Rainfall In June Research Articles

Drought and nitrogen deposition regulate plant nutrient resorption in a typical steppe

The impending rise in drought events in grasslands of northern China over the next few decades, coupled with escalating nitrogen (N) deposition, will have an important impact on nutrient resorption. Previous research has mostly focused on examining the individual effect of specific drought scenario or N enrichment on nutrient resorption. Nonetheless, the impacts of different drought regimes on the resorption of nutrients have rarely been distinguished, especially under the scenario of N enrichment in temperate typical steppe in Inner Mongolia, we studied how intense drought (excluding 100 % rainfall in June), chronic drought (excluding 50 % rainfall during June-August), reduced rainfall frequency (reducing half rainfall events without changing rainfall amount in June-August), and N deposition (0 and 10 g N m−2 yr−1) affected the efficiency of plants in reabsorbing N and phosphorus (P). Both intense and chronic drought significantly reduced N (by 9.41 % and 9.53 %, respectively) and P resorption efficiency (by 6.71 % and 6.62 %, respectively) in plant communities (defined as the proportion of N and P nutrient resorption from senescing leaves), however reducing rainfall frequency had less effects on plant community N and P resorption efficiency. Nitrogen deposition had no effects on N and P resorption efficiency. Drought and N addition interacted to affect plant community P resorption efficiency. Structural equation modeling (SEM) showed that drought reduced N and P resorption efficiencies in plant communities by directly decreasing soil moisture, suppressing nutrient concentrations in green leaves, and enhancing soil nutrient content. Nitrogen deposition reduced P resorption efficiency by reducing P concentration in green leaves, but this effect was offset by the reduction of soil P availability. These results imply that rainfall amount is more important than rainfall frequency in determining the nutrient resorption efficiency of plant communities in the typical steppe. This study highlights the importance of soil water and N availabilities as well as nutrient concentrations in green leaves in modulating the responses of plant nutrient resorption to global change in the typical steppe.

Analisis Semantik Dalam Puisi “Hujan Bulan Juni” Karya Sapardi Djoko Damono

Poetry is the recording and interpretation of important human experiences transformed into a memorable and interesting form. Poetry is one type of literary work that is of interest to various groups and age groups. Not only reading poetry, some people are also interested in creating poetry. Linguistically, poetry is mostly free, not bound by specific rules. The use of figurative language sometimes makes it difficult to understand the meaning of beautiful words in poetry. The poem June Rainfall by Sapardi Djoko Damono is the subject of this research. The problem raised by the author is only limited to the meaning of words in this poem. The purpose of this research is to describe the meaning contained in the poem June Rainfall through semantic analysis, which focuses on analyzing lexical meaning, grammatical meaning, referential meaning, and figurative meaning. Qualitative descriptive method is used as the research method. The techniques used in this research include documentation techniques, library techniques, and note-taking techniques. The results of the discussion show that this poem can be analyzed through semantic studies.

Mapping flooded areas utilizing Google Earth Engine and open SAR data: a comprehensive approach for disaster response

One of the most destructive and frequent natural disasters in the world, flash floods cause millions of people to be displaced annually in addition to seriously harming livelihoods and infrastructure. It affects many ecological components and applications related to water management, natural resources, agriculture, human health, and economics. Himachal Pradesh saw an unprecedented amount of rainfall in June and July 2023, which resulted in exceptionally strong monsoon conditions from July 7 to July 10. Operating out of the Meteorological Centre in Shimla, the India Meteorological Department reported widespread, unusually heavy rainfall throughout the state during this time. Cloud cover often obstructs optical satellite data during the monsoon season, which has led to the investigation of alternate techniques for mapping floods. the whole satellite image processing process was carried out using Google Earth Engine (GEE). The Kangra District's 5739 km2 area was chosen as the study's area of interest. In these circumstances, traditional methods of mapping and monitoring flood-prone areas frequently fall short because of poor visibility and overcast skies during bad weather. Technology such as Synthetic Aperture Radar (SAR) becomes a ray of hope during these desperate times. When SAR data is combined with Google Earth Engine, it becomes even more user-friendly. Using SAR data and the powerful cloud processing platform Google Earth Engine (GEE), this study suggests a flood mapping technique. When it comes to mapping flood areas, the strength of SAR data and GEE surpasses boundaries and challenges. It is evidence of both technological advancement and human inventiveness. Such tools are more critical than ever as the world deals with the increasing effects of climate change and the rising frequency of extreme weather events.

