Total Amount Of Precipitation Research Articles

Generating synthetic rainfall fields by R‐vine copulas applied to seamless probabilistic predictions

AbstractMany post‐processing methods improve forecasts at individual locations but remove their correlation structure. However, this information is essential for forecasting larger‐scale events, such as the total precipitation amount over areas like river catchments, which are relevant for weather warnings and flood predictions. We propose a method to reintroduce spatial correlation into a post‐processed forecast using an R‐vine copula fitted to historical observations. The method rearranges predictions at individual locations and ensures that they still exhibit the post‐processed marginal distributions. It works similarly to well‐known approaches, like the “Schaake shuffle” and “ensemble copula coupling.” However, compared to these methods, which rely on a ranking with no ties at each considered location in their source for spatial correlation, the copula serves as a measure of how well a given arrangement compares with the observed historical distribution. Therefore, no close relationship is required between the post‐processed marginal distributions and the spatial correlation source. This is advantageous for post‐processed seamless forecasts in two ways. First, meteorological parameters such as the precipitation amount, whose distribution has an atom at zero, have rankings with ties. Second, seamless forecasts represent an optimal combination of their input forecasts and may spatially shifted from them at scales larger than the areas considered herein, leading to non‐reasonable spatial correlation sources for the well‐known methods. Our results indicate that the calibration of the combination model carries over to the output of the proposed model, that is, the evaluation of area predictions shows a similar improvement in forecast quality as the predictions for individual locations. Additionally, the spatial correlation of the forecast is evaluated with the help of object‐based metrics, for which the proposed model also shows an improvement compared to both input forecasts.

The impact of aerosols as ice nucleating particle on microphysics and electrification in the cumulus model

Here, a heterogeneous ice nucleation parameterization associated with aerosol acting as ice nucleating particle (INP) was implanted into a two-dimensional numerical cumulus model. To explore the impact of INP on microphysical and electrical processes, a comparison was conducted with the original approach, which employs an empirical formula. Simulation results indicate that INP greatly impacted microphysical evolution in the heterogeneous ice nucleation process, reducing total liquid precipitation amounts and causing a slight precipitation delay, as well as increasing the diameter and mixing ratio of ice crystals and the expansion of the vertical distribution of ice crystals. This led to a notable change in electrification in thunderstorms. The increase of ice crystal diameter was the dominant contributor to the enhancement of electrification using the new parameterization. Additionally, unlike the structure of thunderclouds in a mature stage, which always retains a normal dipole structure adopting the empirical formula, a tripole structure developed a lower positive charge, and the polarity inversion with upper negative and lower positive charges occurred when the new parameterization was adopted. This predominately was the result of abundant ice crystals present below the reversal temperature. The electrification characteristics of thunderstorms may have a close connection with lightning activity. It has been found that charge structure changed significantly in the two cases, with the tripolar charge structure facilitating the production of inverted intra-cloud (IC) flashes and negative Cloud-to-ground (CG) flashes simulated by the new parameterization; additionally, clouds simulated using the empirical formula may be able to develop normal IC lightning and positive CG flashes. Therefore, it will be very meaningful to obtain greater insight into the characteristics of thunderstorms electrification in cumulus model with aerosols.

Exploring Spatio-Temporal Precipitation Variations in Istanbul: Trends and Patterns from Five Stations across Two Continents

This study aims to reveal the long-term station-based characteristics of precipitation in Istanbul, a mega city located on the continents of Europe and Asia, with complex topography and coastline along the Marmara and Black Seas. Using data from five different stations, three located in the European continent and two in the Asian continent, with measurement periods ranging from 72 to 93 years, wet and dry days have been identified, statistics on precipitation conditions during the warm and cold seasons have been generated, categorization based on precipitation intensities has been performed, and analyses have been conducted using extreme precipitation indices. At stations located in the northern part of the city, higher annual total precipitation has been observed compared to those in the south. A similar situation applies to the number of wet days. While during the cold season, the wet and dry day counts are nearly the same across all stations, this condition exhibits significant differences in favor of dry days during the warm season. Apart from dry conditions, “moderate” precipitation is the most frequently observed type across all stations. However, “extreme” events occur significantly more often (6%) during the warm season compared to the cold season (2%). Long-term anomalies in terms of annual precipitation totals have shown similarity between stations in the north and south, which has also been observed in longitudinally close stations. Despite the longer duration of the cold season and stronger temperature gradients, extreme rainfall events are more frequent during the warm season, primarily due to thunderstorm activity. While trend analyses revealed limited significant trends in precipitation intensity categories and extreme indices, the study highlights the importance of comprehensive examination of extreme rainfall events on both station-based and regional levels, shedding light on potential implications for regional climate change. Lastly, during the cold season, the inter-station correlation in terms of annual total precipitation amounts has been considerably higher compared to the warm season.

