Search

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]

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]

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]

The spatial distribution of automatic weather stations in regions of western China (e.g., Tibet and southern Xingjiang) is relatively sparse. Due to the considerable spatial variability of precipitation, estimations of rainfall that are interpolated in these areas exhibit considerable uncertainty based on the current observational networks. In this paper, a new statistical method for estimating precipitation is introduced that integrates satellite products and in situ observation data. This method calculates the differences between raster data and point data based on the theory of data assimilation. In regions in which the spatial distribution of automatic weather stations is sparse, a nonparametric kernel-smoothing method is adopted to process the discontinuous data through correction and spatial interpolation. A comparative analysis of the fusion method based on the double-smoothing algorithm proposed here indicated that the method performed better than those used in previous studies based on the average deviation, root mean square error, and correlation coefficient values. Our results indicate that the proposed method is more rational and effective in terms of both the efficiency coefficient and the spatial distribution of the deviations. [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]

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]

This work added semi-Lagrangian convected air particles to the Intermediate Complexity Atmospheric Research (ICAR) model. The ICAR model is a simplified atmospheric model using quasi-dynamical downscaling to gain performance over more traditional atmospheric models. The ICAR model uses Fortran coarrays to split the domain amongst images and handle the halo region communication of the image's boundary regions. The newly implemented convected air particles use trilinear interpolation to compute initial properties from the Eulerian domain and calculate humidity and buoyancy forces as the model runs. This paper investigated the performance cost and scaling attributes of executing unsaturated and saturated air particles versus the original particle-less model. An in-depth analysis was done on the communication patterns and performance of the semi-Lagrangian air particles, as well as the performance cost of a variety of initial conditions such as wind speed and saturation mixing ratios. This study found that given a linear increase in the number of particles communicated, there is an initial decrease in performance, but that it then levels out, indicating that over the runtime of the model, there is an initial cost of particle communication, but that the computational benefits quickly offset it. The study provided insight into the number of processors required to amortize the additional computational cost of the air particles. [ABSTRACT FROM AUTHOR]

This paper focuses on the atmospheric dust transport effect on the changes in chlorophyll-A concentration in the Black Sea surface layer. In order to assess the input of nutrients with atmospheric precipitations at the Crimean coast of the Black Sea, the collected samples were analyzed for the content of inorganic nitrogen, phosphates, and silicon. The samples were taken into a wet-only sampler and into a permanently open one, to assess the effect of dust on the nutrients concentration in dry depositions. Cases of multi-fold excess of the nutrients content in the open sampler collected precipitation over that in the wet-only sampler were identified. For such high concentration cases, the 7-day back-trajectories analyses was carried out using the model of the international network AERONET and the HYSPLIT model. The results of our research showed that the influx of nutrients with the atmospheric depositions can result in increasing of chlorophyll-A concentration in 11–36% in the surface layer of the Black Sea. After atmospheric depositions, concentration of phosphates in the surface layer can increase more than five times compared with the background concentration. The increase of silicon concentration can reach 30%. The influx of atmospheric precipitation containing significant amounts of nutrients into the bay can shifts the Redfield ratio compared with background value up to three times. [ABSTRACT FROM AUTHOR]

The Budyko framework provides a quantitative description of long-term average annual evapotranspiration at catchment scales in terms of macro-climatic variables. This framework, however, makes no reference to the vadose zone because it neglects changes in subsurface storage in the catchment water balance. Recent studies have shown clearly that vadose-zone water storage cannot be neglected at sub-catchment or sub-annual space and time scales, resulting in numerous model attempts to extend the original Budyko framework to incorporate the full water balance equation. Here we apply the approach taken in a companion paper on the original Budyko framework to show that it can be generalized rigorously to include changes in vadose-zone water storage in a manner that is both parsimonious in hypotheses and broad in scope. [ABSTRACT FROM AUTHOR]

