and were significantly different in total root length, specific root length, specific root surface area, and nonstructural carbohydrate content of root, stem, and leaves. However, 5–6-year seedlings (height > 40 cm) showed the largest biomass. Principal component analysis indicated that altering root morphology, nonstructural carbohydrate, and biomass allocation played significant roles in the drought adaptation of seedlings in the understory. In conclusion, drought stress together with seedling adaptation influenced the dynamics of seedling bank in the understory of Q. acutissima plantations. [ABSTRACT FROM AUTHOR]

Fatal landslides are a widespread geohazard that have affected millions of people and have claimed the lives of thousands around the globe. A change in climate has significantly increased the frequency and magnitude of rainfall, which affect the susceptibility of slopes to shallow landslides. This paper presents a methodological framework to assess the future changes in extreme and seasonal rainfall magnitudes with climate model projections. This framework was applied to project summer rainfall over Wanzhou County, China, using an ensemble of four regional climate models (RCMs) from the East Asian domain of the Coordinated Downscaling Experiment (CORDEX) under the Phase 5 Coupled Intercomparison Modeling Project (CMIP5). The results find that extreme daily rainfall was projected to decrease in the mid-21st century, with an uncertainty measured by a coefficient of variation between 5% and 25%. The mean seasonal rainfall is projected to increase in the mid-21st century up to a factor of 1.4, and up to a factor of 1.8 in the late-21st century. The variation in the mid-21st century ranged from 10% to 35%, and from 30% to 50% in the late-21st century. This case study delivered a proof-of-concept for a methodological framework to derive shallow landslide-triggering rainfall scenarios under climate change conditions. The resulting spatially distributed climate change factors (CCFs) can be used to incorporate future rainfall scenarios in slope susceptibility models and climate impact assessments. [ABSTRACT FROM AUTHOR]

Climate change risk has become an important challenge for global sustainable development. The insurance industry can play an important role in coping with the increasingly severe climate change risk. This paper first describes the increasing climate change risk and the difficulties of the insurance mechanism in dealing with it. Then this paper summarizes the international practice of using the insurance mechanism to deal with climate change risk from ten different aspects. Based on the summary of the role of the insurance mechanism in dealing with this risk in developing countries, this paper puts forward the main application areas for climate change risk insurance and discusses the policy implications of developing climate change risk insurance in China.

Facing climate risks has become a common problem for mankind and a topic of great importance for the Chinese government. To thoroughly implement the overall requirements for the construction of an ecological civilization and effectively improve the capacity of cities to adapt to climate change, China launched the pilot construction of "Climate Resilient Cities" in 2017. In this paper, 16 prefecture level cities in Anhui Province of China were selected as the research objects, and the multi-level grey system evaluation method was used to measure the climate resilience of these regions. We used the difference in differences method to evaluate the effect of the pilot policy of "Climate Resilient Cities." The pilot policies of the "Climate Resilient Cities" showed a significant contribution to the regional climate resilience, and, after isolating the impact of other factors on the regional climate resilience, the pilot policies of the "Climate Resilient Cities" increased the climate resilience of the pilot cities by four percentage points. The pilot policies of the "Climate Resilient Cities" had a significant contribution to the urban infrastructure development and ecological space optimization, as well as non-significant impacts to the urban water security, emergency management capacity-building, and science and technology innovation initiatives.

Climate change is having a significant impact on farmers and agriculture. Rising temperatures and extreme weather events, such as droughts and floods, are causing crop failures and reducing yields. This study evaluated existing publications from 2006 to 2022, using the Web of Science database, Citespace, and the Bibliometrix package in R language for a systematic analysis. A total of 426 publications were identified, written by 1449 authors from 56 countries. The results showed that China has the highest share of publications (16.4%), followed by the United States (14.3%) and Australia (6.8%), with China and Pakistan collaborating most frequently. The keyword timeline analysis from 2006 to 2022 identified 11 clusters of research topics related to farmers' climate change risk (CCRF). Cluster #1, "water conservation measures," had the longest duration, highlighting its significance. Key areas of CCRF research include the vulnerability of land-lost farmers, farmers' use of meteorological information, sources of risk, barriers to implementing conservation agriculture, farmers' attitudes towards heavy metal pollution, and the use of protection motivation theory in agricultural adaptation. In conclusion, climate change poses significant threats to farmers, impacting crop yields, soil fertility, and water availability. Farmers adopt adaptation strategies, but effectiveness varies due to resource limitations and policy gaps. The research on farmer adaptation to climate change is growing, emphasizing the need for supportive policies, resources, and knowledge-sharing to achieve sustainable agriculture and food security. [ABSTRACT FROM AUTHOR]

