Total Precipitation Research Articles

Resilience Assessment and Influencing Factors Analysis of Water Security System in the Yellow River Basin

The assessment and regulation of water security system resilience (WSSR) are important ways to alleviate water resource crises. On the basis of the three characteristics of resilience, namely, resistance, restoration, and adaptability, an evaluation index system of the WSSR was built for the Yellow River Basin (YRB). A projection pursuit model based on the sparrow search algorithm (SSA-PP model) was constructed to assess the WSSR at the overall and provincial scales from 2009 to 2022. The factors influencing the spatial distribution patterns of WSSR was identified through factor detector and interactive detector techniques. The results revealed that: (1) From 2009 to 2022, the YRB exhibited a rising trend in WSSR, with the western region attaining the peak level, and the eastern region recording the lowest magnitude. (2) the adaptability capacity of most provinces and regions in the YRB tended to decrease to about 1.0, whereas the resistance and restoration capacities tended to increase towards 2.0. (3) Factors relevant to the restoration and adaptability subsystems, such as the development and utilization ratio of water resources and surface water resources (B2 and B3), water resource load index (C1), water consumption per 10,000 yuan of GDP (C8), and total precipitation (A1), significantly affected the WSSR. The interaction between factors exhibited significant enhancement effects on explanatory. Therefore, targeted countermeasures and recommendations were provided to improve the WSSR in the YRB.

Spatiotemporal variability of flash floods and their human impacts in the Czech Republic during the 2001–2023 period

Abstract. Flash floods, characterized by their sudden onset, extreme discharge, short duration, material damage, and human loss, represent a significant natural hazard. Not well covered by standard hydrological observations, flash flood data can primarily be derived from various types of documentary evidence. This evidence served as the main data source for creating a flash flood database for the Czech Republic from 2001 to 2023. This database enabled detailed analysis of different aspects of flash floods. The annual series of 233 flash flood events, 160 flash flood days, and 424 affected municipalities showed significant inter-annual variability but no linear trends. The triggering rainfall that generates flash floods was analyzed with respect to 1–3-hourly and daily precipitation totals and circulation types from the objective classification. While flash floods can occur anywhere, they were more frequently recorded at the foots of mountain slopes, often coinciding with “critical points” where built-up areas meet concentrated surface runoff pathways. The division of material damage caused by flash floods into eight categories indicated that the highest proportion of damage was to streets and communications (24.3 %), to houses (21.7 %), and to their cellars and basements (18.3 %). There were also 36 recorded fatalities. The understanding of flash floods in the Czech Republic aligns generally well with studies of flash floods in other European regions.

Climatology of Orographic Precipitation Gradients Over High Mountain Asia Derived From Dynamical Downscaling

AbstractWithin High Mountain Asia (HMA), the annual melting of glaciers and snowpack provides vital freshwater to populations living downstream. Precipitation over HMA can directly affect the freshwater availability in this region by altering the mass balance of glaciers and snowpack. However, available reanalyses and downscaling simulations lack the resolution required to understand important glacier‐scale variations in precipitation. This study aimed to determine the current characteristics of orographic precipitation gradients (OPG) by curve‐fitting daily precipitation as a function of elevation from a 15‐year, 4‐km grid spaced Weather Research and Forecasting (WRF) model simulation focused on the Himalayan, Karakoram, and Hindu‐Kush mountain ranges. To facilitate precipitation curve‐fitting, the WRF model grid points were separated into regions of similar orientation, referred to as facets. Akaike Information Criterion‐corrected values and an F‐test p‐value identified the need for a curvature term to account for a varying OPG with elevation. Regions with similar seasonal variability were found using ‐means clustering of the monthly mean OPG coefficients. The central Himalayan slope's intra‐seasonal variability of OPG depended on synoptic scale conditions, in which cyclonically‐forced heavy‐precipitation events produced strong sublinear increases in precipitation with elevation. Initial testing of precipitation estimates using monthly coefficients showed promising results in downscaling daily WRF precipitation; the daily mean absolute error at each grid point had a lower magnitude than the daily mean precipitation total, on average. Results provide a physically‐based context for machine learning algorithms being developed to predict OPG and downscale precipitation output from global climate models over HMA.

