Extreme Rainfall Research Articles

Extreme rainfall events eliminate the response of greenhouse gas fluxes to hydrological alterations and fertilization in a riparian ecosystem

Riparian ecosystems are essential carbon dioxide (CO2) sources, which considerably promotes climate warming. However, the other greenhouse gas fluxes (GHGs), such as methane (CH4) and nitrous oxide (N2O), in the riparian ecosystems have not been well studied, and it remains unclear whether and how these GHG fluxes respond to extreme weather, fertilization and hydrological alterations associated with reservoir management. Here, we assessed the impacts of hydrological alterations (i.e., flooding frequency) and fertilization (nitrogen and/or phosphorus) induced by human activities (hydroengineering construction and agricultural activities) on GHG fluxes, and further investigated the underlying mechanisms in two contrasting years (normal vs. extreme rainfall years) in a reservoir riparian zone dominated by grasses. The significant combined effects of extreme rainfall events and human activities (hydrological alterations and fertilization) on the GHGs were observed. Continuous flooding reduced CO2 emissions by 24% but increased CH4 emissions by ∼4 times in a normal rainfall year. In addition, nitrogen fertilization promoted CO2 emissions by 37%. However, these phenomena were not observed in the year with extreme rainfall events, which made the flooding levels homogeneous across the treatments. Furthermore, we found that CO2 fluxes were driven by the soil moisture, nutrient content, aboveground biomass, and root carbon content, while CH4 and N2O fluxes were merely driven by the soil properties (pH, moisture, and nutrient content). This study provides valuable insights into the crucial role of extreme rainfall events, hydrological alteration, and fertilization in regulating GHG fluxes in riparian ecosystems, as well as supports the integration of these changes in GHG emission models.

Decadal trends and climatic influences on flash droughts and flash floods in Indian cities

Flash droughts/floods are extreme weather phenomena that are expected to become increasingly frequent and severe with the changing climate. Flash droughts result from a rapid decline in soil moisture, while flash floods occur due to a high extreme rainfall intensity over a short duration. This study analyzes the ERA5 reanalysis data (hourly temperature, soil moisture, and precipitation) from 1992 to 2022 to assess flash drought/flood attribute variations across fourteen Indian cities. Flash drought events are identified based on specific conditions using the obtained Soil Moisture Index (SMI) values. At the same time, we propose a novel approach to attribute flash floods by setting thresholds for precipitation and soil moisture.This study examines the frequency and trends of flash drought and flood events across India's various Köppen-Geiger climatic zones from 1992 to 2022. Jaipur and Dehradun show a statistically significant decrease in flash drought events with magnitudes of −0.0833 events/year and −0.0769 events/year, respectively. Conversely, Hyderabad exhibits a highly significant increase in flash flood events with a magnitude of 1.1851 events/year. Similarly, Bengaluru, Varanasi, and Vishakhapatnam also show substantial increases in flash flood events. These findings underscore the impact of climate change on flash droughts/floods, highlighting the necessity for sustainable strategies.

The impact of long-term organic horticultural systems on energy outputs and carbon storages in relation to extreme rainfall events

Enhancing resilience of agroecosystems of Mediterranean area is a challenge that involves both researchers and different stakeholders and, in this context, increasing crop diversity by redesigning agricultural systems can be considered among the most important tools. Therefore, the response of agroecological practices to climate change effects was tested in a long-term experiment on organic horticultural crops (MITIORG), which is characterized by a soil hydraulic arrangement in ridges, strips and the use (with different management options) of cover crops within cash crops rotations. The main objective of this study was to show how powerful is the sustainability assessment of agroecological practices by converting crops yield and biomass into energy outputs and carbon storages, in diversified horticultural systems. The obtained outputs (expressed in energy and carbon equivalents) were evaluated and analyzed considering the site-specific meteorological data in more than 10 horticultural cropping cycles, from autumn-winter 2014-15 to autumn-winter 2020-21. The Ridge and Strips (RS) system 1 (RS1 - cover crops as living mulch on ridges and break crops in strips, both with no-till termination) showed an enhancement of about 18% of energy output and carbon (C) storages compared to RS2 (ridges and strips with green manured cover) when extreme precipitation events occurred. Moreover, RS3 (ridges and strips without cover crops) recorded a reduction of about 5 and 9% of energy output and C storage, respectively, compared to the mean of RS1 and RS2 in periods with extreme events. Our results highlighted that using more diversified agroecological systems improved their overall average outputs, ensuring greater resilience during extreme weather events, since at least part of crop productions was safeguarded. Therefore, it is important to combine techniques that allow long-term resilience, such as choosing and well managing cover crops (agroecological service crops), according to site and systems specific conditions.

Optimize Stormwater Management for Green Roofs Based on IoT Technology and Sensor Networks

The intensification of global climate change and the significant increase of extreme rainfall events have made traditional urban drainage systems face great adjustments. As a key component of green infrastructure to avoid urban flood risk, green roofs are receiving increasing attention due to their unique capabilities in stormwater management. This paper explores the potential of Internet of Things (IoT) technology and sensor networks to enhance green roof stormwater management. Integrated real-time monitoring of key environmental parameters, including soil moisture, temperature and rainfall, enables the system to dynamically adjust roof drainage and irrigation strategies. This greatly improves the rainwater retention capacity and reduces the pressure on the municipal sewage system. The results show that the combination of IoT technology and sensor networks not only improves the efficiency of green roof management in combating climate change, but also promotes the efficient use of water resources and enhances the adaptability and sustainability of the system. This research provides the necessary technical support for the wider adoption of green roofs in urban environments and offers viable solutions to future challenges posed by climate change.

