Moderate Rainfall Research Articles

Advancing the dominance of winter annuals under changing rainfall patterns in a temperate desert of Central Asia

Understanding how climate change-induced alterations in rainfall patterns impact the composition and structure of plant diversity is crucial. Examining variations in plant life forms is vital for comprehending ecosystem dynamics. This study analyzes plant life form diversity, rainfall variability, and the impact of different rainfall patterns on plant diversity over 15 years in the Gurbantünggüt Desert, a temperate desert in Central Asia. The study identified 96 plant species in the area, comprising 27 winter annuals and 33 perennial herbs. Over the past 15 years, species diversity has remained stable, while diversity among plant life forms has varied significantly. Winter annuals consistently dominated, showing a significant linear increase in importance values (slope=1.135). Conversely, perennial shrubs like Haloxylon ammodendron and Haloxylon persicum exhibited a marked declining trend. Over the past 15 years, the occurrence of minor (<1 mm) and moderate (1–5 mm) rainfall during the spring and autumn seasons initially rose and subsequently fell. Concurrently, heavy rainfall events (≥5 mm) in spring have waned, showing notable yearly fluctuations. The frequency of heavy autumnal downpours has remained steady, with consistent interannual variations. Minor rainfall events primarily influenced winter annuals and perennial herbs during the spring and autumn, whereas heavy rainfall mainly affected perennial shrubs during these seasons. Our investigations suggest that winter annuals, despite their expanding presence, are ineffective in their ecological functions such as dune stabilization and reducing aeolian erosion in the peak summer months. In contrast, perennial shrubs like H. ammodendron and H. persicum, crucial for these roles, are markedly declining. The variable plant life forms might lead to modifications in the ecological roles of the region, thereby rendering it imperative to persistently observe this desert and proactively formulate conservation strategies for the vegetation.

Seasonal distribution and upsurge of respiratory viruses among indigenous tribes with ILI and SARI in a far-flung Car Nicobar Island

BackgroundRespiratory viral illnesses among children are a prominent cause of morbidity and mortality in the developing world. The aim of this study is to understand the seasonal pattern and surge of respiratory viruses among the Nicobarese tribe.MethodsRespiratory specimens were collected from both ARI and SARI cases attended the BJR district hospital in Car Nicobar Island, India, between 2021 and 2022. Respiratory viruses were identified from the specimens by using the qRT-PCR assay. Meteorological parameters were collected and evaluated using Microsoft Excel and SPSS 21. The significant association between the surge of respiratory viruses and each climatic parameter was evaluated.ResultsIn this hospital-based cross-sectional study, 471 ILI cases were enrolled, and 209 of these were positive for respiratory viral infections. Of these respiratory virus infections, 201 (96.2%) were infected with a single respiratory virus infection, and 8 (3.8%) had mixed viral infections. Fever, cough, and chills were the most common symptoms of respiratory illness among this indigenous population. There was a significant link between respiratory viruses and influenza-like illness in children (below 5 years and 6 to 15 years).ConclusionThis prevalence study revealed that viral respiratory infections were more common in children than adults. Among these respiratory viruses, respiratory syncytial virus A (RSV) and influenza B virus were predominantly reported among tribal children up to age five years. In the year 2021, these viruses were recorded frequently during the winter season. Climate factors such as high humidity, high precipitation, moderate temperature, and moderate rainfall are found to be correlated with respiratory viral infections. This study implicates important information for preventing a further outbreak of respiratory viral infections in Car Nicobar Island.

Analisis Pengaruh Curah Hujan terhadap Frekuensi Kejadian Banjir di Kabupaten Kotabaru, Kalimantan Selatan

The accumulation of water on the ground is what causes flooding, whereas rainfall is the amount of rainwater collected in a flat area Rainfall and flooding are two related but distinct concepts. While rainfall refers to the amount of precipitation that falls in a particular area, flooding is the result of excessive water accumulation on the ground. Indonesia can be classified as a country with high rainfall due to its average annual rainfall ranging from 2000 to 3000 millimeters. In cases of heavy rainfall, excessive water discharge can occur. This can result in flooding when the land, rivers, or water reservoirs cannot manage the overflow effectively. Flooding is a common occurrence in Kotabaru Regency located in South Kalimantan, as it is a combination of mountainous regions, coastal areas, land areas, and small islands. The objective of the study is to examine whether flooding is always caused by high rainfall in Kotabaru Regency from January 2018 to December 2022. The correlation analysis is part of the research design, which utilizes secondary data collected and analyzed in Microsoft Excel. The findings suggest that moderate intensity rainfall is responsible for 73.3% of floods that occurred between 2018-2022. Moreover, 26.7% of floods occurred when rainfall is less than 20 mm/day. However, other factors such as monsoons, tropical cyclones, and El-Nino Southern Oscillation (ENSO) can affect the variability of rainfall and inffluence the occurrence of flood in the area.

