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Tropical cyclones (TCs) can cause severe wind and rain hazards. Unusual TC tracks and their extreme precipitation forecasts have become two difficult problems faced by conventional models of primitive equations. The case study in this paper finds that the numerical computation of the climatological component in conventional models restricts the prediction of unusual TC tracks. The climatological component should be a forcing quantity, not a predictor in the numerical integration of all models. Anomaly-based variable models can overcome the bottleneck of forecast time length or the one-week forecasting barrier, which is limited to less than one week for conventional models. The challenge in extreme precipitation forecasting is how to physically get the vertical velocity. The anomalous moisture stress modulus (AMSM), as an indicator of heavy rainfall presented in this paper, considers the two conditions associated with vertical velocity and anomalous specific humidity in the lower troposphere. Vertical velocity is produced by the orthogonal collision of horizontal anomalous airflows. [ABSTRACT FROM AUTHOR]

Gridded precipitation (PRP) data have been largely used in diagnostic studies on the climate variability in several time scales, as well as to validate model results. The three most used gauge-based PRP datasets are from the Global Precipitation Climatology Centre (GPCC), University of Delaware (UDEL), and Climate Research Unit (CRU). This paper evaluates the performance of these datasets in reproducing spatiotemporal PRP climatological features over the entire South America (SA) for the 1901–2015 period, aiming to identify the differences and similarities among the datasets as well as time intervals and areas with potential uncertainties involved with these datasets. Comparisons of the PRP annual means and variances between the 1901–2015 period and the non-overlapping 30-year subperiods of 1901–1930, 1931–1960, 1961–1990, and the 25-year subperiod of 1991–2015 for each dataset show varying means of the annual PRP over SA depending on the subperiod and dataset. Consistent patterns among datasets are found in most of southeastern SA and southeastern Brazil, where they evolved gradually from less to more rainy conditions from 1901–1930 to the 1991–2015 subperiod. All three datasets present limitations and uncertainties in regions with poor coverage of gauge stations, where the differences among datasets are more pronounced. In particular, the GPCC presents reduced PRP variability in an extensive area west of 50° W and north of 20° S during the 1901–1930 subperiod. In monthly time scale, PRP time series in two areas show differences among the datasets for periods before 1941, which are likely due to spurious or missing data: central Bolivia (CBO), and central Brazil (CBR). The GPCC has less monthly variability before 1940 than the other two datasets in these two areas, and UDEL presents reduced monthly variability before 1940 and spurious monthly values from May to September of the years from 1929 to 1941 in CBO. Thus, studies with these three datasets might lead to different results depending on the study domain and period of analysis, in particular for those including years before 1941. The results here might be relevant for future diagnostic and modelling studies on climate variability from interannual to multidecadal time scales. [ABSTRACT FROM AUTHOR]

The Woka-Cuona rift zone on the southeastern side of the Qinghai-Tibet Plateau is characterized by complex geological background conditions, comprising three independent or semi-grabens that traverse from south to north across the Himalayan and Gangdise terranes. Conducting research on the distribution patterns and genesis mechanisms of geothermal resources within the Woka-Cuona rift zone has certain guiding significance for understanding the genesis mechanisms of the geothermal system in the southern Tibetan rift and its exploitation. This paper utilized methods such as data collection, ground investigations, and geochemical analyses to analyze the distribution characteristics and evolutionary processes of geothermal waters in the Cuona rift area based on the geological background conditions of the study area. The research findings demonstrate a significant correlation between the occurrence of geothermal waters in the Cuona rift zone and geological structures, with most geothermal waters primarily distributed near intersections of graben boundary faults and east–west-trending faults. Different regions exhibit variations in the intensity of geothermal activity and geochemical characteristics, with the genesis of geothermal waters associated with deep magmatic activity, characterized by Na+ and K+ as the primary cations and Cl− as the primary anions. Geothermal waters mainly originate from atmospheric precipitation and snowmelt water from surrounding mountainous areas, with recharge elevations ranging from 4500 to 6200 m and an average elevation of 5400 m. [ABSTRACT FROM AUTHOR]