Climate change impacts on precipitation and water resources in Northwestern China

A statistical analysis is conducted on the hourly precipitation at 105 national meteorological stations in the Xinjiang region of China from 2011 to 2020. The purpose of this analysis is to determine the total quantity of precipitation during the warm and cold seasons, as well as the precipitation frequency at each station. The results of the analysis indicate that there is a more pronounced inter-annual variation in precipitation during the warm and cold seasons in Xinjiang, where the total amount of precipitation during the warm season is significantly higher than that during the cold season, with the warm season experiencing at least twice as much precipitation. In terms of the inter-annual incremental amount of precipitation, both the warm and cold seasons exhibit a trend of increasing and then decreasing over the years. However, the curvature change is more prominent in the cold season compared to the warm season, suggesting that the inter-annual variation in precipitation during the warm season remains relatively stable. The spatial distribution characteristics of the total precipitation in Xinjiang indicate that warm-season precipitation is more scattered, while cold-season precipitation is mainly concentrated in the northern region of the territory. This pattern holds true at low, medium, medium–high, and high altitudes. The cumulative precipitation during the warm season is always higher than that in the cold season, and this difference becomes more prominent with increasing altitude. This indicates that warm-season precipitation in Xinjiang is closely related to its complex topographic structure, particularly the convective weather generated by topographic clouds. On the other hand, precipitation in the cold season is mainly concentrated in the low- and medium-altitude stations, and there is not much correlation between precipitation and altitude. Precipitation is mainly affected by the mesoscale and the direct influence of large-scale weather systems. Analysis of precipitation in extreme moments reveals that during the warm season, extreme precipitation events occur mainly from the evening to early morning (19:00–03:00 the next day), accounting for 75.24% of the 105 stations. In contrast, during the cold season, extreme precipitation events occur between 03:00 and 18:00, corresponding to 74.29% of the stations. The relationship between precipitation and altitude differs in the warm and cold seasons. In the warm season, there is a linear relationship between precipitation and altitude, with a slope of 0.72 and a correlation coefficient of 0.52, indicating that precipitation increases with increasing altitude. However, in the cold season, the linear relationship between precipitation and altitude is very inconspicuous, with a slope of −0.05 and a correlation coefficient of −0.05, suggesting that there is no significant relationship between precipitation and altitude during this season. These findings provide valuable insights into the changing patterns of precipitation gradients at different altitudes and seasons in Xinjiang. This information can be used for the site selection of smokestack operations in high-altitude areas and for ground and air weather modification operations in low-altitude areas. Additionally, these data contribute to the understanding of precipitation patterns in Xinjiang, further improving the effectiveness of weather modification efforts and increasing the utilization of airborne water resources.

Impact of Rainfall on the Detection Performance of Non-Contact Safety Sensors for UAVs/UGVs.

This study comprehensively investigates how rain and drizzle affect the object-detection performance of non-contact safety sensors, which are essential for the operation of unmanned aerial vehicles and ground vehicles in adverse weather conditions. In contrast to conventional sensor-performance evaluation based on the amount of precipitation, this paper proposes spatial transmittance and particle density as more appropriate metrics for rain environments. Through detailed experiments conducted under a variety of precipitation conditions, it is shown that sensor performance is significantly affected by the density of small raindrops rather than the total amount of precipitation. This finding challenges traditional sensor-evaluation metrics in rainfall environments and suggests a paradigm shift toward the use of spatial transmittance as a universal metric for evaluating sensor performance in rain, drizzle, and potentially other adverse weather scenarios.

Rice mutants, selected under severe drought stress, show reduced stomatal density and improved water use efficiency under restricted water conditions.