Nonstationary is one of the prominent phenomena in the current hydrological time series due to climate change and urban expansion. In this study, using the long time series rainfall data from rain gauges and satellite rainfall data, the trend and abrupt change of rainfall in the Taihu Lake basin, China, are examined by the Mann–Kendall (MK) test and the Pettitt test, using rain gague data. For seven water conservancy zones in this basin, the intensity–duration–frequency curves (IDFs) are obtained using satellite rainfall and the stochastic storm transposition (SST) method, providing a method for rainfall frequency analysis based on nonstationary assumption. The IDFs results between the conventional frequency analysis method with the stationary assumption and the SST-based method are compared. The results show an overall increasing trend of annual total rainfall in the Taihu Lake Basin, with significant changes at most stations. The SST-based results show a significant difference of IDFs in seven conservancy zones, which are linked to nonstationary changes in rainfall series. Our results provide an important reference for understanding the nonstationary changes and nonstationary frequency analysis of extreme rainfall in the Taihu Lake basin. [ABSTRACT FROM AUTHOR]

As one of the largest agricultural countries in the world, China has always paid close attention to the sustainable development of agricultural production efficiency. However, with global climate change, extreme weather has become an exogenous factor that cannot be ignored, as it affects agricultural production. Most of the existing studies only consider the domestic natural resources and economic factors, without fully considering the external climate factors. This paper uses the super undesirable dynamic Slacks-Based Measures (SBM) under an exogenous variable model to simulate the external environmental factors by adding extreme weather days. The Dagum Gini coefficient and kernel density estimation are used to explore the regional differences in agricultural production in China. The results show that the agricultural production efficiency is higher in the eastern region, and the difference in agricultural production efficiency among the provinces in the middle and western regions is large, showing a trend of polarization. The difference in the Gini coefficient between the middle and western regions is more significant. The main contribution factor of the Dagum Gini coefficient is the inter-regional difference. The regional concentration degree of agriculture in China is decreasing, the regional distribution of agricultural water resources is more balanced, and the national regional difference gradually decreases. Finally, some suggestions are put forward, such as extreme weather control, agricultural water supply, and water-saving measures., Competing Interests: The authors declare no conflict of interest.

Improving local farmers' climate change adaptive capacity is an important policy issue in rural China. This study investigates farmers' risk cognition, risk preferences and climate change adaptive behavior. Based on unique data from a survey and a paired lottery experiment completed by 240 rural farmers in Chongqing City of China, this paper finds that farmers have a pessimistic risk cognition towards climate change and the typical farmers are risk-averse and loss-averse. Risk cognition and adaptation cognition have significantly positive influences on climate change adaptive behavior, and loss aversion has a significantly positive influence on farmers' adaptation decisions. Loss aversion exerts a positive impact on risk cognition and adaptation cognition, and risk aversion has a positive impact on adaptation cognition. This paper contributes to the emerging literature that relates risk preference in experiments and risk cognition to farmers' climate change adaptive behavior., Competing Interests: What is your average monthly household income?

Under the background of the construction of ecological civilization in China, since China has a vast amount of territory and large distances between cities, the intensity of environmental supervision in different regions may affect the enthusiasm of enterprises to disclose carbon information. Based on the listed companies of the Chinese heavy pollution industry from 2009 to 2014, using the content analysis method, the paper researches the influence of the spatial heterogeneity regarding government regulation and spatial distance on enterprise carbon information disclosure and puts forward some suggestions. The empirical results show that carbon information disclosure is significantly positively related with the spatial heterogeneity of government regulation. The spatial heterogeneity of government regulation is significantly positively associated to non-financial carbon information disclosure. The relationship between government supervision and financial carbon information disclosure is not significant. Spatial distance and carbon information disclosure are significantly positively related. There is also a significant positive correlation between spatial distance and financial carbon information disclosure. Further study finds that in public enterprises, the spatial heterogeneity of government regulation will promote carbon information disclosure, and the influence of spatial heterogeneity of government regulation on non-financial and financial carbon information disclosure both are significantly positively related. In non-public enterprises, spatial distance and carbon information disclosure are significantly positively related, and spatial distance and non-financial carbon information disclosure are significantly positively related as well.