Investigating the Mechanisms Impacting Soil Ca, Mg, and Na in Wastewater Land Application Systems Using Machine Learning Models

ABSTRACTWastewater land application is a widely accepted solution for addressing global water crisis, particularly in arid and semiarid regions, but it may cause soil Ca, Mg, and Na accumulation and result in soil degradation. The objective of this study was to investigate the underlying mechanisms impacting soil Ca, Mg, and Na in wastewater land application systems using tree‐based machine learning models. Using data collected from previous field studies, decision tree (DT), random forest (RF), and extreme gradient boosting decision trees (XGBoost) models were developed to predict soil Ca, Mg, and Na in wastewater land application systems. XGBoost models showed the best performance, with R2 and RMSE values of 0.999 and 18.9 mg/kg, 0.999 and 3.2 mg/kg, and 0.912 and 104 mg/kg on the training data and 0.989 and 345 mg/kg, 0.925 and 56.1 mg/kg, and 0.908 and 112 mg/kg on the test data for soil Ca, Mg, and Na prediction, respectively. Permutation importance analysis reveals that initial soil Ca and electrical conductivity (EC) and total irrigation amount, initial soil Mg, total precipitation and initial soil EC, and initial soil Na, total irrigation amount and wastewater Na were the top three predictive variables for soil Ca, Mg, and Na, respectively. Partial dependence analysis demonstrates how soil Ca, Mg, and Na changed with the predictive variables, and indicates that wastewater irrigation caused soil Ca, Mg, and Na accumulation. This study highlights the need for sustainable wastewater land application management to control soil sodium adsorption ratio and mitigate the risks of land degradation.

IRainSnowHydro v1.0: A distributed integrated rainfall-runoff and snowmelt-runoff simulation model for alpine watersheds

Snowmelt runoff is an essential runoff component in alpine watersheds. On the Tibetan Plateau, the complex hydrometeorological and underlying surface conditions make a single runoff generation mode (either snowmelt-runoff or rainfall-runoff) cannot accurately simulate the runoff process. In this study, we developed a new method that combines the curve number, topographic index, and fractional snow cover to identify the sub-basin seasonal dominant runoff generation mode within the Jinsha River Basin. By constructing a surface ‘snow reservoir’ to depict snow melting impact on runoff generation, and quantitatively classifying the precipitation composition, an innovative integrated hydrological model named the distributed integrated Rainfall-runoff and Snowmelt-runoff simulation Hydrological model (iRainSnowHydro) is developed. With model, a method for identifying the seasonal varying dominant runoff generation mode is proposed. The results show that most sub-basins experience both snowmelt and rainfall driven runoff generation in spring, with snowmelt occurring earlier in regions of lower latitude and elevation. Besides, iRainSnowHydro performs well in daily runoff simulations at Zhimenda and Shigu stations with Nash coefficients of 0.81 and 0.85 in the calibration period, and 0.72 and 0.81 in the validation period. The correlation coefficient ranges from 0.92 to 0.96. Additionally, calculation through iRainSnowHydro indicates a noteworthy percentage of spring snowmelt water. Notably, the Zhimenda watershed, characterized by higher latitudes and elevations, displays an escalating trend from 56.6 % to 78.9 % of total precipitation for spring snowmelt water between 2014 and 2020, while the Shigu watershed maintains stable within 27 % ± 6 %. The methodologies outlined bear significance for simulating and predicting runoff in alpine watersheds and offers valuable insights into how snow cover responds to climate change on the Tibetan Plateau.

Does ERA5-Land Effectively Capture Extreme Precipitation in the Yellow River Basin?

ERA5-Land is a valuable reanalysis data resource that provides near-real-time, high-resolution, multivariable data for various applications. Using daily precipitation data from 301 meteorological stations in the Yellow River Basin from 2001 to 2013 as benchmark data, this study aims to evaluate ERA5-Land’s capability of monitoring extreme precipitation. The evaluation study is conducted from three perspectives: precipitation amount, extreme precipitation indices, and characteristics of extreme precipitation events. The results show that ERA5-Land can effectively capture the spatial distribution patterns and temporal trends in precipitation and extreme precipitation; however, it also exhibits significant overestimation and underestimation errors. ERA5-Land significantly overestimates total precipitation and indices for heavy precipitation and extreme precipitation (R95pTOT and R99pTOT), with errors reaching up to 89%, but underestimates the Simple Daily Intensity Index (SDII). ERA5-Land tends to overestimate the duration of extreme precipitation events but slightly underestimates the total and average precipitation of these events. These findings provide a scientific reference for optimizing the ERA5-Land algorithm and for users in selecting data.

