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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]

This paper presents a holistic approach to biological and agricultural research focused on the use of interconnected technologies in the context of climate change. Researchers from different countries have analyzed how smart technologies can help agriculture adapt to these changes. The most representative works in the field are analyzed. Among these technologies are graph database systems such as Neo4j, which have demonstrated success in predicting the studied phenomena. The paper describes the development of a soybean crop productivity prediction model using monthly and annual data of meteorological phenomena such as precipitation, air temperature, hydrothermal coefficient, soil moisture and others. Some of the results of this promising research are also presented. [ABSTRACT FROM AUTHOR]

Karst groundwater is a crucial water source, but it has faced significant environmental risks in recent years. The complexity of the groundwater system necessitates innovative approaches to studying karst groundwater. This paper focuses on the karst groundwater of the Baotu Spring area in Jinan. Using the nanoparticle tracking analysis instrument, it is observed that the collected groundwater contains many natural nanoparticles, with particle sizes mainly ranging from 76.3 to 621.8 nm and concentrations primarily between 0.31 and 5.0 × 105 Particles/L. The transmission electron microscope (TEM) is used to study the characteristics of naturally occurring nanoparticles in the karst groundwater. The results show that the karst groundwater mainly contains particles of Ca, Na, Fe, Al, Si, and other elements, which exist in granular and irregular forms. The size of individual particles varies from 40 to 600 nm, and they are mostly amorphous and monocrystalline. The characteristics of the particles suggest that the direct recharge area mainly receives infiltration from atmospheric precipitation, with minimal influence from human activities and agriculture. In contrast, the indirect recharge and discharge areas are more significantly affected by external environments, including domestic sewage, industrial wastewater discharge, and agricultural fertilizers. These findings also suggest that elements can be transported in particle form during water-rock interactions, potentially playing a significant role in the cycling of elements between water and rocks. The particles in the study area are situated in a relatively oxidized environment, suggesting that fracture and oxidation are the main processes for particle formation. Particles can effectively transport metallic elements in groundwater, offering fresh perspectives on the migration of these elements and acting as carriers for inorganic substances, thereby increasing their mobility in aquatic environments. Given the widespread presence of natural nanoparticles in the water cycle system, some stable nanoparticles can serve as new types of groundwater tracing agents during the groundwater migration process. [ABSTRACT FROM AUTHOR]

Estimating precipitation is of critical importance to climate systems and decision-making processes. This paper presents Espresso, a deep learning model designed for estimating precipitation from satellite observations on a global scale. Conventional methods, like ground-based radars, are limited in terms of spatial coverage. Satellite observations, on the other hand, allow global coverage. Combined with deep learning methods, these observations offer the opportunity to address the challenge of estimating precipitation on a global scale. This research paper presents the development of a deep learning model using geostationary satellite data as input and generating instantaneous rainfall rates, calibrated using data from the Global Precipitation Measurement Core Observatory (GPMCO). The performance impact of various input data configurations on Espresso was investigated. These configurations include a sequence of four images from geostationary satellites and the optimal selection of channels. Additional descriptive features were explored to enhance the model's robustness for global applications. When evaluated against the GPMCO test set, Espresso demonstrated highly accurate precipitation estimation, especially within equatorial regions. A comparison against six other operational products using multiple metrics indicated its competitive performance. The model's superior storm localization and intensity estimation were further confirmed through visual comparisons in case studies. Espresso has been incorporated as an operational product at Météo-France, delivering high-quality, real-time global precipitation estimates every 30 min. [ABSTRACT FROM AUTHOR]

This research paper explores the implementation of machine learning (ML) techniques in weather and climate forecasting, with a specific focus on predicting monthly precipitation. The study analyzes the efficacy of six multivariate machine learning models: Decision Tree, Random Forest, K-Nearest Neighbors (KNN), AdaBoost, XGBoost, and Long Short-Term Memory (LSTM). Multivariate time series models incorporating lagged meteorological variables were employed to capture the dynamics of monthly rainfall in Rabat, Morocco, from 1993 to 2018. The models were evaluated based on various metrics, including root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). XGBoost showed the highest performance among the six individual models, with an RMSE of 40.8 (mm). In contrast, Decision Tree, AdaBoost, Random Forest, LSTM, and KNN showed relatively lower performances, with specific RMSEs ranging from 47.5 (mm) to 51 (mm). A novel multi-view stacking learning approach is introduced, offering a new perspective on various ML strategies. This integrated algorithm is designed to leverage the strengths of each individual model, aiming to substantially improve the precision of precipitation forecasts. The best results were achieved by combining Decision Tree, KNN, and LSTM to build the meta-base while using XGBoost as the second-level learner. This approach yielded a RMSE of 17.5 millimeters. The results show the potential of the proposed multi-view stacking learning algorithm to refine predictive results and improve the accuracy of monthly precipitation forecasts, setting a benchmark for future research in this field. [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 paper we present the results of quality control and homogenization procedures applied to long time series of daily atmospheric precipitation sums (Rr) and daily mean (Tm), maximum (Tx) and minimum (Tn) air temperature collected in Ukraine. The daily data from 178 meteorological stations covering the period of 1946–2020 were analysed. In order to perform a thorough quality assurance check, we used the R package INQC, while the Climatol homogenization software was used to detect and remove breaks from the time series. The INQC quality assurance tests revealed a relatively small number of erroneous records (around 0.01% for each variable) and suspicious values (up to 0.09%). The application of Climatol resulted in 195, 296, 355 and 359 break points, detected for Rr, Tm, Tx and Tn, respectively. These quantities coincide roughly with the results of the HOMER homogenization procedure applied to monthly time series for the same stations and almost the same period (performed in the previous works of the authors). To verify the homogenization results, statistical comparison of the raw and homogenized time series was performed. The verification demonstrated that the quality control and homogenization procedures detected and removed errors and breaks very well, and air temperature and precipitation fields after the homogenization are more self‐consistent compared to the original raw data. [ABSTRACT FROM AUTHOR]