Rice is among the least water-use-efficient crops, and rice plants utilise most of their water uptake for transpirational cooling via stomata. To improve water-use efficiency (WUE) in rice, reducing stomatal density and size could help optimise transpiration and photosynthesis. In this study, we compared two series of purple rice stomata mutants: the Stomatal Model Mutant (SMM) identified by microscopic observation of flag-leaf stomata, and the Drought-selected Model Mutant (DMM) generated through screening under severe water stress. After undergoing two rounds of severe water stress between -60 to -80 Ym, right before the R1-2 reproductive stage, three DMMs were selected based on their rapid recovery rate and % filled-grain percentage. The three DMMs displayed 618-697 stomatal units per mm2, similar to the SMMs low-density stomata mutant (JHN 8756 (LD)). Furthermore, the four SMMs, three DMMs and the Jao Hom Nin wild type (JHN WT) were treated with two restricted water condition schemes from seedlings to harvest. The total amount of irrigation and precipitation during the experiment was 78.1 L/plant (69.1 mm/plant) for the less restricted water condition (LR) and 47.5 L/plant (42 mm/plant) for the more restricted water condition (MR). Water condition treatments had no effects on stomatal density and stomatal index. In contrast, genotypes and restricted water condition schemes affected plant height, tillers/plant, % filled grains and shoot dry weight (SDW). The three DMMs and the JHN 8756 (LD), the SMM's low-density stomata mutant, displayed greater resilience towards more restricted water conditions than the SMMs and the JHN wild type. Particularly, DMMs were tolerant to more restricted water condition treatments, showing no SDW penalties. Together, the DMMs and the JHN 8756 (LD) displayed higher WUE under these conditions of more restricted water conditions. A rigorous screening process to distinguish tolerant mutants with a rapid drought recovery rate from severe water stress could pave the way to isolate more mutants with better stomatal functionality and resilience in preparation for imminent climate changes.

The Effect of Convective/Advective Precipitation Partitions on the Precipitation Isotopes in the Monsoon Regions of China: A Case Study of Changsha

Abstract Convective/advective precipitation partitions refer to the divisions of precipitation that are either convective or advective in nature, relative to the total precipitation amount. These distinct partitions can have a significant influence on the stable isotope composition of precipitation. This study analyzed and compared the effect of precipitation partitions on δ18O in precipitation (δ18Op) by using daily precipitation stable isotope data from Changsha station and monthly precipitation stable isotope data from the Global Network of Isotopes in Precipitation (GNIP), under different time scales, time intervals (i.e., annual, warm season, and cold season), and precipitation intensities. The results showed that the correlation between the convective precipitation fraction (CPF) and total precipitation amount was influenced by the intensity of convection in different time intervals. On both the daily and monthly scales, the CPF decreased as the precipitation amount increased in the warm season, while it increased with increasing precipitation amount in the cold season. Regardless of the season, daily δ18Op at Changsha station consistently increased with an increase in daily CPF. On a daily scale, the effect of convective activity on δ18Op was stronger than that of the “precipitation amount effect” in the cold season, as compared to the situation in the warm season. As a result, the regression line slope between δ18Op and CPF increased with increasing precipitation intensity in the warm season, meaning that as the CPF increased, the δ18Op increased at a faster rate under higher precipitation intensity. Similarly, the slope increased with increasing precipitation intensity in the cold season. This suggests that precipitation intensity and convection intensity can affect the relationship between δ18Op and CPF. Our findings shed light on how different precipitation partitions affect stable isotope composition of precipitation, thus enhancing our understanding of the variability of precipitation stable isotopes in the monsoon regions of China. Significance Statement This study aims to better elucidate the influence of different precipitation partitions on precipitation stable isotopes. In the eastern monsoon region of China, stable isotopes in precipitation showed a robust positive relationship with convective precipitation faction. On a daily scale, the convective activity enhanced the influences of the “precipitation amount effect” on precipitation stable isotopes in the warm season and reduced such influences in the cold season. These results improve our understanding of stable isotopic variability of precipitation in the eastern monsoon region, China.