IMPLEMENTATION OF MAPPING-BASED MACHINE LEARNING ALGORITHM AS NON-STRUCTURAL DISASTER MITIGATION TO DETECT LANDSLIDE SUSCEPTIBILITY IN TAKARI DISTRICT

This research is primarily dedicated to providing a comprehensive exposition of the methodology applied in the deployment of a cartographic-based machine learning algorithm designed for the precise identification of areas susceptible to landslides within the geographical confines of the Takari District.This research delves into the application of mapping-based machine learning algorithms in the domain of non-structural disaster mitigation, with a specific emphasis on the detection of landslide susceptibility within the Takari District. A range of machine learning algorithms, including Support Vector Machine, Naive Bayes Classifier, Ordinal Logistic Regression, Random Forest, and Decision Tree, were harnessed to evaluate rainfall data within the context of landslide susceptibility. An evaluation of model performance, anchored in accuracy and Kappa metrics, unveiled that both the Ordinal Logistic Regression and Random Forest models exhibited noteworthy precision, reaching a commendable 74.36%. Nevertheless, a meticulous examination of Kappa values disclosed the ascendancy of the Random Forest model, which achieved a superior Kappa value of 0.5397. As portrayed in the visual representation provided, it becomes manifest that the Random Forest algorithm's prognostications yield 66 instances of cloudy atmospheric conditions, 48 occurrences of light precipitation, and 3 episodes of moderate rainfall. These predictions are influenced by several factors, including average temperature, humidity levels, wind speed, duration of sunlight, and wind direction at maximum speed. Consequently, this comprehensive analysis underscores the Random Forest algorithm as the most efficacious model for landslide susceptibility prediction. Furthermore, the study seamlessly integrated overlay maps, encompassing the Slope Inclination Map of the Takari District, Geological Map of the Takari District, and Soil Type Map of the Takari District, to contribute to the formulation of a definitive map delineating the susceptibility to landslides in the Takari District. Furthermore, further research could conduct spatial validation of the model predictions using additional datasets or remote sensing data to validate the accuracy of the landslide susceptibility map and ensure its applicability across different geographical regions.

Assessment of environmental and spatial factors influencing the establishment of Anopheles gambiae larval habitats in the malaria endemic province of Woleu-Ntem, northern Gabon

BackgroundThis study aimed to assess the spatial distribution of Anopheles mosquito larval habitats and the environmental factors associated with them, as a prerequisite for the implementation of larviciding.MethodsThe study was conducted in December 2021, during the transition period between the end of the short rainy season (September–November) and the short dry season (December-February). Physical, biological, and land cover data were integrated with entomological observations to collect Anopheles larvae in three major towns: Mitzic, Oyem, and Bitam, using the "dipping" method during the transition from rainy to dry season. The collected larvae were then reared in a field laboratory established for the study period. After the Anopheles mosquitoes had emerged, their species were identified using appropriate morphological taxonomic keys. To determine the influence of environmental factors on the breeding of Anopheles mosquitoes, multiple-factor analysis (MFA) and a binomial generalized linear model were used.ResultsAccording to the study, only 33.1% out of the 284 larval habitats examined were found to be positive for Anopheles larvae, which were primarily identified as belonging to the Anopheles gambiae complex. The findings of the research suggested that the presence of An. gambiae complex larvae in larval habitats was associated with various significant factors such as higher urbanization, the size and type of the larval habitats (pools and puddles), co-occurrence with Culex and Aedes larvae, hot spots in ambient temperature, moderate rainfall, and land use patterns.ConclusionsThe results of this research mark the initiation of a focused vector control plan that aims to eradicate or lessen the larval habitats of An. gambiae mosquitoes in Gabon's Woleu Ntem province. This approach deals with the root causes of malaria transmission through larvae and is consistent with the World Health Organization's (WHO) worldwide objective to decrease malaria prevalence in regions where it is endemic.