The Multiplicative Discrete Random Cascade (MDRC) class of model is used to temporally disaggregate rainfall volumes through multiplying the volumes by random weights, which is repeated through multiple disaggregation levels. The model development involves the identification of probability density functions from which to sample the weights. The parameters of the probability density functions are known to be dependent on the rainfall volume. This paper characterises the volume dependency over the scarcely observed extreme ranges of rainfall, introducing the concept of volume-bounded MDRC models. Probable maximum precipitation (PMP) estimates are used to define theoretically-based points and asymptotes to which the observation-based estimates of the MDRC model parameters are extrapolated. Alternative models are tested using a case study of rainfall data from Brisbane, Australia covering the period 1908 to 2015. The results show that moving from a baseline model with constant parameters to incorporating the volume dependency of the parameters is essential for acceptable performance in terms of the frequency and magnitude of modelled extremes. As well as providing better estimates of parameters at each disaggregation level, the volume dependency provides an in-built bias correction when moving from one level to the next. A further, relatively small performance gain is obtained by extrapolating the observed dependency to the theoretically-based bounds. The volume dependency of the parameters is found to be reasonably time-scaleable, providing opportunity for advances in the generalisation of MDRC models. Sensitivity analysis shows that the subjectivities and uncertainties in the modelling procedure have mixed effects on the performance. A principal uncertainty, to which the results are sensitive, is the PMP estimate. Therefore, in applications of the bounded approach, the PMP should ideally be described by a probability distribution function. [ABSTRACT FROM AUTHOR]

This paper analyzes the potential of a nu-support vector regression (nu-SVR) model for the reconstruction of missing data of hydrological time series from a sensor network. Sensor networks are currently experiencing rapid growth of applications in experimental research and monitoring and provide an opportunity to study the dynamics of hydrological processes in previously ungauged or remote areas. Due to physical vulnerability or limited maintenance, networks are prone to data outages, which can devaluate the unique data sources. This paper analyzes the potential of a nu-SVR model to simulate water levels in a network of sensors in four nested experimental catchments in a mid-latitude montane environment. The model was applied to a range of typical runoff situations, including a single event storm, multi-peak flood event, snowmelt, rain on snow and a low flow period. The simulations based on daily values proved the high efficiency of the nu-SVR modeling approach to simulate the hydrological processes in a network of monitoring stations. The model proved its ability to reliably reconstruct and simulate typical runoff situations, including complex events, such as rain on snow or flooding from recurrent regional rain. The worst model performance was observed at low flow periods and for single peak flows, especially in the high-altitude catchments. [ABSTRACT FROM AUTHOR]

The relation between the atmospheric entropy flow field and local rainfall is examined in terms of the theory of dissipative structures. In this paper, the entropy balance equation in a form suitable for describing the entropy budget of the Earth's atmosphere is derived starting from the Gibbs relation, and, as examples, the entropy flows of the two severe weather events associated with the development of an extratropical cyclone and a tropical storm are calculated, respectively. The results show that negative entropy flow (NEF) has a significant effect on the precipitation intensity and scope with an apparent matching of the NEF's pattern with the rainfall distribution revealed and, that the diagnosis of NEF is able to provide a good indicator for precipitation forecasting. [ABSTRACT FROM AUTHOR]

Coal mining has a certain influence on and causes disturbances in groundwater. To investigate the variation trend of groundwater around the open-pit mine in grassland area, taking Shengli No. 1 open-pit mine as an example, the impact and variation trend of groundwater level in Quaternary aquifer around the mine area was studied by using the data of hydrological monitoring wells. The results show that the water level around the mining area varies from one year to the next. Since 2008, the water level has experienced a process of reduction, stability and increase. Compared with the background water level value, the current water level of each monitoring well is lower than the background water level. The influence radius calculated by Kusakin formula ranges from 94.15 m to 906.80 m, and the aquifer is heterogeneous. On the basis of the correlations between changes in waterline in monitoring wells and the stope distance, the disturbance radius of open-pit mining on surrounding diving water in grassland area is less than 2000 m. Based on the comprehensive analysis of the alteration of diving waterline and its influencing factors, the main factors affecting the variation in the phreatic water level are atmospheric precipitation, evaporation, groundwater usage and dewatering water. All factors act on the diversification of diving water level synthetically. The internal waste dump of an open-pit mine has a positive effect on the surrounding groundwater recovery. The aim of this study is to reveal the impact of open-pit mining on surrounding groundwater and providing scientific basis for future mining in other open-pit mines. [ABSTRACT FROM AUTHOR]