The migratory locust, Locusta migratoria (L.), a significant grasshopper species known for its ability to form large swarms and cause extensive damage to crops and vegetation, is subject to the influence of climate change. This research paper employs geographic information system (GIS) and MaxEnt ecological modelling techniques to assess the impact of climate change on the distribution patterns of L. migratoria. Occurrence data and environmental variables are collected and analysed to create predictive models for the current and future distribution of the species. The study highlights the crucial role of climate factors, particularly temperature and precipitation, in determining the locust's distribution. The MaxEnt models exhibit high-performance indicators, accurately predicting the potential habitat suitability of L. migratoria. Additionally, specific bioclimatic variables, such as mean temperature and annual precipitation, are identified as significant factors influencing the species' presence. The generated future maps indicate how this species will invade new regions especially in Europe. Such results predict the risk of this destructive species for many agriculture communities as a direct result of a warming world. The research provides valuable insights into the complex relationship between locust distribution and environmental factors, enabling the development of effective strategies for locust management and early warning systems to mitigate the impact on agriculture and ecosystems. [ABSTRACT FROM AUTHOR]

Karst spring water dynamic characteristics and its response to atmospheric precipitation are of great significance for water resources utilization under the background of climate change. This paper selects Longzici spring area, North China, as the study area. Based on a long series of spring water flow and precipitation data, the dynamic characteristics of spring flow were analyzed and the numerical simulation of the groundwater flow model was established. The results show that the groundwater kept the sustained decline over the past decades which is in a negative equilibrium state, with a storage variable of − 2.26 million m3/year. The sensitivity of spring flow to precipitation under different precipitation scenarios shows that the water level changes in the recharge and drainage areas are similar about (3–5 cm) and slightly larger than that in the runoff area(1.5 cm) when minimum rainfall (287.24 mm) happens. When the precipitation is at its maximum (867.66 mm), the water level change in the runoff area can reach 95 cm which is much larger than those in the recharge and discharge areas. The results indicate that Longzici karst spring has a relatively good regulation water resource capacity and the runoff area is more sensitive which plays an important role in response to climate change. [ABSTRACT FROM AUTHOR]

The combination of precipitation formation and fallout affects atmospheric flows through the release of latent heat and through the removal of mass from the atmosphere, but because the mass of water vapor is only a small fraction of the total mass of Earth's atmosphere, precipitation mass sinks are often neglected in theory and models. However, a small number of modeling studies suggest that water mass sources and sinks can intensify heavily precipitating weather systems. These studies point to a need to more systematically verify the impact of neglecting precipitation mass sinks, particularly for warmer and moister climates in which precipitation rates can be much higher. In this paper, we add precipitation mass sources and sinks to an idealized general circulation model and examine their effects on steady-state midlatitude storm track statistics. The model has several idealizations, including that all condensates immediately fall out of the atmosphere, and is run across a wide range of climates, including very warm climates. We find that modifying the model to include mass sources and sinks has no detectable effect on midlatitude variability or extremes, even in climates much warmer and moister than the modern. However, we find that a 10-fold exaggeration of mass sources and sinks is sufficient to produce more intense midlatitude weather extremes and increase surface pressure variance. This result is consistent with theoretical potential vorticity analysis, which suggests that the dynamical effects of mass sources and sinks are much smaller than the dynamical effects of accompanying latent heating unless mass sinks are artificially amplified by at least a factor of 10. Finally, we use simulations of "tropical cyclone worlds" to attempt to reconcile our results with earlier work showing stronger deepening in a simulation of a tropical cyclone case study when precipitation mass sinks were included. We demonstrate that abruptly "turning on" mass sources and sinks can lead to stronger transient deepening in some individual storms (consistent with results of past work) but weaker transient deepening in other storms, without modifying the steady-state statistics of storms in equilibrium with the large-scale environment (consistent with our other results). Our results provide a firmer foundation for using general circulation models that neglect moist mass sources and sinks in climate simulations, even in climates much warmer than today, while leaving open the possibility that their inclusion might lead to short-term improvements in forecast skill. [ABSTRACT FROM AUTHOR]

This paper examines the empirical relationship between the Madden–Julian oscillation (MJO), the quasi‐biennial oscillation (QBO), and atmospheric river (AR) activity and precipitation in California on subseasonal time scales. We introduce an experimental forecast tool that uses observed anomaly patterns during a 38 yr period to predict the probability of above‐ and below‐normal AR activity and precipitation at lead times of 1–6 weeks based on the phase and amplitude of the MJO and QBO. The hindcast prediction skill of probabilistic AR activity and precipitation forecasts is evaluated for Northern, Central, and Southern California, as well as two sets of smaller geographical domains. These smaller domains are more relevant for water resource management and allow us to investigate the sensitivity of prediction skill to domain size. Consistent with previous studies, our results demonstrate that subseasonal AR activity and precipitation in California are strongly modulated by the MJO and QBO. The anomaly patterns of AR activity and precipitation vary considerably throughout the cool season, with a tendency toward below‐normal AR activity and precipitation during easterly QBO and above‐normal AR activity and precipitation during westerly QBO in JFM. The opposite patterns are generally observed in OND, but the anomaly signals are weaker and less coherent for AR activity. Certain combinations of MJO phase, QBO phase, lag time, and season yield notably higher skill scores, reinforcing the notion of "windows of opportunity" for skillful subseasonal‐to‐seasonal predictions. In California, these forecasts of opportunity are predominantly associated with easterly QBO in JFM and FMA. Plain Language Summary: This paper examines the relationship between the Madden–Julian oscillation (MJO), the quasi‐biennial oscillation (QBO), and atmospheric river (AR) activity and precipitation in California. We introduce an experimental forecast tool that shows the likelihood of above‐normal and below‐normal AR activity and precipitation based on the phases of the MJO and QBO. Consistent with previous studies, our results demonstrate that AR activity and precipitation in California are strongly influenced by the MJO and the QBO. There is a tendency for below‐normal AR activity and precipitation during easterly QBO conditions in January–March. The opposite pattern (above‐normal AR activity and precipitation) generally occurs during westerly QBO conditions. The results also suggest that our forecast tool has some potential to improve the prediction of AR activity and precipitation. The reliability and usefulness of this forecast tool depend on multiple factors, including the MJO phase, the QBO phase, and the time of year. In California, this forecast tool is likely to provide the most beneficial value during easterly QBO conditions in mid‐winter through early spring. Key Points: The modulation of atmospheric river (AR) activity and precipitation in California by the Madden‐Julian oscillation and quasi‐biennial oscillation is quantifiedA hindcast skill assessment of probabilistic AR activity and precipitation forecasts is presentedAn experimental subseasonal AR activity and precipitation forecast tool is introduced [ABSTRACT FROM AUTHOR]

Copyright of Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The paper explores the occurrence of minimal precipitation extremes at specific meteorological stations in the southeastern region of Serbia. With climate change leading to increased instances of droughts, these natural phenomena have garnered heightened interest due to their negative impacts on society, environment, and the economy. Employing the SPI-12 index in the southeastern part of Serbia from 1946 to 2021, the study sheds light on the vulnerability of this natural phenomenon in the observed stations. Understanding historical manifestation of these events helps water resource managers, farmers and policymakers manage these risks in the southeastern region of Serbia. [ABSTRACT FROM AUTHOR]