Bрожайність ранньостиглих сортів картоплі за впливу природно-кліматичних умов західного лісостепу

Agriculture remains a key branch of the economy, ensuring food security and the production of important types of raw materials, but due to the fact that predictive assessments demonstrate the negative impact of climate change on the yield of agricultural crops, there is a need to modernize existing models of agricultural production and improve methods of managing agricultural systems with taking into account climate change. The issues of optimization of obtaining high productivity of potatoes, in certain regions, should first of all be aimed at the rational use of natural and climatic conditions of the year, with the aim of timely revision of the cultivation technology, which would correspond to the most complete realization of the agro-climatic potential of the research area, namely the Western Forest-Steppe. Solving this task depends on the degree of study of the interaction and interdependence of the influence of natural and climatic conditions on the processes of formation of yield and quality of potatoes. The results of the analysis of weather and climate conditions in the Western Forest-Steppe during 2010-2023 and their impact on the yield of early-ripening potato varieties are presented. It was investigated that the meteorological conditions in some years fluctuated sharply in comparison both to previous years and to the average long-term data. This is especially true about the total amount of precipitation and its distribution during the potato growing season, because with regard to the dynamics of the temperature regime, as already noted, a significant increase has been observed. Despite this during the potato growing season the temperature and water conditions provided favorable conditions for obtaining a high yield of early-ripening potato varieties. The presented results of the analysis of weather and climate conditions in the Western Forest-Steppe during 2010-2023 indicate a positive effect on the yield of potato varieties of the early-ripening group. It was established that the weather and climatic conditions of the research area favor the cultivation of potato varieties of the early-ripening group, and the yield compared to the standard variety increased by 0.9-5.2 t/ha. The conducted analysis makes it possible to select the most productive potato varieties of the early-ripening group, which are the most optimal for cultivation in the conditions of the Western Forest-Steppe of Ukraine as well as further study and improvement regarding the formation of their productivity

A 131-year evidence of more extreme and higher total amount of hourly precipitation in Hong Kong

Based on the observations of hourly precipitation for 131 years from Hong Kong Observatory Headquarters, this study examined the long-term changes in the characteristics of hourly precipitation extremes in terms of intensity, total precipitation amount, duration, and frequency. Results show that the hourly precipitation extremes have significantly intensified by 29%–38% from 1885 to 2022. The 131-year observations evidence that the more extreme the hourly precipitation is (i.e. higher percentiles), the faster the increasing rate it has. Specifically, the magnitudes of hourly precipitation with the 95th, 97.5th, 99th, and 99.9th percentiles increased by rates of 0.03 mm, 0.05 mm, 0.07 mm, and 0.12 mm per year, respectively. Through the secular trend analysis, we found that only the maximum intensity of extreme precipitation events (i.e. events with maximum intensity exceeding the 95th percentiles) shows a significant increasing trend during 1885–2022, while the trends in the total precipitation amount, duration, and mean intensity are not significant. However, by comparing the percentile bin values between three sub-periods of the 131-year record, we found a significant rise over time in the total precipitation amount, mean intensity, and maximum intensity of extreme precipitation events with different intensities (i.e. 95th, 96th, 97th, 98th, and 99th percentiles), while the change in the duration is not significant. The analysis of the frequency of precipitation events shows significant increases in the proportion of extreme precipitation events during 1885–2022. The observations of 53 stations across Hong Kong from 1986 to 2022 show significant intensification and increasing frequency in the hourly precipitation extremes in most areas of Hong Kong. Meanwhile, the precipitation duration shows a decreasing tendency, which may explain the insignificant changes in the total precipitation amount. These findings provide important insights into the longer-term variations in the characteristics of hourly precipitation extremes.

Reconstructing Historical Intense and Total Summer Rainfall in Central North Carolina Using Tree-Ring Data (1770–2020)

