Multi-year Average Research Articles

A Circulation Study Based on the 2022 Sino–Vietnamese Joint Survey Data from the Beibu Gulf

This study analyzed the horizontal and vertical distribution characteristics of temperature and salinity in the central and eastern regions of the Beibu Gulf, based on conductivity measurements in summer 2022, temperature, and depth (CTD) measurement data from the Sino–Vietnamese cooperative project “Demonstration Study on Ecological Protection and Management in Typical Bays: Seasonal Survey of the Beibu Gulf”. Furthermore, the study utilized the computational results from the numerical Finite-Volume Coastal Ocean Model (FVCOM) to elucidate the intrinsic patterns that formed the temperature and salinity distribution characteristics in August 2022 from both thermodynamic and dynamic perspectives. The circulation in the Beibu Gulf drives external seawater to move northward from the bay mouth. During this movement, numerous upwelling areas are created by lateral Ekman transport. The formation of different scales of cyclonic and anticyclonic vortices and current convergence zones is influenced by topography, runoff, and the water flux from the Qiongzhou Strait, which are key factors in the formation of upwelling and downwelling. The surface circulation in August 2022 significantly differed from the 20-year average surface circulation, with an influx of 1.15 × 104 m3/s more water entering the Beibu Gulf compared to the multi-year average. The water flux from the Qiongzhou Strait is a critical factor affecting the circulation patterns in the Beibu Gulf. The northeastern waters of the Beibu Gulf are characterized by current convergence zones, where extensive upwelling occurs. The rich nutrient salts in these areas promote the reproduction and growth of phytoplankton and zooplankton, making this the most favorable ecological environment in the Beibu Gulf and serving as a natural reserve for fisheries, coral reefs, dugongs, and Bryde’s whales.

Spatial–Temporal Variations in Water Use Efficiency and Its Influencing Factors in the Li River Basin, China

As a vital indicator for measuring the coupled carbon–water cycle of an ecosystem, water use efficiency (WUE) can also reflect the adaptive capacity of plants in different ecosystems. Located in Southwest China, the Li River Basin has a representative karst landform, and the uneven rainfall in the region leads to severe water shortage. In this study, we analyzed the spatial–temporal transformation characteristics of the WUE of the basin and its relationship with different influencing factors from 2001 to 2020 based on a correlation analysis and trend analysis. The main conclusions are as follows: (1) The average value of WUE in the Li River Basin was 1.8251 gC· mm−1·m−2, and it kept decreasing at a rate of 0.0072 gC· mm−1·m−2·a−1 in the past 20 years. With respect to the spatial distribution of the multi-year average of WUE, it exhibits a gradual increasing trend from west to east. (2) Between gross primary productivity (GPP) and evapotranspiration (ET), it was found that ET was the primary influencing factor of WUE. Precipitation was positively correlated with WUE in the Li River Basin, accounting for 67.22% of the total area of the basin. The air temperature was negatively correlated with WUE, and the area was negatively correlated with WUE, accounting for 92.67% of the basin area. (3) The normalized difference vegetation index (NDVI) and leaf area index (LAI) were negatively correlated with WUE, and the proportions of negatively correlated areas to the total area of the basin were similar; both were between 60 and 70%. The growth of vegetation inhibited the increase in WUE in the basin to a certain extent. Regarding Vapor Pressure Deficit (VPD), the proportions of positive and negative correlation areas with WUE were similar, accounting for 49.58% and 50.42%, respectively. (4) The occurrence of drought events and the enhancement in its degree led to a continuous increase in WUE in the basin; for different land cover types, the correlation of the standardized precipitation evapotranspiration index (SPEI) was in the following order from strongest to weakest: grassland > cropland > forest > shrubland.