Spatiotemporal assessment of rainfall and drought projection for integrated dam management in Benut River Basin, Malaysia under CMIP6 scenarios

This study assesses the influence of climate change on rainfall and drought in the Benut River Basin (BRB) to support the integrated management of Machap dam. The study seeks to reshape dam safety components in the near future through a technical focus group discussion (FGD) with key stakeholders. Utilizing the Coupled Model Intercomparison Project Phase 6 (CMIP6) dataset for the immediate future (2020–2040) under various scenarios (SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5), historical Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) rainfall datasets (1981 to 2021) are employed. A multi-filtration process ranks twenty-three Global Climate Models (GCMs) using seven relative importance metrics (RIMs). This work's novelty lies in a focused GCM evaluation through individual contributions quantified with RIMs, providing a refined ranking that address the limitations of traditional metrics. Then, the Compromise Programming Index (CPI) aggregates the ranked models, with the top subset selected through Jenks Optimised Classification (JOC), identifying ACCESS-ESM1–5 and CMCC-ESM2 as top models. The selected GCMs were bias-corrected using the linear scaling method and validated before ensembled, effectively replicating the probability distribution function (PDF) of CHIRPS data, as confirmed by a Taylor diagram indicating the closest agreement. The study reveals a generally wetter tendency in the northernmost area of Machap dam under all SSPs, with 5–9 % maximum increasing percentages. Conversely, downstream areas anticipate decreasing rainfall. June (6–22 %) and July (7–22 %) project higher rainfall, indicating increased water availability in a relatively dry period, while December (3–11 %) suggests increasing flood risk. The South West monsoon (SWM) anticipates lower rainfall in early March (3–7 %) and more severe dry periods in August (8–11 %). Future drought occurrences are projected homogeneously across scenarios, with drought months ranging from 15 % to 19 % of the 252 months under all severity classifications. In particular, the study enhances myDAMS by proposing continuous development of the guideline under diverse scenario for Machap dam's overall management and sustainability. Projected scenarios, coupled with outcomes from FGD, can inform basin-scale dam management, aiding risk mitigation for critical dam infrastructure.

AGROCLIMATIC FACTORS AND PRODUCTIVITY OF DIFFERENT MATURITY GROUPS SOYBEAN CULTIVARS UNDER ENDOPHYTIC-RHIZOBIAL INOCULATION IN CONDITIONS OF NON-IRRIGATED AGRICULTURE OF THE SOUTHERN STEPPE OF UKRAINE

The article presents the results of studies of weather and climatic conditions during the production processes in the period of formation of the yield of soybean varieties of different maturity under pre-sowing endophytic-rhizobial inoculation of seeds, as well as the functional activity of the rhizosphere microbiota and plant productivity in conditions of non-irrigated agriculture. The average temperature, relative humidity, and rainfall were determined for the interphase periods, which are the main indicators that affect the evapotranspiration, moisture deficit, and moisture coefficient. The experiments were conducted at the experimental field of the Odesa State Agricultural Research Station, located in the southern steppe zone of Ukraine. The soil of the experimental field is a heavy loamy southern chernozem. The moisture availability indicators show that the southern part of the Steppe zone of Odesa region during the growing season of crops (April-September) in 2023 belonged to the semi-arid zone, and in July and September - to the desert. Evapotranspiration and moisture deficit during the growing season of plants of both soybean varieties (ultra-early maturing Diona and mid-season Sviatohor) varied significantly and depended on the average monthly temperature, relative humidity and rainfall. Evapotranspiration reached 1045.1-1244.0 mm, resulting in a moisture deficit of 913.3-1100.4 mm. Due to insufficient rainfall in June, July and August, most crops experienced an increase in moisture deficit. The use of complex thermotolerant and drought-resistant microbial inoculants with stress-protective functions reduced the negative impact of agroclimatic factors on soybean plants and rhizosphere microbiota. The main directions of scientific and economic activity for obtaining consistently high crop yields in the southern part of the Steppe zone are the structuring of sown areas with an optimal share of irrigated land and the use of innovative biological preparations.