Slope ecological restoration and climate change are important factors affecting the hydrological processes of the Huangshui River Basin in Qinghai province, China. How to quantitatively identify the impact of slope ecological restoration on runoff and whether slope ecological restoration can mitigate the impact of future climate change on runoff are both very important. In this paper, the Huangshui River above the center of Minhe county was taken as the research area, and the Pinus tabulaeformis and shrubs were taken as the main forest land types of slope ecological restoration. First, based on the law of forest land variation, the construction scales of slope ecological restoration in different periods were identified. The influence of slope ecological restoration on runoff was then quantitatively evaluated by using a distributed hydrological model. Second, the future climate scenarios of five general circulation models (GCMs) under three representative concentration pathways (RCPs) (i.e., RCP2.6, RCP4.5, and RCP8.5) from 2021 to 2050 were selected and modified by model integration. Combined with the slope ecological restoration scenarios, the influence of slope ecological restoration on runoff under future climate scenarios was explored. The results showed that the effect of slope ecological restoration was significant. Compared with 1980, the area of slope ecological restoration increased by 24% in 2017. Under the present climate conditions (1960-2017), different periods of slope ecological restoration have an effect on the process of runoff in the wet season (June, July, August, and September) and dry season (January, February, March, and December), which eliminates the maximum, replenishes the minimum, and reduces the variability of runoff processes in the watershed. Under the future climate scenario (2021-50), slope ecological restoration will reduce runoff. On the other hand, climate change will increase runoff, and the combination of the two effects will have a certain offsetting effect. On the whole, comparing the influence of slope ecological restoration on the runoff process with that of climate change in different seasons, due to the main influence of slope ecological restoration, the runoff decreased by about 55% in the temperate season (April, May, October, and November), and increased by about 50% in the dry season or wet season due to the main influence of future climate scenarios.

An object-based method is proposed in this paper for change detection in urban areas with multi-sensor multispectral (MS) images. The co-registered bi-temporal images are resampled to match each other. By mapping the segmentation of one image to the other, a change map is generated by characterizing the change probability of image objects based on the proposed change feature analysis. The map is then used to separate the changes from unchanged areas by two threshold selection methods and k-means clustering (k = 2). In order to consider the multi-scale characteristics of ground objects, multi-scale fusion is implemented. The experimental results obtained with QuickBird and IKONOS images show the superiority of the proposed method in detecting urban changes in multi-sensor MS images. [ABSTRACT FROM AUTHOR]

The regions in China that intersect the 400 mm annual precipitation line are especially ecologically sensitive and extremely vulnerable to anthropogenic activities. However, in the context of climate change, the response of vegetation Net Primary Production (NPP) in this region has not been scientifically studied in depth. NPP suffers from the comprehensive effect of multiple climatic factors, and how to eliminate the effect of interfering variables in the correlation analysis of NPP and target variables (temperature or precipitation) is the major challenge in the study of NPP influencing factors. The correlation coefficient between NPP and target variable was calculated by ignoring other variables that also had a large impact on NPP. This increased the uncertainty of research results. Therefore, in this study, the second-order partial correlation analysis method was used to analyze the correlation between NPP and target variables by controlling other variables. This can effectively decrease the uncertainty of analysis results. In this paper, the univariate linear regression, coefficient of variation, and Hurst index estimation were used to study the spatial and temporal variations in NPP and analyze whether the NPP seasonal and annual variability will persist into the future. The results show the following: (i) The spatial distribution of NPP correlated with precipitation and had a gradually decreasing trend from southeast to northwest. From 2000 to 2015, the NPP in the study area had a general upward trend, with a small variation in its range. (ii) Areas with negative partial correlation coefficients between NPP and precipitation are consistent with the areas with more abundant water resources. The partial correlation coefficient between the NPP and the Land Surface Temperature (LST) was positive for 52.64% of the total study area. Finally, the prediction of the persistence of NPP variation into the future showed significant differences on varying time scales. On an annual scale, NPP was predicted to persist for 46% of the study area. On a seasonal scale, NPP in autumn was predicted to account for 49.92%, followed by spring (25.67%), summer (13.40%), and winter (6.75%).