The Flora of Kodaikanal

Kodaikanal is of special botanical interest for its temperate flora, rare in Peninsular India, on account of high altitude and consequent moderate temperaturé. The climate is characterized by moderate temperature with low annual range, and moderate and well-distributed rainfall.- Its flora is relatively rich, but not yet studied exhaustively and critically.The flora of Kodaikanal has been subjected to excessive human interference, as first by noted by' Beddome already in 1858, and this has been true ever since, especially of late. A disastrous form of interference is the putting of fire during summer to ensure good growth of grass with the first showers. Recently many places have been completely cleared for cultivation. The more ornamental species like orchids and lilies, and the rarer pteridophytes have been removed by visiting botanists. Thus the flora of Kodaikanal, as it is today, is but a relic of a rich flora in the past.The present paper restricts itself to the plants above alt. 1675 m. The flora of this area may be classified at first into the Indigenous and the Exotic, treated separately, below.

Quantifying annual microplastic emissions of an urban catchment: Surface runoff vs wastewater sources

Urban clusters are recognized as hotspots of microplastic pollution, and the associated urban rivers convey microplastics into the global oceans. Despite this knowledge, the relative contributions of various sources to the annual microplastic emissions from urban catchments remain scarcely quantified. Here, we quantified microplastic emissions from a riverine urban catchment in Japan. The total microplastics (size range: 10–5000 μm) released from the catchment amounted to 269.1 tons/annum, of which 78.1% is contributed by surface runoff and other uncontrolled emissions (UCE), and 21.1% emerges from the regulated wastewater (controlled emissions; CE), implying that approximately one-fifth is intercepted and removed by the wastewater treatment plants (WWTPs). This further indicated higher microplastic pollution by unmanaged surface runoff compared to untreated wastewater. In the dry season, WWTPs contributed significantly to the reduction of total microplastic emissions (95%) compared to wet periods (8%). On an annual scale, the treated effluent occupies only 0.1% of the total microplastics released to the river network (212.4 tons/annum), while the remaining portion is dominated by UCE, i.e., primarily surface runoff emissions (98.9%), and trivially by the background microplastic inputs that are potentially derived through atmospheric depositions in dry days (1.0%). It was shown that moderate and heavy rainfall events which occur during 18% of the year (within the context of Japan), leading to 95% of the annual microplastic emissions, are crucial for pollution control of urban rivers. Furthermore, our study demonstrated that surface area-normalized microplastic emissions from an urban catchment (∼0.8 tons/km2/annum) is globally relevant, especially for planning microplastic interventions for developed cities.

Adapting to climate disparities: Implications for fruit fly populations in citrus orchards

In the context of climate change, the agricultural sector faces the imperative of devising adaptive strategies to mitigate emerging challenges. This study aimed to know the relationship between climatic disparities and fruit fly populations in citrus orchards. Utilizing Steiner trap and Methyl Eugenol wooden blocks as monitoring, male annihilation controlling devices, and spraying protein bait, we conducted a comprehensive investigation across two distinct agroclimatic conditions. The main variations were monthly rainfall (mm), accentuating diverse agroclimatic dynamics. Surprisingly, an analysis comparing low rainfall (1-4 mm) to moderate rainfall (14-21 mm) periods did not affect fruit fly populations. Contrary to conventional assumptions, variations in only precipitation did not significantly impact fruit fly population dynamics. The critical factors were the dominant role of host plant availability and the efficacy of pest management practices in steering population trends. This study showed that climatic factors contribute to pest dynamics; however, they might not function as a single change factor. Instead, host plant availability and the implementation of effective pest control interventions held more significant influence. Based on the interconnected relationship between climate and pests, amid a continuously developing climate scenario, more careful management strategies are needed to increase the sustainability of citrus yields for food security and farmer welfare.