Response of gully morphology and density to the spatial and rainy-season monthly variation of rainfall at the regional scale of the Chinese Loess Plateau

Gully erosion normally causes land degradation, especially on the Chinese Loess Plateau (CLP). However, research on gully spatial variations at larger regional scales, particularly considering the influence of rainfall, remains limited. The effects of monthly rainfall variations during the rainy season (June-September) on gully morphology and density have also been less explored. To narrow these gaps, we conducted a study in five representative catchments along a rainfall gradient in the CLP.Using high-resolution satellite imagery, we visually interpreted 2511 gullies and computed multiple gully morphological and density parameters, including length, area, volume, number density, length density and area density, etc. Long-term (1961–2014) monthly rainfall data and other environmental factors were analyzed to understand their impact on gully morphology and density.Results revealed that gully length, width and depth were crucial parameters representing gully morphology, exhibiting obvious variation across catchments. Generally, gully dimensions increased with higher rainfall, while densities decreased. Gully number density (GND) had a more significant impact on regional gully erosion degree than gully scales (length and area).Gully length and depth were primarily influenced by upslope runoff, while width mainly by runoff and potentially freezing and thawing. Rainfall in July and June-September, respectively, and especially the upslope catchment maximum slope were critical factors influencing gully head retreating and bottom incising, while June rainfall played a key role in sidewall collapsing. These result from the interplay of gully development sub-processes, monthly rainfall characteristics, and concurrent vegetation conditions. NDVI and July-September rainfall primarily determined GND at this scale, with the protective effect of rainfall-contributed vegetation outweighing rainfall’s promotion on gully occurrence.Our study highlights the importance of tailored gully erosion control measures that consider monthly rainfall variations and the significance of vegetation restoration in controlling new gully formation in the CLP and similar regions with comparable climatic and underlying-surface features.

Weathering the storm: Decreased activity and glucocorticoid levels in response to inclement weather in breeding Columbian ground squirrels

Inclement weather can rapidly modify the thermal conditions experienced by animals, inducing changes in their behavior, body condition, and stress physiology, and affecting their survival and breeding success. For animals living in variable environments, the extent to which they have adapted to cope with inclement weather is not established, especially for hibernating species with a short active season that are constrained temporally to breed and store energy for subsequent hibernation. We examined behavioral (foraging activity) and physiological (body mass and fecal cortisol metabolites) responses of Columbian ground squirrels (Urocitellus columbianus), small hibernating rodents inhabiting open meadows in Rocky Mountains, to 3 events of inclement weather (two snow storms in May 2021 and May 2022, one heavy rainfall in June 2022). We found that individuals adapted to inclement weather conditions by (1) reducing above-ground activity, including foraging, (2) decreasing the mobilization of stored resources as indicated by a decrease in the activity of the hypothalamo-pituitary-adrenal (HPA) axis and lower fecal cortisol metabolites in the hours/days following periods of inclement weather; and (3) compensating through increased foraging and more local activity when favorable conditions resumed. As a result, body mass and growth did not decrease following short periods of inclement weather. Columbian ground squirrels were well-adapted to short periods of inclement weather, coping via modifications of their behavior and the activity of the HPA axis.

Data-driven multi-step prediction and analysis of monthly rainfall using explainable deep learning

Monthly rainfall prediction is a crucial topic for the management of water resources and prevention of hydrological disasters. To make a multi-step monthly rainfall prediction and discover the primary factors affecting rainfall, this study presents an explainable deep learning approach that integrates four consecutive modules, namely the Gated-Recurrent-Unit-based (GRU-based) encoder module, attention mechanism module, GRU-based decoder module, and expected-gradient-based explanation module, respectively. The first three modules constitute an encoder-decoder with an attention mechanism which could predict monthly rainfall in multiple continuous months in the future, while the explanation module computes attribution values for the input weather and climate features to quantify their importance. A case study is conducted on monthly rainfall data collected from Darwin and Perth, Australia, from Jan 1921 to Dec 2020. The results mainly indicate that: (1) Compared with the baseline methods, the multi-step monthly rainfall predictions made by the encoder-decoder with an attention mechanism show better agreement with the ground truth. (2) For January rainfall in Darwin, the most important weather factors are last January and February rainfall, while the most critical climate index is last February’s Southern Oscillation Index, with its high values inhibit while low values promote January rainfall in Darwin. (3) The most significant feature for June rainfall in Perth is last June’s solar radiation whose feature value is weakly negatively correlated with its attribution value. The study’s significance lies in enhancing the accuracy of multi-step rainfall prediction and providing interpretability through identification of influential factors, which facilitates long-term planning of water resources and deeper understanding of complex weather systems.