Hydraulic traits exert greater limitations on tree-level maximum sap flux density than photosynthetic ability: Global evidence

Transpiration is a key process that couples the land-atmosphere exchange of water and carbon, and its maximum water transport ability affects plant productivity. Functional traits significantly influence the maximum transpiration rate; however, which factor plays the dominant role remains unknown. SAPFLUXNET dataset, which includes sap flux density of diverse species worldwide, provides fundamental data to test the importance of photosynthetic and hydraulic traits on maximum tree-level sap flux density (Js_max). Here, we investigated variations in Js_max of 2194 trees across 129 species using data from the SAPFLUXNET dataset, and analysed the relationship of Js_max with photosynthetic and hydraulic traits. Our results indicated that Js_max was positively correlated with photosynthetic traits at both leaf and tree level. Regarding hydraulic traits, Js_max was positively related to xylem hydraulic conductivity (Ks), leaf-specific hydraulic conductivity (Kl), xylem pressure inducing 50 % loss of hydraulic conductivity (P50), xylem vessel diameter (Vdia), and leaf-to-sapwood area ratio (AlAs). Random forest model showed that 87 % of the variability in Js_max can be explained by functional traits, and hydraulic traits (e.g., P50 and sapwood area, As) exerted larger effects on Js_max than photosynthetic traits. Moreover, trees with a lower sapwood area or depth could increase their sap flux density to compensate for the reduced whole-tree transpiration. Js_max of the angiosperms was significantly higher than that of the gymnosperms. Mean annual total precipitation (MAP) were positively related to Js_max with a weak correlation coefficient. Furthermore, Js_max showed a significant phylogenetic signal with Blomberg's K below 0.2. Overall, tree species with acquisitive resource economics or more efficient hydraulic systems show higher water transport capacity, and the efficiency of xylem hydraulic system rather than the demand for carbon uptake predominantly determines water transport capacity.

Variability of Dry Days and Sequences of Dry Days in the Polish Carpathians in the Period 1986–2020

This study provides a climatological analysis of annual and long-term variability of dry days and sequences of dry days in the Polish Carpathians between 1986 and 2020. The input data for the research was compiled from the daily precipitation totals from 17 meteorological stations and the T. Niedźwiedź catalogue of circulation types over southern Poland. The frequency of sequences of dry days of different duration has been presented. The incidence of long-term periods has been related to the atmospheric circulation. The number of dry days and the averages of both the number and of the duration of sequences of dry days were analysed for the study period. This yielded an average annual number of dry days ranging from 142 to 195 (39–53% of days in a year), depending on the altitude above sea level and longitude. A general pattern emerged in which the number of dry days increased from west to east. At most stations, there were no statistically significant trends with respect to the dry day characteristics analysed. This is an obvious result of high year-to-year variability, which means that only large changes can achieve statistical significance. Dry days have been evidenced to relate to circulation types. The vast majority of dry days are related to anticyclonic systems, particularly the anticyclonic wedge (Ka) and western anticyclonic (Wa) situations. On the other hand, no clear-cut relationship was established between an increase in air temperature and the occurrence of dry days.

Climate-Related Risks and Agricultural Yield Assessment in the Senegalese Groundnut Basin

Climate change and variability pose significant threats to agricultural production, particularly in regions heavily dependent on rainfed agriculture like Senegal. The problem addressed in this study revolves around the impact of climate-related risks on agricultural yields in the Senegalese Groundnut Basin as a key agricultural region. Daily rainfall, temperatures, and yield over 1991–2020 were used. The data were analyzed using multiple regression, trend analysis, and correlation approaches. The results indicate that the overall seasonal precipitation increases over time (98 mm in the north and 103 mm in the south). However, we found that the south Groundnut Basin has a much slower seasonal precipitation rate than the northern zone. Our results also show that the northern zone exhibits a more consistent and predictable growing season, with onset and offset, in contrast with the southern zone, which shows higher variability. The analysis further reveals that both the northern and southern zones are experiencing a warming trend, with the southern zone showing a more pronounced increase in maximum temperatures (+0.7 °C) than to the northern zone (+0.4 °C). Estimates from the regression analysis revealed that total seasonal precipitation and maximum temperature positively and significantly influence groundnut, millet, and maize yields in the northern and southern zones. All the other weather-related parameters have different influences depending on the zone. These findings highlight the heterogeneous nature of the study area and the significant role climatic factors play in crop yield variability in the Groundnut Basin. Understanding these influences is crucial for developing targeted agricultural strategies and climate adaptation measures to mitigate risks and enhance regional productivity. The study provides valuable insights for policymakers and farmers aiming to improve crop resilience and sustain agricultural outputs amidst changing climatic conditions.