Estimating the dynamics and driving factors of gross primary productivity over the Chinese Loess Plateau by the modified vegetation photosynthesis model

Gross primary productivity (GPP) is a key parameter in research on the global carbon cycle and changes. Understanding the spatiotemporal dynamics and influencing factors of GPP on the Loess Plateau (LP) helps identify the health status of ecosystems, thereby enabling the implementation of effective conservation and restoration measures. In this study, we used a modified vegetation photosynthesis model (VPM) to simulate a long-term series of GPP in the LP from 2001 to 2022, and the impacts of different land-use patterns and meteorological factors on GPP were investigated using a transition matrix, linear regression, and partial correlation analyses. The findings suggested that the modified simulation yielded a more reliable performance (coefficient of determination (R2) = 0.89, root mean square error (RMSE) = 143.47 gC·m−2·yr−1) and was suitable for further research endeavors. (1) The GPP on the LP significantly increased by 232.65 TgC from 2001 to 2022. The southeastern region contributed more than the northwestern region, and the GPP exhibited higher multi-year averages and growth rates below 1000 m elevation. (2) Forests in the southeastern region of the LP, characterized by a heightened growth rate, will influence future spatial variations in GPP increases across the LP. Despite the decline in grassland and cultivated land areas, substantial land coverage has significantly contributed to the overall GPP growth. Urbanization encroaching on cultivated land has emerged as a key contributor to the decline in GPP in low-altitude regions. (3) Air temperature was the main physical driving force for GPP change in the LP. Additionally, the GPP in forested regions exhibited a negative correlation with rainfall, whereas the GPP in areas undergoing the return of cropland to forest–grassland and cropland reclamation correlated negatively with solar radiation. (4) The attribution analysis indicated that the surge in vegetation GPP on the LP was collectively driven by human activities and meteorological changes, with human activities dominating these changes by 61.41 %. This study deepens the understanding of terrestrial ecology in semi-humid regions and provides scientific insights for implementing ecological governance strategies in the LP.

Anti-Erosion Effectiveness of Selected Crops in Sustainable Mountain Agriculture in a Warming Climate

Mountain ecosystems are among the most difficult areas for plant cultivation due to water erosion occurring on the slopes. Growing plants in these areas may lead to a weakening of ecosystem functions and in degradation of these areas and threatens sustainability. In this experiment, the anti-erosion effectiveness of maize, oat and spring vetch were assessed through the measuring of LAI and sheet wash from a slope where cultivation had occurred. Averaged values from the six years field experiment (2017–2022) reveal that maize achieved maximum soil protection between the 115th and 128th day of vegetation (14 days), when the LAI value equals to 3.8–4.0. The corresponding values for oats were 63–81 days of vegetation (19 days; LAI 2.4–2.7). The longest period of maximum soil protection was achieved from the cultivation of spring vetch compared to maize and oats (between the 49th and 82nd day of its vegetation, i.e., 34 days), when the LAI value was in the range of 2.2–3.0. Soil cover at their maximum development is conservative compared to mountain ecosystems, and in the case of the studied plants, the protection time varied. These relationships were quantified by simple regression equations. Additionally, taking into account the compiled climate data, the average air temperature in the years of research (2017–2022) was higher than the multi-year average (1961–2000) by 2.15 °C, which may confirm the fact that the climate is warming in the region of Southern Poland.

Estimating irrigation water use from remotely sensed evapotranspiration data: Accuracy and uncertainties at field, water right, and regional scales

Irrigated agriculture is the dominant user of water globally, but most water withdrawals are not monitored or reported. As a result, it is largely unknown when, where, and how much water is used for irrigation. Here, we evaluated the ability of remotely sensed evapotranspiration (ET) data, integrated with other datasets, to calculate irrigation water withdrawals and applications in an intensively irrigated portion of the United States. We compared irrigation calculations based on an ensemble of satellite-driven ET models from OpenET with reported groundwater withdrawals from hundreds of farmer irrigation application records and a statewide flowmeter database at three spatial scales (field, water right group, and management area). At the field scale, we found that ET-based calculations of irrigation agreed best with reported irrigation when the OpenET ensemble mean was aggregated to the growing season timescale (bias = 1.6–4.9 %, R2 = 0.53–0.74), and agreement between calculated and reported irrigation was better for multi-year averages than for individual years. At the water right group scale, linking pumping wells to specific irrigated fields was the primary source of uncertainty. At the management area scale, calculated irrigation exhibited similar temporal patterns as flowmeter data but tended to be positively biased with more interannual variability. Disagreement between calculated and reported irrigation was strongly correlated with annual precipitation, and calculated and reported irrigation agreed more closely after statistically adjusting for annual precipitation. The selection of an ET model was also an important consideration, as variability across ET models was larger than the potential impacts of conservation measures employed in the region. From these results, we suggest key practices for working with ET-based irrigation data that include accurately accounting for changes in soil moisture, deep percolation, and runoff; careful verification of irrigated area and well-field linkages; and conducting application-specific evaluations of uncertainty.

