Precipitation Events Research Articles

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.

On the present and future changes in Indian summer monsoon precipitation characteristics under different SSP scenarios from CMIP6 models

AbstractMonsoons are a vital part of the agriculture and economy of India which most of its population rely on for their livelihoods. It still is not clear how climate change will impact precipitation events over India due to the complexity of accurately modelling precipitation. Using twelve Coupled Model Intercomparison Project Six (CMIP6) models, we compared their performance to observed data taken from CRU as well as looking at the future changes in precipitation until the end of the twenty first century for the six precipitation homogenous regions over India. The individual models showed varying degrees of wet and dry biases and the ensemble mean of these models showed relatively lesser bias and improved spatial correlation. Out of 12 models, NorESM and MIROC6 models outperform other models in terms of capturing the spatial variability of precipitation over the Indian region. It is also found that due to lesser moisture transport from the adjoining seas represented through vertically integrated moisture transport (VIMT) analysis, there is consistent dry bias across the models. Further a comprehensive analysis of model performance across six homogeneous precipitation regions indicates that NorESM demonstrates better performance in the CNE and HR regions, EC-Earth excels in the PR, WC, and NE regions, while CMCC shows better performance specifically in the NW region compared to other models. Shared Socioeconomic Pathways (SSPs) were used for future projections and a slight increase in June, July, August, and September (JJAS) precipitation until the end of the century with SSP5-8.5 showing the largest increase. We found an increase in precipitation of 0.49, 0.74 and 1.4 mm/day under SSP1-2.6, SSP2-4.5 and SSP5-8.5 in the far future. The northeast region was shown to receive the largest increase in precipitation (2.9 mm/day) compared to other precipitation homogenous regions and northwest will experience largest shift in precipitation. Interestingly, the number of wet days is expected to increase in the northwest region implying more VIMT towards the region. Our results indicate that monsoon precipitation extremes across all the homogenous regions will increase into the future with a higher severity under fossil-fuelled development, although the models still show large biases lowering confidence in our results.

The Coupled Application of the DB-IWHR Model and the MIKE 21 Model for the Assessment of Dam Failure Risk

The phenomenon of global climate change has led to an increase in the frequency of extreme precipitation events, an acceleration in the melting of glaciers and snow cover, and an elevation of the risk of flooding. In this study, the DB-IWHR model was employed in conjunction with the MIKE 21 hydrodynamic model to develop a simulation system for the dam failure flow process of an earth and rock dam. The study concentrated on the KET reservoir, and 12 dam failure scenarios were devised based on varying design flood criteria. The impact of reservoir failures on flood-risk areas was subjected to detailed analysis, with consideration given to a range of potential failure scenarios and flood sizes. It was determined that under identical inflow frequency conditions, the higher the water level, the more rapid the breakout process and the corresponding increase in flood peak discharge. Conversely, for a given frequency of incoming water, an elevated water level results in a transient breach process, accompanied by a reduction in flood peak flow. Moreover, for a given water level, an increase in water frequency results in a reduction in breaching time, an extension of flood duration, and an increase in flood peak flow. The observed trend of flood spreading is generally north-south, and this process is highly compatible with the topographic and geomorphological features, demonstrating good adaptability.

Resonant Forcing by Solar Declination of Rossby Waves at the Tropopause and Implications in Extreme Precipitation Events and Heat Waves—Part 2: Case Studies, Projections in the Context of Climate Change