With the implementation of the Grain for Green Project, the apple plantation area is increasing in Loess Plateau. However, due to severe water scarcity, the sustainability of apple tree growth is threatened. In this paper, we used meteorological data (1990⁻2013) and forecasted climate data (2019⁻2050) to estimate water demand and establish a water suitability model to study the water balance between available water and water consumption of the apple trees. The results show that: (i) the order of the average water demand of apple plantation in each subarea is Shaanxi Province > Yuncheng area > Gansu Province > Sanmenxia Region, ranging from 500 to 950 mm; (ii) the temporal variability of water suitability from 1990 to 2013 is large, and the higher values are concentrated in the late growth stage of the apple trees and the lower values are concentrated in the early growth stage; (iii) the temporal and spatial distribution of water suitability is relatively stable and even in the Loess Plateau in the period of 2019⁻2050; (iv) the water suitability is mainly affected by effective precipitation and reference evapotranspiration and the reference evapotranspiration is mainly affected by the solar radiation (36%) and average temperature (38%). Furthermore, due to the joint influence of precipitation increases and solar radiation (average temperature) increases, the future water suitability of the apple plantation area in the Loess Plateau is showing a non-significant downward trend under RCP4.5 scenario., Competing Interests: The authors declare no conflict of interest.

Projecting future changes in extreme flood is critical for risk management. This paper presented an analysis of the implications of the Fifth Coupled Model Intercomparison Project Phase (CMIP5) climate models on the future flood in the Jinsha River Basin (JRB) in Southwest China, using the Xinanjiang (XAJ) hydrologic model. The bias-corrected and resampled results of the multimodel dataset came from the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP). Relatively optimal general circulation models (GCMs) were selected with probability density functions (PDFs)-based assessment. These GCMs were coupled with the XAJ model to evaluate the impact of climate change on future extreme flood changes in the JRB. Two scenarios were chosen, namely: a midrange mitigation scenario (Representative Concentration Pathway 4.5, RCP4.5) and a high scenario (RCP8.5). Results show that: (1) The XAJ model performed well in simulating daily discharge and was suitable for the study area, with E NS and R ² higher than 0.8; (2) IPSL-CM5A-LR and MIROC-ESM-CHEM showed considerable skill in representing the observed PDFs of extreme precipitation. The average skill scores across the total area of the JRB were 0.41 to 0.66 and 0.53 to 0.67, respectively. Therefore, these two GCMs can be chosen to analyze the changes in extreme precipitation and flood in the future; (3) The average extreme precipitation under 20- and 50-year return period across the JRB were projected to increase by 1.0⁻33.7% under RCP4.5 and RCP8.5 during 2020 to 2050. The Upper basin is projected to experience the largest increase in extreme precipitation indices, possibly caused by a warmer climate. The extreme flood under 20- and 50-year return period will change by 0.8 to 23.8% and -6.2 to 28.2%, respectively, over this same future period. Most of scenarios projected an increase during the near future periods, implying the JRB would be likely to undergo more flooding in the future.

Gradually increasing durations of high temperature caused by climate change harm the health of individuals and then lead to death. This study aimed to investigate the relationship between durations of different daily mean air-temperature categories and mortality in China and forecast future mortality changes in China for 2020–2050 under Representative Concentration Pathways (RCP)4.5 and RCP8.5 scenarios. The daily mean air temperature was divided into 10 categories, and the days under each air-temperature category were counted during the period of 2000–2015. Then, the connection between the days of each of the 10 air-temperature categories and mortality was established using the semi parametric regression model. Results indicate that the days of the >32 °C category have the largest impact on mortality in China, with the death rate increasing by 23‰ for one additional day. Predictions reveal that mortality in China will increase 25.48% and 26.26% under the RCP4.5 and RCP8.5 scenarios, respectively. Moreover, the mortality of 86 regions in western China will increase 30.42%. Therefore, in the future, the increasing duration of days of high temperatures will raise the mortality rate in China and aggravate the mortality gap between developed and underdeveloped regions. [ABSTRACT FROM AUTHOR]