Changing climate conditions increase rainfall intensity and cause a growing number of flash flood events. Due to flash floods, problems with water damage prevention (urban area floods, erosion of natural watersheds) are increasing; these events also make the sustainable maintenance of watersheds more challenging. Hungarian watersheds are ungauged; very little historical or real-time data is available, making it difficult to understand the hydrological processes occurring during flash flood events. When only limited data is available, numerical models help predict peak flows and runoff volume. Additionally, a comparison of different models and parametrizations could be a helpful tool to reduce prediction uncertainty. This paper evaluates the impact of different rainfall intensities and durations on flash flood events at the ungauged watershed of the Morgó-creek on the northern side of Hungary. Land use conditions are primarily natural, with urban areas close to the outlet point. Hydrological and hydrodynamical models were used to apply different scenarios to determine the sensitivity of the whole watershed system based on typical precipitation events in time and intensity. The models predicted different peak flows and timing for flash floods. Using both models for comparison is recommended for flash flood prediction to compensate for the lack of measured data. [ABSTRACT FROM AUTHOR]

Prediction of drought, a dangerous hydrometeorological phenomenon, is based on meteorological data (temperature, precipitation, air pressure, dew point, evaporation), combined into a single formula – an index describing the qualitative state of aridity depending on the quantitative value of the index in a given climatic region. Due to the lack of a universal definition of drought, different aridity indices provide an effective assessment of aridity in different climatic regions of the world in different ways. In order to adapt methods for assessing aridity in a specific climatic region accounting for both the physical mechanism of the occurrence and evolution of drought and the influence of geographical and climatic features of the region under study, it is necessary to investigate the informativeness of a particular aridity index in relation to that region. This paper provides a solution for prediction of drought for the Central Asian region. [ABSTRACT FROM AUTHOR]

The paper presents results of long-term (for seven years) monitoring and analysis of the formation of rainwater acidity in Primorsky Krai, southern Russian Far East. These results reveal the variability in the concentrations of both of total dissolved solids (TDS) and individual elements. Two groups of years were distinguished in the general dataset during which the character of the atmospheric circulation played an important role in the ionic balance of the rainwater. The study area was shown to be characterized by predominantly acidic precipitation, with the pH of the water rarely exceeding 5.3 even at low concentrations of strong acid anions. In order to identify the factors that form the acidity of rainwater, the paper proposes to rank the initial dataset according to the value of fractional acidity. In the course of this separation, data blocks with a high and low degree of neutralization of the acid potential are distinguished. Within each data block, the relationships between the pH value and the concentrations of the major ions are defined. During the analysis, it was found that the mechanism of acid rain generation is determined by the mutual influence of natural and human-made sources. It was shown that compounds of dissolved organic carbon (DOC) were involved in both acidification and neutralization of rainwater. The results obtained indicate the predominance of natural rain acidification factors. However, during periods of cyclonic activity, even a slight increase in anthropogenic load leads to an increase in the acidity of atmospheric precipitation to critical values. [ABSTRACT FROM AUTHOR]

These sensors exploit already-existing, but usually unrelated, microwave (MW) or millimeter wave (mmW) links to infer the rainfall amount or intensity by interpreting the extra attenuation induced by the precipitation on the received signal level. In situ weather sensors aiming at the measurement of liquid atmospheric precipitation (rainfall) experienced limited conceptual innovation in recent decades, except for the data recording and transmission components. Non-Conventional Techniques Yakovleva et al. [[29]] used measurements of -radiation dose rate ambient equivalent performed with a scintillation detector BDKG-03 (at 1 min sampling rate) to confirm that a change in the intensity of precipitation leads to a change in the -radiation dose rate increase speed (time derivative). Though the height range is limited, a significant difference of 3-4% was observed in the collected precipitation at the yearly scale, which the authors attribute entirely to the aerodynamic impact of wind on the sensor. [Extracted from the article]

Greenockite, mineral containing cadmium, is of significant scientific interest owing to its remarkable properties and potential applications in the field of science. It commonly forms in hydrothermal ore deposits as a secondary mineral due to complex cadmium-sulfur interactions influenced by temperature. Typically, it replaces zinc minerals like sphalerite, depending on the abundance of cadmium and zinc in hydrothermal fluids. Solid solubility in the ZnSCdS system occurs at high temperatures, with immiscibility observed under specific conditions. Greenockite forms at relatively low temperatures (100°C to 200°C) and is influenced by factors such as oxygen fugacity, hydrogen sulfide activity, and chloride ion content. Its hexagonal zinc blende structure results in diverse crystal forms, including rods, wires, and whiskers, often adorned with bismuth drops. Experimental studies have identified factors affecting its formation, such as solid solubility, precipitation mechanisms, pH levels, and fluid composition. Its vibrant coloration, attributed to cadmium concentration, varies from pale yellow to deep orange-red. Geologically, it is present in diverse settings, including metamorphosed zinc oxide mineralization, granites, fumaroles, pegmatites, and sediment-hosted Pb-Zn deposits, with a unique occurrence in gabbroic rock at the Babbitt deposit. In India, Greenockite has been identified in the Bagada orogenic gold prospect, situated within the Paleoproterozoic Mahakoshal belt, Central Indian Tectonic Zone (CITZ). Additionally, it is found in hydrothermal deposits of Pb-Zn mines of Zawar. It is exclusively encountered within specific geological settings, primarily as a secondary product. This paper provides a comprehensive exploration of its geological occurrences, distribution, associations, and exceptions. [ABSTRACT FROM AUTHOR]

