Irrigation-driven greening is essential for northwest China’s dryland ecosystems, where vegetation growth depends on key hydrological factors, including precipitation (PRE), evapotranspiration (ET), soil moisture (SM), and irrigation water use (IWU), which affect water availability to a certain extent. To assess greening sustainability, a 1 km IWU dataset was created for 2001–2022 by combining remote sensing and ancillary data using machine learning, overcoming limited irrigation records. By linking IWU with the normalized difference vegetation index (NDVI) and analyzing trends in irrigated areas, we implemented a regional zonation approach to identify specific risk areas and evaluated both greening sustainability and vegetation responses using water balance (WB) and various hydrological variables. The results show that NDVI has increased widely over the past two decades, with sustained positive WB and stable irrigation, indicating improved water availability. However, spatial differences exist: 35.98% of irrigated areas have rising NDVI but falling IWU, especially in the east, where higher NDVI, IWU, WB, PRE, and ΔSM (soil moisture difference between growing season end and start) reflect favorable climate and hydrology; attention should also be directed toward potential deep percolation and saline sinks. In contrast, areas with high IWU often displayed elevated NDVI but declining water availability, suggesting unsustainable greening due to excessive irrigation. In addition, the SCDIWU-SCDNDVI class dominates among significant NDVI-IWU trends, indicating potential for sustainable irrigation under certain drought and climate conditions. Overall, the northwestern portion of the study area exhibits the lowest water availability; cities such as Urumqi warrant particular attention. These findings identify at-risk areas and those with better water resilience, supporting targeted water–vegetation management.

Monitoring the hydrochemistry of groundwater and the H-O isotopes in the Jingpo Lake volcanic area, China, is fundamental to studying the mechanisms of volcanic and seismic events, as well as the associated hazards. To study the hydrogeochemistry of fluids in the Jingpo Lake volcanic area, water samples from seven sites were tested for hydrogeochemistry, H-O isotopes, and radon (Rn) content. The genesis and evolution of the groundwater system were elucidated through an integrated approach employing Gibbs diagrams, ionic ratio analyses, reservoir temperature estimation (silica–enthalpy method), and inverse geochemical modeling with PHREEQC. The results showed that the dominant water chemistry type was HCO3−, primarily influenced by volcanic rock weathering and deep hydrothermal activity. Spring and well water were influenced by cation exchange, adsorption, and rock weathering dissolution. The H-O isotope composition and radon content indicate that atmospheric precipitation is the main source of supply, while well water is influenced by deep fluids. According to the Na-K-Mg triangle diagram, most of the groundwater was shallow and immature, whereas the well water was partially balanced. The temperature of the geothermal water was controlled by the geothermal gradient, depending on its occurrence and circulation depth. Additionally, the equilibrium temperature of the thermal reservoir was calculated using the silica–enthalpy equation method, with the concentrations of dissolved components in the water taken into account. The temperature of the thermal reservoir of the well water and the depth of groundwater circulation were estimated. The original reservoir temperature in the study area was calculated to range from 108 °C to 156 °C, with a geothermal water-to-shallow groundwater mixing ratio of between 71% and 85%. The estimated shallow temperature ranged from 64.9 °C to 74.9 °C. These hydrogeochemical signatures reflect active water–rock interactions and the contribution of deep-seated geothermal fluids, providing robust evidence for ongoing geothermal activity in the Jingpo Lake volcanic system. The findings enhance our understanding of the recent geological evolution and present-day hydrothermal processes of this potentially active volcanic field, which establishes a crucial hydrogeochemical baseline for future monitoring and hazard assessment studies.