In recent years, the runoff of numerous rivers has experienced substantial changes owing to the dual influences of climate change and human activities. This study focuses on the Lixian hydrological station's controlled basin, located in the upper reaches of the Jialing River in China. The objective is to assess and quantify the impacts of human activities and climate change on runoff variations. This study analyzed runoff variations from 1960 to 2016 and employed the Soil and Water Assessment Tool (SWAT) model, the long short-term memory (LSTM) model, and eight Budyko framework formulations to assess factors influencing runoff. Additionally, it used the patch-generating land use simulation (PLUS) and SWAT models to simulate future runoff scenarios under various conditions. The results indicate the following. (1) The study area has witnessed a significant decline in runoff (p < 0.01), while potential evapotranspiration shows a significant upward trend (p < 0.01). Precipitation displays a nonsignificant decreasing trend (p > 0.1). An abrupt change point in runoff occurred in 1994, dividing the study period into baseline and change periods. (2) The Budyko results reveal that human activities contributed 50% to 60% to runoff changes. According to the SWAT and LSTM models, the contribution rates of human activities are 63.21% and 52.22%, respectively. Human activities are thus identified as the predominant factor in the decline in runoff. (3) Human activities primarily influence runoff through land cover changes. Conservation measures led to a notable increase in forested areas from 1990 to 2010, representing the most significant change among land types. (4) Future land use scenarios suggest that the highest simulated runoff occurs under a comprehensive development scenario, while the lowest is observed under an ecological conservation scenario. Among the 32 future climate scenarios, runoff increases significantly with a 10% increase in precipitation and decreases substantially with a 15% reduction in precipitation. These findings underscore the significant impact of human activities and climate change on runoff variations in the upper reaches of the Jialing River, highlighting the importance of incorporating both factors in water resource management and planning. [ABSTRACT FROM AUTHOR]

Assessing Sustainable Development Goal (SDG) indicator 15.3.1, which refers to the proportion of degraded land to total land area, and analysing its status and drivers is essential for the development of policies to promote the early achievement of SDG target 15.3 of Land Degradation Neutrality (LDN). In this study, Northeast China was selected as the study area, and the progress of indicator 15.3.1 was assessed based on the perspective of Net Primary Productivity (NPP) calculated by the CASA model. WorldPop population spatial distribution data were used as a proxy for human activities, combined with climate data to calculate the effects of changes in temperature, precipitation and population spatial distribution on vegetation NPP based on the partial correlation coefficient method and the Geodetector method. The results showed that 92.81% of the areas that passed the test of significance showed an increasing trend in vegetation NPP from 2000 to 2020. The vegetation NPP was affected by a combination of temperature, precipitation and population. The effects of temperature and precipitation on spatial differences in NPP for various vegetation types were significantly greater than those of population, but in high-density population zones, the effects of population on spatial differences in NPP were generally greater than those of temperature and precipitation. Precipitation was the main driver for spatial variation in NPP in deciduous broad-leaved forests, cultivated vegetation and thickets, while temperature was the main driver for spatial variation in NPP in evergreen coniferous forests. Generally, the warming and wetting trend in Northeast China contributed to the accumulation of NPP in cultivated vegetation, thickets, steppes and grasslands. The sensitivity of NPP to temperature and precipitation in deciduous broad-leaved and deciduous coniferous forests varied according to geographical location. [ABSTRACT FROM AUTHOR]