In order to more reasonably predict runoff under extreme precipitation and corresponding meteorological conditions, and explore the influences of annual precipitation and extreme precipitation on the runoff process, this paper proposes an improved precipitation stochastic simulation model and combine it with Weather Generator based on Dry and Wet Spells (WGDWS) and Soil and Water Assessment Tool (SWAT) model. Taking a typical mountainous basin in North China, the basin above the Wangkuai Reservoir, as the study area, the daily precipitation process and corresponding meteorological data for six extreme precipitation scenarios are generated as inputs of the SWAT model to predict monthly runoff. The results reveal that the annual runoff under the six scenarios is 5.41 m3/s, 5.95 m3/s, 6.57 m3/s, 7.02 m3/s, 7.74 m3/s and 8.04 m3/s, with maximum monthly runoff of 18.10 m3/s, 21.71 m3/s, 21.94 m3/s, 32.69 m3/s, 34.33 m3/s, 43.72 m3/s, respectively. For the same annual precipitation, the extreme precipitation magnitude has a significant effect on annual runoff, but this impact weakens as annual precipitation increases, and the influence on monthly runoff is reflected mainly in August. Moreover, under the same extreme precipitation conditions, the annual runoff increases by approximately 10% if the annual precipitation increases by 100 mm, and the influence on monthly runoff is reflected only in July. The coupling of the improved precipitation stochastic simulation, WGDWS and SWAT model not only presents a technical reference for water conservancy project operation management and water resource management under extreme precipitation scenarios, but also provides a new idea for predicting runoff under extreme precipitation and corresponding meteorological conditions. [ABSTRACT FROM AUTHOR]

This paper assesses the water vapor flux performance of three reanalysis datasets (ERA5, JRA55, NCEP-2) on the South China Sea. The radiosonde data were from the South China Sea Scientific Expedition organized by Sun Yat-sen University in the 2019 summer (SCSEX2019). The comparison shows that all reanalyses underestimate the temperature and specific humidity under 500 hPa. As for the wind profile, the most significant difference appeared at 1800 UTC when there was no conventional radiosonde observation around the experiment area. As for the water vapor flux, ERA5 seems to give the best zonal flux but the worst meridional one. A deeper analysis shows that the bias in the wind mainly caused the difference in water vapor flux from ERA5. As for JRA55 and NCEP-2, the humidity and wind field bias coincidentally canceled each other, inducing a much smaller bias, especially in meridional water vapor flux. Therefore, to get a more realistic water vapor flux, a correction in the wind profile was most needed for ERA5. In contrast, the simultaneous improvement on both wind and humidity fields might produce a better water vapor flux for JRA55 and NCEP-2. Significance Statement: This paper mainly aims to assess three atmospheric reanalyses from the viewpoint of the water vapor flux over the South China Sea during the monsoon period. The observation data contain more than 120 radiosonde profiles. Our work has given an objective comparison among the reanalyses and observations. We also tried to explain the bias in the water vapor flux over the ocean from the reanalyses. The results of our work might help understand the monsoon precipitation given by atmospheric reanalyses or regional climate models and enlighten the development of atmospheric assimilation products. [ABSTRACT FROM AUTHOR]

This paper mainly takes Zhacun goldmine as the research object and carries out research on the metallogenic system and genesis of the deposit in the area. The research methods are as follows: according to the geological characteristics of the metallogenic mining area in the Zhacun goldmine, combined with the comparative analysis of the metallogenic age, alteration characteristics and lithology between the Zhacun goldmine and the Lianhuashan alkali rich rock mass, and the analysis of the isotopic data in the Zhacun goldmine, this paper demonstrates that the magmatic activity of the Lianhuashan quartz monzonite porphyry plays an important role in the mineralization of this area, which is both an important ore source and fluid source. It is also the main heat source driving the circulation of ore-forming fluid. On this basis, through the analysis of the metallogenic elements in the area, the metallogenic system model of Zhacun goldmine is constructed. It is considered that under the action of long-term horizontal compressive stress, the atmospheric precipitation infiltrated into the formation continued to penetrate along the fractures and fissures to deep crust and finally mixed with the magmatic water sealed in the thick formation. During this period, a series of shallow and ultrashallow porphyry mass intruded, providing heat source and part of the ore-forming materials and resulting in incomplete homogenization under high temperature and high pressure, which formed highly mineralized mixed water. Under the combined action of long-term compressive stress and various deep thermal dynamics, these mineral fluids migrated along the buried deep faults near the core of the duplex anticline to the upper part of the crust. When the metal elements in these ore fluids migrated over long distances to the nappe fault zone of the gold-bearing fracture zone, the gold began to precipitate, and the native gold and the coarse-grained pyrite and quartz formed at the same time or earlier started to fill the fracture zone along the fissures and accumulate into minerals. Then, through the analysis of metallogenic conditions, gold mineralization process, and genetic mechanism of the deposit, it is proposed that the genetic type of the ore deposit in this area is magmatic mesothermal-epithermal gold deposit. The establishment of metallogenic system model in Zhacun goldmine will provide theoretical and practical guidance for deep and peripheral prospecting prediction in the area, carry out targeted and reasonable prediction, and improve prospecting effectiveness. [ABSTRACT FROM AUTHOR]

The high variability of the sub-desert climate, especially in the south-eastern region of Morocco, has severe socioeconomic impacts on the inhabitant's way of life, as is the case in the Daoura watershed, where this variability is associated with droughts or exceptional rainfall. The data collected from the Guir-Ziz-Rheriss hydraulic agency were processed, corrected and analyzed using the Climatol package (version 4.0.0) developed in R software to homogenize rainfall data. Through this work, the significance and amplitudes of annual and monthly rainfall trends were defined using the Mann-Kendall test and Sens's slope estimator, while comparing the results of raw and homogenized data. The Daoura watershed has a sub-desert climate, and the homogenization process revealed a few rainfall stations with significant positive trends at confidence levels ranging from 90% to 95%. According to the raw and homogenized data, the majority of these stations are located in the High Atlas (CR1) and Anti-Atlasic (CR2) zones, where there is considerable spatiotemporal variability in rainfall from one year to the next. The essential objective of this scientific paper was to analyze the spatiotemporal variability of monthly and annual precipitation and to study their trends through rainfall data homogenized by the climatol package (version 4.0.0) from 13 stations over a period (1957-2018). The contribution of this study to science is the rainfall data it offers, which is useful for managing natural resources in sub-desert areas. [ABSTRACT FROM AUTHOR]

The article places emphasis on the latest advancements in this field, particularly focusing on indoor and outdoor microplastic (MP) pollution, including their emission, behaviour and potential health hazards. Gaining an in-depth understanding of these factors is crucial for devising effective strategies to mitigate the impact of microplastics (MPs) on human health and the environment. Indoor MP abundance is generally higher than outdoor levels, with textiles serving as a primary source of indoor airborne MPs. Traffic-derived MP particles, MP fibres in residential areas, agricultural plastic mulch, marine MPs and landfill sites appear to be contributors to outdoor atmospheric MP pollution. Factors such as wind direction, wind speed, precipitation and snowfall, along with the physical characteristics and secondary suspension of MPs, collectively influence their behaviour, distribution and fate. Inhalation and ingestion constitute the main exposure pathways for airborne MPs, potentially leading to health issues like respiratory inflammation. Therefore, gaining a deeper insight into the behaviour and impact mechanisms of atmospheric MPs aids in formulating effective risk management strategies to safeguard human health and maintain environmental sustainability. [ABSTRACT FROM AUTHOR]