Towards the sustainable development of water security: A new copula-based risk assessment system

Water security is deeply intertwined with ecosystems, society and the economy, and is a frequent focal point of sustainable development. This study investigates the water security (WS) of Inner Mongolia, a dryland region in northern China. Specifically, an indicator system integrating the principles of Stability (S), Cooperation (C), and Resilience (R) was constructed to quantitatively and comprehensively assess water security. Risk probability was calculated by the constructed system by combining its output with a copula function. The findings revealed that: (1) WS-SCR levels in the study area gradually increased from 2001 to 2021. The periods multi-year average was 0.42, which is at the threshold level of security; (2) Based on a standard deviation ellipse, the spatial distribution of WS-SCR levels changed from northeast to southwest and back to northeast. The centroid of change shifted from east to west and back to east; (3) Key drivers influencing water security included GDP per capita (S3), agricultural irrigation water consumption per unit area (C4), comprehensive water use per capita (C5), reclaimed water use rate (C8), fertilizer application per unit of farmland (R4), and population density (R5); (4) The S-C, S-R, and C-R relationships within the study area were best modelled by the Clayton copula function, whereas the S-C-R relationship was most accurately modelled by using a Frank copula function. The three-dimensional joint risk probability S-C-R (S≤0.4, C≤0.4, R≤0.4) is 0.226; the probability that R was insecure increased with increasing S or C index values. This study provides a scientific foundation for the long-term planning and management of sustainable water security development in Eurasian drylands.

Assessing spatiotemporal dynamics of water use efficiency in ecologically vulnerable karst landscapes

Study regionSouthwest China Study focusWater use efficiency (WUE) is a key variable for understanding the carbon and water cycling processes in ecosystems. There exists the world's largest continuous karst topography in Southwest China, rendering it a quintessential ecologically vulnerable region. Therefore, determining the trends in WUE changes between karst and non-karst ecosystems is of great significance for understanding the carbon and water cycling processes in ecologically vulnerable areas. In this study, various analysis methods were utilized to explore the spatiotemporal variations, persistence characteristics, and the impact of complex climatic variability of WUE in Southwest China. New hydrological insights for the region(i) The rate of increase and multi-year average of WUE in karst ecosystem is higher than that in non-karst ecosystem. (ii) In the future, the proportion of area with decreasing in WUE will be higher in karst ecosystem than in non-karst ecosystem. (iii) There are significant differences in the correlation between WUE and climatic factors in different ecosystems. (iv) The dominant climatic factor for WUE in karst ecosystem is vapor pressure, while in non-karst ecosystem, it is precipitation. The research results can provide a theoretical basis for studying the carbon and water cycling processes in karst ecosystem.

Evaluation and Comparison of Five Long-Term Precipitation Datasets in the Hang-Jia-Hu Plain of Eastern China

This study analyzed the applicability of five long-term precipitation datasets in the Hang-Jia-Hu Plain of eastern China based on meteorological observation data. The accuracy of each dataset at different time scales (yearly, monthly) was analyzed. Besides, their spatiotemporal distributions and differences in precipitation event frequency were also compared. The results indicate that the high-resolution (1 day, 1 km) and long-term (1961–2019) gridded dataset for temperature and precipitation across China (HRLT) exhibited the best overall performance at the annual scale, while the long-term, gauge-based gridded precipitation dataset for the Chinese mainland (CHM_PRE) performed the best at the monthly scale. The dataset of monthly precipitation with a resolution of 1 km in China from 1960 to 2020 (HHU) and the China Meteorological Forcing Dataset (CMFD) tend to overestimate the local precipitation amounts, while the other three products are characterized by an underestimation. The mean result of the five datasets indicates a slight, statistically insignificant rise in precipitation, by 4.19 mm annually, with an overall multi-year average of 1303.28 mm. The analysis of the five datasets successfully captures the spatial precipitation patterns across the Hang-Jia-Hu Plain, characterized by higher precipitation levels in the southwest and lower in the northeast. Although the interannual variability displays general consistency, there are significant discrepancies in the interannual growth rates and the spatial distribution of significance across different regions. This study can provide a reference for the accuracy of precipitation data in the fields of hydrology, meteorology, agriculture, and ecology, facilitating the analysis of uncertainties in related research.