Objective: (1) To determine the population dynamics of R. microplus ticks in cattle under three grazing systems in the humid tropics; (2) to compare the effect of the grazing system on R. microplus infestations in cattle; and (3) to correlate R. microplus infestations in each treatment with environmental variables. Methodology: The experiment was conducted from April 2023 to March 2024 in Tlapacoyan, Veracruz, Mexico. Thirty heifers (3/4 Brahman × 1/4 Holstein), aged 4–7 months, were used and allocated to three experimental groups (n = 10; grazing systems). The animals in groups 1 and 2 were managed under rotational grazing with 30 (SP30) and 45 days of rest (SP45), respectively, whereas those in group 3 were kept under a continuous grazing system (no rest) (SP00). Tick counts on the animals were performed every 14 days. Daily measurements of ambient temperature (TA), precipitation (PP), and relative humidity (RH) were recorded. Results: During the evaluation year, five peaks in R. microplus population dynamics were observed in the calves of groups SP30 and SP00, whereas only two peaks were recorded in the SP45 group. The tick population peaks in all three groups occurred mainly during the dry and rainy seasons. Animals in the SP45 group had the lowest infestation rates of R. microplus throughout the year (P < 0.0001). In contrast, animals in the SP30 group showed the highest infestations (P < 0.0001). Conclusions: Grazing management and pasture rest periods in the humid tropics influence the population dynamics of R. microplus in cattle

Permafrost thickness serves as a critical indicator of hydrogeological conditions in cold regions and significantly influences the safety of engineering infrastructure. Due to the combined effects of climate, ecology, and human activities, the thermal characteristics and spatial distribution of permafrost in the Greater and Lesser Khingan Mountains of Northeast China exhibit high complexity, rendering existing permafrost thickness estimation methods largely inapplicable in this region. We developed an integrated estimation framework that bridges the gap between limited deep ground temperature measurements and regional-scale mapping. To overcome the scarcity of deep borehole (>20m) data, a physical-statistical inversion method was employed to derive permafrost base depths from shallow borehole temperature profiles, thereby expanding the foundational dataset to 104 representative sites. Integrating these ground observations with satellite-derived products (e.g., MODIS NDVI) and auxiliary environmental covariates (e.g., DEM-based topography and gridded climatic data), a Random Forest algorithm (RF) was applied to generate a 1 km-resolution permafrost thickness distribution map across Northeast China with a classification accuracy of 0.74. The results indicate that the average permafrost thickness in the study area is 47.71 ± 10 m, exhibiting a spatial pattern of thicker in the north and west, thinner in the south and east, and greater in mountainous areas than in plains. The top three influencing factors of permafrost thickness are atmospheric precipitation, surface thawing degree days (TDDs), and topographic position index (TPI), revealing that the thickness of discontinuous permafrost in northeastern China is primarily governed by local factors such as soil moisture, represented by the thick permafrost existed under a small patch of ground surface. This study provides a new methodological framework for estimating permafrost thickness in regions with limited ground temperature gradient measurement in deep boreholes.

The diurnal sea surface temperature variation (DSV) influences atmospheric convection and precipitation through air–sea interactions in the equatorial Pacific. Deep learning-based DSV forecasting has been less explored compared to traditional methods, presenting the potential for a substantial leap in forecast accuracy. In this study, a forecast model is developed for 24 h DSV in the equatorial Pacific using an improved coupled Transformer-CNN (CoTCN-DSV) by incorporating a new loss function including maximal and minimal values. The CoTCN-DSV forecasts diurnal variation in SST at the interval of 3 h based on 3 h SST from the WHOI dataset. The CoTCN-DSV captures DSV well with root mean square error (RMSE) of DSA below 0.03 °C/0.13 °C at 3 h/12 h lead times and maintains high forecast skill with the temporal correlation coefficient (R) of 0.78 at the lead times of 12 h in the equatorial Pacific. The CoTCN-DSV reduces RMSE for daily max/min SST by 10.9% and 12.8% due to replacing the new loss function, then significantly improving DSV forecast. There are systematic SST biases in the WHOI dataset and this leads to relatively large RMSEs when DSV forecasts trained using WHOI are evaluated against TAO observations. Replaced WHOI SST by TAO SST, the forecasted DSA RMSE by CoTCN-DSV is reduced by an average of 43%. This confirms that the CoTCN-DSV has good generalization ability and high-quality data are important to advance the forecast accuracy. These finding show that CoTCN-DSV has the potential to forecast extreme values for different scenarios.