The northern margin of the North China Craton is one of the most important porphyry-skarn molybdenum ore belts in the world. The eastern Hebei Province, which contains a high number of molybdenum and gold (molybdenum) resources, is an important portion of the northern margin of the North China Craton. Xichanggou and Huashi, located in eastern Hebei, are quartz-molybdenum vein deposits that are intimately associated with intrusions that are deeply concealed in the mining area. This work presents two zircon U-Pb dates and ten molybdenite Re-Os ages from samples of the aforementioned two deposits in order to determine the timing of the intrusion and mineralization. The zircon U-Pb ages of the quartz monzonite porphyry from Xichanggou are determined to be 163.3 ± 0.3 Ma and 162.8 ± 0.4 Ma. The molybdenite Re-Os dating yielded ages of 160.3 ± 4.6 Ma for Xichanggou and 171.4 ± 19 Ma for Huashi, respectively. The isotopic composition of oxygen and hydrogen of the ore-forming fluid from Huashi, as indicated by the δDV-SMOW values (−80.0‰ to −67.6‰) and δ18OH2O values (−1.86‰ to 2.33‰), suggests that the fluid is primarily composed of water derived from magma, with some contribution from atmospheric precipitation. The sulfur isotope values (δ34S) of sulfides from Xichanggou range from 6.5‰ to 7.1‰, while the δ34S values from Huashi range from 3.3‰ to 4.9‰. The lead isotope ratios (206Pb/204Pb and 207Pb/204Pb) of sulfides from Xichanggou and Huashi average at 17.414, 15.428, and 17.591, 15.379, respectively. The Re-Os isotopic compositions of ore sulfides mostly fall within the range of 318 ppm to 50,114 ppm. These isotopic compositions indicate that the materials responsible for the formation of the ores in Xichanggou and Huashi primarily originate from the melting of lower crust materials that have been contaminated by the mantle. Based on the regional data, the molybdenum deposits in eastern Hebei were formed in multiple periods, specifically approximately 170 Ma and 160 Ma in Huahsi and Xichanggou, respectively. The subduction of the Paleo-Pacific plate during the middle–late Jurassic period led to the partial remelting of lower crust material, resulting in the acquisition of a significant quantity of metal elements (Mo), which were subsequently deposited. [ABSTRACT FROM AUTHOR]

Soil moisture is the most direct evaluation index for agricultural drought. It is not only directly affected by meteorological conditions such as precipitation and temperature but is also indirectly influenced by environmental factors such as climate zone, surface vegetation type, soil type, elevation, and irrigation conditions. These influencing factors have a complex, nonlinear relationship with soil moisture. It is difficult to accurately describe this non-linear relationship using a single indicator constructed from meteorological data, remote sensing data, and other data. It is also difficult to fully consider environmental factors using a single drought index on a large scale. Machine learning (ML) models provide new technology for nonlinear problems such as soil moisture retrieval. Based on the multi-source drought indexes calculated by meteorological, remote sensing, and land surface model data, and environmental factors, and using the Cubist algorithm based on a classification decision tree (CART), a comprehensive agricultural drought monitoring model at 10 cm, 20 cm, and 50 cm depth in Gansu Province is established. The influence of environmental factors and meteorological factors on the accuracy of the comprehensive model is discussed, and the accuracy of the comprehensive model is evaluated. The results show that the comprehensive model has a significant improvement in accuracy compared to the single variable model, which is a decrease of about 26% and 28% in RMSE and MAPE, respectively, compared to the best MCI model. Environmental factors such as season, DEM, and climate zone, especially the DEM, play a crucial role in improving the accuracy of the integrated model. These three environmental factors can comprehensively reduce the average RMSE of the comprehensive model by about 25%. Compared to environmental factors, meteorological factors have a slightly weaker effect on improving the accuracy of comprehensive models, which is a decrease of about 6.5% in RMSE. The fitting accuracy of the comprehensive model in humid and semi-humid areas, as well as semi-arid and semi-humid areas, is significantly higher than that in arid and semi-arid areas. These research results have important guiding significance for improving the accuracy of agricultural drought monitoring in Gansu Province. [ABSTRACT FROM AUTHOR]