Analyzing Spatio-Temporal Dynamics of Grassland Resilience and Influencing Factors in the West Songnen Plain, China, for Eco-Restoration.

Grassland plays an indispensable role in the stability and development of terrestrial ecosystems. Quantitatively assessing grassland resilience is of great significance for conducting research on grassland ecosystems. However, the quantitative measurement of resilience is difficult, and research on the spatio-temporal variation of grassland resilience remains incomplete. Utilizing the Global Land Surface Satellite (GLASS) leaf area index (LAI) product derived from MODIS remote sensing data, along with land cover and meteorological data, this paper constructed the grassland resilience index (GRI) in the west Songnen Plain, China, a typical region with salt and alkali soils. This paper analyzed the spatio-temporal changes of the GRI and explored the contribution of climate factors, human activities, and geographical factors to the GRI. The results revealed that from 2000 to 2021, the GRI in the study area ranged from 0.1 to 0.22, with a multi-year average of 0.14. The average GRI exhibited a pattern of high-value aggregations in the north and low-value distributions in the south. Trend analysis indicated that areas with an improved GRI accounted for 59.09% of the total grassland area, but there were still some areas with serious degradation. From 2000 to 2015, the latitude and mean annual temperature (MAT) were principal factors to control the distribution of the GRI. In 2020, the mean annual precipitation (MAP) and MAT played important roles in the distribution of the GRI. From 2000 to 2021, the influence of human activities was consistently less significant compared to geographical location and climate variables.

Impact of touristification and landscape pattern on habitat quality in the Longji Rice Terrace Ecosystem, southern China, based on geographically weighted regression models

Although the roles of tourism development and landscape value in the conservation of rice terraces have become increasingly important in recent years, rice terraces still face abandonment and desertification. Habitat quality (HQ) assessments evaluate the biodiversity conservation status of rice terraces; however, the spatial and temporal evolution of rice terrace HQ and influencing factors remains poorly understood. Here, a case study was performed of the Longji Terraces, Longsheng, Guangxi, China. Land use and land cover (LULC) remote sensing data for 1985–2020 (every 5 years) were obtained, and the InVEST-HQ model was used to evaluate and map the spatial and temporal evolution patterns and characteristics of HQ. A landscape pattern index and tourism indicators were combined and used to construct geographically weighted regression (GWR) models to explore the impact of tourism and landscape pattern on HQ. Our findings indicated that: (1) forests and terraces are the dominant landscape patches in Longji because they are better protected. Forest area and HQ changes exhibited temporal consistency, meaning that as forest area increased, HQ improved. (2) Longji Terraces exhibited optimum HQ overall, with a multi-year average of 0.977 and insignificant inter-annual variations. Spatially, the geographical HQ distribution was characterised by ‘low local levels, high global level.’ However, the percentage of HQ degraded areas in the final period reached 68 %, which is significantly greater than that of the improved areas (28 %). Moreover, improved areas had more spatial overlap with administrative villages that present superior tourism development. (3) HQ was significantly negatively correlated with night-time light (NTL), showed periodic changes (first negative, then positive) with Shannon’s diversity index (SHDI), was positively correlated with the contagion index (CONTAG), and showed periodic transitions (initially positive, then negative) with the mean of patch area (AREA_MN), population (POP), and patch density (PD). (4) Over time, the study area HQ showed weaker clustering and increased dispersion. In addition, the influence of tourism and landscape patterns increased from 2000 to 2020, with R2 increasing from 0.42 to 0.95. The results establish a scientific foundation for the conservation of rice terraces and sustainable use of landscape resources.