Based on the properties of Rossby waves at the tropopause resonantly forced by solar declination in harmonic modes, which was the subject of a first article, case studies of heatwaves and extreme precipitation events are presented. They clearly demonstrate that extreme events only form under specific patterns of the amplitude of the speed of modulated airflows of Rossby waves at the tropopause, in particular period ranges. This remains true even if extreme events appear as compound events where chaos and timing are crucial. Extreme events are favored when modulated cold and warm airflows result in a dual cyclone-anticyclone system, i.e., the association of two joint vortices of opposite signs. They reverse over a period of the dominant harmonic mode in spatial and temporal coherence with the modulated airflow speed pattern. This key role could result from a transfer of humid/dry air between the two vortices during the inversion of the dual system. Finally, focusing on the two period ranges 17.1–34.2 and 8.56–17.1 days corresponding to 1/16- and 1/32-year period harmonic modes, projections of the amplitude of wind speed at 250 mb, geopotential height at 500 mb, ground air temperature, and precipitation rate are performed by extrapolating their amplitude observed from January 1979 to March 2024. Projected amplitudes are regionalized on a global scale for warmest and coldest half-years, referring to extratropical latitudes. Causal relationships are established between the projected amplitudes of modulated airflow speed and those of ground air temperature and precipitation rate, whether they increase or decrease. The increase in the amplitude of modulated airflow speed of polar vortices induces their latitudinal extension. This produces a tightening of Rossby waves embedded in the polar and subtropical jet streams. In the context of climate change, this has the effect of increasing the efficiency of the resonant forcing of Rossby waves from the solar declination, the optimum of which is located at mid-latitudes. Hence the increased or decreased vulnerability to heatwaves or extreme precipitation events of some regions. Europe and western Asia are particularly affected, which is due to increased activity of the Arctic polar vortex between longitudes 20° W and 40° E. This is likely a consequence of melting ice and changing albedo, which appears to amplify the amplitude of variation in the period range 17.1–34.2 days of poleward circulation at the tropopause of the Arctic polar cell.

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.

Kilometer-Scale Precipitation Forecasting Utilizing Convolutional Neural Networks: A Case Study of Jiangsu’s Coastal Regions

High-resolution precipitation forecasts play a pivotal role in formulating comprehensive disaster prevention and mitigation plans. As spatial resolution enhances, striking a balance between computation, storage, and simulation accuracy becomes imperative to ensure optimal cost-effectiveness. Convolutional neural networks (CNNs), a cornerstone of deep learning, are examined in this study for their downscaling capabilities in precipitation simulation. During a precipitation event on 23 June 2022, in Jiangsu Province, China, distinct rain belts emerged in both southern and northern Jiangsu, precisely captured by a numerical model (the Weather Research and Forecasting, WRF) with a 3 km spatial resolution. Specifically, precipitation was prevalent in northern Jiangsu from 00:00 to 11:00 Beijing Time (BJT), transitioning to southern Jiangsu from 12:00 to 23:00 BJT on the same day. Upon dynamic downscaling, the model reproduced precipitation in these periods with an average error of 12.35 mm at 3 km and 12.48 mm at 1 km spatial resolutions. Employing CNN technology for statistical downscaling to a 1 km spatial resolution, samples from the initial period were utilized for training, while those from the subsequent period served for validation. Following dynamic downscaling, CNNs with four, five, six, and seven layers exhibited average errors of 8.86 mm, 8.93 mm, 9.71 mm, and 9.70 mm, respectively, accompanied by correlation coefficients of 0.550, 0.570, 0.574, and 0.578, respectively. This analysis indicates that for this precipitation event, a shallower CNN depth yields a lower average error and correlation coefficient, whereas a deeper architecture enhances the correlation coefficient. By employing deep network architectures, CNNs are capable of capturing nonlinear patterns and subtle local features from complex meteorological data, thereby providing more accurate predictions during the downscaling process. Leveraging faster computation and reduced storage requirements, machine learning has demonstrated immense potential in high-resolution forecasting research. There is significant scope for advancing technologies that integrate numerical models with machine learning to achieve higher-resolution numerical forecasts.

Development of Vertical Radar Reflectivity Profiles Based on Lightning Density Using the Geostationary Lightning Mapper Dataset in the Subtropical Region of Brazil

The study aims to develop vertical radar reflectivity profiles based on lightning density data from the Geostationary Lightning Mapper (GLM) on the GOES-16 satellite in the subtropical region of Brazil. The primary objective is to improve the assimilation of lightning data in numerical weather prediction models. The methodology involves the analysis of polarimetric radar data from Chapecó-SC and Jaraguari-MS, spanning from January 2019 to December 2023, and their correlation with lightning data from the GLM. Radar reflectivity profiles were created for different lightning density classes, categorized into six classes based on geometric progression. Results show a significant relationship between lightning activity and radar reflectivity, with distinct profiles for convective and stratiform events. These findings demonstrate the potential of using GLM data to enhance short-term weather forecasting, particularly for severe weather events. The study concludes that the integration of GLM data into weather models can lead to more accurate predictions of intense precipitation events, contributing to better preparedness and response strategies.