Copyright of Environmental Science & Technology (10036504) is the property of Editorial Board of Environmental Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Hydrologic models to simulate river flows are computationally costly. In addition to the precipitation and other meteorological time series, catchment characteristics, including soil data, land use, land cover, and roughness, are essential in most hydrologic models. The unavailability of these data series challenged the accuracy of simulations. However, recent advances in soft computing techniques offer better approaches and solutions at less computational complexity. These require a minimum amount of data, while they reach higher accuracies depending on the quality of data sets. The Gradient Boosting Algorithms and Adaptive Network-based Fuzzy Inference System (ANFIS) are two such systems that can be used in simulating river flows based on the catchment rainfall. In this paper, the computational capabilities of these two systems were tested in simulated river flows by developing the prediction models for Malwathu Oya in Sri Lanka. The simulated flows were then compared with the ground-measured river flows for accuracy. Correlation of coefficient (R), Per cent-Bias (bias), Nash Sutcliffe Model efficiency (NSE), Mean Absolute Relative Error (MARE), Kling-Gupta Efficiency (KGE), and Root mean square error (RMSE) were used as the comparative indices between Gradient Boosting Algorithms and Adaptive Network-based Fuzzy Inference Systems. Results of the study showcased that both systems can simulate river flows as a function of catchment rainfalls; however, the Cat gradient Boosting algorithm (CatBoost) has a computational edge over the Adaptive Network Based Fuzzy Inference System (ANFIS). The CatBoost algorithm outperformed other algorithms used in this study, with the best correlation score for the testing dataset having 0.9934. The extreme gradient boosting (XGBoost), Light gradient boosting (LightGBM), and Ensemble models scored 0.9283, 0.9253, and 0.9109, respectively. However, more applications should be investigated for sound conclusions. [ABSTRACT FROM AUTHOR]

In this paper, we have evaluated high-resolution spatial gridded climate data from two long-term global datasets, WorldClim V.2.0 and Chelsa V.1.2, in representing variables like precipitation and temperature for the Urabá region of Colombia. Additionally, climate variables from these datasets have been used to estimate evapotranspiration using traditional methods such as the Turc, Hargreaves and Thornthwaite equations. Finally, the results of long-term spatial climate characterization are used to apply the water balance equation in the surface at the watershed scale, to obtain the long-term average streamflow of the main streams of the Urabá region; these streamflows are compared with the observations of hydrological stations. We find that the WorldClim and Chelsa rainfall estimates show average differences between 20% and 23% compared to the average annual rainfall in the area from in situ measurements. Both datasets are able to reproduce the rainfall average annual cycle, although Chelsa shows a slightly better performance. Regarding near surface air temperature we find that WorldClim shows a good performance, while Chelsa significantly underestimates the average temperature. Finally, we found that the Hargreaves and Thornthwaite methods lead to the best performance in estimating streamflow from the water balance, probably because details of the seasonal behavior of variables like temperature and radiation are explicitly included in these methods. On the other hand, the Turc method yields larger estimates of evapotranspiration and therefore the corresponding derived streamflows are lower than those observed. The good performance of the WorldClim and Chelsa datasets in representing variables like precipitation, temperature, and the derived watershed-scale streamflow, suggest that these long-term global climate datasets can be used to study the spatial distribution of important hydrological variables in the Urabá region of Colombia, and consequently the estimation of average streamflows through the method of the long-term water balance. [ABSTRACT FROM AUTHOR]

Nature-based solutions (NBSs), a sustainable landscape restoration approach, covers rebuilding ecological functionality and integrity in a watershed. The objective of restoration with NBSs is to revitalize the ecosystems to provide and sustain multiple services. Therefore, it is more than just planting trees or rewetting wetlands. NBSs can provide effective landscape restoration and management tools but should be applied on a methodological basis to get the full benefits. The methodological basis includes the type and nature of NBS, the application principles, and the tools to assess the efficiency of the set of NBS applied. However, most literature on NBSs is theoretical, while practitioners need applicable guidance. In this paper, we reviewed the latest literature on the NBSs and tried to connect the theory with some practical examples. We also underlined that NBSs applied in landscape restoration should relate to watershed processes since streamflow and/or stream quality are significant performance indicators. The NBSs should strengthen the resiliency towards multiple stressors and disturbances in a landscape. The widespread stressors in Türkiye landscapes are related to water balance that compares precipitation and evapotranspiration. Therefore, we suggest Budyko theory application in evaluating NBS options instead of typical climate models. Overall, this paper defines NBSs, provides examples, discusses possible methodologies, and comes up with some practical conclusions. The points we discuss are the resiliency assessment approach, scale, and location of the application, identifying the problems in a watershed through adequate quantitative indicators, and setting up the thresholds planned to be achieved. [ABSTRACT FROM AUTHOR]

The Atmospheric X-ray Imaging Spectrometer (AXIS) described in this work is a compact, wide field-of-view, hard x-ray imager. The AXIS instrument will fly onboard the Atmospheric Effects of Precipitation through Energetic X-rays (AEPEX) 6U CubeSat mission and will measure bremsstrahlung x-ray photons in the 50–240 keV range with cadmium–zinc–telluride (CZT) detectors using coded aperture optics. AXIS will measure photons generated by energetic particle precipitation for the purpose of determining the spatial scales of precipitation and estimating electron precipitation characteristics. This paper describes the design and testing of the AXIS instrument, including a summary of simulations performed that motivate the shielding, optics, and mechanical design. Testing and characterization is reported that validates the instrument design and shows that the instrument design meets or exceeds the measurement requirements necessary for AEPEX mission success. [ABSTRACT FROM AUTHOR]