In this study, spanning from 1938 to 2020, the hydrological changes in the Volga River and their repercussions on the Caspian Sea level were examined. Analysis reveals a correlation between high Volga River runoff and increased atmospheric precipitation in its basin. However, in recent years (2005–2020), a significant decline in the runoff coefficient at the Verkhneye Lebyazhie hydrological station, attributable to climate warming surpassing global temperature anomalies, has been observed. This warming's impact on river flow and sea level was quantified, resulting in a 133 cm decrease in sea level from 1977 to 2020. Notably, while, historically, Caspian Sea level changes mirrored Volga River runoff fluctuations until 2005, since 2006, the sea level has markedly dropped, decoupling from river runoff variations. Comparison with recent studies suggests that altered wind characteristics over the Caspian Sea, influencing surface evaporation, may have significantly contributed to this rapid sea level decline in recent years. [ABSTRACT FROM AUTHOR]

The intensification of anthropogenic activities (agriculture, industry, and exploitation of water resources) during urbanization has posed significant challenges to the aquatic environment, particularly in karst regions. Karst aquifers are highly susceptible to surface contaminants and exhibit minimal natural remediation capabilities. Our understanding of the anthropogenic activities involved in these sensitive karst systems remains limited. To address this gap, we conducted a comprehensive study, collecting 285 groundwater samples in Feicheng, northern China, from 1996 to 2015. The overexploitation of karst groundwater has resulted in several concerns. The whole dataset was classified into four groups according to land use. Water quality assessments revealed a yearly decline, particularly in industrial and agricultural areas. The water chemistry transitioned from Ca-Mg-HCO3 to Ca-Mg-HCO3-SO4. Such evolution was attributed to natural hydrogeochemical processes, atmospheric precipitation, and anthropogenic inputs. Natural factors included water-rock interactions (the mineral dissolution) and ion exchange. Absolute principal component scores with multiple linear regression (APCS-MLR) were used to quantitatively estimate the sources of pollution. The results showed that hydrogeological settings (recharge, runoff, and discharge) were crucial in the hydrochemistry evolution of karst groundwater systems. In agricultural areas, it is recognized that much of the NO3− accumulation in aquifers came from upstream inputs in the groundwater system, not just irrigation and fertilization. Urban areas were affected by Cl− pollution, primarily due to domestic waste. Industrial regions of recharge zones were more susceptible to atmospheric precipitation and industrial waste, with pollutants infiltrating through rainfall and degrading water quality. Mining areas exhibit higher SO42− and lower pH due to the oxidation of sulfur-containing minerals. Therefore, the rapid response and low self-purification capacity of groundwater in karst regions necessitate caution in urban planning to mitigate impacts on these fragile systems. [ABSTRACT FROM AUTHOR]

Near real-time aerosol fields from the Copernicus Atmospheric Monitoring Services (CAMS), operated by the European Centre for Medium-Range Weather Forecasts (ECMWF), are configured for use in the HARMONIE-AROME Numerical Weather Prediction model. Aerosol mass mixing ratios from CAMS are introduced in the model through the first guess and lateral boundary conditions and are advected by the model dynamics. The cloud droplet number concentration is obtained from the aerosol fields and used by the microphysics and radiation schemes in the model. The results show an improvement in radiation, especially during desert dust events (differences of nearly 100 W/m2 are obtained). There is also a change in precipitation patterns, with an increase in precipitation, mainly during heavy precipitation events. A reduction in spurious fog is also found. In addition, the use of the CAMS near real-time aerosols results in an improvement in global shortwave radiation forecasts when the clouds are thick due to an improved estimation of the cloud droplet number concentration. [ABSTRACT FROM AUTHOR]