Climate change is heavily altering plant distributions, posing significant challenges to conventional agricultural practices and ecological balance. Meanwhile, mixed species planting emerges as a potent strategy to enhance agricultural resilience, counteract climate change, preserve ecological balance, and provide a solution to economic instability. The MaxEnt model was used to predict the suitable area of Citrus reticulata under five climate scenarios and to explore affecting environmental factors. Litchi chinensis, Punica granatum, and Lycium chinense were selected as mixed species to analyze the spatial distribution and centroid migration trend of potentially suitable areas. The research results show the following: (1) The primary environmental factors impacting C. reticulata distribution are annual precipitation (1000–4000 mm), precipitation of driest quarter over 100 mm, and mean temperature of coldest quarter (12–28 °C). Crucially, the mixed species exhibited similar environmental sensitivities, indicating mutual mixing suitability. (2) Currently, the C. reticulata suitable area is of 240.21 × 104 km2, primarily in South, East, Central, and Southwest China, with potential for expansion to 265.41 × 104 km2 under the 2090s SSP1-2.6 scenario. (3) The geometric center of the moderately-to-highly suitable areas for C. reticulata is located in Hunan Province. Future scenarios show the C. reticulata's centroid migrating northwest, with distances of less than 110 km. Mixed planting trends toward higher latitudes, fluctuating from 6 km to 210 km. (4) Mixed planting area planning: C. reticulata and L. chinensis are suitable for mixed planting in South China. C. reticulata and P. granatum, C. reticulata and L. chinense are suitable for mixed planting in most areas of Central, East, Southwest, and South China. This research presents a new perspective on using mixed design principles for ecological adaptation and the sustainable mixed planting of C. reticulata, in response to China's changing climate. This approach is expected to help the economic fruit tree industry enhance ecological resilience and economic stability in the face of future climate change challenges. [ABSTRACT FROM AUTHOR]

Tourism climate research is of great significance to the tourism industry because tourism is closely linked to climate. Based on an analysis of related core papers, this paper reviews the research progress on tourism climate in China in terms of research method, research process, and research topic. Research on tourism climate in China started later than similar research in some Western countries and the topics mainly focused on tourism climate resources, climate comfort for tourism, the impact of climate on tourists' behavior and emotion, climate and tourism seasonality, climate change and tourism development, etc. To provide scientific support for the sustainable development of China's tourism industry, we propose the following for future research, based on our review of the literature: (1) strengthening the theoretical study of tourism climatology, (2) constructing and improving the research content system, and (3) enriching relevant research in climate-sensitive areas. [ABSTRACT FROM AUTHOR]

Land use and land cover (LULC) change in tropical regions can cause huge amounts of carbon loss and storage, thus significantly affecting the global climate. Due to the differences in natural and social conditions between regions, it is necessary to explore the correlation mechanism between LULC and carbon storage changes in tropical regions from a broader geographical perspective. This paper takes Hainan Island as the research object, through the integration of the CA-Markov and Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) models, based on multi-source data, analyses the dynamics of LULC and carbon storage from 1992 to 2019 and the relationship between the two, and predicts future LULC and carbon storage under different scenarios. The results show that (1) the built-up land area of Hainan Island expanded from 103.59 km2 to 574.83 km2 from 1992 to 2019, an increase of 454.91%; the area of cropland and shrubland decreased; and the area of forest increased. (2) Carbon storage showed an upward trend during 1992–2000, and a downward trend during 2000–2019. Overall, LULC changes during 1992–2019 reduced carbon storage by about 1.50 Tg. (3) The encroachment of cropland in built-up land areas is the main reason for the reduction of carbon storage. The conversion of shrubland to forest is the main driving force for increasing carbon storage. The increase and decrease of carbon storage have obvious spatial clustering characteristics. (4) In the simulation prediction, the natural trend scenario (NT), built-up land priority scenario (BP) and ecological priority scenario (EP) reduce the carbon storage of Hainan Island, and the rate of decrease is BP> NT > EP. The cropland priority scenario (CP) can increase the LULC carbon storage, and the maximum increase in 2050 can reach 0.79 Tg. This paper supplements and improves the understanding of the correlation between LULC and carbon storage changes in tropical regions, and can provide guidance for the optimization of LULC structure in tropical regions with high economic development from a low-carbon perspective. [ABSTRACT FROM AUTHOR]