Structural and Spatial Shifts in the Viticulture Potential of Main European Wine Regions as an Effect of Climate Change

Climate change modifies the base climate of the wine regions and, with it, the structure of their traditional types of wine production, imposing measures to adapt, mitigate, or capitalize on the newly emerging conditions. In order to assess the impact of climate change and establish the appropriate adaptation measures for each wine region, regional and local studies are needed, which allow knowledge of their current climate profile. The aim of this research was to identify the changes that appeared as an effect of climate change in the initial climate profile and the initial structure of the traditional types of wine production of Bordeaux (France), Loire Valley (France), Rhine-Main-Nahe (Germany), La Rioja (Spain) and Cotnari (Romania) wine regions, and also in climate suitability for wine production of the Sussex area from the UK. The study uses multi-year averages for the 1951–1990 and 1991–2010 time periods of reference bioclimatic indices for viticulture, namely the Average Temperature of the Growing Season (AvGST), the Huglin Index (HI), and the Oenoclimatic Aptitude Index (IAOe). The results of this research reveal significant changes in climate suitability for wine production of the studied wine regions: in the Bordeaux wine region, climate change led to the appearance of conditions for the cultivation of the Mediterranean climate varieties Grenache, Syrah, and Carignan; in the cool climate wine regions Rhine-Main-Nahe and Cotnari, traditional producers of white wines, the climate has also become suitable for the cultivation of Pinot noir and Cabernet franc varieties, and implicitly for the production of red wines; in all studied wine regions, the classes of climate suitability for viticulture shifted higher in altitude, as is the case of the La Rioja region, where, in the recent period, the grapevine can be grown up to 922.9 m asl, higher by 206.2 m compared to the 1951–1990 time period; in the low area of each wine region, one or even two new climate suitability classes for wine grape growing appeared. The shifts revealed by this research generate solid conclusions regarding the effect of climatic change on the viticultural potential of geographical areas, namely: in the context of climate change, the altitude of the wine region has a major influence on the evolution of the local viticulture potential; a higher topography allows a better adaptation of the wine region to climate change; low-elevation wine regions are more vulnerable to climate changes, especially the further south they are located; as an effect of climate change, conditions appear in the wine regions for the cultivation of new grapevine varieties and the production of new types of wine.

CAMELE: Collocation-Analyzed Multi-source Ensembled Land Evapotranspiration Data

Abstract. Land evapotranspiration (ET) plays a crucial role in Earth's water–carbon cycle, and accurately estimating global land ET is vital for advancing our understanding of land–atmosphere interactions. Despite the development of numerous ET products in recent decades, widely used products still possess inherent uncertainties arising from using different forcing inputs and imperfect model parameterizations. Furthermore, the lack of sufficient global in situ observations makes direct evaluation of ET products impractical, impeding their utilization and assimilation. Therefore, establishing a reliable global benchmark dataset and exploring evaluation methodologies for ET products is paramount. This study aims to address these challenges by (1) proposing a collocation-based method that considers non-zero error cross-correlation for merging multi-source data and (2) employing this merging method to generate a long-term daily global ET product at resolutions of 0.1° (2000–2020) and 0.25° (1980–2022), incorporating inputs from ERA5L, FluxCom, PMLv2, GLDAS, and GLEAM. The resulting product is the Collocation-Analyzed Multi-source Ensembled Land Evapotranspiration Data (CAMELE). CAMELE exhibits promising performance across various vegetation coverage types, as validated against in situ observations. The evaluation process yielded Pearson correlation coefficients (R) of 0.63 and 0.65, root-mean-square errors (RMSEs) of 0.81 and 0.73 mm d−1, unbiased root-mean-square errors (ubRMSEs) of 1.20 and 1.04 mm d−1, mean absolute errors (MAEs) of 0.81 and 0.73 mm d−1, and Kling–Gupta efficiencies (KGEs) of 0.60 and 0.65 on average at resolutions of 0.1 and 0.25°, respectively. In addition, comparisons indicate that CAMELE can effectively characterize the multiyear linear trend, mean average, and extreme values of ET. However, it exhibits a tendency to overestimate seasonality. In summary, we propose a reliable set of ET data that can aid in understanding the variations in the water cycle and has the potential to serve as a benchmark for various applications. The dataset is publicly available at https://doi.org/10.5281/zenodo.8047038 (Li et al., 2023b).