Contextualizing historic hydroclimate variability in the southeastern USA has relied significantly on proxy indicators such as tree-ring data, and while previous studies have reconstructed total precipitation, less is known about the historic variability of intense rainfall events, which are climatologically and ecologically important and distinct from non-intense rainfall events. Here, a combined longleaf pine and shortleaf pine adjusted latewood chronology spanning 1770–2020 was used to reconstruct July–September total precipitation and intense rainfall event precipitation in central North Carolina, USA. The adjusted latewood chronology explains 46% of the variance in July–September total precipitation and 37% of the variance in July–September intense rainfall event precipitation during the 1940–2020 instrumental period with intense rainfall event precipitation amounts comprising 52% of total precipitation amounts. The models provide context about historic hydroclimate variability at this location and suggest overall stability in both total and intense rainfall event precipitation amounts during the instrumental and reconstruction periods with three identical significant regime shifts during 1770–2020: 1770–1935, 1936–1959 (above-average moisture), 1960–2020 (below-average moisture). To compare model strength, the models were split into early (1940–1980) and late (1981–2020) analysis periods with the intense rainfall event precipitation model exhibiting greater skill during the early analysis period. The early analysis period has a greater frequency and magnitude of intense rainfall events, and these results suggest the influence of intense rainfall event precipitation on latewood growth and the potential susceptibility of reconstruction models to decreased skill and/or error with differing frequencies of extreme events, a finding of particular importance to future dendroclimatic research.

Vertical structure of tropical cyclone precipitation over the North Indian Ocean: a spaceborne precipitation radar perspective

ABSTRACT Spaceborne precipitation radars provide an unprecedented opportunity to study three-dimensional structure of precipitation, particularly over the open ocean where in-situ observations are rather meagre. In this study, instantaneous surface precipitation characteristics and their vertical structures during the tropical cyclones (TCs) over the North Indian Ocean (NIO) between 2014 and 2022 have been analysed using the dual-frequency precipitation radar onboard the Global Precipitation Measurement Core Observatory. Although stratiform precipitation accounts for more than 70% of total TC surface precipitation area, convective precipitation contributes about half of the total TC surface precipitation amount over the NIO. About 90% of stratiform TC precipitation area yields surface precipitation of less than 10 mm/hour. The vertical structures of stratiform and convective TC precipitation vary with surface precipitation intensity and have nearly similar characteristics over both basins of the NIO. This preliminary quantitative TC precipitation analysis would be useful for better understanding of precipitation processes during TCs over the NIO, and for further advancement in numerical models through improved parameterization schemes for TC precipitation forecasting.

An integrated climate and water resource climate service prototype for long term water allocation in the Upper Yellow River region of China

Water Resourcing in China has historically been a complex issue requiring the ability to deal with regular floods, droughts and diverse water needs. Climate change represents another challenge to this sector, albeit one that is not traditionally considered by water managers. In this sector in China water management is predominantly based on historic, seasonal and annual forecast data while multi-annual and (multi-)decadal data are seldom used. In this paper, we present the co-development of a climate service prototype designed to provide water managers with insights into the impacts of climate change on the Upper Yellow River region for the next century. The paper is an outcome from our project that encouraged water resource planners and water resource managers to utilise long-term climate information to understand the uncertainties and the challenges our changing climate is likely to have in the region. Using an interdisciplinary team and adopting a user-centred, co-production approach, a prototype web-based data visualisation tool was developed. The development of the prototype was based on a design specification constructed from the findings of detailed interviews that allowed it to be developed and tested under SARS-CoV-2 pandemic restrictions that prevented the typical development process to be undertaken. The developed prototype presents climate information and communicates uncertainties regarding climate change in the remainder of the century through data sets that are typically used by the water sector in China in a simple, easy to understand style. Models that estimate river levels under different extraction scenarios and results about estimated river level and flow, and flood risk are also presented. The prototype was shown to be successful, as key messages relating to the impact of climate change and the challenges for water resource management could be effectively communicated through the tool interface. Practical implicationsUnderstanding the impacts of climate change on water resourcing is complicated and multifaceted. There is a need for better data about what water there is and how it is moving around between and within catchments. Estimates of past, present and future climate variables along with historical measurements of river flow can be used to help visualise some of the uncertainties and changes that may happen in the next 50 years. In addition, there is a need to understand changing water demands and water resource management practices. Current water resource management practices are based on historical conditions and assumptions that are less likely to hold true in a more variable and warmer climate. Communicating how future changes will impact future water resourcing is critical to water resources in a changing climate (Belcher et al. 2018).This research outlines the construction of a tool to visualise the impacts of climate change on water availability in part of China that is typically water scarce, using models developed using the Soil Water Assessment Toolkit (SWAT). A model of the Upper Yellow River (UYR) was developed to demonstrate the impact of climate change on river levels in the catchment based on climate variables. The rainfall-runoff model was based on climate predictions from the CMIP5 assessment HadGEM3-GC3.05 climate model and incorporated information about water resource allocations for different administrative regions of the catchment The general climate trend for the region is that it is expected to become significantly warmer. The total amount of precipitation is likely to be about the same, and yet it is expected that overall, the catchment will become significantly drier over time as winter shortens and summer lengthens. The outputs from the model reflect the changes in climate variables. The uncertainties were communicated via a Web based tool. Water resource managers in China helped to coproduce the tool by participating in workshops and providing feedback on prototypes. The workshops helped scientists and water resource managers to communicate about climate change impacts on water resources and water resource management.