Instability evolution of expansive soil slope due to short duration-varying intensities of rainfall

This study examines the evolution of instability in induced expansive soil slopes under varying rainfall intensities. The destabilization evolution of expansive soil slopes and their stability under three different rainfall intensities were revealed through indoor modelling and numerical simulation. The findings indicate that slopes are not destabilized by short duration and low rainfall intensity alone. Slope failure under strong rainfall intensity follows a three-stage evolutionary process. As rainfall intensity increases, the slope's water content, soil pressure, and pore water pressure increase. However, the stress at each monitoring point of the slope affected by rainfall follows a different pattern. Under low rainfall intensity, the water content of the slope surface is higher than that of the slope foot. Under strong rainfall conditions, the water content of the slope foot is higher than that of the slope surface. Therefore, it is important to implement seepage prevention measures on the slope surface and drainage measures on the slope foot to ensure slope stability. Rainfall affects the soil pressure at each monitoring point of the slope, causing unloading on the slope surface and fluctuation at the foot of the slope. This phenomenon becomes more pronounced with increasing rainfall intensity. Numerical simulation confirms that the overall horizontal displacement distance of the slope increases with rainfall intensity, but the horizontal displacement range becomes shallower. The slope's maximum horizontal displacement occurs at its foot, with values of 0.29 m and 0.34 m under moderate and heavy rainfall, respectively, representing a growth rate of 17 %. Settlement of the slope under different rainfall intensities was greatest at the top of the slope area. The lateral stresses generated by the settlement resulted in soil uplift at the leading edge of the slope.

Multiscale dynamics of transient merging between western disturbances and monsoonal lows: Connections to the July 2023 flood in Himachal Pradesh

The Himachal Pradesh region experienced an unprecedented 436% more rainfall than normal from July 7th to 10th, 2023, resulting in devastating landslides, flash floods, and significant socio-economic losses. Using rainfall observations and reanalysis data, this study investigates the dynamics of the extreme precipitation event that caused the disastrous 2023 flood in Himachal Pradesh, India. Prior to the flood, moderate rainfall events saturated the mountainsides, making them more vulnerable to landslides and flash floods. Local features such as rapid convergence, vertical velocity, relative humidity, relative vorticity, and moisture flux accumulation were observed to decrease following the extreme precipitation event, indicating a short-duration cloud burst event. Further, the study reveals a large-scale circulation pattern linking the extratropic and monsoonal low, which is favorable for extreme precipitation in the Himachal Pradesh region. This pattern includes a transient merging of eastward-propagating trough in the mid-upper-level, which extends southward and a monsoon low moving westward across India. The key elements of this transient merging feature consist of a Rossby wave train structure and a blocking high west of the features. The Rossby wave breaking effect occurred downstream of the blocking high two days prior to extreme precipitation event occurrence. This allowed high potential vorticity stratospheric air to intrude the equator and troposphere, causing surface pressure change and enhanced vertical motion, thereby advecting high amount of moisture causing extreme precipitation leading to catastrophic floods. Overall, the findings of this study hold direct implications for early warning systems and disaster preparedness in the face of extreme precipitation-induced flood situations triggered by such transient merging features.

Enhancing Rice Production Prediction: A Comparative Machine Learning Analysis of Climate Variables

This study aims to enhance rice production prediction through a comparative analysis of machine learning models utilizing climate variables. Eight models were assessed on a predetermined dataset, with Support Vector Regression (SVR) emerging as the top performer. Following the identification of significant climate variables influencing rice production, the models underwent evaluation using two hyperparameter approaches: random search and manual tuning. SVR outperformed other models, achieving impressive metrics with MAE 0.180, MSE 0.186, RMSE 0.431, and an exceptionally low MAPE of 0.020. Key factors influencing rice production included productivity and area, along with humidity, rainfall, temperature, wind velocity, and sunshine duration. Favorable conditions for rice output encompassed low humidity, moderate rainfall, increased wind speed, and prolonged sunshine, while rainfall and temperature exhibited minimal impact. The success of random search emphasizes the importance of effective hyperparameter tuning. This research provides valuable insights for enhancing rice production prediction.

Transformation, leaching and plant uptake simulations of 6:2 and 8:2 polyfluoroalkyl phosphate diesters (diPAPs) and related transformation products under near-natural conditions