The Qinghai-Xizang Plateau hosts more than 50% of China's lakes, making the investigation of their hydrochemical characteristics and controls essential for elucidating regional water cycles and climate-hydrosphere interactions. While prior studies have examined broad hydrochemical patterns of Qinghai-Xizang Plateau lakes, systematic analyses distinguishing the unique signatures of different lake types-particularly within the Qinghai Province-remain scarce. This study presents a comprehensive assessment of 12 lakes (freshwater, saline, and salt lakes) in northeastern Qinghai-Xizang Plateau, integrating surface water and sediment porewater sampling. The results show the following: (1) In northeastern Qinghai-Xizang Plateau, for all three lake types, ion concentrations in sediment porewater are consistently higher than those in the overlying water, forming a near-bed concentration gradient. Salt lakes exhibit the highest absolute ion concentrations and the lowest spatial coefficient of variation. (2) All lakes are alkaline. Dominant cations and anions in freshwater and saline lakes are Ca2+, Na+, and SO42-, whereas salt lakes exhibit elevated proportions of Cl-, SO42-, Ca2+, and Mg2+. (3) Lake hydrochemistry is predominantly classified as either SO4-Ca·Mg or Cl-Na types. Freshwater and saline lakes are primarily characterized as SO4-Ca·Mg type, while salt lakes show notable differences between overlying water and porewater compositions. Lake water chemistry is primarily controlled by silicate weathering, carbonate dissolution, and evaporation-crystallization processes, with minimal influence from atmospheric precipitation across all lake types across the northeastern Qinghai-Xizang Plateau. This research provides crucial hydrochemical baselines for the understudied lake systems of the northeastern Qinghai-Xizang Plateau, offering vital insights into the ongoing hydrogeochemical responses to climate change on the Qinghai-Xizang Plateau.

The North China Plain is one of the largest plains in China, where domestic water supply, agricultural irrigation, and industrial production rely on groundwater resources. Groundwater quality is increasingly affected by the combined effects of intense human activity and geological conditions. To ensure sustainable groundwater utilization, it is crucial to investigate the hydrogeochemical processes linked to hydrogeological conditions. In this study, 85 samples were collected from cold wells and 56 samples from geothermal wells in North China. By integrating self-organizing mapping (SOM), hydrochemical and isotopic analysis, nitrate distribution, water quality index (WQI), and human health risk assessment (HHRA) methodologies, we systematically evaluated the spatial variability of groundwater quality and the associated health risks in the region. Hydrochemical analysis indicates that groundwater recharge is primarily driven by atmospheric precipitation. Shallow cold groundwater in Cluster 1 exhibited a mixed phase, whereas geothermal water in Clusters 2 and 3 and cold groundwater in Cluster 4 predominantly displayed a Na-Cl type. Cation exchange processes are the primary factors controlling ion composition. Water quality assessment studies indicate that 75.15% of the groundwater is suitable for drinking. The average water quality index of the geothermal water was higher than that of the cold water. Shallow groundwater in plains is significantly affected by agricultural activities, typically manifested as elevated NO3− concentrations. Arsenic and boron are the primary non-carcinogenic risk pollutants in geothermal water, and children are more vulnerable than adults. The non-carcinogenic risk zones for cold wells were primarily distributed in Shijiazhuang, Baoding, and the coastal areas downstream of the Yellow River. Tianjin has high-risk geothermal water. Therefore, effective strategies must be implemented to protect this valuable water resource and achieve sustainable development in the region.