Fungicidal Protection as Part of the Integrated Cultivation of Sugar Beet: An Assessment of the Influence on Root Yield in a Long-Term Study

Despite the major role of non-chemical treatments in integrated plant protection, fungicides often need to be applied as a crop protection treatment in sugar beet farming. They should be used based on a good understanding of the requirements and effectiveness of the active ingredients. In 11-year field experiments, the effect that one and three foliar applications of fungicides containing various active ingredients (triazoles, benzimidazoles, strobilurines) had on sugar beet root yields was assessed, depending on various thermal and rainfall conditions. It was found that in eight of the 11 years, foliar application of fungicides increased yields compared to unprotected plants, and three foliar treatments during the growing season were more effective than a single application. The negative correlation of the root yield of fungicidally protected plants with total June rainfall was weaker than the same relationship for unprotected plants. At the same time, the positive correlation between the yield of fungicidally protected sugar beets and average June air temperature was stronger than the same relationship for unprotected plants. The research results indicate the need to conduct long-term field experiments and to continuously improve integrated production principles for sugar beet, especially regarding the rational use of pesticides.

Statistical Analysis of Rainfall Pattern and Trend of Chhattisgarh State, India

To analyze, the present rainfall status and trend, the long term weather data (1980-2020) viz., gridded annual rainfall data was analysed which was obtained from the department of Agrometeorology, Indira Gandhi Krishi Vishwavidyalaya Raipur, Using linear trend, the long-term change in rainfall has been evaluated. It is obsered that the state receives the maximum amount of rainfall from the southwest monsoon, around 87 percent from June to September, the state receives 15 percent in June and the state receives 29,26 and 16 percent rainfall in the Month of July, August and September, respectively. Long term weather data of state rainfall, it is seen that rainfall in June and September is less consistent since the coefficient of variation is around 33 and 30 percent, respectively; we would conclude that rainfall in June has more variability than in monsoon months, SW monsoon and annual rainfall. Average Monsoon and Annual rainfall variability are less than monsoon months. District-wise minimum rainfall variability of June, July, August, and September is around 37 percent (Kanker) and 22 percent (Jashpur). Kabirdham district has a 25 percent variability in August and September. District Kanker and Raigarh varied around 14 percent for monsoon and annual rainfall. As the linear regression in southwest monsoon and annual rainfall in the state of Chhattisgarh is increasing in order, but from 1980 to 2020 it shows more fluctuation in trend line, then if a system is developed for agrometeorology station at cluster level or block level and for forecasting, it will be able to account for these changes.From this, farmers will receive timely information about the monsoon and rainfall, allowing them to conduct agricultural activities at the appropriate time.

Large-scale background and role of quasi-biweekly moisture transport in the extreme Yangtze River rainfall in summer 2020

A severe flooding hit southern China along the Yangtze River in summer 2020. The floods were induced by extreme rains, and the associated dynamic and thermodynamic conditions are investigated using daily gridded rainfall data of China and NCEP-NCAR reanalysis. It is found that the June–July rainfall over the Yangtze River Basin (YRB) experienced pronounced subseasonal variation in 2020, dominated by a quasi-biweekly oscillation (QBWO) mode. The southwestward-moving anomalous QBWO circulation was essentially the fluctuation of cold air mass related to the tropospheric polar vortex or trough-ridge activities over the mid-high latitude Eurasian in boreal summer. The southwestward-transport of cold air mass from mid-high latitudes and the northeastward-transport of warm and moist air by the strong anomalous anticyclone over the western North Pacific provided important large-scale circulation support for the extreme rainfall in the YRB. The analysis of streamfunction of water vapor flux demonstrates that a large amount of water vapor eastward zonal transport from the Bay of Bengal and Indo-China and northward transport from the South China Sea provided the background moisture supply for the rainfall. The quasi-biweekly anomalies of potential and divergent component of vertically integrated water vapor flux played an important role in maintaining the subseasonal variability of rainfall in June–July of 2020. The diagnosis of moisture tendency budget shows that the enhanced moisture closely related to the quasi-biweekly fluctuated rainfall was primarily attributed to the moisture convergence. Further analysis of time-scale decomposition in the moisture convergence indicates that the convergence of background mean specific humidity by the QBWO flow and convergence of QBWO specific humidity by the mean flow played dominant roles in contributing to the positive moisture tendency. In combination with adiabatic ascent over the YRB induced by the warm temperature advection, the boundary layer moisture convergence strengthened the upward transport of water vapor to moisten the middle troposphere, favoring the persistence of rainfall during June–July. The vertical moisture transport associated with boundary layer convergence was of critical importance in causing low-level tropospheric moistening. By comparison, the horizontal moisture advection played a secondary important role in the quasi-biweekly oscillation of rainfall in June–July 2020.