In response to the growing concern over PFAS contamination, employing models to simulate PFAS behavior in the environment becomes necessary. This facilitates evaluating risks tied to leaching into groundwater, adsorption in soil, plant uptake, entry into the food chain, and the conversion of precursors into persistent PFAS. We utilized the MACRO model to simulate the behavior of the precursors 6:2 diPAP and 8:2 diPAP using data from a 2-year lysimeter experiment, key compound parameters were optimized via the caRamel evolutionary algorithm. We assumed that the transformation of both diPAP precursors into stable PFAAs is influenced by temperature and soil moisture, similar to pesticide degradation by microorganisms. Results reveal that the model accurately represents transformation, leaching, soil retention, and plant uptake of diPAP and transformation products. A comparison with a lab-based soil column study supports the slower natural degradation of precursors, affirming our modeling approach. Temperature and soil moisture could indicate that a worst-case scenario for transformation product leaching into groundwater could occur during a mild summer with moderate evapotranspiration and heavy rainfall. Plant uptake involves multiple elements: PFAS availability in the root zone depends on prior degradation or presence. Increased moisture in the root zone favors PFAS uptake combined with temperatures high enough for prior biotransformation. The calculation of temperature and moisture-based conversion rates was adopted directly from MACRO. It is recommended to further investigate these effects to validate and possibly modify them.

Projecting Spring Consecutive Rainfall Events in the Three Gorges Reservoir Based on Triple-Nested Dynamical Downscaling

Spring consecutive rainfall events (CREs) are key triggers of geological hazards in the Three Gorges Reservoir area (TGR), China. However, previous projections of CREs based on the direct outputs of global climate models (GCMs) are subject to considerable uncertainties, largely caused by their coarse resolution. This study applies a triple-nested WRF (Weather Research and Forecasting) model dynamical downscaling, driven by a GCM, MIROC6 (Model for Interdisciplinary Research on Climate, version 6), to improve the historical simulation and reduce the uncertainties in the future projection of CREs in the TGR. Results indicate that WRF has better performances in reproducing the observed rainfall in terms of the daily probability distribution, monthly evolution and duration of rainfall events, demonstrating the ability of WRF in simulating CREs. Thus, the triple-nested WRF is applied to project the future changes of CREs under the middle-of-the-road and fossil-fueled development scenarios. It is indicated that light and moderate rainfall and the duration of continuous rainfall spells will decrease in the TGR, leading to a decrease in the frequency of CREs. Meanwhile, the duration, rainfall amount, and intensity of CREs is projected to regional increase in the central-west TGR. These results are inconsistent with the raw projection of MIROC6. Observational diagnosis implies that CREs are mainly contributed by the vertical moisture advection. Such a synoptic contribution is captured well by WRF, which is not the case in MIROC6, indicating larger uncertainties in the CREs projected by MIROC6.

Analysis of long-term trends of rainfall and extreme rainfall events over Andaman &amp; Nicobar and Lakshadweep Islands of India

The two major archipelagos of India, the Andaman &amp; Nicobar Islands and the Lakshadweep situated in the climate-hazardous areas of the Bay of Bengal and Arabian Sea respectively are largely affected by weather systems developing over the sea and heavy rainfall activities. The recent two daily gridded rainfall data sets published by IMD; Rajeevan et al. (2010) at 1° × 1° spatial resolution and Pai et al. (2014) at 0.25° × 0.25° spatial resolution extending for a period of more than 100 years have been extensively used by researchers to study the rainfall characteristics at various spatiotemporal scales over the Indian mainland. However, these data sets do not include the grids over these two island meteorological subdivisions of India mainly because of the absence of daily rainfall observation for this long period. In this study, an attempt has been made to develop daily gridded rainfall data over these island subdivisions for the recent 70 years (1951 to 2020) in two spatial resolutions, viz., 1° × 1° and 0.25° × 0.25° using all the available islands station data during the period and carry out statistical analyses of various rainfall characteristics over these islands. The 0.25° × 0.25° data set was observed to be more comparable with the official rainfall time series of IMD for both these two Island subdivisions, and hence this data set has been used to carry out the trend analysis of Daily events of rainfall DER (&gt; = 5 mm) for these two island subdivisions for the whole data period of 1951-2020 and the climate regime shift period of 1971-2020. DER was classified into two categories DMR (5-100 mm), daily moderate rainfall events and DHR (100 mm and above) daily heavy rainfall events. Signs and magnitude of the long-term trends in the frequency of DER (with DMR &amp; DHR) showed significant changes during the recent period 1971-2020.