Groundwater contamination in chemical industrial parks (CIPs) is a significant threat to global water security due to spills, leaks, and discharges, as well as the complexity of concealing a diverse range of industrial pollutants. In this article, we collected 30 groundwater samples from zones of presumed influence across a CIP, including upstream background, within-park, periphery, and downstream, located in Luxian County, Sichuan, China. We employed excitation–emission matrix (EEM) fluorescence spectroscopy with parallel factor analysis (PARAFAC) coupled with comprehensive hydrochemical analysis to deconvolve the dissolved organic matter (DOM) signature and statistically link its fluorescent components to specific hydrogeochemical processes and anthropogenic sources. Results revealed that industrial activities have transformed the groundwater to Ca-HCO3·Cl and Ca·Na-HCO3·Cl types from the hydrochemical facies comprising Ca-HCO3 and Ca·Mg-HCO3 types. Hydrogeology and groundwater chemistry depend primarily on weathering and atmospheric precipitation, but industrial effluents and evaporation concentration also significantly affect them. EEM-PARAFAC identified three dominant fluorescent components: fulvic-like (C1), humic-like (C2), and tryptophan-like (C3), with the latter serving as a sensitive indicator of recent anthropogenic inputs. The spatial distribution of these components, particularly the enrichment of C3, is primarily governed by anthropogenic inputs (e.g., sewage leakage), modulated by local hydrological conditions. This work demonstrates the integration of optical spectroscopy with conventional hydrochemistry for source apportionment in complex industrial settings. It provides a mechanistic understanding of pollution propagation and a practical, rapid diagnostic tool for targeted groundwater protection in CIPs.

From qualitative prediction to quantitative insight: combined meteorological patterns and regional dynamics of severe fever with thrombocytopenia syndrome in Liaoning Province, China, 2010–2024

Northwestern Jiangxi Province is rich in metasilicic acid (as H2SiO3) mineral water resources. Investigating their hydrogeochemical characteristics and formation mechanism is crucial for the rational utilization of water resources and the sustainable development of the local mineral water industry. Taking the Taoping water source area in northwestern Jiangxi as a case study, 11 sets of groundwater and surface water samples were systematically collected. By comprehensively applying mathematical statistics, ionic ratios, and isotopic analyses, the hydrogeochemical characteristics and formation processes of metasilicic acid-type mineral water were examined. The results indicate that: (1) The mineral waters in the area are weakly alkaline and belong to the metasilicic acid type, with concentrations ranging from 22.0 to 67.0 mg/L, of which 75% exceed 30 mg/L. (2) The primary hydrochemical types are HCO3−–Ca·Na, HCO3−–Ca·Mg, and HCO3−–Ca. Analysis of stable isotopes (δ18O and δ2H) and tritium (3H) indicates that metasilicic acid mineral water is primarily recharged by atmospheric precipitation, with an apparent groundwater age of approximately 60 years. (3) The enrichment of metasilicic acid primarily results from the weathering and leaching of silicate minerals, coupled with cation exchange. K+ and Na+ are mainly derived from silicate minerals such as feldspars and halite, whereas Ca2+ and Mg2+ originate primarily from carbonate minerals like calcite and dolomite. During recharge, atmospheric precipitation infiltrates the aquifer, dissolving aluminosilicate and siliceous minerals in the surrounding rocks, thereby releasing metasilicic acid into the groundwater and ultimately forming the metasilicic acid-type mineral water.

Abstract In recent decades, high demand from stakeholders and policymakers has intensified research efforts toward operational seasonal forecasts. This is particularly relevant when it comes to El Niño-Southern Oscillation (ENSO), the primary driver of year-to-year climate variability with global socioeconomic impacts. While various state-of-the-art systems effectively target ENSO forecasting, the relative influence of different modeling approaches on prediction skill remains incompletely understood. Here, we evaluate an upgraded version of the Norwegian Climate Prediction Model (NorCPM), which employs anomaly initialization and ocean-only data assimilation (DA), against Copernicus Climate Change Service (C3S) systems using full-field DA in both atmosphere and ocean components. Through systematic comparison, we examine the spatio-temporal prediction skill of sea surface temperatures (SST) in the tropical Pacific and associated teleconnection patterns. Despite its ocean-only constraint and lower resolution, NorCPM demonstrates competitive performance for ENSO’s SST signature. However, the system exhibits systematic amplitude errors in representing mid-latitude atmospheric circulation and precipitation patterns. Our comparative analysis reveals three key findings: (1) ocean-only initialization achieves comparable skill to comprehensive approaches during the mature phase of ENSO; (2) model resolution impacts forecast quality in the eastern Pacific boundary region, where fine-scale ocean-atmosphere coupling processes are crucial, particularly during transition seasons; and (3) initialization strategy more strongly influences the representation of global teleconnection patterns than local ENSO dynamics. While targeted experiments are encouraged to establish causal links between specific model features and prediction capabilities, these results provide practical insights for optimizing prediction systems, particularly for applications requiring accurate precipitation predictions in regions affected by ENSO.