Relationship between Tibetan Plateau Surface Heat Fluxes and Daily Heavy Precipitation in the Middle and Lower Yangtze River Basins (1980–2022)

Variable heat fluxes over the Tibetan Plateau (TP) interact thermally with the atmosphere, affecting weather in surrounding areas, particularly in the Middle and Lower Yangtze River (MLYR). However, the circulation patterns and time-lag effects between TP heat fluxes and MLYR precipitation remain unclear. This study identified 577 large-scale daily heavy precipitation events (LSDHPEs) in the MLYR from 1980 to 2022. We analyzed the weather causation and spatiotemporal correlations between the TP surface heat fluxes and MLYR LSDHPEs using self-organizing map clustering, singular value decomposition, and harmonic analysis of time series. The results found two dominant synoptic patterns of LSDHPEs at 500 hPa: one, driven by anticyclonic and cyclonic circulations coinciding with shifts in the West Pacific subtropical high and South Asian high, increased from 2000 to 2022; the other, influenced by MLYR cyclonic circulation, showed a significant decrease. For the first time, we revealed lagged effects of the latent heat anomalies (with a lag time of 1–10 d and 130–200 d) and sensible heat anomalies (with a lag time of 2–4 months) over the TP during LSDHPEs in the MLYR. The results may enhance our understanding of TP heat flux anomalies as precursor signals for early warning of heavy rainfall and flooding in the MLYR.

GA-SmaAt-GNet: Generative adversarial small attention GNet for extreme precipitation nowcasting

In recent years, data-driven modeling approaches have gained significant attention across various meteorological applications, particularly in weather forecasting. However, these methods often face challenges in handling extreme weather conditions. In response, we present the GA-SmaAt-GNet model, a novel generative adversarial framework for extreme precipitation nowcasting. This model features a unique SmaAt-GNet generator, an extension of the successful SmaAt-UNet architecture, capable of integrating precipitation masks (binarized precipitation maps) to enhance predictive accuracy. Additionally, GA-SmaAt-GNet incorporates an attention-augmented discriminator inspired by the Pix2Pix architecture. This innovative framework paves the way for generative precipitation nowcasting using multiple data sources. We evaluate the performance of SmaAt-GNet and GA-SmaAt-GNet using real-life precipitation data from The Netherlands, revealing notable improvements in overall performance and for extreme precipitation events compared to other models. Specifically, our proposed architecture demonstrates its main performance gain in summer and autumn, when precipitation intensity is typically at its peak. Furthermore, we conduct uncertainty analysis on the GA-SmaAt-GNet model and the precipitation dataset, providing insights into its predictive capabilities. Finally, we employ Grad-CAM to offer visual explanations of our model’s predictions, generating activation heatmaps that highlight areas of input activation throughout the network.

An extreme precipitation event over Dronning Maud Land, East Antarctica - A case study of an atmospheric river event using the Polar WRF Model