Synergy of carbon and water use efficiencies in the Huai River Basin

Carbon Use Efficiency (CUE) and Water Use Efficiency (WUE) are pivotal ecological indicators. Clarifying whether they change synergistically can aid in understanding how terrestrial ecosystems adapt to climate and environmental changes. Utilizing meteorological and remote sensing data, our study assessed the spatial and temporal patterns of CUE and WUE in terrestrial ecosystems within the Huai River Basin from 2001 to 2020. Our findings revealed multi-year annual averages of 0.43 and 0.82 gC kg−1H2O for CUE and WUE, respectively. While CUE showed a significant increasing trend (P < 0.01), the increase in WUE was not significant (P > 0.05). The primary drivers behind temporal variations in these efficiencies included leaf area index (LAI), temperature, and soil moisture. In 77.96 % of the basin area, CUE and WUE exhibit synergistic changes. LAI was the predominant factor affecting both CUE and WUE, and dual-factor interactions amplify their influence. These insights enhance our understanding of regional carbon and water cycles and offer valuable guidance for the refinement of ecological conservation strategies.

Downscaling and Wind Resource Assessment of Climatic Wind Speed Data Based on Deep Learning: A Case Study of the Tengger Desert Wind Farm

Analyzing historical and reanalysis datasets for wind energy climatic characteristics offers crucial insights for wind farms and short-term electricity generation forecasting. However, large-scale wind farms in Chinese deserts, the Gobi, and barren areas often lack sufficient wind measurement data, leading to challenges in assessing long-term power generation revenue and introducing uncertainty. This study focuses on the Tengger Desert as the study area, processes the Coupled Model Intercomparison Project Phase 6 (CMIP6) data, and analyzes and compares wind energy’s future characteristics utilizing a developed deep learning (DL) downscaling algorithm. The findings indicate that (1) the Convolutional Neural Network (CNN) downscaling model, with the Weather Research and Forecasting Model (WRF) numerical simulation results as the targets, exhibits spatial distribution consistency with WRF simulation results in the experimental area. (2) Through testing and validation with three practical wind measurements, the annual average wind speed error is below 4%. (3) In the mid-term future (~2050), the average wind speed in the experimental area remains stable with a multi-year average of approximately 7.00 m·s−1. The overall wind speed distribution range is significant, meeting the requirements for wind farm development.

Spatiotemporal variation of the major meteorological elements in an agricultural region: A case study of Linyi City, Northern China

&lt;abstract&gt; &lt;p&gt;Evaporation is a key element of the water and energy cycle and is essential in determining the spatial and temporal variations of meteorological elements. In particular, evaporation is crucial for thoroughly understanding the climate variations of a region. In this study, we discussed evaporation, precipitation, and temperature by adopting Linyi City in Shandong Province, China, which is an important agricultural region, as a research case. Linear regression analysis, the empirical orthogonal decomposition function, and the Morlet wavelet function were used to reveal the trends, spatiotemporal modes, and multi-time scale characteristics of the three climate factors and provide a theoretical basis for the efficient use of climate resources in the future development of regional agriculture. Results showed that the precipitation (2.09 mm/a) and temperature (0.04 ℃/a) in Linyi City exhibited a synchronous growth trend. Conversely, evaporation (−6.47 mm/a) showed a decreasing trend and the evaporation paradox because of the considerable decrease in evaporation energy. Regional development of water-consuming agriculture in consideration of global warming is a key point for improving water use efficiency in Linyi City. In terms of spatial distribution, precipitation was dominated by the first mode wherein low precipitation was observed at the early stage, and high precipitation occurred at the late stage. The first mode was supplemented by the second mode wherein an inverse phase change occurred in the southeast-northwest direction. Large interannual fluctuations were observed only in Yinan County. Temperature exhibited a pattern of warming change with high homogeneity. Evaporation demonstrated obvious heterogeneity and was dominated by two major modes, and the difference in evaporation between Junan County and the other regions of Linyi City was large. Therefore, the local regional climate changes in Yinan and Junan should be given attention. All three meteorological elements showed interannual and interdecadal variations in the short (5 a), medium (16 a), and long (25 a) terms, with precipitation, temperature, and evaporation dominated by 16 a, 24 a, and 31 a, respectively. In the short-term future, the regional precipitation and temperature in Linyi will experience decrements that are above the multiyear average, and evaporation will increase to above the multiyear average. Given the changing trends of precipitation, temperature, and evaporation, urgent requirements for the regional development of efficient water-saving irrigation and the promotion of digital agriculture should be proposed.&lt;/p&gt; &lt;/abstract&gt;

Heat Waves Analysis of 2023 Summer in Eastern Part of Romania and Atmospheric Instability Events Which Succeeded Between Them