Understanding how global climate drivers manifest at regional scales is critical for designing targeted adaptation strategies, particularly in vulnerable mountainous countries. This study provides an integrated assessment of atmospheric CO2 concentrations, surface temperature, and precipitation trends at both global and Armenian levels from the early 20th century to 2024. Using long-term observational datasets and ordinary least squares regression models with HAC-robust errors, this study quantifies the magnitude and statistical significance of historical climate shifts. Results confirm pronounced global warming (+0.021 °C/year) alongside a moderate rise in global precipitation (+1.13 mm/year). Armenia, however, exhibits substantially accelerated warming (+0.052 °C/year) coupled with a non-significant and spatially heterogeneous precipitation trend, including notable declines in humid regions. CO2 emissions per capita strongly predict temperature change both globally (0.59 °C/ton) and, even more prominently, in Armenia (1.33 °C/ton), indicating heightened regional climate sensitivity. These findings align closely with Armenia’s Fourth National Communication to the UNFCCC, reinforcing the robustness of the analysis. By revealing how global climate forcings translate into region-specific outcomes—and by discussing the emerging thermal contribution of digital infrastructure—this study underscores the urgency of localized climate adaptation, water resource planning, and agricultural resilience measures.

Abstract Microwave imagers play a key role in numerical weather prediction (NWP), providing information about atmospheric humidity, temperature, cloud, and precipitation, as well as surface information such as skin temperature and sea ice. The atmospheric information has been directly assimilated from radiances for many years, but until recently much of the surface information has come to NWP systems through external retrievals and analyses. The aim now is to extract all available information within the NWP process, directly from radiances, using coupled data assimilation. The scientific benefits should include more complete and consistent use of the surface information in NWP. The practical benefits include the elimination of significant time delays between the microwave observations and when the information reaches the NWP system. The focus of the current work is to infer ocean surface skin temperature from microwave observations using a sink variable approach within the atmospheric data assimilation system, with the future aim of transferring this information to the ocean data assimilation. Improvements in the skin temperature are seen particularly in the region of tropical instability waves, and the updated skin temperature allows an improved simulation of the microwave brightness temperatures.

The vine phenological development is closely related to the regional climatic specifics. Changes in temperature, precipitation, and other factors can significantly affect the course of individual phenophases. The red wine varieties Cabernet Dorsa and Cabernet Mitos, characterized by increased resistance to fungal diseases and low winter temperatures, have been selected for specific conditions of countries with a cool climate and a short growing season. The presence of valuable economic and technological qualities in these varieties justifies research related to their regenerative and reproductive performance under conditions of a long growing season and extreme temperatures. The phenological phases and their duration were monitored – bud burst, first leaf appearance, first inflorescence appearance, flowering, veraison, and the onset of technological ripeness. Climatic and soil indicators were recorded - average air temperature, relative atmospheric humidity and precipitation, soil humidity and temperature, while the total and active temperature sum for the onset of the main phases and the entire growing season was determined. The period from bud burst to technological ripeness lasted from 141 to 157 days, which is 30 to 50 days less than the most widely distributed local and introduced varieties. The active temperature sum required to reach technological ripeness is between 1655°C and 1831.1°C. Varieties have low thermal requirements and good adaptation to the climatic conditions of the semi-continental climate. The data would serve to determine suitable terroirs for growing these varieties, depending on the set agrotechnical and technological goals. Keywords: PIWI, phenology, climate change, vegetation, terroir