In this study, three typical plants capable of restoring in the Heidaigou open-pit mine dump, namely, Pinus sylvestris var. mongolica, Caragana korshinskii, and Medicago sativa, were taken as the research objects. The δ2H and δ18O values of atmospheric precipitation, soil water, stem water, and leaf water were measured using the stable isotope technique, and the distribution characteristics of the δ2H and δ18O values of different water sources were identified. The IsoSource model (version1.3.1) was used to calculate the contribution rate of different water sources to the plants, and the differences and dynamic changes in the water sources for P. sylvestris var. mongolica, C. korshinskii, and M. sativa during the rainy season were examined. Results showed that the water source of the three plants was found to be mainly soil water, and the utilization of each potential water source varied in different periods of the rainy season. In June, when SWC was sufficient, P. sylvestris var. mongolica and M. sativa primarily absorbed and utilized shallow and middle soil water, with relative utilization ratios of 55.5% and 59%, respectively, while C. korshinskii has a more balanced utilization ratio of soil water in each layer, with shallow soil water utilization at 33.7%, middle soil water at 34.2%, and deep soil water at 32.2%. In August, when SWC decreased, P. sylvestris var. mongolica, C. korshinskii, and M. sativa were all transferred to deep soil water, with utilization ratios of 75.8%, 78.8%, and 71.1%, respectively. The values showed that these three typical plants are capable of restoring can respond to external water changes through the plastic transformation of water absorption sources. Among them, C. korshinskii can flexibly use soil water in each layer, has stronger survival competitiveness in drought, and can better adapt to the fragile ecological environment of a mining dump. [ABSTRACT FROM AUTHOR]

The aim of this study is to present the spatial and temporal variability of the frequency of dry and wet days and dry and wet spells against the background of changes in precipitation and atmospheric circulation. The study is based on daily precipitation totals from 46 meteorological stations in Poland from 1966 to 2023. Additionally, seven circulation indices were used, namely GBI, NAO, AO, EA, EA/WR, SCAND, and AMO. Dry days are defined as days without precipitation. Wet days are days with at least 1 mm of precipitation. It was shown that dry spells are much more common than wet spells, are longer, and cover larger areas. Long-term changes in the annual characteristics of dry and wet days and spells are not statistically significant. Only the length of the most extended dry spell in the year increases. However, there are significant changes in their annual cycles. Spring is drier; in summer, precipitation decreases in the south and increases in the north; November and December, symbols of gloomy rainy weather, are increasingly drier; and rainy weather has shifted to January and February. The impact of circulation varies according to the season, with the NAO, AO, SCAND, and GBI indices having the greatest influence. [ABSTRACT FROM AUTHOR]

The Eastern Kunlun Fault (EKF) is situated in an area with a history of significant seismic events, yet it has witnessed a dearth of major earthquakes in recent years. This study conducted a detailed analysis of the hydrogeochemical characteristics of the springs in the EKF and their temporal variation, aiming to address the gaps in the research on the hydrogeochemistry in the region and to investigate the changes in water chemistry during the seismogenic process. In this study, the main elements, trace elements, hydrogen isotopes, oxygen isotopes, and strontium isotopes of 23 springs in the EKF were analyzed. The results indicated that the groundwater recharge in the eastern part of the Eastern Kunlun Fault Zone mainly originates from atmospheric precipitation, as supported by its isotopic characteristics. The spring water is immature, showing weak water–rock interactions. A hydrochemical analysis classified the springs into 11 main types, reflecting varying degrees of water–rock interaction. Based on measurements using quartz geothermometers, the estimated geothermal reservoir temperatures ranged from 39.6 to 120.3 °C, with circulation depths of 1.3 to 3.8 km. By means of regularly monitoring three selected springs, this study also explored the relationship between earthquakes and hot spring chemical variations. Finally, a conceptual model of hydrogeochemistry was proposed to describe the groundwater circulation in the study area. [ABSTRACT FROM AUTHOR]