Field and Numerical Investigation of Taihu Resort Cut Slope Failure in Suzhou, China

This study aims to determine the cause of a landslide located at Taihu Resort in Suzhou, Jiangsu Province, China. Field monitoring has been conducted, including two-dimensional finite element method (FEM) numerical analyses to examine slope stability under different working conditions. The monitoring data indicated that the slope deformation was triggered in two phases: at a certain period when the slope was cut and moderate rainfall occurred in November; and afterward, when a continuous heavy rainfall intensity hit during the monsoon rainfall, which accelerated sliding of the slope. Numerical simulations show that stress and maximum displacement distributions formed and redistributed during the excavation process, where the shear strain occurred mainly in the front layer of soil. Additionally, the factor of safety of the slope decreases gradually with the increase in the excavation rate. During 15 days of continuous rainfall infiltration, the slope under heavy rainfall indicates a minimum factor of safety (FoS). Specifically, the slope excavation followed by heavy rainfall intensities contributed significantly to the landslide.

A comparative spatial analysis of flood susceptibility mapping using boosting machine learning algorithms in Rathnapura, Sri Lanka

AbstractIdentifying flood‐prone areas is essential for preventing floods, reducing risks, and making informed decisions. A spatial database with 595 flood inventory and 13 flood predictors were used to implement five boosting algorithms: gradient boosting machine (GBM), extreme gradient boosting, categorical boosting, logit boost, and light gradient boosting machine (LGBM) to map flood susceptibility in Rathnapura while evaluating trained model's generalizing ability and assessing the feature importance in flood susceptibility mapping (FSM). The model performance was evaluated using the F1‐score, kappa index, and area under curve (AUC) method. The findings revealed that all the models were effective in identifying the overall flood susceptibility trends while LightGBM model had superior results (F1‐score = 0.907, Kappa value = 0.813 and AUC = 0.970), securing the top scores across all performance metrics compared to the other models (for testing dataset). Based on kappa evaluation, most of the models had finer performance (AUC min = 0.737) while LightGBM had moderate performance for predictions beyond the training region. According to the results, regions with lower altitudes and topographic roughness values, moderate rainfall, and proximity to rivers are more susceptible to flooding. This framework can be adapted for rapid FSM in data‐deficient regions.

Assimilation of the deep learning-corrected global forecast system fields into the regional model for improving medium-range persistent precipitation forecasts

The error of regional models gradually increases during long-term integration, and it can be reduced with the constraint of large-scale circulation by assimilating global forecast system (GFS) fields to improve the forecast performance. However, with increasing forecast time, the GFS forecast error also significantly increases and varies widely. Directly assimilating GFS fields into the regional model without processing may generate negative impacts, whereas employing postprocessing correction methods can effectively mitigate GFS forecast errors. In this study, the GFS data of European Centre for Medium-Range Weather Forecasts (ECMWF) are first corrected using a convolutional long- and short-term memory network method. The correction model can correct multiple forecasts simultaneously by training only one model, and the correction results are less erroneous than those obtained by training multiple correction models for the different forecast times and pressure layers. The corrected ECMWF forecast fields are then assimilated into Weather Research and Forecasting Model (WRF) using the nudging method. This assimilation method generally improves the forecasts for precipitation above the moderate rainfall level at forecast times of 4–7 days, and reduces the errors of surface wind speed forecasts. This study improves the accuracy of correction models and the forecasts of persistent precipitation weather event with regional model.

False Alarm Causes and Wind Field Sensitivity Analysis of a Severe Rainfall Event in the Guangdong-Hong Kong-Macao Greater Bay Area Urban Cluster

On May 11, 2022, despite the favorable upper and lower-level circulation patterns of the high-altitude trough, shear line, and southwest jet stream, the urban cluster of the Guangdong-Hong Kong-Macao Greater Bay Area experienced light to moderate rainfall, deviating significantly from the forecasted heavy rain and local heavy rainstorm. This study explores the reasons for false alarms and predictability using ground observation data, radar data, ECMWF-ERA5 reanalysis field data, and ECMWF and CMA-TRAMS forecast data. The results indicate that the warm and moist airflow transported by the low-level jet stream was intercepted by the upstream MCS (mesoscale convective system) along the coastal area of western Guangdong, and inadequate conditions of negative vorticity dynamics led to insufficient moisture, thermodynamic, and dynamic conditions over the urban cluster, preventing the triggering of heavy precipitation. In addition, the 700 hPa westerly flow guiding the airflow and the stable low-level shear line, coupled with surface convergence lines, influenced the northward or southward movement of MCSs along the coastal and inland regions of western Guangdong. The weak and discontinuous intensity of echoes in the upstream Zhaoqing region further hindered the influence of surrounding echoes on the urban cluster. Numerical forecast models ECMWF and CMA-TRAMS overestimated the 850 hPa windspeed and 925 hPa meridional windspeed, resulting in the forecasted urban cluster experiencing heavy rain. Sensitivity tests of wind fields indicate that the 850 hPa wind field information is more sensitive to precipitation in the urban cluster. In this process, weak signal correction can be achieved in strong precipitation forecasts using the distinct signal of lower 850 hPa water vapor flux divergence compared to 925 hPa. Therefore, in the future, when the Guangdong-Hong Kong-Macao Greater Bay Area encounters similar warm-sector heavy rainfall events, adjustments to model forecasts can be made using specific 850 hPa elements such as wind speed, water vapor flux divergence, or specific humidity to enhance predictive accuracy.