Extreme precipitation events (EPEs) are crucial in Antarctica, impacting the Antarctic ice sheet's surface mass balance and stability. Comprehensive case studies are essential for better understanding these events and the underlying processes driving them. Here, we investigate an extreme snowfall event in Dronning Maud Land (DML), East Antarctica on November 8 and 9, 2015. This event contributed approximately 22 % of the annual accumulation in less than two days and exhibited high spatial variability in precipitation distribution. We employed a high-resolution atmospheric model specifically optimized for the polar regions (Polar WRF) and ERA5 reanalysis data to analyze the event in detail. Our findings highlight the importance of a blocking high-pressure ridge of record strength that effectively blocked and diverted a strong extratropical cyclone into DML, ultimately leading to the heavy snowfall event. The sudden deepening of the cyclone was initiated by a ‘jet streak’ in the upper atmosphere that steered the system southeastwards towards the Antarctic coast. Notably, we observed an anomalously high poleward moisture transport in the form of a strong atmospheric river on November 7, 2015. This atmospheric river originated in the South Atlantic Ocean and tracked poleward from the 30°S-40°S latitude band. Vertical cross-sections of the model outputs indicate that most of the precipitation was concentrated in regions with steep orography along the path of the atmospheric river. This interaction between the atmospheric river and the steep terrain led to the uplift of maritime air, resulting in heavy snowfall. This study highlights the significance of extreme upper and lower atmospheric conditions in driving intense moisture transport towards coastal DML. The interaction between the atmospheric river and the steep orography contributed to heavy snowfall, underscoring the importance of considering orographic influences in understanding EPEs in Antarctica.

Amplified temperature sensitivity of extreme precipitation events following heat stress

This study investigates global extreme precipitation events (EPEs) during warm seasons, with a particular focus on EPEs preceded by extreme heat stress (EPE-Hs) and a comparative analysis with those not (EPE-NHs). Using reanalysis product and Earth System Model data, the spatiotemporal characteristics and temperature sensitivities of EPEs are analyzed. Results show that EPE-Hs, while less frequent, have longer duration and greater magnitude compared to EPE-NHs, particularly in high latitude regions. In the future, a significant increase is projected in the characteristics of EPE-Hs, in contrast to the stable duration and magnitude of EPE-NHs. EPE-Hs demonstrate substantially higher temperature sensitivity than EPE-NHs, especially in low latitudes. The precipitation-temperature scaling relationships diverge markedly between EPE-Hs and EPE-NHs, with notable regional variations. These insights are pivotal for crafting region-specific early warning and adaptation strategies to mitigate the risks associated with extreme precipitation under the backdrop of global warming.

Exploring perceived relationships between weather, climate and mental health: biometeorological perspectives of healthcare practitioners.

Over the last decade, there has been an increase in research examining the influence of weather and climate in mental health caseloads. Variations in temperature, sunshine hours, cloud cover, precipitation and extreme weather events have been statistically linked to diagnoses and increases in hospital admissions for several mental health conditions. This study aimed to explore whether mental health practitioners perceive there to be a link between mental health and daily, seasonal, or inter-annual shifts in various climate variables in South Africa, and the timing and causal mechanisms thereof. Semi-structured interviews were conducted with 50 practicing healthcare practitioners, and the data was analysed using thematic analysis. The findings of this research show that all 50 participants were aware of the link between weather, climate and mental health, primarily through their awareness of seasonal affective disorder. Of the 50 participants, 38 participants could explain the aetiology of seasonal affective disorder. Participants perceived sunlight and temperature to exert an influence on mental health. All 50 participants perceived exposure to sunlight to exert a positive influence on several mental health conditions. Of the 50 participants, 36 participants perceived increases in temperature to exert an adverse effect on mental health symptomology. A minority of 11 participants perceived precipitation to influence mental health conditions such as seasonal affective disorder, bipolar disorder, and substance abuse disorder. Participants' perceptions of the influence of precipitation on mental health provided a unique potential explanation of this relationship, which, at the time of writing, has not been discussed in formal research.

Comparison of the Morrison and WDM6 Microphysics Schemes in the WRF Model for a Convective Precipitation Event in Guangdong, China, Through the Analysis of Polarimetric Radar Data