EVALUATION OF NITROGEN FERTILIZER AND ROW SPACING EFFECT ON YIELD AND PHYSIOLOGICAL CHARACTERISTICS OF MAIZE (Zea mays L.) UNDER IRRIGATED AND RAINFED CONDITIONS

This research aims to investigate the yield and physiological traits of Hybrid 704 Single Cross maize under rainfed (without any irrigation) and irrigation based on plant needs as a split-plot experiment in a randomized complete block design with three replications during 2017-2018. It was carried out in Langrud, Gilan province, Iran. The total amount of precipitation during the plant growth period was 580.4 and 463.4 mm in 2017 and 2018 respectively, which happened to improper distribution during the plant growth. In experimental treatments, the first factor includes irrigation and rainfed in the main plots and the second factor is the combination of urea fertilizer levels (control, applying 100, 200, and 300 kg per hectare) with row spacing (10, 20, and 30 cm distance) were in sub-plots. The results showed that the effect of nitrogen source and row spacing under rainfed and irrigation conditions was significant on all investigated traits in this experiment. In the conditions of rainfed, the maximum grain yield was 11394.6 kg.h-1 for applying 300 kg of nitrogen fertilizer per hectare and 20 cm row spacing. In irrigation conditions, the highest grain yield was assigned to 200 and 300 kg of nitrogen fertilizer per hectare and of 20 cm row spacing, with an average of 14050.5 and 14993 kg per hectare, respectively. In addition, an increase in physiological traits, including relative water content, proline, antioxidant activity, and improvement of electrolyte leakage under rainfed conditions was observed by applying nitrogen fertilizer and increasing the row spacing. As a result, in rainfed conditions, using 200 to 300 kg of nitrogen fertilizer and increasing the row spacing should be used to obtain the highest maize yield in the experimental area.

Variations in Precipitation at the Shimantan Reservoir, China

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.

Simulation of surface wastewater flow volume using the city of Kyiv as an example

Although observations of the negative impact of surface runoff on water quality in water bodies have a long history, the development of national regulations governing surface water quality took place only in 2022. Accordingly, the Law of Ukraine "On Water Drainage and Wastewater Treatment" was adopted, which entered into force in August 2023 and is part of the process of adapting Ukrainian legislation in the field of water drainage in accordance with Council of Europe Directive 91/271/EEC "On Urban Wastewater Treatment". The paper analyzes the problem of determining the volume of annual surface runoff to be cleaned at treatment facilities. Using the data of the hydrometeorological service on the maximum daily amount of precipitation of various types for the city of Kyiv, the dependences of the distribution of the probability of daily layers of precipitation, in the form of the total depth of precipitation and the total duration of precipitation, were obtained. The conclusion that the predominant volume of runoff is determined by microrains that have a high frequency of recurrence is confirmed. The problem of finding the ratio of precipitation and runoff in the regulatory documents of Ukraine is covered very primitively, which is why there is a need to determine the depth of runoff on a more advanced basis, taking into account infiltration, which is determined by the parameters of the filtration properties of soils. The effect of imperviousness on the drain layer is determined through simulations using the SWMM distributed model. The F/P parameter is proposed, which characterizes the volume of losses from the total amount of precipitation depending on the properties of the surface, namely waterproofness and filtration coefficient. The modeling made it possible to obtain the dependence of the F/P parameter on the filtration coefficient and the imperviousness of the catchment area. Calculations confirmed that the runoff in most cases is formed as a result of precipitation, the value of which exceeds 2.5 mm. For the city of Kyiv, the volume of surface wastewater runoff for the warm period of the year from an area of 1 ha was calculated depending on the soil filtration coefficient at 60% imperviousness. As a result of these studies, it is recommended that sewage treatment plants capture and treat rainwater with a probability of 70 to 90% of the total depth of precipitation that falls on the catchment area. At the same time, the value of the flow volume of surface wastewater must be confirmed by calculation.