Less concentrated precipitation and more extreme events over the Three River Headwaters region of the Tibetan Plateau in a warming climate

Widely known as the “China Water Tower”, changes in precipitation concentration and insufficient rainfall days over the Three River Headwaters region (TRH) may threaten food and water security in TRH and downstream areas. However, current research on rainfall occurrences in TRH lacks a comprehensive understanding of the spatiotemporal variations in precipitation concentration, the seasonal distribution of rainy days across different rainfall categories, and their correlations with global warming. In this study, we utilize the CN05.1 gridded daily rainfall dataset to address these gaps. Our analysis reveals a decreasing trend in both the precipitation concentration index (PCI) and precipitation concentration degree (PCD), accompanied by an earlier onset of the precipitation concentration period (PCP) in TRH over the past six decades. The distribution of monthly rainfall becomes more uniform throughout the year, with precipitation spreading across a greater number of days annually. Notably, the reduction in both the number of rainy days and the proportion of rainfall during the summer, along with the decrease in the frequency of light and moderate rainfall days and the increase in the frequency of moderately heavy and heavier rainfall days, will lead to a more extreme distribution of summer precipitation in TRH. The increase in the number of rainy days across all intensity categories and the proportion of precipitation during winter, has favorable implications for the sustainable development and water security in TRH. Additionally, the relative increase in the occurrence of heavy and extremely heavy rainfall days in spring demonstrates the most pronounced change, with a notable rise of 12.74% to 28.24% per decade. This provides a certain degree of alleviation for spring drought in TRH. Furthermore, we find that for every 1-degree Celsius increase in global temperature, both PCI and PCD decrease by 9.82% and 8.5%, and PCP advances by 6.41 days. With the 50th percentile of precipitation serving as a dividing line, stronger precipitation events beyond this threshold exhibit a higher positive response to global warming. Conversely, weaker precipitation events falling below the 50th percentile show a higher negative response to global warming. Light rainy days decrease by 16.2% per degree Celsius of warming, while very heavy and extremely heavy rainy days increase by 26.4% and 27.5%, respectively. These findings have important implications for mitigating droughts and floods, as well as for developing a comprehensive framework for the management of water resources and agricultural irrigation in TRH.

Greater change in arbuscular mycorrhizal fungal richness as a response to short-term rainfall exclusion across the North American monsoon season

Arid ecosystems around the world are projected to experience reduced and more infrequent precipitation events. The effects of reduced rainfall are well understood for plants and aboveground interactions; however, the effect of rainfall on belowground microbial interactions remains understudied. Here, we illustrate the strength and direction of change that short-term manipulative rainfall exclusion treatments have on the composition and relative abundance of Arbuscular Mycorrhizal (AM) fungal communities throughout the monsoon season in the Sonoran Desert. Additionally, we use these rainfall exclusion treatments to further understand the impact of soil moisture on labile forms of nitrogen and phosphorous in a natural environment. Rainfall exclusion treatments were installed to remove 0% (control), 60% (moderate exclusion), and 80% (high exclusion) of rainfall over a single wet season. AM fungal community composition varied among rainfall treatments and across the monsoon season, where change was dependent on initial conditions within treatments. Phosphorous content was also a strong predictor of AM fungal composition and relative abundance. By the end of the season, the difference in AM fungal richness was influenced by rainfall exclusion, with the greatest positive change in richness in the moderate rainfall exclusion treatments. Lastly, AM fungal community turnover was predicted by rainfall exclusion treatments, where rainfall contributed to greater turnover in the control treatment in comparison to other treatments. This study further illustrates the complex association between soil abiotic factors, how they are influenced by environmental stress, and how in turn cause shifts in AM fungal communities.