Light Precipitation rather than Total Precipitation Determines Aerosol Wet Removal.

Precipitation scavenging is an important sink for both organic and inorganic pollutants in the atmosphere. However, there has been controversy over the relative importance of different precipitation characteristics (i.e., the precipitation amount, intensity, frequency, and duration) in the removal of air pollutants. It is critical to reach a consensus on which precipitation characteristics are most significant for aerosol wet removal. In this study, the analysis of multisource in situ observations of aerosol wet deposition worldwide indicates that the precipitation frequency plays a first-order role in scavenging aerosols. This is because large amounts of air pollutants are efficiently removed at precipitation initiation. As the duration and amount of precipitation increase, the scavenging efficiency decreases sharply. Consequently, it is featured that light precipitation, due to its high frequency of occurrence, rather than total precipitation, determines climatological aerosol wet deposition. To further confirm this, a state-of-the-art global climate model with convection resolved is modified to reduce the light precipitation frequency in both stratiform and convective cloud regimes. Results show widespread increases in aerosol optical depth (AOD) due to the reduced wet deposition. The spatial distribution of aerosol wet deposition changes resembles that of changes in the light precipitation frequency rather than that of total precipitation changes. These findings are consistent with those from the Coupled Model Intercomparison Project Phase 6 multimodel simulations, which show that the intermodel uncertainty in simulated AOD correlates more strongly with the uncertainty in light precipitation frequency than with that in total precipitation.

Studies on Heavy Precipitation in Portugal: A Systematic Review

This systematic review, based on an adaptation of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement from 2020, focuses on studies of the atmospheric mechanisms underlying extreme precipitation events in mainland Portugal, as well as observed trends and projections. The 54 selected articles cover the period from 2000 to 2024, in which the most used keywords are “portugal” and “extreme precipitation”. Of the 54, 23 analyse trends and climate projections of precipitation events, confirming a decrease in total annual precipitation, especially in autumn and spring, accompanied by an increase in the frequency and intensity of extreme precipitation events in autumn, spring and winter. Several articles (twelve) analyse the relationship between synoptic-scale circulation and heavy precipitation, using an atmospheric circulation types approach. Others (two) establish the link with teleconnection patterns, namely the North Atlantic Oscillation (NAO), and still others (three) explore the role of atmospheric rivers. Additionally, five articles focus on evaluating databases and Numerical Weather Prediction (NWP) models, and nine articles focus on precipitation-related extreme weather events, such as tornadoes, hail and lightning activity. Despite significant advances in the study of extreme precipitation events in Portugal, there is still a lack of studies on hourly or sub-hourly scales, which is critical to understanding mesoscale, short-lived events. Several studies show NWP models still have limitations in simulating extreme precipitation events, especially in complex orography areas. Therefore, a better understanding of such events is fundamental to promoting continuous improvements in operational weather forecasting and contributing to more reliable forecasts of such events in the future.

Integrated Geospatial and Analytical Hierarchy Process Approach for Assessing Sustainable Management of Groundwater Recharge Potential in Barind Tract

Groundwater depletion in Bangladesh’s Barind tract poses significant challenges for sustainable water management. This study aims to delineate groundwater recharge potential zones in this region using an integrated geospatial and Analytical Hierarchy Process (AHP) approach. The methodology combines remote-sensing data with GIS analysis, considering seven factors influencing groundwater recharge: rainfall, soil type, geology, slope, lineament density, land use/land cover, and drainage density. The AHP method was employed to assess the variability of groundwater recharge potential within the 7586 km2 study area. Thematic maps of relevant factors were processed using ArcGIS software. Results indicate that 9.23% (700.22 km2), 47.68% (3617.13 km2), 37.12% (2816.13 km2), and 5.97% (452.70 km2) of the study area exhibit poor, moderate, good, and very good recharge potential, respectively. The annual recharge volume is estimated at 2554 × 106 m3/year, constituting 22.7% of the total precipitation volume (11,227 × 106 m3/year). Analysis of individual factors revealed that geology has the highest influence (33.57%) on recharge potential, followed by land use/land cover (17.74%), soil type (17.25%), and rainfall (12.25%). The consistency ratio of the pairwise comparison matrix was 0.0904, indicating acceptable reliability of the AHP results. The spatial distribution of recharge zones shows a concentration of poor recharge potential in areas with low rainfall (1200–1400 mm/year) and high slope (6–40%). Conversely, very good recharge potential is associated with high rainfall zones (1800–2200 mm/year) and areas with favorable geology (sedimentary deposits). This study provides a quantitative framework for assessing groundwater recharge potential in the Barind tract. The resulting maps and data offer valuable insights for policymakers and water resource managers to develop targeted groundwater management strategies. These findings have significant implications for sustainable water resource management in the region, particularly in addressing challenges related to agricultural water demand and climate change adaptation.