ФОРМУВАННЯ ПРОДУКТИВНОСТІ КВАСОЛІ ЗВИЧАЙНОЇ ЗАЛЕЖНО ВІД СОРТІВ, ІНОКУЛЯЦІЇ ТА УДОБРЕННЯ

Ріст і розвиток рослин квасолі прямо залежить від умов навколишнього середовища, основними складниками якого є температура повітря та ґрунту, освітленість, вологість, мінеральне живлення. Продуктивність рослин зумовлюється наявністю цих факторів, і чим більше вони відповідають біологічним особливостям культури, тим повніше реалізуються потенційні можливості сортів квасолі. Під час дослідження насіння квасолі звичайної перед сівбою обробляли різними штамами мікроорганізмів. Залежно від цього дещо змінювалися міжфазні періоди росту й розвитку рослин. Тривалість періоду «сівба – повна стиглість» у рослин квасолі сорту Ластівка, інокульованих різними штамами Rhizobium phaseoli, у середньому за період дослідження 2020–2022 рр. відрізнялася на 1–3 доби.

Preliminary Study on the Distribution, Source, and Ecological Risk of Typical Microplastics in Karst Groundwater in Guizhou Province, China.

Karst groundwater is one of the important drinking water sources in karst areas, and it has an important role in maintaining the regional ecosystem and human health. The study of microplastics (MPs) in karst groundwater has rarely been reported, and the occurrence and migration behavior of MPs under the unique environmental conditions of karst is unclear. This study selected cave groundwater and common MPs in karst areas to explore the occurrence characteristics of MPs in groundwater to clarify the factors affecting the distribution and migration of MPs. The results showed that the abundance of MPs in karst groundwater was between 2.33 and 9.50 items·L-1, with an average abundance of 4.50 items·L-1. The microplastic size, type, color, and chemical composition were primarily 1~5 mm, film and fiber, color and transparent, and PS and PE, respectively. The risk characterization ratio (RCR) index results indicated that 80% of the samples were at a low ecological risk level, whereas 60% of the sampling points after concentrated rainfall in June were a medium ecological risk. The study showed that rainfall events significantly changed the abundance and migration of MPs in karst groundwater. The Pearson analysis showed a positive correlation between microplastic distribution and suspended particles (SP), total organic carbon (TOC), and water velocity (WV) in water. The study indicated that strong soil erosion in karst areas may also be one of the main sources of MPs in karst groundwater, and that karst groundwater microplastic pollution is an environmental problem that should not be ignored.

Dipole response of early‐summer rainfall in eastern China to 1.5 and 2.0°C global warming

AbstractThe 2015 Paris Agreement set the goal of pursuing efforts to keep global warming to 1.5°C above the pre‐industrial level. It remains unclear how the early‐summer rainfall in China responds to moderate warming. Here, we investigate the changes in June rainfall in eastern China by 1.5 and 2.0°C warming using the ensemble projection from Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) dataset. Results reveal a dipole response in the June rainfall with the significant increase in southeastern China but a slight decrease in northeastern China at 1.5 and 2.0°C warming compared to the present day. The moisture budget analysis indicates that the dipole rainfall response is caused predominantly by the dynamic response in the anomalous circulation and in turn moisture transport. Compared to the present day, the upper‐tropospheric temperature is significantly enhanced in the tropics but decreased in middle latitudes at the 1.5 and 2.0°C warming. The enhanced poleward temperature gradient strengthens zonal wind on the south of the East Asian westerly jet, leading to a southward displacement of jet. As a result, an anomalous upward motion is reinforced over the north of 30°N with an anomalous downward motion over the south of 30°N, leading to a dipole rainfall anomaly in eastern China. In addition, the warming also induces a thermodynamic response with increased moisture and in turn rainfall across eastern China, further enhancing rainfall in southeastern China, but weakening the decreased rainfall in northeastern China.

