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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]

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]

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]

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]

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]

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]

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]

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]

This paper presents adjustment routines for Geonor totalizing precipitation gauge data collected from the headwaters of the Oldman River, within the southwestern Alberta Canadian Rockies. The gauges are situated at mountain valley and alpine ridge locations with varying degrees of canopy cover. These data are prone to sensor noise and environment-induced measurement errors requiring an ordered set of quality control (QC) corrections using nearby weather station data. Sensor noise at valley sites with single-vibrating wire gauges accounted for the removal of 5% to 8% (49-76 mm) of annual precipitation. This was compensated for by an increase of 6% to 8% (50-76 mm) from under-catch. A three-wire ridge gauge did not experience significant sensor noise; however, the under-catch of snow resulted in 42% to 52% (784-1342 mm) increased precipitation. When all QC corrections were applied, the annual cumulative precipitation at the ridge demonstrated increases of 39% to 49% (731-1269 mm), while the valley gauge adjustments were 􀀀4% to 1% (􀀀39 mm to 13 mm). Public sector totalizing precipitation gauge records often undergo minimal QC. Care must be exercised to check the corrections applied to such records when used to estimate watershed water balance or precipitation orographic enhancement. Systematic errors at open high-elevation sites may exceed nearby valley or forest sites. [ABSTRACT FROM AUTHOR]

Crop yields are threatened by global climate change. Maize has high water requirements, and precipitation fluctuations can impact its yield. In this study, we used the Environmental Policy Integrated Climate (EPIC) model to simulate maize yields in eight northeastern U.S. states. We used precipitation fluctuations and the coefficient of variation (CV) of yield as indicators to construct a vulnerability curve for the CV of yield and precipitation fluctuations. We then evaluated the vulnerability of maize yields under precipitation fluctuations in the region. We obtained the following results: (1) the fitted vulnerability curves were classified into three categories (positive slope, negative slope, and insignificant fit), of which the first category accounted for about 92.7%, indicating that the CV of maize yield was positively correlated with precipitation fluctuations in most parts of the study area; and (2) the CV of maize yield under 11 precipitation fluctuation scenarios was mapped to express the CV at the spatial level, and the maize yield in Connecticut and Maryland proved to be the most sensitive to precipitation fluctuations. This study provided a theoretical and experimental basis for the prevention of maize yield risk under fluctuating precipitation conditions. [ABSTRACT FROM AUTHOR]

The Brazilian savannah-like Cerrado is classified as a fire-dependent biome. Human activities have altered the fire regimes in the region, and as a result, not all fires have ecological benefits. The indigenous lands (ILs) of the Brazilian Cerrado have registered the recurrence of forest fires. Thus, the diagnosis of these events is fundamental to understanding the burning regimes and their consequences. The main objective of this paper is to evaluate the fire regimes in Cerrado's indigenous lands from 2008 to 2017. We used the Landsat time series, at 30 m spatial resolution, available in the Google Earth Engine platform to delineate the burned areas. We used precipitation data from a meteorological station to define the rainy season (RS), early dry season (EDS), middle dry season (MDS), and late dry season (LDS) periods. During 2008-2017, our results show that the total burned area in the indigenous lands and surrounding area was 2,289,562 hectares, distributed in 14,653 scars. Most fires took place between June and November, and the annual burned area was quite different in the years studied. It was also possible to identify areas with high fire recurrence. The fire regime patterns described here are the first step towards understanding the fire regimes in the region and establishing directions to improve management strategies and guide public policies. [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]

This paper is a literature review focused on permeable pavements and especially the permeable subbase material. Run-off water from the surface is traditionally let through a drainage system, and the roads are kept dry. Due to climate changes, heavy precipitation and high-intensity rainfalls are putting pressure on the infrastructure. Traditionally, water in subbase materials reduces the resilient E-moduli and the lifespan of the pavement design. Studies show that increasing saturation reduces the bearing capacity of a traditional subbase material. Unbound materials with highly grained fines and high moisture content have higher tendency to show reduced resilient E-moduli. One study was found where the E-moduli of five different coarse grained aggregates used in permeable pavements were examined through a triaxial test. It was found that the E-moduli varied from 110–371 MPa. Other studies examining the E-moduli of permeable subbases based on moisture content were not found. However, this paper discusses different experiences regarding the bearing capacity of traditional vs. permeable subbase materials. It also covers a discussion and an analysis of missing research areas that needs to be investigated for further knowledge about the usage of permeable pavements in areas with a risk of flooding. [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]