Extreme rainstorms have important socioeconomic consequences, but understanding their fine spatial structures and temporal evolution still remains challenging. In order to achieve this, in view of an evolutionary property of rainstorms, this paper designs a process-oriented algorithm for identifying and tracking rainstorms, named PoAIR. PoAIR uses time-series of raster datasets and consists of three steps. The first step combines an accumulated rainfall time-series and spatial connectivity to identify rainstorm objects at each time snapshot. Secondly, PoAIR adopts the geometrical features of eccentricity, rectangularity, roundness, and shape index, as well as the thematic feature of the mean rainstorm intensity, to match the same rainstorm objects in successive snapshots, and then tracks the same rainstorm objects during a rainstorm evolution sequence. In the third step, an evolutionary property of a rainstorm sequence is used to extrapolate its spatial location and geometrical features at the next time snapshot and reconstructs a rainstorm process by linking rainstorm sequences with an area-overlapping threshold. Experiments on simulated datasets demonstrate that PoAIR performs better than the Thunderstorm Identification, Tracking, Analysis and Nowcasting algorithm (TITAN) in both rainfall tracking and identifying the splitting, merging, and merging-splitting of rainstorm objects. Additionally, applications of PoAIR to Integrated Multi-satellitE Retrievals for Global Precipitation Mission (GPM/IMERG) final products covering mainland China show that PoAIR can effectively track rainstorm objects. [ABSTRACT FROM AUTHOR]

A rainfall threshold for landslide occurrence at a national scale in China has rarely been developed in the early warning system for landslides. Based on 771 landslide events that occurred in China during 1998–2017, four groups of rainfall thresholds at different quantile levels of the quantile regression for landslide occurrences in China are defined, which include the original rainfall event–duration (E–D) thresholds and normalized (the accumulated rainfall is normalized by mean annual precipitation) (EMAP–D) rainfall thresholds based on the merged rainfall and the Climate Prediction Center Morphing technique (CMORPH) rainfall products, respectively. Each group consists of four sub-thresholds in rainy season and non-rainy season, and both are divided into short duration (<48 h) and long duration (≥48 h). The results show that the slope of the regression line for the thresholds in the events with long durations is larger than that with short durations. In addition, the rainfall thresholds in the non-rainy season are generally lower than those in the rainy season. The E–D thresholds defined in this paper are generally lower than other thresholds in previous studies on a global scale, and a regional or national scale in China. This might be due to there being more landslide events used in this paper, as well as the combined effects of special geological environment, climate condition and human activities in China. Compared with the previous landslide model, the positive rates of the rainfall thresholds for landslides have increased by 16%–20%, 10%–17% and 20%–38% in the whole year, rainy season and non-rainy season, respectively. [ABSTRACT FROM AUTHOR]

A drop-freeze array (PeKing University Ice Nucleation Array, PKU-INA) was developed based on the cold-stage method to investigate heterogeneous ice nucleation properties of atmospheric particles in the immersion freezing mode from -30 to 0 °C. The instrumental details as well as characterization and performance evaluation are described in this paper. A careful temperature calibration protocol was developed in our work. The uncertainties in the reported temperatures were found to be less than 0.4 °C at various cooling rates after calibration. We also measured the ice nucleation activities of droplets containing different mass concentrations of illite NX, and the results obtained in our work show good agreement with those reported previously using other instruments with similar principles. Overall, we show that our newly developed PKU-INA is a robust and reliable instrument for investigation of heterogeneous ice nucleation in the immersion freezing mode. [ABSTRACT FROM AUTHOR]