Surface runoff is a major component of the hydrological cycle, and it is essential for supporting the ecosystem services provided by grassland and forest ecosystems. It is of practical importance to understand the mechanisms and the dynamic processes of runoff in a river's basin, and in this study, we focused on the restored montane Pailugou Basin in the Qilian Mountains, Gansu Province, China, since its water status is extremely important for the large arid area and local economies therein. Our purpose was to determine the annual variation in the surface runoff in the Pailugou Basin because it is important to understand the influence of climate fluctuations on surface water resources and the economy of the basin. In addition, little is known about the annual variations in precipitation and runoff in this region of the world. Daily atmospheric precipitation, air temperature and runoff data from 2000 to 2019 were analyzed by the calculation of the uneven annual distribution of surface runoff, the calculation of the complete adjustment coefficient, and the vector accumulation expressed by the concentration degree. We also used the cumulative anomaly approach to determine the interannual variation trend of runoff, while the change trend was quantified by the sliding average method. Finally, we used the Mann–Kendall mutation test method and regression analysis to establish the time-series trend for precipitation and runoff and to determine the period of abrupt runoff changes. The results indicated concentrated and positive distributions of surface runoff on an annual basis, with a small degree of dispersion, and an explicit concentration of extreme flows. The relative variation ranges exhibited a decreasing trend, and the distribution of the surface runoff gradually was uniform over the year. The runoff was highest from July to September (85% of the annual total). We also determined that annual surface runoff in the basin fluctuated over the 20-year period but showed an overall increasing trend, increasing by 3.94 × 105 m3, with an average increase rate of 0.42 × 105 m3 every ten years. From 2005 to 2014, the annual runoff and the proportion of runoff in the flood season (July to September) to the annual runoff fluctuated greatly. The correlation between the runoff and precipitation was significant (r = 0.839, p < 0.05), whereas the correlation between air temperature and surface runoff was low (r = 0.421, p < 0.05). [ABSTRACT FROM AUTHOR]

Remote sensing precipitation data have the characteristics of wide coverage and revealing spatiotemporal information, but their spatial resolution is low. The accuracy of the data is obviously different in different study areas and hydrometeorological conditions. This study evaluated four precipitation products in the Yellow River basin from 2001 to 2019, constructed the optimal combined product, conducted downscaling with various machine algorithms, and performed corrections using meteorological station precipitation data to analyze the spatiotemporal trends of precipitation. The results showed that (1) GPM and MSWEP had the best four evaluation indicators, with R2 values of 0.93 and 0.90, respectively, and the smallest FSE and RMSE, with a BIAS close to 0. A high-precision mixed precipitation dataset, GPM-MSWEP, was constructed. (2) Among the three methods, the downscaling results of DFNN showed higher accuracy. (3) The results, after correction with GWR, could more effectively enhance the accuracy of the data. (4) Precipitation in the Yellow River Basin showed a decreasing trend in January, September, and December, while it exhibited an increasing trend in other months and seasons, with 2002 and 2016 being points of abrupt change. This study provides a reference for the production of high-precision satellite precipitation products in the Yellow River basin. [ABSTRACT FROM AUTHOR]

The services provided by green infrastructures may lead to a decrease in climate-related ecological, social, and health risks, especially in the urban environment. Consequently, the best guarantee to make this environment as safe as possible is to increase the extent of green areas, taking into consideration the functional importance, and climatic–ecological peculiarities of the area. These are also issues for the Republic of Armenia's (RA) capital Yerevan. There the current conditions of the green areas of Yerevan city do not meet the expected requirements of the climatic–ecological development of urban areas. The green area per capita is 8 m2, which is unevenly distributed within 12 different administrative districts of Yerevan city. The aim of this research was to study the natural climatic and ecological conditions of Yerevan city and the status of the green areas of the city. The eco-biological indicators of the trees and shrubs growing in Yerevan green areas have been assessed, and the more resilient plant species have been singled out. All 12 administrative districts of Yerevan have been mapped and the green area per capita for each administrative district has been calculated. The received data have been combined with health indicators and suggestions have been made to add green areas in Yerevan according to the functional significance and sustainability of shrubby species and to their decorative and phyto-filtration properties. The city has unfavorable climatic conditions. It is located in the northern section of the subtropical climatic zone and has a distinct dry continental climate. Temperatures above +40 °C are typical, while winter is rather cold and sometimes temperatures may drop below −20 °C (in January 2008, it dropped to −27.6 °C). The amount of atmospheric precipitation has reduced by 9%. The city is counted as one of the driest urban areas of the South Caucasus. The other unfavorable ecological conditions are heavy traffic, the city's open landfill, the concentration of industrial enterprises, large-scale construction works, etc. The atmospheric concentrations of particulate matter (PM), gases and heavy metals have been detected to exceed the permitted limits. In terms of health care, the death cases due to various diseases (acute respiratory, vascular, and cancer) have increased, which requires complex activities to reduce environmental pollution and to improve the microclimate. [ABSTRACT FROM AUTHOR]