Global warming substantially intensifies hydrologic cycles, causing increasing frequency and magnitude of catastrophic floods and droughts. Understanding the patterns and mechanisms of precipitation in historical periods is pivotal for regional disaster prevention and mitigation. Here, we analyzed the daily precipitation of six stations at the Shimantan Reservoir from 1952 to 2013 to examine precipitation characteristics at different time scales. The Mann–Kendall test, moving t-test, and Innovative Polygon Trend Analysis (IPTA) were employed to detect the trends and change points in total precipitation amount, frequency, and duration. Influences of atmospheric circulations on precipitation were then explored via cross-wavelet analysis. Our results showed increased average precipitation and decreased precipitation days annually at the Shimantan Reservoir in the past decades. Specifically, increased seasonal precipitation was only detected in summer, while precipitation days were mainly reduced in winter. There was a noticeable increasing to decreasing transition trend in precipitation from July to August, and a transition from decreasing to increasing from June to July in precipitation days. Summer rainfall was predominantly moderate and light, accompanied by shortening and highly fluctuating rainstorm durations. July exhibited the highest precipitation frequency and always experienced rainstorms. The Arctic Oscillation and East Asian summer monsoon showed positive and negative correlations, respectively, with the changes in precipitation at the Shimantan Reservoir. Our analyses provide a fine-scale portrait of precipitation patterns and mechanisms under a changing climate and benefit regional flood control and sustainable development. [ABSTRACT FROM AUTHOR]

The Amazon Basin is the largest rainforest in the world, and studying the rainfall in this region is crucial for understanding the functioning of the entire rainforest ecosystem and its role in regulating the regional and global climate. This work is part of the application of complex networks, which refer to a network modeled by graphs and are characterized by their high versatility, as well as the extraction of key information from the system under study. The main objective of this article is to examine the precipitation system in the Amazon basin during the austral summer. The networks are defined by nodes and connections, where each node represents a precipitation time series, while the connections can be represented by different similarity functions. For this study, three rainfall networks were created, which differ based on the correlation function used (Pearson, Spearman, and Kendall). By comparing these networks, we can identify the most effective method for analyzing the data and gain a better understanding of rainfall's spatial structure, thereby enhancing our knowledge of its impact on different Amazon basin regions. The results reveal the presence of three important regions in the Amazon basin. Two areas were identified in the northeast and northwest, showing incursions of warm and humid winds from the oceans and favoring the occurrence of large mesoscale systems, such as squall lines. Additionally, the eastern part of the central Andes may indicate an outflow region from the basin with winds directed toward subtropical latitudes. The networks showed a high level of activity and participation in the center of the Amazon basin and east of the Andes. Regarding information transmission, the betweenness centrality identified the main pathways within a basin, and some of these are directly related to certain rivers, such as the Amazon, Purus, and Madeira. Indicating the relationship between rainfall and the presence of water bodies. Finally, it suggests that the Spearman and Kendall correlation produced the most promising results. Although they showed similar spatial patterns, the major difference was found in the identification of communities, this is due to the meridional differences in the network's response. Overall, these findings highlight the importance of carefully selecting appropriate techniques and methods when analyzing complex networks. [ABSTRACT FROM AUTHOR]

The karst water resources in the Fengfeng mining area are an important water supply source in the region. In recent years, due to the influence of external environment changes, the hydrochemical environment of karst groundwater has changed. In order to scientifically understand karst groundwater and rationally develop groundwater resources, it is necessary to study the hydrochemical characteristics and evolution laws of groundwater in this area. In this study, major elements in karst groundwater of different years and hydrogen and oxygen isotopes in 2022 were analyzed. The hydrochemical types of the study area were analyzed by using the Piper trilinear diagram drawn with Aquachem10.0 software. D and 18O were used to analyze the source and elevation of groundwater recharge. Origin software was used to draw Gibbs diagrams, three-terminal diagrams, ion ratio diagrams, etc., to analyze the influencing factors of groundwater chemical components. The research results show that: (1) The groundwater in the study area is mainly HCO3-Ca(Mg) type water; (2) The main supply source of karst water in the study area is atmospheric precipitation, and the supply elevation ranges from 1166.7 to 1533.3 m; (3) Most of the karst groundwater in the study area is affected by the dissolution of carbonate and silicate rocks; (4) With the influence of human activities, mainly agricultural activities, with the passage of time, the impact of industrial activities on groundwater has been weakening. [ABSTRACT FROM AUTHOR]