Increased drought and extreme events over continental United States under high emissions scenario

The frequency, severity, and extent of climate extremes in future will have an impact on human well-being, ecosystems, and the effectiveness of emissions mitigation and carbon sequestration strategies. The specific objectives of this study were to downscale climate data for US weather stations and analyze future trends in meteorological drought and temperature extremes over continental United States (CONUS). We used data from 4161 weather stations across the CONUS to downscale future precipitation projections from three Earth System Models (ESMs) participating in the Coupled Model Intercomparison Project Phase Six (CMIP6), specifically for the high emission scenario SSP5 8.5. Comparing historic observations with climate model projections revealed a significant bias in total annual precipitation days and total precipitation amounts. The average number of annual precipitation days across CONUS was projected to be 205 ± 26, 184 ± 33, and 181 ± 25 days in the BCC, CanESM, and UKESM models, respectively, compared to 91 ± 24 days in the observed data. Analyzing the duration of drought periods in different ecoregions of CONUS showed an increase in the number of drought months in the future (2023–2052) compared to the historical period (1989–2018). The analysis of precipitation and temperature changes in various ecoregions of CONUS revealed an increased frequency of droughts in the future, along with longer durations of warm spells. Eastern temperate forests and the Great Plains, which encompass the majority of CONUS agricultural lands, are projected to experience higher drought counts in the future. Drought projections show an increasing trend in future drought occurrences due to rising temperatures and changes in precipitation patterns. Our high-resolution climate projections can inform policy makers about the hotspots and their anticipated future trajectories.

Classification of precipitation types in Poland using machine learning and threshold temperature methods

The phase in which precipitation falls—rainfall, snowfall, or sleet—has a considerable impact on hydrology and surface runoff. However, many weather stations only provide information on the total amount of precipitation, at other stations series are short or incomplete. To address this issue, data from 40 meteorological stations in Poland spanning the years 1966–2020 were utilized in this study to classify precipitation. Three methods were used to differentiate between rainfall and snowfall: machine learning (i.e., Random Forest), daily mean threshold air temperature, and daily wet bulb threshold temperature. The key findings of this study are: (i) the Random Forest (RF) method demonstrated the highest accuracy in rainfall/snowfall classification among the used approaches, which spanned from 0.90 to 1.00 across all stations and months; (ii) the classification accuracy provided by the mean wet bulb temperature and daily mean threshold air temperature approaches were quite similar, which spanned from 0.86 to 1.00 across all stations and months; (iii) Values of optimized mean threshold temperature and optimized wet bulb threshold temperature were determined for each of the 40 meteorological stations; (iv) the inclusion of water vapor pressure has a noteworthy impact on the RF classification model, and the removal of mean wet bulb temperature from the input data set leads to an improvement in the classification accuracy of the RF model. Future research should be conducted to explore the variations in the effectiveness of precipitation classification for each station.

Mesoscale Characteristics of Exceptionally Heavy Rainfall during 4–6 May 2023 in Jiangxi, China

A long-lasting rainfall event exceeding historical extremes took place in Jiangxi, China, from May 4 to 6, 2023. Because of the concentrated duration of precipitation, it led to significant water accumulation in the northern, central, and southern regions of Jiangxi. The objective of this study was to investigate the weather mechanisms underlying this extreme rainstorm in Jiangxi. By examining detailed observational data, the mesoscale weather characteristics and environmental conditions of the event can be obtained. These findings offer valuable insights for future weather forecasting and warnings. It was observed that after the Huanghuai cyclone moved eastward into the sea, the cold air on its western side shifted northward and converged with the warm, moisture-laden air mass in Hunan and Jiangxi provinces. This convergence of air masses triggered the heavy rainstorm event. The peak precipitation period occurred from midnight on May 5 to 0800 BJT on May 6. Concerning the macroscopic precipitation characteristics, multiple mesoscale convective systems (MCSs) originated in Hunan during this period and progressed eastward along the shear line toward the central part of Jiangxi. As for the microscopic precipitation features, the total precipitation amount was closely linked to the duration of heavy rain droplets. The rainfall distribution in the raindrop spectrum also served as a valuable reference for understanding the persistence and size of precipitation. The temporal pattern of the combined reflectivity echo along 27.5° N indicated that from 2000 BJT on May 5 to the early morning of May 6, there was a rapid development of a weaker MCS after passing through the Luoxiao Mountains. This development resulted in a “train effect” in the central region of Jiangxi. The presence of a 200 hPa divergence area, high vertical ascent rate, and abundant water vapor contributed to the formation of a narrow area of heavy rainstorms in central Jiangxi. Additionally, the falling area of heavy rain coincided with the front of the 500 hPa low trough. In the northern part of Jiangxi, the occurrence of heavy precipitation was influenced by the equivalent temperature front area. Favorable conditions, including water vapor, dynamics, and thermal factors, further supported the occurrence of heavy precipitation.