Urbanization has caused the groundwater environment to face varying degrees of threats， among which nitrate pollution of groundwater is one of the problems that cannot be ignored. To research the sources of nitrate pollution and biogeochemical processes in the karst groundwater system under the influence of urbanization， 86 karst groundwater samples were collected from the Guiyang urban area from July to August 2021， and their major water chemical indicators and dual nitrate isotope composition （δ15N-NO3- value and δ18O-NO3- value） were measured. The MixSIAR model was used to analyze the contribution rate of different pollution sources of nitrate in the groundwater. The results showed that the main hydrochemical facies of karst groundwater in the study area was HCO3-Ca type. Nitrate concentrations ranged from 0.1 to 74.6 mg·L-1， with an average value of （21.1±17.3） mg·L-1； δ15N-NO3- values ranged from -1.1‰ to 37.7‰， with an average value of （10.8±5.1）‰； and δ18O-NO3- values varied from -10.1‰ to 17.8‰， with an average value of （3.3±3.7）‰， suggesting that nitrate in the groundwater mainly came from fecal sewage and soil organic nitrogen. The δ18O-NO3- values of the groundwater fell on both sides of the nitrification theoretical value line， indicating that a biogeochemical process dominated by nitrification occurred in the groundwater system. The ratio of （δ15N-NO3-）∶（δ18O-NO3-） for the groundwater was 0.15， which was much lower than the denitrification ratio ranging from 1.3 to 2.1， suggesting that no denitrification had occurred in groundwater in the study area. The contribution rate of manure and sewage to nitrate sources in groundwater in the study area was as high as 48.6%， and soil organic nitrogen， chemical fertilizers， and atmospheric precipitation were 22.7%， 21.6%， and 7.1%， respectively. The research results provide theoretical basis for the prevention and control of nitrate pollution in urban groundwater.

In this work, V-Pt-Ti catalysts were synthesized employing impregnation (IP), precipitation (PC), sol-gel (SG), thermal decomposition (TD), and hydrothermal (HD) methods. A systematic study has been carried out to investigate impacts of various preparation methods on the performance of NH3 selective catalytic oxidation (SCO) at temperatures from 150 °C to 450 °C. N2 adsorption/desorption, XPS, XRD, H2-TPR, NH3-TPD, O2-TPD, SEM, TEM, and in situ DRIFTS were adopted to characterize the physico-chemical property of V-Pt-Ti catalysts. The results suggested that V-Pt-Ti catalysts synthesized by precipitation methods (denoted as VPT-PC) exhibited notably better SCO performance across the 150-450 °C temperature range compared with those produced by impregnation (IP), sol-gel (SG), thermal decomposition (TD), and hydrothermal (HD) methods. The outstanding performance of the VPT-PC catalyst could be ascribed to its larger surface area, higher relative contents of Pt0, V5+, and Oα, more abundant surface acid sites, and better redox property. In situ DRIFTS results suggested that NO2 species could participate in NH3 oxidation reaction on the surface of the VPT-PC catalyst, which was beneficial for improving the SCO activity.

Landscape science is a key branch of geography that examines the structure, development, and functioning of natural territorial complexes. As interactions between nature and society intensify, landscapes need to be studied not only through individual components but also as integrated systems. This paper systematizes the conceptual foundations of a systems approach in landscape science and outlines the role of landscape ecology in evaluating ecological functions, stability, and responses to anthropogenic pressure. Landscapes are treated as open systems connected to the external environment through exchanges of matter and energy, driven primarily by solar energy, atmospheric precipitation, and biological processes. The synthesis highlights key analytical tasks—assessing landscape stability, identifying ecological risks, regulating anthropogenic loads, and supporting rational use of natural resources—as a scientific basis for sustainable development. The systems approach is presented as practically important for landscape planning, territorial development management, and ecological monitoring.

Abstract Atmospheric rivers (ARs) are major drivers of orographic precipitation along the mountainous west coasts and often involve embedded convection delivering extreme local bursts of rainfall associated with flash floods. Understanding the convection within ARs is critical for assessing flood risks and improving precipitation forecasts and numerical weather models. This study evaluates the sensitivity of the 2019 Valentine’s Day atmospheric river precipitation in California to Grell–Freitas and Tiedtke convective schemes using the Weather Research and Forecasting (WRF) Model. Ensemble simulations at a 9-km horizontal resolution—a scale within the gray zone where some convection is resolved but parameterization remains necessary—are compared with seven observational datasets, including ground- and satellite-based products. Over the ocean, WRF successfully captures key synoptic features and the overall distribution of total precipitation due to the dominance of stratiform precipitation. Convective precipitation, however, is simulated differently: Grell–Freitas produces it within the AR, whereas Tiedtke simulates it west of the AR. Given the challenges in diagnosing AR-embedded convection at 9 km, an alternative method based on outgoing longwave radiation and precipitation rate thresholds is tested. The alternative convective precipitation aligns with observations, occurring within the AR over the ocean, coastal mountains, and Sierra Nevada. Using the alternative convective method, the overall pattern of convective precipitation is generally consistent between the Grell–Freitas and Tiedtke schemes. This agreement contrasts with the patterns of parameterized convective precipitation. These results are relevant for global models or regional large ensembles approaching gray-zone resolution, guiding diagnostics of embedded convection in ARs.