The Jizhong Depression boasts rich geothermal resources with a lengthy history of geothermal exploitation. Buried hill geothermal reservoirs, which serve as primary thermal sources for hydrothermal resource exploitation, are prevalent in this region and have advantages such as extensive development potential, significant geothermal reservoir capacity, superior water quality, and straightforward recharge. This study investigates the formation and evolution of deep geothermal water in the Jizhong Depression by analyzing the hydrochemical and isotopic data of geothermal water samples collected from buried hill geothermal reservoirs in the northern part of the depression. The findings reveal that the subsurface hot water samples from the carbonate geothermal reservoirs in this region were predominantly weakly alkaline water with a pH ranging between 6.61 and 8.87. The hot water samples collected at the wellhead exhibited temperatures varying from 33.9 °C to 123.4 °C and total dissolved solids (TDS) lying between 473.9 mg/L and 3452 mg/L. Based on the δ2H-δ18O stable isotope analysis, the geothermal fluids in the Jizhong Depression are predominantly sourced from atmospheric precipitation and exist in a somewhat isolated hydrogeological environment, exhibiting pronounced water–rock interactions and deep water circulation (with depths ranging from 1324 m to 3455 m). Through a comparison of various methods, it is deduced that the most appropriate geothermometer for deep karst geothermal reservoirs in the Jizhong Depression is a chalcedony geothermometer, and when using it, the deep reservoir temperature was estimated at 63–137.6 °C. The precipitation in the adjacent mountainous areas enables the groundwater to infiltrate and descend deep into the earth along piedmont faults. Subsequently, lateral runoff over extended periods replenishes the groundwater into the depression. This process allows for the groundwater to fully absorb heat from deep heat sources, resulting in the formation of the deep geothermal reservoirs in the northern Jizhong Depression. The insights obtained from this study offer a theoretical and scientific foundation for the exploitation and utilization of regional geothermal resources and the transformation of the energy structure in China. [ABSTRACT FROM AUTHOR]

Geothermal water (GW) resources are highly valued as clean, renewable energy sources. In this study, a comprehensive analysis of water chemistry and isotope data from 25 GW samples was conducted to gain insights into the hydrochemical characteristics and formation mechanisms of the GW in the northern Jinan region (NJR). Statistical analysis and hydrochemical methods were employed for relevant analysis. The findings reveal that the GW in the NJR exhibits high salinity, with an average total dissolved solids (TDS) concentration of 9009.00 mg/L. The major ions identified are Na+ and Cl−, with mean concentrations of 2829.73 mg/L and 4425.77 mg/L, respectively, resulting in a hydrochemical type of Cl−Na. The analysis of δ2H and δ18O isotopes indicates that the GW originates from atmospheric precipitation that undergoes deep cycling and interaction with older groundwater. The composition of 3H suggests that the GW in the NJR is a mixture of waters, while radiocarbon dating (14C) suggests that the recharge of the GW may have occurred in the late Pleistocene era. The GW in the NJR is classified as partially equilibrated waters. The temperature range of geothermal reservoirs is 57.13 to 99.74 °C. The hydrochemical components primarily result from water–rock interactions, including silicate weathering, cation exchange, as well as carbonate weathering and the dissolution of halite and gypsum. Moreover, taking into account the hydrogeological conditions, hydrochemistry, and isotope analysis, a conceptual model of the geothermal reservoir in the NJR was developed. The research findings serve as a valuable reference and foundation for the development and utilization of geothermal resources in the Jinan region. These originate from the Taiyi mountains in the south or the Taihang mountains in the west, and experience deep circulation and long runoff times. This study provides a reference for the sustainable development and utilization of regional geothermal resources. [ABSTRACT FROM AUTHOR]