Analyzing the Spatiotemporal Changes in Climatic Extremes in Cold and Mountainous Environment: Insights from the Himalayan Mountains of Pakistan

This study assessed the past changes in extreme precipitation and temperature events across the Himalayan Mountains of Pakistan. This cold and mountainous environmental region has witnessed a significant increase in climate-related disasters over the past few decades. Spatiotemporal changes in extreme temperature and precipitation events were analyzed using 24 indices developed by the Expert Team on Climate Change Detection and Indices (ETCCDI). For this study, in situ data of 16 national meteorological stations were obtained from the Pakistan Meteorological Department (PMD) for the past three decades (1991–2020). The significance of the trends was assessed using the modified Mann–Kendall (MMK) test, and the Theil–Sen (TS) slope estimator was used to estimate the slope of the trend. The results showed that there has been a consistent decline in the total precipitation amount across the Himalayan Mountains of Pakistan. The trend exhibited a decrease in the annual average precipitation at a rate of −6.56 mm/year. Simultaneously, there was an increasing trend in the annual average minimum and maximum temperatures at rates of 0.02 °C/year and 0.07 °C/year, respectively. The frequencies of consecutive wet days (CWDs) and maximum 5-day precipitation (RX5day) have decreased significantly, with decreasing rates of −0.40 days/year and −1.18 mm/year, respectively. The amount of precipitation during very wet days (R95p) and extremely wet days was decreased by −19.20 and −13.60 mm/decade, respectively. The warm spell duration (WSDI) and the frequency of warm days (TX90p) across the Himalayan Range both increased by 1.5 and 1.4 days/decade. The number of cold days (TX10p) and cold nights (TN10p) decreased by 2.9 and 3.4 days/decade. The average temperature of the hottest nights (TXn) and the diurnal temperature range (DTR) were increased by 0.10 and 0.30 °C/decade. The results indicated an increasing tendency of dry and warm weather in the Himalayan region of Pakistan, which could have adverse consequences for water resources, agriculture, and disaster management in the country. Therefore, it is essential to prioritize the implementation of localized adaptation techniques in order to enhance sustainable climate resilience and effectively address the emerging climate challenges faced by these mountainous regions.

Temporal variation, sources, fluxes, and risk assessment of heavy metals and arsenic in rainwater from Zhanjiang Bay, Northern South China Sea: Impact of typhoons Lion and Kompasu

This study investigates the impact of typhoon-induced rainfall on coastal pollution dynamics in Zhanjiang Bay during the 2021 wet season, focusing on typhoons Lion and Kompasu. Typhoon-induced rainfall (217 mm) contributed 23 % of the total wet season precipitation. Concentrations of Cu, Zn, Cd, and As in rainwater during typhoons were significantly diluted, showing decreases of 48 %, 48 %, 54 %, and 42 %, respectively. In contrast, Pb concentrations remained consistent (29.5 vs. 29.3 μg L−1), indicating that increased local emissions offset the dilution effect. The deposition fluxes of these elements during typhoons accounted for 12–21 % of total wet season deposits. A positive matrix factorization model identified six primary sources, highlighting a rise in coal combustion contributions during typhoons. Overall, while typhoons reduced risks for Cu, Cd, and As, risks for Zn and Pb increased upon deposition on Zhanjiang Bay, illustrating the complex impact of typhoon-induced rainfall on coastal pollution dynamics.