Machine learning models for prediction of rainfall over Nigeria

Investigating climatology and predicting rainfall amounts are crucial for planning and mitigating the risks caused by variable rainfall. This study utilized two multivariate polynomial regressions (MPR) and twelve machine learning algorithms, namely three artificial neural networks (ANN), four adaptive neuro-fuzzy inference system (ANFIS) and five support vector machine (SVM) algorithms, to estimate monthly and annual rainfalls in a tropical location. The ground measured rainfall data were collected from the Nigerian Meteorological Agency (NIMET), Lagos spanning 31 years (1983–2013) spatially distributed across Nigeria. The proposed models employed geoclimatic coordinates such as longitude, latitude, and altitude as input variables. Analyses based on general performance index (c) showed that the adaptive neuro-fuzzy inference system (ANFIS) model’s algorithms outscored the MPR, ANN and SVM models in the ten months of the year. Its the generalized bell-shaped algorithm (ANFIS-GBELL) performed best for January, April, May, July, October and annual rainfalls, the Gaussian algorithm (ANFIS-GAUSS) for November and December, the subtractive clustered algorithms (ANFIS-SC) for August and September rainfalls, and fuzzy c-means algorithms (ANFIS-FCM) for June rainfall. Also, the multivariate polynomial regression of second order (MPR-2) model performed best for February and March rainfalls. These models’ algorithms have general performance index ranging from 0.906 to 0.996 and they are thereby proposed for the estimation of rainfall amounts over Nigeria.

Possible Lagged Impact of the Arctic Sea Ice in Barents–Kara Seas on June Precipitation in Eastern China

Based on the Hadley Centre sea ice concentration, the ERA5 reanalysis, and three precipitation datasets, the possible lagged impact of the Barents–Kara sea ice on June rainfall across China is investigated. Using the singular value decomposition, it is revealed that the state of sea ice concentration in Barents–Kara Seas from November to December is closely related to regional precipitation in June, which is most evident across the Yangtze–Huai Rivers Valley and South China. Possible pathways from preceding Arctic sea ice concentration to June precipitation are examined and discussed. First, the sea ice concentration usually has a long memory, which exerts a long-lasting and lagged impact, although the sea ice anomaly amplitude gradually weakens from early winter to early summer. Second, an increase in Barents–Kara sea ice usually corresponds to a stronger stratospheric polar vortex in midwinter by suppressing extratropical wave activities, which is projected to the positive phase of northern annular mode (NAM). Strong vortex gradually recovers to its normal state and even weakens in spring, which corresponds to the negative NAM response from spring to early summer. Third, the stratospheric anomalies associated with the Barents–Kara sea ice variations propagate downward. Due to the out-of-phase relationship between the lower and upper stratospheric circulation anomalies after midwinter, westerly anomalies in midwinter are followed by easterly anomalies in later months in the circumpolar region, consistent with the positive NAM response in midwinter, negative NAM response in spring, and a wave train-like response in early summer to Barents–Kara sea ice increase (and vice versa). The observed lagged impact of Barents–Kara sea ice on China rainfall in June is limitedly simulated in the ten CMIP6 models used in this study.

Trend Pattern of Heavy and Intense Rainfall Events in Colombia from 1981–2018: A Trend-EOF Approach

The Andes mountain range divides Colombia into various climatic regions over the country, as the Andean, Caribbean, Pacific, Amazon, and Orinoco regions. Given this scenario, knowing the current change in total precipitation and their extremes values are relevant. In this study, the main goal is to assess the spatio-temporal trends of heavy and intense rainfall at a seasonal scale during the last 38 years (1981–2018) using the trend empirical orthogonal function (TEOF). An increase in maximum precipitation during five consecutive days (RX5day), Simple daily intensity index (SDII), and the number of days with precipitation above 20 mm (R20mm) and 30 mm (R30mm) during December–February and March–May was observed in most of the Colombian territory, except for the Amazon region for RX5day. A decrease in total rainfall in June–August was observed in the Andean, the Caribbean, and southern Pacific regions, while, in the northern Pacific, it increased, consistent with the trend patterns of RX5day, SDII, and R20mm. During September–November, there was a reduction in rainfall in the Amazon region and the South Pacific, and an increase in RX5day, SDII, R20mm, and R30mm in the Andean, the Caribbean, and North Pacific regions. The TEOF showed more pronounced spatial trend patterns than those obtained with the traditional Mann–Kendall test. The findings offer a better understanding of the climate extremes impacts in tropical latitudes and help planners to implement measures against the future effects of climate change.

Seasonal variation significantly affected bacterioplankton and eukaryoplankton community composition in Xijiang River, China.