Abstract The purpose of this paper is a general climatic characterization of the 2023 summer in the region of Moldavia, in eastern part of Romania, but with focus on the analysis of the context in which the most intense tropical air advections occurred in this region. The most significant and lasting tropical air waves were in July and August. From a thermal point of view, June had only one interval in which the maximum temperatures exceeded, for 3 consecutive days, the threshold of 30ºC (between 22nd and 24th of June). Instead, in July there were three intervals in which the maximum temperature exceeded this threshold, for more than 3 days in a row (between 01st - 06th, 15th - 22nd, 24th - 26th of July), and August had the longest such intervals (between 03rd – 06th, 13th – 29th of August). Also, in August, there were four consecutive days with maximum temperatures that exceeded the heat wave threshold (26th -29th of August). Without achieving new absolute temperature records (the highest maximum temperature in this interval being 39˚C at the Iași meteorological station, on August 28th), the last heat wave was notable in terms of duration of manifestation. Considering the definition given by the World Meteorological Organization (WMO) in 2020, according to which heat waves are considered those periods of time that are unusually warm in relation to multi-year averages and that last more than 2 consecutive days, we can frame the tropical air advection of July and August in the category of heat waves.

The impact of climate change and human activities on the change in the net primary productivity of vegetation-taking Sichuan Province as an example.

Vegetation is an essential component of terrestrial ecosystems, influenced by climate change and human activities. Quantifying the relative contributions of climate change and human activities to vegetation dynamics is crucial for addressing global climate change. Sichuan Province is one of the essential ecological functional areas in the upper reaches of the Yangtze River, and its vegetation change is of great significance to the environmental function and ecological security of the Yangtze River Basin and southwest China. In this paper, the modified Carnegie-Ames-Stanford Approach(CASA) model was used to estimate the monthly NPP (Net Primary Productivity) of vegetation in Sichuan Province from 2000 to 2018, and the univariate linear regression analysis was used to analyze the temporal and spatial variation of vegetation NPP in Sichuan Province from 2000 to 2018. In addition, taking vegetation NPP as an index, Pearson correlation analysis, partial correlation analysis, and second-order partial correlation analysis were carried out to quantitatively analyze the contribution of climate change and human activities to vegetation NPP. Finally, the Hurst index and nonparametric Man-Kendall significance test were used to predict the future change trend of vegetation NPP in Sichuan Province. The results show that (1) from 2000 to 2018, the NPP of vegetation in Sichuan Province has a significant increasing trend (Slope = 6.09gC·m-2·a-1), with a multi-year average of 438.72 gC·m-2·a-1, showing a trend of low in the east and high in the middle. The response of vegetation NPP to altitude is different at different elevations; (2) the contribution rates of climate change and human activities to vegetation NPP change are 4.12gC·m-2·a-1 and 1.97gC·m-2·a-1, respectively. In contrast, the impact of human activities on NPP is more significant than climate change. Human activities are the main factors affecting vegetation restoration and degradation in Sichuan Province. However, the positive contribution to NPP change is less than climate change; (3) the future vegetation NPP change trend in Sichuan Province is mainly rising, and the same direction change trend is much larger than the reverse change trend. The areas with an increasing trend in the future account for 89.187% of the total area. This research helps understand the impact of climate change and human activities on vegetation change in Sichuan Province. It offers scientific bases for vegetation restoration and ecosystem management in Sichuan and the surrounding areas.

Effects of Extreme Weather Events on Nitrous Oxide Emissions from Rice-Wheat Rotation Croplands.

Cropland ecosystems are significant emission sources of N2O, but a limited number of studies have focused on the impact of extreme weather events on N2O fluxes from cropland. This present study integrated field observations and model simulations to explore the responses of N2O fluxes to extreme weather events in typical rice and wheat rotation croplands in the middle and lower reaches of the Yangtze River (MLRYR) in China. The findings revealed that the studied rice-wheat rotation cropland exhibited a net source of N2O over the three-year monitoring period, with annual cumulative N2O emissions ranging from 190.4 to 261.8 mg N m-2. N2O emissions during the rice and wheat growing seasons accounted for 29% and 71% of the total yearly emissions, respectively. Extreme heat events led to a 23% to 32% increase in observed N2O emissions from cropland. Observed N2O emissions from irrigated rice fields during extreme precipitation events were 45% lower than those during extreme drought events. In contrast, extreme precipitation events raised observed N2O emissions from rain-fed wheat fields by 36% compared to the multi-year average, while extreme drought events reduced N2O emissions from wheat fields by 20%. Regional simulations indicated that annual cumulative N2O emissions from croplands in the MLRYR are projected to increase from 207.8 mg N m-2 under current climate to 303.4 mg N m-2 in the future. Given the episodic nature and uncertainties associated with N2O emissions from cropland, further validation is necessary for utilizing the model to explore the effects of extreme weather events on N2O in cropland ecosystems.