This paper presents a precipitation reconstruction that is based on the continuous observations by Louis Morin in Paris from 1665–1713. Morin usually recorded precipitation intensity and duration, when it snowed/rained, three times each day (sometimes up to six times). The consistency of his observations can be calculated from his other measurements and observations (e.g. temperature, cloud cover), where at least one entry of his different measurements and observations for 98.7 % of all days is noted. To 5 convert these observations to common units, we calibrated them with a multiplicative interacting model using Philippe and Gabriele-Philippe de la Hire's instrumental measurements from Paris. The two series of measurements by de la Hire (father and son) and observations by Morin overlap from 1688–1713. To test the quality of the reconstruction, we analyzed it with the de la Hire's measurements, proxy data, an internal analysis of Morin's measurements of different climate variables, and modern data. Thus, we assess the reliability of the precipitation reconstructions based on Morin's data as follows. We have moderate confidence regarding the exact quantities of daily, seasonal, and annual precipitation totals. We have low confidence regarding exceptionally high precipitation amounts, but we have high confidence in the indices of an impact analysis (i.e., dry days, wet days, consecutive dry days, consecutive wet days), in monthly frequencies of rainfall, and in interannual, interseasonal, and interdecadal variability. Rainy seasons with precipitation totals greater than 250 mm occurred in MAM 1682, JJA 1682, SON 1687, JJA 1697 and JJA 1703. Furthermore, compared to other DJF seasons, the winter of 1666/67 stands out with a precipitation total of 248 mm. Dry seasons with precipitation totals less than 60 mm occurred in SON 1669, DJF 1690/91 and DJF 1693/94. An impact analysis shows no abnormalities regarding consecutive dry days or wet days in MAM. In JJA a longer dry period of 31 days appeared in 1686 and a dry period of 69 days appeared in DJF 1671/72. [ABSTRACT FROM AUTHOR]

Moisture-damaged buildings are a prominent issue in Finland, but with limited information on damage prevalence, degree of severity and risk factors. This paper analyses 14,996 Finnish detached and semidetached houses that have undergone a standardised moisture assessment of interior spaces and at-risk structures inside the building envelope. Confirmed damage (a binary indicator of damage presence) and a damage index (an ordinal indicator of severity) were calculated for each home and their association with different building and area characteristics estimated. Frequently damaged structures include pre-1950s log walls, walls contacting soil, wooden ground floors and false plinths. Around 15% of surveyed houses had risk structure damage, 19% had at least one confirmed damage anywhere in the house and 49% had either confirmed, likely or possible damage. The greatest risk factor for confirmed damage was house age (odds ratio = 1.48 (95% confidence interval (CI) = 1.45-1.51) for each decade since construction), with nearly half of all houses built pre-1939 damaged. Other risk factors explained a third of the effect of building age, and included log external walls, fibreboard roofs, absence of mechanical ventilation, detached properties and wind-driven rain precipitation. Results can support targeted remediation efforts, protect health and estimate exposure-response relationships for moisture damage. [ABSTRACT FROM AUTHOR]

This paper presents a research study of expected precipitation extremes across the Gandaki Province, Nepal. The study used five indices to assess extreme precipitation under climate change. Precipitation output of two Global Climate Models (GCMs) of Coupled Model Intercomparison Project Phase Six (CMIP6) were used to characterize the future precipitation extremes during the rainfall season from June to September (JJAS) and overall days of the year. To characterize extreme precipitation events, we used daily precipitation under the SSP2–4.5 and SSP5–8.5 scenarios from the Beijing Climate Center and China Meteorological Administration, China; and Meteorological Research Institute (MRI), Japan. Considering large uncertainties with GCM outputs and different downscaling (including bias correction) methods, direct use of GCM outputs were made to find change in precipitation pattern for future climate. For 5-, 10-, 20-, 50-, and 100-year return periods, observed and projected 24 h and 72 h annual maximum time series were used to calculate the return level. The result showed an increase in simple daily intensity index (SDII) in the near future (2021–2040) and far future (2081–2100), with respect to the base-year (1995–2014). Similarly, heavy precipitation days (R50 mm), very heavy precipitation days (R100 mm), annual daily maximum precipitation (RX1day), and annual three-day maximum precipitation (RX3day) indices demonstrated an increase in extreme precipitation toward the end of the 21st century. A comparison of R50 mm and R100 mm values showed an extensive (22.6% and 63.8%) increase in extreme precipitation days in the near future and far future. Excessive precipitation was forecasted over Kaski, Nawalparasi East, Syangja, and the western half of the Tanahun region. The expected increase in extreme precipitation may pose a severe threat to the long-term viability of social infrastructure, as well as environmental health. The findings of these studies will provide an opportunity to better understand the origins of severe events and the ability of CMIP6 model outputs to estimate anticipated changes. More research into the underlying physical factors that modulate the occurrence of extreme incidences expected for relevant policies is suggested. [ABSTRACT FROM AUTHOR]

EPA SWMM hydraulic modeling requires the estimation of different parameters allowing the determination of the basin's response upon a given precipitation event. Some physical parameters, such as area or perimeter, are measurable and can be accurately determined; however, other parameter estimation presents greater uncertainty, such as the width parameter. For regular and uniform drainage areas, width parameter estimation is relatively simple; however, when a complete irregular basin analysis is required, width determination presents greater uncertainty, and its representativeness becomes complicated to define. Width determination is idealized with the representation of a rectangle, where, for an equal area, a higher width will result in a faster response of the basin, while a lower width will result in a slower response of the basin. This paper attempts to estimate a representative value of width for a realistic, irregularly shaped basin by defining the equivalent rectangle, which takes into account the area, perimeter, and compactness index of the basin. The compactness index can be used to classify the basin by its shape. The shape of the basin is an indicator of how the precipitation histograms are temporally distributed and how the water flows through the basin, i.e., it defines the response speed of the basin, as the width parameter does in modeling. The width parameter has a high sensitivity in the EPA SWMM modeling results; therefore, an inaccurate estimation of the parameter leads to unrepresentative results. For this reason, this study seeks to find an optimal methodology to reduce modeling uncertainty and achieve more accurate simulations of an irregular watershed's hydrological response. [ABSTRACT FROM AUTHOR]

Formation water is produced during gas well production, which can seriously affect gas well productivity, especially in deep and ultradeep tight sandstone gas reservoirs. In this paper, we have studied the formation water production and geochemical properties of the ultradeep tight sandstone gas reservoir in the Cretaceous Bashijiqike Formation of the DK gas field in the Kuqa Depression. The results indicate that the formation water can be classified as condensate water, gas-water transition zone water, mixed water, and isolated formation water, all of which are acidic and of CaCl2 type, with increasing salinity or chloride ion content. Sodium/chloride ratio (ρNa+/ρCl-), metamorphic coefficient (ρ(Cl--Na+)/ρ(Mg2+)), desulfurization coefficient (100 × ρ S O 4 2 − / ρ C l − ), and trace elements concentration parameters show that the formation water was formed in a closed and reductive environment and experienced strong water-rock interaction. The formation water may have been influenced by early seawater, but it is not significant and is more likely to have been influenced by water seepage from the overlying gypsum strata. The Sr, H, and O isotopes reveal that the different types of formation water have good homology and are not affected by atmospheric precipitation, which is propitious to the preservation of natural gas. [ABSTRACT FROM AUTHOR]