Both bacterioplankton and eukaryoplankton communities play important roles in the geochemical cycles and energy flows of river ecosystems. However, whether a seasonal change in bacterioplankton and eukaryoplankton communities is synchronous remains unclear. To test the synchronicity and analyze how physical and chemical environmental factors affect these communities, we compared bacterioplankton and eukaryoplankton communities in surface water samples between March (dry season) and June (rainfall season) considering water environmental factors. Our results showed that there was no significant difference in operational taxonomic unit number, Shannon index, and Chao1 index in bacterioplankton and eukaryoplankton communities between March and June. However, principal component analysis showed that the communities were significantly different between the sampling times and sampling sites. Water temperature (WT), oxidation-reductionpotential (ORP), water transparency (SD), NO3-N, and NH3 significantly influenced bacterioplankton communities, and WT, SD, ORP, and NH4-N significantly influenced eukaryoplankton communities in the river. These results implied that compared with the sampling sites, sampling times more significantly affected the bacterioplankton and eukaryoplankton river communities by influencing WT, ORP, SD, and nitrogen forms.

Seasonal and Inter-Annual Variations of Stable Isotopic Characteristics of Rainfall and Cave Water in Shennong Cave, Southeast China, and Its Paleoclimatic Implication

Speleothem calcite stable oxygen isotope (δ18OC) is one of the most widely used proxies in paleoclimate research, and understanding its seasonal-annual variability is very significant for palaeoclimate reconstruction. Five-year precipitation and karst cave water from 2016 to 2021 were monitored in Shennong cave, Jiangxi Province, Southeast China. The local meteoric water line (LMWL) is δD = 8.20 × δ18O + 13.34, which is similar to the global meteoric water line. The stable hydrogen and oxygen isotope (δD and δ18O) characteristics of precipitation and cave water were studied. δ18O and δD of precipitation and cave water show obvious seasonal variations. Lower precipitation δ18O and δD generally occur during summer and autumn compared with higher δ18O and δD values during winter and spring. Meanwhile, low precipitation δ18O values do not only appear in June–July when precipitation is the highest of the year but also appear in August–September when precipitation is limited. The back-trajectory analysis of monsoon precipitation moisture sources shows that the moisture uptake regions vary little on inter-annual scales; the water vapor of rainfall in June–July comes from the South China Sea and the Bay of Bengal, while the moisture source in August–September is mainly from the West Pacific and local area. The El Niño-Southern Oscillation is an important factor affecting the value of δ18O by modulating the percentage of summer monsoon precipitation in the annual precipitation and moisture source. The relationship between amount-weighted monthly mean precipitation δ18O and Niño-3.4 index shows that the East Asian summer monsoon (EASM) intensifies during La Niña phases, resulting in more precipitation in monsoon season (May to September, MJJAS) and lower δ18O values, and vice versa during El Niño phases.

Soil bacterial community composition following cover crops and corn nitrogen management

AbstractUtilizing cover crops (CC) prior to corn (Zea mays L.) creates opportunities to increase plant diversity and influence soil bacterial communities, but data are scarce when CC are combined with corn N fertilizer management. Field studies were conducted in 2014–2015 and 2015–2016 to evaluate corn N strategies following daikon radish [Raphanus sativus (L.)], forage oat [Avena sativa (L.)], or no CC on soil bacterial community composition. Nitrogen strategies consisted of 179 kg N ha–1 applied pre‐plant incorporated as urea, slowly‐available poultry litter (61 kg N ha–1) plus sidedress (SID) N at V11, starter N (45 kg N ha–1) applied 5 cm beside and 5 cm below the furrow (5 × 5) plus SID at V4, V11, or 50:50 (split) V4 and V11, and a zero N control. Each year, soil bacterial community composition was variably influenced by CC and N management. With excessive May–June rainfall, radish and oat CC residues shaped community structure and membership differently regardless of soil sampling zone (between‐row or in‐row), but these residues influenced operational taxonomic units (OTUs) less under deficit May–June rainfall. Soil disturbance associated with coulter‐injection of V4 SID placement influenced bulk soil community structure between‐rows, whereas in 1 of 2 yr, the 5 × 5 N placement appeared to induce an in‐row corn rhizosphere response resulting in OTU membership differences from other N strategies. Including slowly‐available organic N as part of a well‐balanced fertility program may stabilize the impact of inorganic N fertilizer to soil bacterial communities.