Deep convection is a threat to many human activities, with a great impact on aviation safety. On 7 July 2017, a widespread torrential precipitation event (associated with a cut-off low at mid-levels) was registered in the vicinity of Madrid, causing serious flight disruptions. During this type of episode, accurate short-term forecasts are key to minimizing risks to aviation. The aim of this research is to improve early warning systems by obtaining the best WRF model setup. In this paper, the aforementioned event was simulated. Various model configurations were produced using four different physics parameterizations, 3-km and 1-km domain resolutions, and 0.25° and 1° initial condition resolutions. Simulations were validated using data from 17 rain gauge stations. Two validation indices are proposed, accounting for the temporal behaviour of the model. Results show significant differences between microphysics parameterizations. Validation of domain resolution shows that improvement from 3 to 1 km is negligible. Interestingly, the 0.25° resolution for initial conditions produced poor results compared with 1°. This may be linked to a timing error, because precipitation was simulated further east than observed. The use of ensembles generated by combining different WRF model configurations produced reliable precipitation estimates. [ABSTRACT FROM AUTHOR]

Quantifying the impacts of multiple factors on surface runoff and base flow is essential for understanding the mechanism of hydrological response and local water resources management as well as preventing floods and droughts. Despite previous studies on quantitative impacts of multiple factors on runoff, there is still a need for assessment of the influence of these factors on both surface runoff and base flow in different temporal scales at the watershed level. The main objective of this paper was to quantify the influence of precipitation variation, evapotranspiration (ET) and vegetation restoration on surface runoff and base flow using empirical statistics and slope change ratio of cumulative quantities (SCRCQ) methods in Pengchongjian small wate (116°25048"-116°2707" E, 29°31044"-29°32056" N, 2.9 km²), China. The results indicated that, the contribution rates of precipitation variation, ET and vegetation restoration to surface runoff were 42.1%, 28.5%, 29.4% in spring; 45.0%, 37.1%, 17.9% in summer; 30.1%, 29.4%, 40.5% in autumn; 16.7%, 35.1%, 48.2% in winter; and 35.0%, 38.7%, 26.3% in annual scale, respectively. For base flow they were 33.1%, 41.9%, 25.0% in spring; 39.3%, 51.9%, 8.8% in summer; 40.2%, 38.2%, 21.6% in autumn; 24.3%, 39.4%, 36.3% in winter; and 24.4%, 47.9%, 27.7% in annual scale, respectively. Overall, climatic factors, including precipitation and ET change, affect surface runoff generation the most, while ET affects the dynamic change of annual base flowthe most. This study highlights the importance of optimizing forest management to protect the water resource. [ABSTRACT FROM AUTHOR]

Spatial and temporal variation in precipitation patterns can directly alter the survival and growth of plants, yet in China there is no comprehensive and systematic strategy for plant use based on the effects of precipitation patterns. Here, we examined information from 93 published papers (368 plant species) on plant xylem water stable isotopes (δD and δ18O) in China. The results showed that: (1) The slope of the local meteoric water line (LMWL) gradually increased from inland areas to the coast, as a result of continental and seasonal effects. The correlation between δD and δ18O in plant stem water is also well fitted and the correlation coefficients range from 0.78 to 0.89. With respect to the soil water line, the δ18O values in relation to depth (0-100 cm) varied over time; (2) Plants' main water sources are largely affected by precipitation patterns. In general, plants prioritize the use of stable and continuous water sources, while they have a more variable water uptake strategy under drought conditions; (3) There are no spatial and temporal variations in the contribution of the main water source (p > 0.05) because plants maintain growth by shifting their use of water sources when resources are unreliable. [ABSTRACT FROM AUTHOR]