The solid Earth deforms elastically in response to variations of surface atmosphere, hydrology, and ice/glacier mass loads. Continuous geodetic observations by Global Positioning System (CGPS) stations and Gravity Recovery and Climate Experiment (GRACE) record such deformations to estimate seasonal and secular mass changes. In this paper, we present the seasonal variation of the surface mass changes and the crustal vertical deformation in the South China Block (SCB) identified by GPS and GRACE observations with records spanning from 1999 to 2016. We used 33 CGPS stations to construct a time series of coordinate changes, which are decomposed by empirical orthogonal functions (EOFs) in SCB. The average weighted root-mean-square (WRMS) reduction is 38% when we subtract GRACE-modeled vertical displacements from GPS time series. The first common mode shows clear seasonal changes, indicating seasonal surface mass re-distribution in and around the South China Block. The correlation between GRACE and GPS time series is analyzed which provides a reference for further improvement of the seasonal variation of CGPS time series. The results of the GRACE observations inversion are the surface deformations caused by the surface mass change load at a rate of about -0.4 to -0.8 mm/year, which is used to improve the long-term trend of non-tectonic loads of the GPS vertical velocity field to further explain the crustal tectonic movement in the SCB and surroundings. [ABSTRACT FROM AUTHOR]

Land use and land cover changes (LUCC) are thought to be amongst the most important impacts exerted by humans on climate. However, relatively little research has been carried out so far on the effects of LUCC on extreme climate change other than on regional temperatures and precipitation. In this paper, we apply a regional weather research and forecasting (WRF) climate model using LUCC data from Heilongjiang Province, that was collected between the 1900s and 2010s, to explore how changes in forest cover influence extreme temperature indexes. Our selection of extreme high, low, and daily temperature indexes for analysis in this study enables the calculation of a five-year numerical integration trail with changing forest space. Results indicate that the total forested area of Heilongjiang Province decreased by 28% between the 1900s and 2010s. This decrease is most marked in the western, southwestern, and northeastern parts of the province. Our results also reveal a remarkable correlation between change in forested area and extreme high and low temperature indexes. Further analysis enabled us to determine that the key factor explaining increases in extreme high temperature indexes (i.e., calculated using the number of warm days, warm nights, as well as tropical nights, and summer days) is decreasing forest area; data also showed that this factor caused a decrease in extreme low temperature indexes (i.e., calculated using the number of cold days and cold nights, as well as frost days, and ice days) and an increase in the maximum value of daily minimum temperature. Spatial data demonstrated that there is a significant correlation between forest-to-farmland conversion and extreme temperature indexes throughout most of our study period. Spatial data demonstrated that there is a significant correlation between forest-to-farmland conversion and extreme temperature indexes throughout most of our study period. Positive correlations are also present between decreasing forest area, the more frequent occurrence of extreme high temperature events, and a rise in the maximum value of daily minimum temperature. At the same time, we found clear negative correlations between decreasing forest area and less frequent occurrence of extreme low temperature events. [ABSTRACT FROM AUTHOR]

Potential evapotranspiration (PET) is an important factor that needs to be considered in regional water management and allocation; thus, the reasonable estimation of PET is an important topic in hydrometeorology and other related fields. There is evidence that increased CO2 concentration alters the physiological properties of vegetation and thus affects PET. In this study, changes in PET with and without the CO2 effect over China is investigated using seven CMIP6-GCMs outputs under seven shared socioeconomic pathways (SSPs) based scenarios (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP4-3.4, SSP4-6.0, and SSP5-8.5), as well as the contribution rate of CO2 on PET in different climatic regions. Changes in estimated PET based on modified Penman–Monteith (PM) method that considers the CO2 effect is compared with the traditional PM method to examine how PET quantity varies (differences) between these two approaches. The results show that the PET values estimated by the two methods explored opposite trends in 1961–2014 over entire China; it decreases with consideration of CO2 but increases without consideration of CO2. In the future, overall PET is projected to increase under all scenarios during 2015–2100 for China and its three sub-regions. PET generally tends to grow slower when CO2 is taken into account (modified PM approach), than when it is not (traditional PM method). In terms of differences in the estimated PET by the two methods, the difference between the two adopted methods increased in China and its sub-regions for the 1961–2014 period. In the future, the difference in estimated PET is anticipated to continuously increase under SSP3-7.0 and SSP5-8.5. Spatially, a much greater extent of difference is found in the arid region. Across the arid region, the PET difference is projected to be the highest at 138% in the mid-term (2041–2060) with respect to the 1995–2014 period, whereas it tends to increase slower in the long-term period (2081–2100). Importantly, CO2 is found to be the most dominant factor (−154.2% contribution) to have a great effect on PET changes across the arid region. Our findings suggest that ignorance of CO2 concentration in PET estimation will result in significant overestimation of PET in the arid region. However, consideration of CO2 in PET estimation will be beneficial for formulating strategies on future water resource management and sustainable development at the local scale. [ABSTRACT FROM AUTHOR]