A Performance Comparison Study on Climate Prediction in Weifang City Using Different Deep Learning Models

Climate change affects the water cycle, water resource management, and sustainable socio-economic development. In order to accurately predict climate change in Weifang City, China, this study utilizes multiple data-driven deep learning models. The climate data for 73 years include monthly average air temperature (MAAT), monthly average minimum air temperature (MAMINAT), monthly average maximum air temperature (MAMAXAT), and monthly total precipitation (MP). The different deep learning models include artificial neural network (ANN), recurrent NN (RNN), gate recurrent unit (GRU), long short-term memory neural network (LSTM), deep convolutional NN (CNN), hybrid CNN-GRU, hybrid CNN-LSTM, and hybrid CNN-LSTM-GRU. The CNN-LSTM-GRU for MAAT prediction is the best-performing model compared to other deep learning models with the highest correlation coefficient (R = 0.9879) and lowest root mean square error (RMSE = 1.5347) and mean absolute error (MAE = 1.1830). These results indicate that The hybrid CNN-LSTM-GRU method is a suitable climate prediction model. This deep learning method can also be used for surface water modeling. Climate prediction will help with flood control and water resource management.

Oberved trend of precipitation extreme in Kalimantan

<p style="line-height:200%;margin-bottom:.0001pt;text-align:justify;text-justify:inter-ideograph;"><span style="font-family:"Times New Roman";" lang="IN">It has been suggested that the global warming due to an increase in green house gas concentrations could lead to extreme climate events, such as </span><span style="color:#007BB8;font-family:"Times New Roman";" lang="IN">precipitation extreme and droughts</span><span style="font-family:"Times New Roman";" lang="IN">. This study is designed to analyze trend in the long-term time series of preciptation extreme in Kalimantan, focusing on observed data obtained from eight meteorological stations spanning from January 1985 to December 2022. </span><span style="background-color:white;color:#0D0D0D;font-family:"Times New Roman";" lang="EN-ID">Statistical analyses were employed to discern patterns and trends in extreme precipitation events. The findings reveal a discernible long-term trend characterized by fluctuations in precipitation extremes over the study period. The trend of total annual precipitation (PRCPTOT) has increased at all observation stations. Especially at SM. Nangapinoh and Supadio, the PRCPTOT trend increased significantly at the 99% and 90% levels. Meanwhile, the increase in consecutive wet days (CWD) trends only occurred at SM. Nangapinoh and Rahadi Oesman (West Kalimantan), and SM. Iskandar (South Kalimantan). A significant trend was found in SM. Nangapinoh (90%) and Iskandar (95%). In addition to experiencing an increase in the R20mm trend, there was a decrease in the trend. Significant trend increases at the 99% and 90% levels occurred in SM. Nangapinoh and Supadio.  In addition, an increase in CDD trend occurred only in SM. Rahadi Oesman and Tjilik Riwut, although not significant. Meanwhile, the majority of extreme precipitation indices have a greater correlation with ENSO than the IOD. </span></p>

Direct and Legacy Effects of Varying Cool-Season Precipitation Totals on Ecosystem Carbon Flux in a Semi-Arid Mixed Grassland.

In the semi-arid grasslands of the southwest United States, annual precipitation is divided between warm-season (July-September) convective precipitation and cool-season (December-March) frontal storms. While evidence suggests shifts in precipitation seasonal distribution, there is a poor understanding of the ecosystem carbon flux responses to cool-season precipitation and the potential legacy effects on subsequent warm-season carbon fluxes. Results from a two-year experiment with three cool-season precipitation treatments (dry, received 5th percentile cool-season total precipitation; normal, 50th; wet, 95th) and constant warm-season precipitation illustrate the direct and legacy effects on carbon fluxes, but in opposing ways. In wet cool-season plots, gross primary productivity (GPP) and ecosystem respiration (ER) were 103% and 127% higher than in normal cool-season plots. In dry cool-season plots, GPP and ER were 47% and 85% lower compared to normal cool-season plots. Unexpectedly, we found a positive legacy effect of the dry cool-season treatment on warm-season carbon flux, resulting in a significant increase in both GPP and ER in the subsequent warm season, compared to normal cool-season plots. Our results reveal positive legacy effects of cool-season drought on warm-season carbon fluxes and highlight the importance of the relatively under-studied cool-growing season and its direct/indirect impact on the ecosystem carbon budget.