The improvement of water usage efficiency and productivity, as well as the development of effective water management plans, necessitates a comprehensive understanding of how water utilization patterns in different soil layers within arid and semi-arid climates impact the capacity of plant roots to absorb water. However, there is currently no knowledge regarding the water use strategies employed by artificial yellow willow. So, we conducted a study on the hydrogen and oxygen isotopic composition of rainfall in yellow willow (Salix gordejevii) from the semi-arid region located at the southern edge of the Hunshandak Sandland in China. This study utilized measured data on xylem water, groundwater, soil moisture, and rainfall. By employing a combination of the direct comparison method and the MixSIAR model, we investigated the water uptake strategies employed by yellow willow throughout its growing season. The findings revealed that the mean δ D was highest in precipitation and lowest in groundwater, whereas the mean δ18O was highest in stem water and lowest in groundwater. The δ D and δ18O fluctuated significantly in precipitation but were steady in groundwater. Because precipitation was significantly less than evaporation, the slope and intercept were lower for the local than global atmospheric precipitation line. Water availability steadily declined with increasing depth. Lower δ18O values were caused by precipitation diluting the soil water. The MixSIAR results indicated that the primary source in May, September, and October was utilized at 19%, 18%, and 18%, respectively. In contrast, the utilization rate of each source varied considerably in June, July, and August (the primary source was utilized at 19%, 18%, and 18%, respectively). Comparatively high rates of water absorption and utilization were observed in June (19% of the total water source), July (18%), and August (23%). Therefore, the vertical distribution of the root system and variations in the soil water content regulate water usage for the yellow willow. To prevent excessive water usage and promote ecosystem restoration with artificial yellow willow plantations in water-limited desert settings, policy makers should consider the patterns of plant water use and soil water availability. By selecting drought-adapted plant species and optimizing irrigation management, it is possible to reduce water wastage and ensure that water is used efficiently for revegetation and ecosystem restoration, avoiding overuse of water and maintaining the sustainability of revegetation in water-stressed desert areas. [ABSTRACT FROM AUTHOR]

With the development of mathematical statistics, people have developed the spatiotemporal interpolation methods based on the spatial interpolation method or the temporal interpolation method. These methods fully consider the comprehensive effects of time series changes and spatial distribution to better handle complicated and changeable meteorological element data. This article systematically reviews the current research progress of spatiotemporal interpolation methods for spatiotemporal sampling data of meteorological origin. Spatiotemporal interpolation methods of meteorological elements are classified into three categories: spatiotemporal geostatistical interpolation methods, spatiotemporal deterministic interpolation methods, and spatiotemporal mixed interpolation methods. This article summarizes the theoretical concept and practical application of the spatiotemporal interpolation methods of meteorological elements, analyzes the advantages and disadvantages of using spatiotemporal interpolation methods for estimating or forecasting meteorological elements, combined through some measures and their application to explain the accuracy of the spatiotemporal interpolation methods; and discusses the problems and challenges of spatiotemporal interpolation. Finally, the future research focus of spatiotemporal interpolation methods is proposed. This article provides a valuable method reference for estimating or predicting meteorological elements such as precipitation in unsampled points. [ABSTRACT FROM AUTHOR]

Precipitation prediction plays a crucial role in people's daily lives, work, and social development. Especially in the context of global climate variability, where extreme precipitation causes significant losses to the property of people worldwide, it is urgently necessary to use deep learning algorithms based on radar echo extrapolation for short-term precipitation forecasting. However, there are inadequately addressed issues with radar echo extrapolation methods based on deep learning, particularly when considering the inherent meteorological characteristics of precipitation on spatial and temporal scales. Additionally, traditional forecasting methods face challenges in handling local images that deviate from the overall trend. To address these problems, we propose the METEO-DLNet short-term precipitation prediction network based on meteorological features and deep learning. Experimental results demonstrate that the Meteo-LSTM of METEO-DLNet, utilizing spatial attention and differential attention, adequately learns the influence of meteorological features on spatial and temporal scales. The fusion mechanism, combining self-attention and gating mechanisms, resolves the divergence between local images and the overall trend. Quantitative and qualitative experiments show that METEO-DLNet outperforms current mainstream deep learning precipitation prediction models in natural spatiotemporal sequence problems. [ABSTRACT FROM AUTHOR]