Net precipitation (NP) is the primary source of soil water essential for the functioning of vegetated ecosystems. By quantifying NP as the difference between gross precipitation and canopy interception evaporation, this study examined the dynamics of NP in China from 2001 to 2020 and the contribution of environmental factors to NP variations was investigated. The findings revealed a multiyear mean NP of 674.62 mm, showcasing a 2.93 mm/yr increase. The spatiotemporal variations in NP were mainly attributed to a remarkable increase in precipitation rather than canopy interception. Notably, climate (temperature, wind speed, surface solar radiation downward and vapor pressure deficit) and vegetation factors (leaf area index and net primary productivity) played a dominant role in NP in 61.53% and 15.39% of China, respectively. The dominant factors contributing to NP changes were vapor pressure deficit (mean contribution rate: −43.68%), temperature (mean contribution rate: 11.69%), and leaf area index (mean contribution rate: 2.13%). The vapor pressure deficit negatively exerts a negative influence on the southern and eastern regions. Temperature and leaf area index have the greatest effect on the northeastern and southwestern regions, respectively. The results provide valuable insights into the pivotal role of climatic and vegetation factors in ecohydrological cycles. [ABSTRACT FROM AUTHOR]

Previously, the types of impacts of land–atmosphere interactions have scarcely been clarified systematically. In this article, we present a classification of these impacts based on modeling boundary layer variables/parameters, which is grouped into local, regional, and remote impacts. In the narrow sense, land surface processes (LSPs) influence the atmospheric state via vertical land–atmosphere coupling at local scales, which is referred to as local LSP impacts. However, local LSP impacts can lead to the advection effect due to the horizontal heterogeneity in the parameters over a region, which can be defined as regional LSP impacts. Furthermore, remote LSP impacts on the regional atmospheric state are induced by some land/sea surface variables/parameters over remote key areas of the Earth's surface, which are conventionally taken as strong signals of climate variation. Of the three impacts, local impacts are the most important essential, as the other two types of impacts are derived from these impacts. We describe the quantification of local impacts based on our previous studies from the perspective of modeling approaches, and we discuss some issues related to these impacts. Previous investigations showed that local LSP impacts are mostly stronger than regional LSP impacts, e.g., the diabatic process is dominant in the physical processes responsible for daily maximum temperatures, and two first-order physical processes including vertical diffusion largely induce changes in surface wind speed in China. Finally, some aspects for future research are noted. This study provides insights into the research on land–atmosphre interactions at different scales. [ABSTRACT FROM AUTHOR]

Ambrosia trifida is an invasive alien plant species, which has very high reproductive and environmental adaptability. Through strong resource acquisition ability and allelopathy, it could inhibit the growth and reproduction of surrounding plants and destroy the stability of an invasive ecosystem. It is very important to predict the change of suitable distribution area of A. trifida with climate change before implementing scientific control measures. Based on 106 A. trifida distribution data and 14 points of environmental data, the optimal parameter combination (RM = 0.1, FC = LQ) was obtained using the MaxEnt (version 3.4.1) model optimized by Kuenm package, and thus the potential suitable areas of A. trifida in Northeast China under three different climate scenarios (RCP2.6, RCP4.5, RCP8.5) with different emission intensities in the future (2050, 2070) were predicted. The changes of A. trifida suitable area in Northeast China under three climate scenarios were compared, and the relationship between the change of suitable area and emission intensity was analyzed. In general, the suitable area of A. trifida in Northeast China will expand gradually in the future, and the area of its highly suitable area will also increase with the increasing emission intensity, which is unfavorable to the control of A. trifida. [ABSTRACT FROM AUTHOR]