Microphysical characteristics of torrential predecessor rain events over the Yangtze River Delta Area and the related tropical cyclones

The precipitation structures and microphysical characteristics of predecessor rain events (PREs) over the Yangtze River Delta area and related tropical cyclones (TCs) from 2014 to 2019 were investigated using Dual-frequency Precipitation radar data from Global Precipitation Measurement (GPM) for drop size distributions (DSDs). Results showed that the total mean rain rate of PREs was larger than that of TCs, primarily due to higher convective and stratiform rain rates, with enhanced fractional coverage of convective rain in PREs. Examination of microphysical characteristics revealed that a greater quantity of small-sized droplets and a smaller quantity of medium- as well as large-sized droplets contributed towards PREs in comparison with TCs. The conclusion still holds when partitioning DSDs based on different precipitation rate categories. Further investigation of DSDs using gamma functions illustrated that precipitation in PREs had lower average mass-weighted diameters (Dm) and enhanced normalized number concentration (Nw) compared with TCs; partitioning precipitation into convective and stratiform components illustrated a larger Dm and lower Nw in TCs than PREs. The analysis of microphysical and thermodynamical processes using the reanalysis data indicates that relatively intense convective activity with drier conditions may be favorable to enhancing raindrop growth through collision-coalescence processes, as a result of larger Dm in TCs than PREs. The empirical relations (Z–R algorithms) applied in different rain regimes (stratiform, convective, and total PREs) revealed significant diversities, relying on weather conditions and geographical locations. Plain language summaryThe Yangtze River Delta area is an important economic belt in China. Under climate change, observed frequent occurrences of weather extremes of heavy rainfall and tropical cyclones (TCs) exert adverse effects on economic development in this region. Thus, a deep understanding of the mechanism of TCs torrential rainfall in the Yangtze River Delta area is urgently necessary. In this study, we investigated the precipitation patterns and microphysical characteristics of predecessor rain events (PREs) in the Yangtze River Delta region, and their association with TCs in the South China Sea-Western North Pacific Ocean (SCS-WNPO) area from 2014 to 2019. We found that PREs had a higher total mean precipitation rate than TCs. Further examination using gamma functions demonstrated that PREs exhibited lower average mass-weighted diameters (Dm) and higher normalized intercept parameters (Nw) than TCs. This pattern persisted when distinguishing between convective and stratiform precipitation components. We believe that our study makes a significant contribution to the literature because these results provide valuable insights into the distinct precipitation characteristics of PREs and TCs in the study region and contribute to a better understanding of tropical cyclone-related rainfall patterns, and act as a scientific basis for disaster mitigation.

Infestation Rates, Seasonal Abundance of Aphids and Relationships with Some Environmental Factors in Different Forest Habitats in Southwestern Türkiye

Aim of study: It was to determine the infestation rate and seasonal abundance of aphids and their relationships with some environmental factors in forests. Area of study: The study was conducted in Pinus nigra, P. brutia, Cedrus libani, Abies cilicica, Juniperus spp., Quercus spp. and Robinia pseudoacacia forests (34 sampling areas) in Isparta. Materials and methods: Systematic sampling was carried out monthly throughout April-October in 2019-2020. As aphid density and infestation rates were determined, 30 cm shoots were taken from 10 trees. The slope, aspect, elevation, forest characteristics, daily minimum, average, and maximum temperature and total precipitation were recorded. Main results: Abundance were highest in P. nigra, Quercus and Robinia pseudoacacia forests in both years. There was a significant correlation between abundance and development stages (p

Characterizing Wet Season Precipitation in the Central Amazon Using a Mesoscale Convective System Tracking Algorithm

AbstractTo comprehensively characterize convective precipitation in the central Amazon region, we utilize the Python FLEXible object TRacKeR (PyFLEXTRKR) to track mesoscale convective systems (MCSs) observed through satellite measurements and simulated by the Weather Research and Forecasting model at a convection‐permitting resolution. This study spans a 2‐month period during the wet seasons of 2014 and 2015. We observe a strong correlation between the MCS track density and accumulated precipitation in the Amazon basin. Key factors contributing to precipitation, such as MCS properties (number, size, rainfall intensity, and movement), are thoroughly examined. Our analysis reveals that while the overall model produces fewer MCSs with smaller mean sizes compared to observations, it tends to overpredict total precipitation due to excessive rainfall intensity for heavy rainfall events (≥10 mm hr−1). These biases in simulated MCS properties could vary with the constraints on the convective background environment. Moreover, while the wet bias from heavy (convective) rainfall outweighs the dry bias in light (stratiform) rainfall, the latter can be crucial, particularly when MCS cloud cover is significantly underestimated. A case study for 1 April 2014 highlights the influence of environmental conditions on the MCS lifecycle and identifies an unrealistic model representation in both stratiform and convective precipitation features.