The current neighborhood probability (NP) method mainly considers the spatial displacement error in high-resolution precipitation forecasts, but the problem of the forecast time exceeding or lagging behind the observed field has not been properly solved. Therefore, a temporal factor was introduced into the NP method in this paper, and precipitation forecasts were evaluated in different spatial-temporal neighborhoods based on the improved NP method and fractions skill score (FSS), combined with the relative operating characteristic (ROC) curve. The results indicated that the forecasting accuracy of the ensemble forecast was higher than the control forecast. The neighborhood ensemble probability (NEP) and probability matched mean (PMM) methods were superior to the traditional ensemble mean (EM) method in forecasting heavy rainfall, which compensated for the limitations of the heavy rainfall forecasting ability of EM. For such squall line processes, a spatial scale of 15-45 km neighborhood radius could effectively rectify the displacement error of precipitation. There was a corresponding relationship between temporal scale and rainfall intensity in convective-scale precipitation forecast, so the temporal uncertainty of different levels of precipitation could be captured by different temporal scales. The spatial and temporal scales had interdependent influences on precipitation forecast effects, which could be affected by the intrinsic spatial-temporal scale of convective-scale weather systems. The improved NP method could simultaneously reflect the spatial and temporal uncertainties of convective-scale precipitation forecasts in high-resolution models, achieving a comprehensive assessment of spatial-temporal scale and providing probabilistic forecast results that match different levels of precipitation. [ABSTRACT FROM AUTHOR]

Robustness of water distribution networks is related to their connectivity and topological structure, which also affect their reliability. Flow entropy, based on Shannon's informational entropy, has been proposed as a measure of network redundancy and adopted as a proxy of reliability in optimal network design procedures. In this paper, the scaling properties of flow entropy of water distribution networks with their size and other topological metrics are studied. To such aim, flow entropy, maximum flow entropy, link density and average path length have been evaluated for a set of 22 networks, both real and synthetic, with different size and topology. The obtained results led to identify suitable scaling laws of flow entropy and maximum flow entropy with water distribution network size, in the form of power-laws. The obtained relationships allow comparing the flow entropy of water distribution networks with different size, and provide an easy tool to define the maximum achievable entropy of a specific water distribution network. An example of application of the obtained relationships to the design of a water distribution network is provided, showing how, with a constrained multi-objective optimization procedure, a tradeoff between network cost and robustness is easily identified. [ABSTRACT FROM AUTHOR]

The growth of urban population, combined with an increase of extreme events due to climate change call for a better understanding and representation of urban floods. The uncertainty in rainfall distribution is one of the most important factors that affects the watershed response to a given precipitation event. However, most of the investigations on this topic have considered theoretical scenarios, with little reference to case studies in the real world. This paper incorporates the use of spatially-variable precipitation data from a long-range radar in the simulation of the severe floods that impacted the city of Hull, U.K., in June 2007. This radar-based rainfall field is merged with rain gauge data using a Kriging with External Drift interpolation technique. The utility of this spatially-variable information is investigated through the comparison of computed flooded areas (uniform and radar) against those registered by public authorities. Both results show similar skills at reproducing the real event, but differences in the total precipitated volumes, water depths and flooded areas are illustrated. It is envisaged that in urban areas and with the advent of higher resolution radars, these differences will be more important and call for further investigation. [ABSTRACT FROM AUTHOR]

The influence of initial state variables on flood forecasting accuracy by using conceptual hydrological models is analyzed in this paper and a novel flood forecasting method based on correction of initial state variables is proposed. The new method is abbreviated as ISVC (Initial State Variable Correction). The ISVC takes the residual between the measured and forecasted flows during the initial period of the flood event as the objective function, and it uses a particle swarm optimization algorithm to correct the initial state variables, which are then used to drive the flood forecasting model. The historical flood events of 11 watersheds in south China are forecasted and verified, and important issues concerning the ISVC application are then discussed. The study results show that the ISVC is effective and applicable in flood forecasting tasks. It can significantly improve the flood forecasting accuracy in most cases. [ABSTRACT FROM AUTHOR]