Geothermal resources are the vital renewable energy for resolving energy crisis and environmental deterioration. Understanding hydrogeochemical processes, genesis mechanisms and scaling trends is crucial for securing the sustainable utilization of geothermal resources. In this study, fourteen geothermal waters were collected for hydrochemical and δ2H–δ18O isotopic analyses in northwestern Sichuan, SW China to clarify hydrogeochemical processes, genesis mechanisms, and scaling trends. Geothermal waters were recharged via atmospheric precipitation. Three different types of geothermal waters were identified using a piper diagram. Class 1 geothermal water with HCO3–Na and HCO3–SO4–Na types formed in the contact zone with Yanshanian intrusions and heated by residual radioactive heat. The hydrochemical processes were sodium/potassium silicate dissolution and positive cation–exchange. Class 2 geothermal water with HCO3–Ca and HCO3–Ca–Mg type was carbonate–type and heated by geothermal gradient. The dissolution of carbonate minerals dominated the hydrochemical process. Class 3 geothermal water with the SO4–Ca–Mg type was determined within deep faults. The dissolution of carbonatite and gypsum minerals and the oxidation of sulfides played a vital role in the hydrochemical process. The reservoir temperatures of geothermal waters followed the orders of Class 1 (74.9–137.6 °C) > Class 3 (85.9–100 °C) > Class 2 (38.7–93.5 °C). Calcium carbonate scaling should be paid attention to in Class1 and Class 3 geothermal water, and calcium sulfate scaling merely occurs in Class 3 geothermal water. This study provides vital information for geothermal exploitation in western Sichuan and other similar areas. [ABSTRACT FROM AUTHOR]

The results of modeling the conversion factor from rainfall-deposited unit activity of gamma-emitting radon and thoron daughter decay products to their created gamma-radiation dose rate as a function of height above the Earth's surface using the Geant4 toolkit are presented in this paper. Thin layers of water, soil, and air, with the height of 0.1–10 mm, are considered as the source in order to examine whether the composition of the radiation source environment affects the simulation result. Cases with different absorber-atmosphere densities are simulated. The contribution of each radionuclide 212 Bi, 214 Bi, 212 Pb, 214 Pb and 208 Tl to the total gamma background was determined. The dependence of dose rate growth during the precipitation period on the detector position in relation to the area covered by precipitation was investigated numerically. The obtained conversion factors are universal values, because do not depend on soil type (material) on which radionuclides are deposited by precipitation. These coefficients can be used for solving both direct tasks of radiation background recovery during precipitation and inverse tasks of determining the intensity and amount of precipitation by the known gamma background, as well as tasks to decipher the gamma background by the shape of the response to various phenomena. Also in this work, it is shown how thoron decay products can affect the response shape of gamma background on atmospheric precipitation. [ABSTRACT FROM AUTHOR]

This paper presents a system for supporting hydropower production on mountainous areas. The system couples the outputs of a numerical weather prediction model and a snow melting and accumulation temperature-based model. Several procedures are presented for interpolating meteorological variables and calibrating and validating model parameters that can be generalized to any other mountainous area where the estimation of current and forecasted snow water equivalent and melting amount is required. The system reliability has been assessed through the validation of three components: spatial interpolation of meteorological data, mathematical modeling, and quantitative meteorological forecast. The results show that good accuracy of meteorological data spatial interpolation can be achieved when the data from snow gauges are used for assessing the precipitation lapse rate at higher altitudes, and the temperature lapse rate is computed from data at each time step. The temperature-based hydrological model proved to be effective in simulating lake inflow water volume and energy production. No clear result has been found for snow melt forecast due to the difficulties in providing reliable quantitative weather forecast in complex alpine area. [ABSTRACT FROM AUTHOR]