Numerical weather prediction (NWP) models are indispensable for studying severe convective weather events. Research demonstrates that the outcomes of convective precipitation simulations are profoundly influenced by the choice between single or double-moment schemes for ice precipitation particles and the categorization of rimed ice. The advancement of dual-polarization radar has enriched the comparative validation of these simulations. This study simulated a convective event in Guangdong, China, from May 7 to 8, 2017, employing two bulk microphysical schemes (Morrison and WDM6) in the WRF v4.2 model. Each scheme was divided into two versions: one representing rimed ice particles as graupel (Mor_G, WDM6_G) and the other as hail (Mor_H, WDM6_H). The simulation results indicated negligible differences between the rimed ice set as graupel or hail particles, for both schemes. However, the Morrison schemes (Mor_G, Mor_H) depicted a more accurate raindrop size distribution below the 0 °C height level. A further analysis suggested that disparities between the Morrison and WDM6 schemes could be attributed to the intercept parameter (N0) setting for snow and graupel/hail in WDM6 scheme. The prescribed snow and graupel/hail N0 of WDM6 scheme might influence the melting processes, leading to a higher number concentration but a reduced mass-weighted diameter of raindrops. Reducing the intercept parameter for snow and graupel/hail in the WDM6 scheme could potentially enhance the simulation of convective precipitation. Conversely, the increase in N0 might deteriorate the precipitation simulation performance of the WDM6_G scheme, whereas the WDM6_H scheme exhibits minimal sensitivity to such changes.

Hydro-geomorphological assessment of culvert vulnerability to flood-induced soil erosion using an ensemble modeling approach

Intense precipitation events pose growing threats to forest infrastructure causing flooding, and soil erosion and deposition, creating bottlenecks at road-stream crossing structures (RSCS). We describe a hillslope-scale ensemble hydro-geomorphological vulnerability assessment integrating geospatial Streambank Erosion Vulnerability Assessment (SBEVA), Modified Revised Soil Loss Equation (MRUSLE), and process-based Water Erosion Prediction Project (WEPP) model into an ensemble hydro-geomorphologic vulnerability index (EHVI) for USDA Forest Service (USFS) managed 194 road-culverts at the Hubbard Brook Experimental Forest (HBR-EF) in New Hampshire, USA. The results revealed that five and one culvert with diameters of 0.46m and 0.61m, respectively, have extreme EHVI values between 4 and 5, and fifteen and three culverts with diameters of 0.46m and 0.61m, respectively, have severe EHVI values between 3 and 4, some of which were previously identified as hydrologically vulnerable (undersized) to floods. This knowledge will inform USFS efforts to improve the resilience of the RSCS and protect aquatic habitats.

Simulated particle evolution within a winter storm: contributions of riming to radar moments and precipitation fallout

Abstract. Remote sensing radars from airborne and spaceborne platforms provide critical observations of clouds to estimate precipitation rates across the globe. The ability of these radars to detect changes in precipitation properties is advanced by Doppler measurements of particle fall speed. Within mixed-phase clouds, precipitation mass and its fall characteristics are especially sensitive to the effects of riming. In this study, we quantified these effects and investigated the distinction of riming from aggregation in Doppler radar vertical profiles using quasi-idealized particle-based model simulations. Observational constraints of a control simulation were determined from airborne in situ and remote sensing measurements collected during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) for a wintry–mixed precipitation event over the northeastern United States on 4 February 2022. From the upper boundary of a one-dimensional column, particle evolution was simulated through vapor deposition, aggregation, and riming processes, producing realistic Doppler radar profiles. Despite a modest observed amount of supercooled liquid water (0.05 g m−3), riming accounted for 55 % of the ice-phase precipitation mass, cumulatively increasing reflectivity by 44 % and Doppler velocity by 68 %. Independent evaluation of process-based sensitivities showed that, while radar reflectivity is comparably sensitive to either riming- or aggregation-based particle morphology, the Doppler velocity profile is uniquely sensitive to particle density changes during riming. Thus, Doppler velocity profiles advance the diagnosis of riming as a dominant microphysical process in stratiform clouds from single-wavelength radars, which has implications for quantitative constraints of particle properties in remote sensing applications.