The objective of this paper was to analyze the temporal and spatial trends in annual and seasonal precipitation in Slovakia utilizing 487 gauging station data collected statewide in the period from 1981 to 2013. The nonparametric Mann-Kendall (MK) statistical test, which has been widely used to assess the significance of trends in hydrological time series, was applied to detect the significant trends. The Theil-Sen (TS) approach was applied to quantify the trend magnitude, and the Sequential Mann-Kendall (SQMK) test was used to detect abrupt temporal trend shifts. In general, precipitation data in the study area have not changed during the last 33 years, and there are no big gaps. However, predominantly increasing trends in precipitation time series were found at most of the gauging stations in Slovakia. The results corroborate previous climatic studies in the area of central Europe. In Slovakia, there is evidence of different rain distribution from the monthly point of view. Decreasing trends were detected in December in the northern part of Slovakia, while the central and southern parts revealed increasing trends. Most of the stations showed increasing summer precipitation trends, especially in July. It is expected that the findings of this comprehensive study of precipitation trends over Slovakia will produce more insights for the understanding of the regional hydrological behavior in Slovakia over the last several decades. [ABSTRACT FROM AUTHOR]

Copula functions have been extensively used to describe the joint behaviors of extreme hydrological events and to analyze hydrological risk. Advanced marginal distribution inference, for example, the maximum entropy theory, is particularly beneficial for improving the performance of the copulas. The goal of this paper, therefore, is twofold; first, to develop a coupled maximum entropy-copula method for hydrological risk analysis through deriving the bivariate return periods, risk, reliability and bivariate design events; and second, to reveal the impact of marginal distribution selection uncertainty and sampling uncertainty on bivariate design event identification. Particularly, the uncertainties involved in the second goal have not yet received significant consideration. The designed framework for hydrological risk analysis related to flood and extreme precipitation events is exemplarily applied in two catchments of the Loess plateau, China. Results show that (1) distribution derived by the maximum entropy principle outperforms the conventional distributions for the probabilistic modeling of flood and extreme precipitation events; (2) the bivariate return periods, risk, reliability and bivariate design events are able to be derived using the coupled entropy-copula method; (3) uncertainty analysis highlights the fact that appropriate performance of marginal distribution is closely related to bivariate design event identification. Most importantly, sampling uncertainty causes the confidence regions of bivariate design events with return periods of 30 years to be very large, overlapping with the values of flood and extreme precipitation, which have return periods of 10 and 50 years, respectively. The large confidence regions of bivariate design events greatly challenge its application in practical engineering design. [ABSTRACT FROM AUTHOR]

Proper water resources management involves the analysis and resolution of various optimization problems according to climate change effects on the availability and distribution of the resources themselves. Specifically, these conditions require the identification of new resource allocation optimization solutions capable of taking into account the water resource losses due to climate change scenarios. As is well known, Southern Italy is a region that is potentially very sensitive to climate change. In this paper, a 1717 km² area, corresponding to the province of Crotone, was analyzed as a study case. This area is characterized by a sufficient availability of resources as a whole as compared to the needs of the users, but has an unbalanced distribution of water through its various systems. After identifying water resource allocations in detail for this area, an optimization solution accounting for the expected reduced availability of water resources in the context of climate change was created and was compared with the optimization solution for current water availability. [ABSTRACT FROM AUTHOR]