The EarthCARE mission aims to probe the Earth's atmosphere by measuring cloud and aerosol profiles using its active instruments, the Cloud Profiling Radar (CPR) and Atmospheric Lidar (ATLID). The correct identification of hydrometeors and aerosols from atmospheric profiles is an important step in retrieving the properties of clouds, aerosols and precipitation. Ambiguities in the nature of atmospheric targets can be removed using the synergy of collocated radar and lidar measurements, which is based on the complementary spectral response of radar and lidar relative to atmospheric targets present in the profiles. The instruments are sensitive to different parts of the particle size distribution, and provide independent but overlapping information in optical and microwave wavelengths. ATLID is sensitive to aerosols and small cloud particles and CPR to large ice particles, snowflakes and raindrops. It is therefore possible to better classify atmospheric targets when collocated radar and lidar measurements exist compared to a single instrument. The cloud phase, precipitation and aerosol type within the column sampled by the two instruments can then be identified. ATLID-CPR Target Classification (AC-TC) is the product created for this purpose by combining the ATLID Target Classification (A-TC) and CPR Target Classification (C-TC). AC-TC is crucial for the subsequent synergistic retrieval of cloud, aerosol and precipitation properties. AC-TC builds upon previous target classifications using CloudSat/CALIPSO synergy, while providing richer target classification using the enhanced capabilities of EarthCARE's instruments: CPR's Doppler velocity measurements to distinguish snow and rimed snow from ice clouds, and ATLID's lidar ratio measurements to objectively discrimination between different aerosol species and optically thin ice clouds. In this paper we first describe how the single-instrument A-TC and C-TC products are derived from ATLID and CPR measurements. Then the AC-TC product, which combines the A-TC and C-TC classifications using a synergistic decision matrix, is presented. Simulated EarthCARE observations are used to test the processors generating the target classifications, with results presented using the Halifax scene. Finally, the target classifications are evaluated by quantifying the fractions of ice and snow, liquid clouds, rain and aerosols in the atmosphere that can be successfully identified by each instrument and their synergy. We show that radar-lidar synergy helps better detect ice and snow, with ATLID detecting radiatively-important optically thin cirrus and cloud-tops while CPR penetrates most deep and highly concentrated ice clouds. The detection of rain and drizzle is entirely due to C-TC, while that of liquid clouds and aerosols is due to A-TC. The evaluation also shows that simple assumptions can be made to compensate for when the instruments are obscured by extinction (ATLID) or surface clutter and multiple scattering (CPR); this allows for the recovery of a majority of liquid cloud not detected by the active instruments. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Any hydro-meteorology variable includes different components, in general, such as periodicity, trend, possibly jump, and uncertain stochastic parts. Among these, the trend analysis gained importance in the last three decades due to global warming and climate change effects. The literature is full of monotonic trend applications by taking into consideration trend analysis methodologies such as Mann–Kendall trend identification, Sen trend slope calculation, and Şen innovative trend analysis (ITA). It is more important in practical studies to deal with less than one-year durations to assess the possible trend pieces for better practical uses such as agricultural productivity purposes. In this paper, a new concept is applied, known as the innovative polygon trend analysis (IPTA), to assess the trend component transitions between two successive months. The application of the methodology is presented for the coastal region of northeast Algerian precipitation records. The emanated results from this study provided information that El Taref station has the most unstable precipitation behavior among all the meteorology stations in the study area, moving from a month to another rather very unsystematically; whereas other meteorology stations have systematic movement from a month to the subsequent one. [ABSTRACT FROM AUTHOR]

Reference evapotranspiration (ETo) is an essential parameter of the hydrologic cycle that plays a key role in the water resources management, especially in arid and semi-arid regions where the total amount of annual precipitation is low. Accurate information on ETo values would be essential for irrigation scheduling and water resources planning as well as water allocation policies. Due to the difficulties involved in the precise direct measuring of this parameter, indirect methods should be adopted and validated in different data completeness scenarios. Although local estimation of ETo using indirect methods is a way to achieve reliable estimations of this parameter, assessing external/regional scenarios for evaluating these methods would be necessary, since lots of regions suffer from the lack of necessary meteorological records for the ETo estimation. The present paper evaluated different spatial (external) data management scenarios for adopting the most commonly used empirical equations under ancillary data supplanting in semi-arid regions. The obtained results revealed that when designed properly, the external data supplying structure for ETo estimations in a target point would be a suitable way to obtain reliable estimates. [ABSTRACT FROM AUTHOR]

Climate disasters such as floods and droughts often bring heavy losses to human life, national economy, and public safety. El Niño/Southern Oscillation (ENSO) is one of the most important inter-annual climate signals in the tropics and has a global impact on atmospheric circulation and precipitation. To address the impact of climate change, accurate ENSO forecasts can help prevent related climate disasters. Traditional prediction methods mainly include statistical methods and dynamic methods. However, due to the variability and diversity of the temporal and spatial evolution of ENSO, traditional methods still have great uncertainty in predicting ENSO. In recent years, with the rapid development of artificial intelligence technology, it has gradually penetrated into all aspects of people's lives, and the climate field has also benefited. For example, deep learning methods in artificial intelligence can automatically learn and train from a large amount of sample data, obtain excellent feature representation, and effectively improve the performance of various learning tasks. It is widely used in computer vision, natural language processing, and other fields. In 2019, Ham et al. used a convolutional neural network (CNN) model in ENSO forecasting 18 months in advance, and the winter ENSO forecasting skill could reach 0.64, far exceeding the dynamic model with a forecasting skill of 0.5. The research results were regarded as the pioneering work of deep learning in the field of weather forecasting. This paper introduces the traditional ENSO forecasting methods and focuses on summarizing the various latest artificial intelligence methods and their forecasting effects for ENSO forecasting, so as to provide useful reference for future research by researchers. [ABSTRACT FROM AUTHOR]