The adaptability of seed metabolism to different environmental conditions represents a crucial aspect to understand the effects of climate change on plant populations in wild environments. Among the indicators of stress and repair in seeds, tocopherols and malondialdehyde have been related to membrane stability in seed deterioration. Alpine plants constitute an interesting system to understand stress response dynamics because of the relevant climate variations challenging seed viability in alpine environments. This study considered five accessions of Viscaria alpina seeds collected over five years, highlighting significant correlations between environmental parameters such as precipitations and temperature, and several indicators of the oxidative stress response. These provide new insights on how changes in indicators of the seed stress response can reflect annual variations in temperature and precipitations affecting their parental plants, with possible implications on the current understanding of seed persistence in alpine environments threatened by climate change and on the effects of seed storage. [ABSTRACT FROM AUTHOR]

As a catastrophic phenomenon, drought has destructive impacts on water resources, the environment, and the ecosystem. Consequently, drought plays a vital role in risk assessment, water resources management, and drought mitigation plans. The main aim of this research is to obtain critical intensity-duration-frequency (IDF) drought curves and to provide a comprehensive understanding of the drought characteristics by considering the meteorological Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), and hydrological Standardized Streamflow Index (SSI). Critical IDF curves for the drought index and return period selection are identified. Also, new terms are defined as the specific drought duration, the maximum drought duration, and the critical intensity based on drought IDF curves. The results show that the SPI3 based on run theory for 500 years return period has higher drought intensity compared with other drought indices. In some IDF curves, the 2-year return period of a 12-month duration timescale is not provided. Regarding the maximum drought duration, the SPEI12 gave a longer duration. With the new concepts in this research, the presented IDF drought methodology has a novel additional practice to identify the critical intensity and maximum drought duration. Using this methodology for any drought index will contribute to converting data with mathematical calculations into IDF curves for design and risk assessment purposes. [ABSTRACT FROM AUTHOR]

In Italy, most of the destructive landslides are triggered by rainfall, particularly in central Italy. Therefore, effective monitoring of rainfall is crucial in hazard management and ecosystem assessment. Global precipitation measurement (GPM) is the next-generation satellite mission, which provides the precipitation measurements worldwide. In this research, we employed the available monthly GPM data to estimate the monthly precipitation for the twenty administrative regions of Italy from June 2000 to June 2021. For each region, we applied the non-parametric Mann–Kendall test and its associated Sen's slope to estimate the precipitation trend for each calendar month. In addition, for each region, we estimated a linear trend and the seasonal cycles of precipitation with the antileakage least-squares spectral analysis (ALLSSA) and showed the annual precipitation variations using box plots. Lastly, we compared machine-learning models based on the auto-regressive moving average for monthly precipitation forecasting and showed that ALLSSA outperformed them. The findings of this research provide a significant insight into processing climate data, both in terms of trend-season estimates and forecasting, and can potentially be used in landslide susceptibility analysis. [ABSTRACT FROM AUTHOR]

Accurate precipitation forecasting is essential for emergency management, aviation, and marine agencies to prepare for potential weather impacts. However, traditional radar echo extrapolation has limitations in capturing sudden weather changes caused by convective systems. Deep learning models, an alternative to radar echo extrapolation, have shown promise in precipitation nowcasting. However, the quality of the forecasted radar images deteriorates as the forecast lead time increases due to mean absolute error (MAE, a.k.a L1) or mean squared error (MSE, a.k.a L2), which do not consider the perceptual quality of the image, such as the sharpness of the edges, texture, and contrast. To improve the quality of the forecasted radar images, we propose using the Structural Similarity (SSIM) metric as a regularization term for the Conditional Generative Adversarial Network (CGAN) objective function. Our experiments on satellite images over the region 83° W–76.5° W and 33° S–40° S in 2020 show that the CGAN model trained with both L1 and SSIM regularization outperforms CGAN models trained with only L1, L2, or SSIM regularizations alone. Moreover, the forecast accuracy of CGAN is compared with other state-of-the-art models, such as U-Net and Persistence. Persistence assumes that rainfall remains constant for the next few hours, resulting in higher forecast accuracies for shorter lead times (i.e., <2 h) measured by the critical success index (CSI), probability of detection (POD), and Heidtke skill score (HSS). In contrast, CGAN trained with L1 and SSIM regularization achieves higher CSI, POD, and HSS for lead times greater than 2 h and higher SSIM for all lead times. [ABSTRACT FROM AUTHOR]