In this paper, we applied the support vector machine (SVM) to the spatial interpolation of the multi-year average annual precipitation in the Three Gorges Region basin. By combining it with the inverse distance weighting and ordinary kriging method, we constructed the SVM residual inverse distance weighting, as well as the SVM residual kriging precipitation interpolation model and compared them with the inverse distance weighting, ordinary kriging, linear regression residual inverse distance weighting and linear regression residual kriging interpolation methods. The TRMM 3B43 V7 satellite precipitation information, which is processed by the latest revision algorithm, is used as the auxiliary variable for ground site precipitation interpolation along with latitude and elevation. Our results show that: (1) adding the TRMM 3B43 V7 satellite precipitation data as an auxiliary variable significantly improves the interpolation accuracy of the linear regression equation and SVM model; (2) the support vector machine hybrid interpolation method obtains superior interpolation results compared to the inverse distance weighting method, ordinary kriging method and linear regression hybrid interpolation method; (3) the interpolation accuracy of the SVM hybrid interpolation method depends on the SVM fitting degree, so we should choose a suitable fitting accuracy rather than the highest fitting accuracy; (4) the linear regression equation has a greater degree of dependency on the TRMM data than the SVM. The SVM accepts the TRMM data information while better maintaining its independence, taking into account that the TRMM data linear regression and linear regression hybrid interpolation method are not suitable for TRMM data evaluation. [ABSTRACT FROM AUTHOR]

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

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

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

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

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

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

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

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

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

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]

This study aims to provide an opinion on the state-of-the-art of changes and reforms of water policies in South Korea, as well as the challenges along with their implications for sustainable water governance and management. In parallel with change in water resource characteristics generated by physical, environmental and socio-economic challenges such as: (1) uncertainties about climate change (flooding and drought) including seasonal and regional variation in precipitation; (2) significant increase in water use caused by rapid urbanization and population growth in industrialized urban areas; (3) inadequate water pricing mechanism which covers only around 80% of the production cost and makes it harder to maintain water systems; and (4) recursive water quality degradation and conflicts over water rights between regions resulting from non-point source pollution in highland versus lowland areas, Korean water policies have been developed through diverse reforms over 100 years. Nevertheless, new challenges for sustainable water management are continuously emerging. To meet those challenges we provide two ideas: (i) provider-gets-principle (payment for ecosystem services) of cost-benefit sharing among stakeholders who benefit from water use; and (ii) water pricing applying full-cost pricing-principle internalizing environmental externalities caused by the intensive water use. Funds secured from the application of those methods would facilitate: (1) support for upstream (rural) low income householders suffering from economic restrictions; (2) improvement in water facilities; and (3) efficient water use and demand management in South Korea's water sectors. We expect that this paper can examine the lessons relevant to challenges that South Korea faces and offer some implications on the formulation of new integration and further reforms of the institutions, laws and organizations responsible for managing water resources in South Korea. [ABSTRACT FROM AUTHOR]

Baseflow is an essential component of river runoff. Accurate measurements and analyses of baseflow change are challenging in permafrost-covered regions. In this paper, the upper reaches of the Shule River were selected as the study area, in which to study the baseflow change regulation and causes. The variable infiltration capacity (VIC) model, based on the ARNO baseflow formulation, was used to simulate the baseflow. Simulated baseflow was validated by the isotopic baseflow separation results and measured runoff in the recession periods throughout an entire year. It was found that approximately 63.1% of the river runoff was sourced by baseflow in the study region; the baseflow change was relatively smooth throughout the year, and it lagged a few days behind the river runoff. Approximately 80% of the total baseflow was generated in the 3500-4500 m alpine regions, with mainly low-temperature and mid-temperature permafrost. Based on the climate, runoff, land use, soil temperature and moisture data of the permafrost active layer, the mechanism of baseflow change in the permafrost zone was analysed. Precipitation and temperature positively enhanced the baseflow in the permafrost region throughout a year, but the baseflow was more influenced by the temperature than precipitation. In the study area, the cold desert and alpine grassland had the largest regulation capacity for baseflow. Affected by the permafrost freeze-thaw process, a baseflow peak occurred in the spring and the baseflow recession slowed in the autumn. This lead to a more uniform distribution of baseflow and runoff throughout the year. [ABSTRACT FROM AUTHOR]