Response of Grassland Vegetation Growth to Drought in Inner Mongolia of China from 2002 to 2020

Drought poses a significant environmental risk and can deeply affect the growth of grasslands. However, there is still uncertainty regarding the precise impact of varying levels of drought on grassland growth. To address this gap, we utilized several key indicators, including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Global Orbiting Carbon Observatory-2-based Solar-induced Chlorophyll Fluorescence (GOSIF), and Gross Primary Productivity (GPP), in conjunction with drought indices (the Standardized Precipitation Evapotranspiration Index (SPEI) and soil moisture (SM). Our study aimed to comprehensively assess the consistency of spatiotemporal patterns in grassland vegetation and its responsiveness to different drought levels in the Inner Mongolia region from 2002 to 2020. The results indicated that NDVI, EVI, GOSIF, and GPP in grassland vegetation across Inner Mongolia exhibited significant increasing trends from 2002 to 2020. Specifically, NDVI, EVI, GOSIF, and GPP all displayed consistent spatial patterns, with 25.83%, 21.18%, 22.65%, and 48.13% of the grassland area showing significant increases, respectively. Drought events, as described through SPEI and SM, from June 2007 to September 2007 and June 2017 to July 2017 were selected to evaluate the response of grassland vegetation to drought. The drought events of 2007 and 2017 resulted in reductions in NDVI, EVI, GOSIF, and GPP relative to the multi-year average (2002–2020). GOSIF exhibited a more intense response to drought, suggesting that GOSIF may reflect the inhibition of water stress on grassland photosynthesis better than NDVI and EVI for the drought in 2007 and 2017. The reductions in NDVI, EVI, GOSIF, and GPP in grassland increased significantly across different drought levels, with the sharpest reductions observed during extreme drought. Under the severe and extreme drought events, the most substantial reductions in NDVI, EVI, GOSIF, and GPP were observed in the temperate steppe (TS). Moreover, the effects of different drought severity levels within the same grassland type varied, with the most significant reductions in NDVI, EVI, GOSIF, and GPP observed during extreme drought. Our results provide new perspectives for developing and implementing effective strategies to address grassland carbon cycling management and climate change in Inner Mongolia.

A single-point modeling approach for the intercomparison and evaluation of ozone dry deposition across chemical transport models (Activity 2 of AQMEII4).

A primary sink of air pollutants and their precursors is dry deposition. Dry deposition estimates differ across chemical transport models, yet an understanding of the model spread is incomplete. Here, we introduce Activity 2 of the Air Quality Model Evaluation International Initiative Phase 4 (AQMEII4). We examine 18 dry deposition schemes from regional and global chemical transport models as well as standalone models used for impact assessments or process understanding. We configure the schemes as single-point models at eight Northern Hemisphere locations with observed ozone fluxes. Single-point models are driven by a common set of site-specific meteorological and environmental conditions. Five of eight sites have at least 3 years and up to 12 years of ozone fluxes. The interquartile range across models in multiyear mean ozone deposition velocities ranges from a factor of 1.2 to 1.9 annually across sites and tends to be highest during winter compared with summer. No model is within 50 % of observed multiyear averages across all sites and seasons, but some models perform well for some sites and seasons. For the first time, we demonstrate how contributions from depositional pathways vary across models. Models can disagree with respect to relative contributions from the pathways, even when they predict similar deposition velocities, or agree with respect to the relative contributions but predict different deposition velocities. Both stomatal and nonstomatal uptake contribute to the large model spread across sites. Our findings are the beginning of results from AQMEII4 Activity 2, which brings scientists who model air quality and dry deposition together with scientists who measure ozone fluxes to evaluate and improve dry deposition schemes in the chemical transport models used for research, planning, and regulatory purposes.