Copyright of Polish Geographical Review / Przegląd Geograficzny is the property of Polska Akademia Nauk, Instytut Geografii i Przestrzennego Zagospodarowania and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In climate models, subgrid parameterizations of convection and clouds are one of the main causes of the biases in precipitation and atmospheric circulation simulations. In recent years, due to the rapid development of data science, machine learning (ML) parameterizations for convection and clouds have been demonstrated to have the potential to perform better than conventional parameterizations. Most previous studies were conducted on aqua-planet and idealized models, and the problems of simulation instability and climate drift still exist. Developing an ML parameterization scheme remains a challenging task in realistically configured models. In this paper, a set of residual deep neural networks (ResDNNs) with a strong nonlinear fitting ability is designed to emulate a super-parameterization (SP) with different outputs in a hybrid ML–physical general circulation model (GCM). It can sustain stable simulations for over 10 years under real-world geographical boundary conditions. We explore the relationship between the accuracy and stability by validating multiple deep neural network (DNN) and ResDNN sets in prognostic runs. In addition, there are significant differences in the prognostic results of the stable ResDNN sets. Therefore, trial and error is used to acquire the optimal ResDNN set for both high skill and long-term stability, which we name the neural network (NN) parameterization. In offline validation, the neural network parameterization can emulate the SP in mid- to high-latitude regions with a high accuracy. However, its prediction skill over tropical ocean areas still needs improvement. In the multi-year prognostic test, the hybrid ML–physical GCM simulates the tropical precipitation well over land and significantly improves the frequency of the precipitation extremes, which are vastly underestimated in the Community Atmospheric Model version 5 (CAM5), with a horizontal resolution of 1.9 ∘ × 2.5 ∘. Furthermore, the hybrid ML–physical GCM simulates the robust signal of the Madden–Julian oscillation with a more reasonable propagation speed than CAM5. However, there are still substantial biases with the hybrid ML–physical GCM in the mean states, including the temperature field in the tropopause and at high latitudes and the precipitation over tropical oceanic regions, which are larger than those in CAM5. This study is a pioneer in achieving multi-year stable climate simulations using a hybrid ML–physical GCM under actual land–ocean boundary conditions that become sustained over 30 times faster than the target SP. It demonstrates the emerging potential of using ML parameterizations in climate simulations. [ABSTRACT FROM AUTHOR]

The problem of assessing carbon fluxes and pools in forests is acute due to climate change and contribution of forests to carbon reserves. The papers provides a comparative assessment of carbon ingress with atmospheric precipitation and leaching the dissolved organic carbon from soils under coniferous–deciduous forests of different types on sandy soil-forming deposits. The dominant types of coniferous–broadleaved forests of the Bryansk region, i.e., dwarf shrub–green moss pine forests, composite boreal–nemoral herbaceous pine forests and polydominant broadleaved nemoral-forb spruce forests are the objects of study. On average, over a three-year-long observation period (2016–2019), the carbon ingress with rain and snow was 60 ± 4 kg C/ha per year in shrub–green moss and composite pine forests; this turned out to be higher than in coniferous-broadleaved forests, where the carbon input was 47 ± 2 kg C/ha per year. The removal of organic carbon from organic horizons in polydominant broadleaved forests was four times lower (14 ± 4 kg C/ha per year) on average than in dwarf shrub–green moss pine forests specified by a thick litter (56 ± 22 kg C/ha per year), and 2.5 times lower than in composite pine forests (36 ± 12 kg C/ha per year). The cases of leaching organic carbon from the lower mineral horizons were scarce for different types of coniferous-deciduous forests and varied on average from 6 to 12 kg C/(ha year). The distribution of dissolved organic carbon in the soil profile shows a more efficient carbon fixation in the mineral part of soil profile in pine forests than in polydominant broadleaved forests. [ABSTRACT FROM AUTHOR]

The study of geothermal field characteristics and the mechanisms involved in the hydrogeochemical evolution of geothermal fluids is an effective means to understand the formation, reservoir conditions and circulation mechanics of geothermal resources. Documenting these parameters within the Rongcheng Bulge of Xiong'an New Area, China, is of great significance to its future development and utilization as a geothermal resource. In this paper, we investigate the hydrochemical characteristics of geothermal fluids and the rock thermal properties of the Rongcheng Bulge Reservoir and the surrounding rocks to identify the mechanisms of geothermal fluid genesis within the geothermal field. The results show that the geothermal water in the Rongcheng Bulge is mainly recharged by atmospheric rainfall. The thermal storage temperature at which the deep geothermal fluid is located ranges between 63 and 105 °C, whereas the depth of geothermal water circulation is between 1500 and 2700 m. Fluid exchange is weak during deep circulation, but as the geothermal fluid rises, the proportion of cold water mixed with the geothermal waters is 75–95%. The overall geothermal gradient of the carbonate thermal reservoir is significantly lower than that within the overburden and basement rocks, probably due to convection during the upward transport of groundwater in the reservoir. The geothermal fluid in the area of the Rongcheng Bulge is recharged by the Taihang Mountains and atmospheric precipitation. Following recharge and deep circulation, the fluids rise along fractures and by interlayer convection and are mixed with cold water to form the current, accessible geothermal water. [ABSTRACT FROM AUTHOR]

The aim of this paper is to identify the trends of changes in atmospheric precipitation percolation under the changing climate conditions of Lithuania (the East Baltic region) based on long-term lysimeter studies. Data from 1987–2022 research (n = 1296) was used to determine trends in precipitation infiltration changes. Two 10-year periods, 1989–1998 and 2011–2020, were selected from the whole observation period (1987–2022) to assess changes in precipitation infiltration due to climate change. The air temperature has increased significantly in November (+3.4 °C) and December (+3.3 °C), with a +2.2 °C increase in the standard climate normal. The distribution of yearly precipitation has changed, with the annual amount decreasing from 686 to 652 mm. Precipitation increased the most in July and August (10.9 and 22.9 mm). In autumn, the amount of precipitation decreased by 7.9–31.1 mm. The number of rainy days did not change during the year, but the frequency of heavy precipitation increased significantly in August. The annual percolation increased by 14.2% over 2011–2020 compared to 1989–1998. Percolation increased by 19.0, 22.3, and 20.1% during the spring, autumn, and winter, respectively, and decreased by 35.0% in summer. The increase in annual percolation is mostly related to the increase in temperature during the cold season: November and December. During these months, the likelihood of early freeze formation, which interrupts gravitational water percolation in soil, is significantly reduced. In the spring, the increase in average air temperatures in March leads to faster melting of the winter snow in a shorter period, which significantly increases percolation processes. In Lithuania, higher percolation in autumn and winter, when part of the agricultural land is not covered by vegetation, may lead to higher leaching of chemical elements. [ABSTRACT FROM AUTHOR]

Copyright of Hydrogeology & Engineering Geology / Shuiwendizhi Gongchengdizhi is the property of Hydrogeology & Engineering Geology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)