Hydrogeomorphic response of steep streams following severe wildfire in the Western cascades, Oregon

AbstractSevere wildfire may alter steep mountain streams by increasing peak discharges, elevating sediment and wood inputs into channels, and increasing susceptibility to landslides and debris flows. In the Pacific Northwest, where mean annual precipitation is high and mean fire‐return intervals range from decades to centuries, understanding of steep stream response to fire is limited. We evaluate the hydrologic and geomorphic response of ~100‐m‐long steep stream reaches to the large‐scale and severe 2020 fires in the Western Cascade Range, Oregon. In the two runoff seasons after the fires, peak flows in burned reaches were below the 2‐year recurrence interval flood, a level sufficient to mobilize the median grain size of bed material, but not large enough to mobilize coarser material and reorganize channel morphology. Sediment inputs to study streams consisted of two road‐fill failure landslides, slumps, sheetwash, and minor bank erosion; precipitation thresholds to trigger debris flows were not exceeded in our sites. There was a 50% increase in the number of large wood pieces in burned reaches after the fires. Changes in fluxes of water, sediment, and wood induced shifts in the balance of sediment supply to transport capacity, initiating a sequence of sediment aggradation and bed‐material fining followed by erosion and bed‐material coarsening. Gross channel form showed resilience to change, and an unburned reference reach exhibited little morphologic change. Post‐fire recruitment of large wood will likely have long‐term implications for channel morphology and habitat heterogeneity. Below‐average precipitation during the study period, combined with an absence of extreme precipitation events, was an important control on channel responses. Climate change may have a complex effect on stream response to wildfire by increasing the propensity for both drought and extreme rain events and by altering vegetation recovery patterns.

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>

A Methodological Approach to Improving Extreme Precipitation Reanalysis Data Using the Clausius-Clapeyron Relationship: A Case Study in a Mediterranean City

Climate change is a crucial issue of the 21st century, leading to more frequent and severe extreme precipitation events globally. These events result in significant social and economic disruptions, including flooding, loss of life, and damage to infrastructure. Projections suggest that extreme rainfall will intensify in the latter half of the century, underscoring the need for accurate and timely forecasting. Despite advancements in meteorological and climate models that offer high accuracy for various weather parameters, these models still struggle to detect extreme values, particularly for precipitation. This research examines the sensitivity of extreme precipitation events to temperature, based on the Clausius-Clapeyron relationship, focusing on Thessaloniki, Greece. It also evaluates the effectiveness of reanalysis data in identifying extreme precipitation and explores how rainfall-temperature relationships can enhance prediction accuracy. The findings are vital for improving the estimation of extreme rainfall events and informing the design of flood-resilient infrastructure.

Quercus acutissima exhibits more adaptable water uptake patterns in response to seasonal changes compared to Pinus massoniana

BackgroundSeasonal precipitation variability significantly affects water use in forests; however, whether water uptake is adapted to changes in precipitation, particularly whether it could affect the coexistence of tree species, has rarely been quantified in forest systems. MethodIn this study, dual stable isotopes and the Li-6400 portable photosynthesis system were used to determine the water sources of a mixed conifer (Pinus massoniana) and broadleaf (Quercus acutissima) forest and changes in hydraulic characteristics during the dry and wet seasons in a southern hilly region of China. ResultsAlthough the hydraulic characteristics of P. massoniana were lower than those of Q. acutissima, it maintained a stable water source from the deep soil layer and a higher stomatal conductance (Gs), leading to a higher transpiration rate (Tr) during the growing seasons. Q. acutissima mainly absorbed water from deeper soil layers in the dry season and took up from shallow soil layers in the wet season. Its Gs values exhibited sensitivity to precipitation, while it maintained a lower Tr value during the growing seasons. The excessive water-use strategy observed in P. massoniana may confer weak drought-tolerance during higher frequency and more intense extreme precipitation events, whereas Q. acutissima may exhibit better ecological adaption to precipitation changes. ConclusionsThe overlap of water niches in mixed forests did not appear to affect the coexistence of tree species. The present study provides insights into reforestation and water management in the southern hilly regions of China.

Evaluation of extreme precipitation events as simulated by CMIP6 models over Central Africa: Spatial patterns

Evaluation of extreme precipitation events as simulated by CMIP6 models over Central Africa: Spatial patterns