Quantitative assessment of the variations in monthly precipitation trends induced by the impact of three gorges dam.

There are many studies that have examined the impact of the Three Gorges Dam (TGD) on changes in meteorological data, and most of them concluded that the TGD significantly reduced precipitation without taking into account the negative trends that had already existed before the impoundment. In this study, the investigation focused on the monthly precipitation data, and the Mann-Kendall (MK) trend analysis was conducted to show that the TGD had little effect on the trends of the precipitation data. Monthly data (1980-2018) from 19 stations upstream and downstream of the TGD and 5 stations located far from the main river were extracted. The analysis and results showed that although the linear long-term (1980-2018) precipitation trend upstream of the TGD was downward, the MK trend analysis showed that the precipitation trends became upward after impoundment. This situation existed even for station data located outside the region. Also, the analysis of monthly trends in different seasons showed that in spring and winter, there was only a very weak downward trend in monthly precipitation, while in summer and autumn, the trends were upward with steeper slopes. Following the upward trends of the monthly precipitation, the TGD generally positively intensified the monthly precipitation trends upstream and downstream of the dam, with the exception of a few months when total precipitation amounts were consistently low. In contrast to the trend analysis, which showed small and insignificant variations in precipitation data, the 12-month SPEI analysis showed a significant deterioration (about 20%) in the wetness index after impoundment both upstream and downstream of the TGD, while this situation did not occur outside the region. Thus, the TGD has extended dry periods both upstream and downstream of the dam over the past two decades.

The increase in extreme precipitation and its proportion over global land

Changes in extreme precipitation (EP) have a significant impact on the ecology and sustainable development of most regions, especially when considered against a backdrop of global warming. Using trend analysis and other methods, we analyze the changes in nine extreme precipitation indices (EPIs), looking at the proportion of EP in total precipitation from the perspectives of intensity, frequency, and duration. We also utilize a geo-detector method to investigate the impacts of 15 climate factors on EPIs. Our results indicate that although EP and their proportions show an overall increasing trend on a global scale, they had undergone a process of initial decrease followed by increase over the past approximately 70 years. And this overall global increases are charged by the significant increases happened in Northern Hemisphere and western of South America, especially EP intensity and frequency indices, and their proportion. Whereas CDD shows an opposite trend in spatial distribution, the CWD is decreasing globally. The EPIs and their proportions are all highest in the low elevation belt (≤2000 m), followed by the high elevation belt (≥4000 m), and lowest in the mid-elevation belts (2000-4000 m). Most of them have returned to or exceeded the levels of the mid-20th century in all three elevation belts. Only CWD shows a decreasing trend, which indicates that more EP amounts are occurring with less precipitation duration. In some arid areas, this makes it possible that a short period of precipitation may complete the total amount of precipitation for that year. Global temperature is proven to be the most important factor influencing the EPI globally, and followed by the temperature of each region. And the regions of high latitudes of the Northern Hemisphere and Antarctica which are greatly influenced by global temperature demonstrate the Polar amplification effect. The East Atlantic-West Russia Pattern, Atlantic Multi-Decadal Oscillation, and El Nino-Southern Oscillation indices are the secondly important factors leading to changes in the proportion of global EP. At the same time, we also find that the influence of non-monsoon factors on EP is gradually strengthening. This study clarifies changes in EP globally and in various regions, which will help provide a scientific reference for preventing disasters caused by extreme climate change.