Distinguishing anthropogenic perturbations from natural carbon cycling in mega-rivers remains a critical challenge. Carbon isotopes (δ13CDIC) provide a powerful tracer to decode these complex interactions which are often masked in traditional hydrochemical assessments. This study investigated the hydrochemistry and multiple stable isotopes (δD, δ18O and δ13CDIC) of the Yangtze River surface water (YRSW) during the dry season to quantify these contributions. δD values ranged from -117.8‰ to -44.6‰ and δ18O from -16.3‰ to -7.0‰, aligning with the Global Meteoric Water Line, which confirms atmospheric precipitation as the primary water source shaped by continental and altitude effects. DIC concentrations ranged from 1560 to 5724.29 µmol L-1 (mean 2993.79 µmol L-1), acting as a net CO2 source with an average pCO2 of 522.08 µatm. Stoichiometric and isotopic analyses reveal that carbonate weathering dominated by soil CO2 is the primary DIC source. However, in the middle and lower reaches, anthropogenic sulfuric/nitric acid weathering and organic matter oxidation were identified as key drivers decoupling DIC from natural climatic controls. This study systematically reveals for the first time the spatial differentiation patterns of DIC and δ13CDIC on the scale of the entire Yangtze River Basin and the main controlling factors, providing a new perspective for the study of carbon cycle in large river basins under high-intensity human activity interference.

Global warming continues to intensify and continues to exert its harmful effects in all regions. The South Caucasus region, especially the Republic of Azerbaijan, is facing an increasing risk to human and food security as water resources are decreasing year by year under the influence of climate change. The Kura intermountain depression, where the country’s large agricultural regions are located, is one of the areas most affected by this crisis. The purpose of the study is to determine the current state of the precipitation regime in the Kura intermountain depression and the influence of climate change on it. In the article, the characteristics of modern time-space changes of atmospheric precipitation in the natural province of the Kura depression were analyzed. In the analysis, we used observational precipitation data from 15 hydrometeorological stations covering the period 1961–2023. The research was conducted with modern mathematical-statistical, physical and cartographic methods. Extreme precipitation events were also analyzed for the same period (1961–2023). In addition, modern time-space distributions of atmospheric precipitation (monthly, seasonal, annual, surface) for the years 1991-2023 were identified. Spatial distribution of precipitation was mapped using GIS technologies. In the Kura depression, the average annual precipitation in 1991-2023 was 285 mm. 44% of these precipitations fell in the warm half of the year and 56% in the cold half of the year. In the warm half of the year, the amount of annual precipitation increases from the southeast to the northwest of the province, and in the cold period, the reverse pattern applies. The amount of annual precipitation in the province decreased by 9% (27 mm) in 2011-2023 compared to the climate norm (1981-2010). In this province, the absolute maximum quantity of precipitation was higher than the climate norm (1981-2010) by 7-8% for the years 1961-1970 and 2001-2010, 17% for 1971-1980, 4% for 1981-1990, 8% for 1991-2000s and lower by 23% for 2011-2020. More recurrence of annual precipitation occurs in the precipitation gradation of less than 10 mm, 11-20 mm, 21-30 mm, and 31-40 mm. The results indicate that increasing air temperatures are associated with reduced precipitation and increased potential evaporation. If the climate changes in the Kura depression continue with this dynamic, the decrease of precipitation and humidity in the large agricultural regions of the republic will lead to an increase in major environmental crises (drought, salinization, etc.).