This publication presents the chemical compositions of carbon reducers used in the production of ferroalloys, with particular emphasis on the content of Al, Ca, Mg, Mn, Cr, Ti, P, or S in their mineral substances. A certain amount of these elements is transferred to the produced alloys, creating inclusions and precipitations in the metal structure. Most of them are undesirable and adversely affect the final effect of the production process. The work includes sample images of the microstructure of alloys, the chemical compositions of the samples at the test point, and diffragrams. On the basis of the obtained metallographic data, technological observations are made, especially of the crushing process and the amount of grain fractions obtained. [ABSTRACT FROM AUTHOR]

In February 1963, a huge landslide (ca. 1,950,000 m3) blocked the Visočica River and, thus, formed Zavoj Lake. The primary objective of this research was to investigate the importance of snowmelt in relation to landslide occurrence and to define the critical climatic conditions that may trigger massive winter landslides. We used monthly precipitation and average monthly maximum temperature data from meteorological and precipitation stations in the Visočica River basin (Dojkinci) and in the immediate proximity of Lake Zavoj (Pirot, Dimitrovgrad and Topli Do) as data inputs to the Snow-Melt Landslide (SML) index. It considers the summed monthly precipitation for previous months that continuously have an average maximum temperature below 0 °C. According to this method, the event at Zavoj Lake stands out among all other precipitation and snowmelt values for the past 72 years. After applying the SML index, all stations showed values of >300 mm for February 1963, which we consider as the threshold value for potential landslides appearance. In addition to meteorological data, we applied the SML index to data from the Coordinated Regional Downscaling Experiment (CORDEX) regional climate model outputs for the region from 2022 to 2100. As expected, climate change will have influenced the temperature values, especially during the winter. Conversely, the study area is experiencing drastic changes in land use caused by depopulation, leading to a reduced risk of winter landslides in the Visočica basin. We suggest that future climatic conditions in the area will make it more likely to experience extreme summer precipitation events, which might trigger large landslides. The SML method can be implemented for all landscapes that experience snowy winters, providing information in a timely manner so that local residents can react properly when the probability of landslide occurrence rises. The SML index, grounded in essential meteorological principles, provides a tailor-made, data-driven methodology applicable across varied geographical settings. Its utility extends to mitigating hydro-meteorological hazards on scales ranging from local to national scales, offering diverse and effective early warning solutions. [ABSTRACT FROM AUTHOR]

In this study, carbon and nitrogen contents in the undisturbed terrestrial ecosystems in the northern taiga zone of Russia's Murmansk region were estimated. The goal of this study was to examine the carbon and nitrogen dynamics in atmospheric precipitation, assimilating organs of coniferous trees (Picea obovata and Pinus sylvestris), needle litter, soils, and soil water. The objects of our research were the most common dwarf shrub-green moss spruce forests and lichen-dwarf shrub pine forests of the boreal zone. The study was carried out on permanent plots between 1999 and 2020. The long-term dynamics of carbon concentrations in snow demonstrated a trend towards increasing carbon concentrations in forested and treeless areas of the Murmansk region. It was shown that in representative spruce and pine forests, the concentrations and atmospheric precipitation of carbon compounds and carbon leaching with soil water were higher below the tree crowns, compared to between the crowns. In soil water, a decrease was found in carbon concentration with the soil profile depth. For soils, the highest carbon concentrations were found in the organic and illuvial soil horizons. The main soil sinks of carbon and nitrogen in northern taiga forests were found to be located in the organic soil horizon below the crowns. In northern taiga forests, the carbon content of living Picea obovata and Pinus sylvestris needles and Pinus sylvestris needle litter had minor variability; no significant interbiogeocoenotic and age differences were found. We found that the nitrogen content in brown needles and needle litter was significantly lower compared to photosynthetically active needles, probably due to retranslocation processes (withdrawal before needle abscission), corroborating the literature in the results session. The largest stocks of carbon and nitrogen in northern taiga forests are concentrated in the soil organic horizon, and the removal of these elements with soil water is insignificant. Carbon and nitrogen stocks in living and fallen needles are lower than in soil. The least amount of carbon and nitrogen is contained in atmospheric precipitation. [ABSTRACT FROM AUTHOR]