Evaluation of WRF model performance with different microphysics schemes for extreme rainfall prediction in Lagos, Nigeria: Implications for urban flood risk management

In Nigeria, particularly in urban areas like Lagos, flooding is a frequent natural hazard. In 2011, Lagos experienced one of its worst floods resulting in significant economic losses and displacement of people. In recent years, Lagos has continued to grapple with flooding challenges, with an equally significant flood episode occurring in 2021. This study focuses on predicting floods and forecasting extremely heavy rainfall in West Africa's equatorial zone using the Weather Research and Forecasting (WRF) model, particularly in humid tropical environments like Lagos. The study discusses the need to review existing flood models and adopt alternative flood models to address the limitations of flood prediction. As potential causes of these rainfall episodes, the interconnections between synoptic systems such as tropical easterly waves, southwesterly winds related to the West African Monsoon, and local topography and oceanic conditions are investigated. Three key metrics: root mean square error (RMSE), mean bias (MB), and mean absolute error (MAE) are used to assess the effectiveness of the computational model. Results indicate that the WRF model, specifically when using the Thompson parameterisation, can estimate the amount of rainfall accumulated over a 24-h period. This suggests that the model can predict the size of daily precipitation during intense rain events. The Thompson scheme shows better performance compared to the WSM6 scheme while evaluating the stations and episodes. During the rainfall episode on July 10, 2011, Thompson's spatial rainfall predictions were better than WSM6, resulting in a decrease in root mean square error (RMSE) of 15–31% depending on the area. Simulations of the July 2021 episode also show better performance, with a decrease in RMSE of 11–25% when comparing Thompson to WSM6 scheme. The Thompson scheme’s improved ability is directly linked to a more accurate depiction of the microphysical mechanisms that control the rainfall formation. By explicitly simulating the dynamics of ice crystals and graupel, it is possible to accurately replicate the processes of orographic lifting and moist convection that are responsible for driving intense monsoon precipitation. In addition, Thompson scheme shows a reduced degree of systemic bias in comparison to WSM6, with a 75% reduction in the average bias in rainfall accumulation over the research area. The combination of the advanced Thompson microphysics method and WRF's atmospheric dynamics shows a high level of accuracy in predicting intense rainfall and the risk of floods in this area with diverse tropical topography.

Comparison of Different Quantitative Precipitation Estimation Methods Based on a Severe Rainfall Event in Tuscany, Italy, November 2023

Accurate quantitative precipitation estimation (QPE) is fundamental for a large number of hydrometeorological applications, especially when addressing extreme rainfall phenomena. This paper presents a comprehensive comparison of various rainfall estimation methods, specifically those relying on weather radar data, rain gauge data, and their fusion. The study evaluates the accuracy and reliability of each method in estimating rainfall for a severe event that occurred in Tuscany, Italy. The results obtained confirm that merging radar and rain gauge data outperforms both individual approaches by reducing errors and improving the overall reliability of precipitation estimates. This study highlights the importance of data fusion in enhancing the accuracy of QPE and also supports its application in operational contexts, providing further evidence for the greater reliability of merging methods.

Daily Rainfall Patterns During Storm “Daniel” Based on Different Satellite Data

Extreme rainfall from a long-lived weather system called storm “Daniel” occurred from 4th to 11th September 2023 over the central and eastern Mediterranean, leading to many devastating flood events mainly in central Greece and the western coastal parts of Libya. This study analyzes the daily rainfall amounts over all the affected geographical areas during storm “Daniel” by comparing three different satellite-based rainfall data products. Two of them are strictly related to Meteosat multispectral imagery, while the other one is based on the Global Precipitation Measurement (GPM) satellite mission. The satellite datasets depict extreme daily rainfall (up to 450 mm) for consecutive days in the same areas, with the spatial distribution of such rainfall amounts covering thousands of square kilometers almost during the whole period that the storm lasted. Moreover, the spatial extent of the heavy rainfall patterns was calculated on a daily basis. The convective nature of the rainfall, which was also recorded, characterizes the extremity of this weather system. Finally, the intercomparison of the datasets used highlights the satisfactory efficiency of the examined satellite datasets in capturing similar rainfall amounts in the same areas (daily mean error of 15 mm, mean absolute error of up to 35 mm and correlation coefficient ranging from 0.6 to 0.9 in most of the examined cases). This finding confirms the realistic detection and monitoring of the different satellite-based rainfall products, which should be used for early warning and decision-making regarding potential flood events.

Tropical intraseasonal oscillations as key driver and source of predictability for the 2022 Pakistan record-breaking rainfall event

In August 2022, Pakistan experienced unprecedented monsoon rains, leading to devastating floods and landslides affecting millions. While previous research has mainly focused on the contributions of seasonal and synoptic anomalies, this study elucidates the dominant influences of tropical and extratropical intraseasonal oscillations on both the occurrence and subseasonal prediction of this extreme rainfall event. Our scale-decomposed moisture budget analysis revealed that intense rainfall in Pakistan was triggered and sustained by enhanced vertical moisture transport anomalies, primarily driven by interactions between intraseasonal circulation anomalies and the prevailing background moisture field when tropical and mid-latitude systems coincided over Pakistan. Evaluation of subseasonal-to-seasonal prediction models further highlighted the critical role of tropical intraseasonal modes in causing this extreme rainfall event in Pakistan. Models that accurately predicted northward-propagating intraseasonal convection with a forecast lead time of 8–22 days demonstrated good skill in predicting the extreme event over Pakistan.

Enhancing Sustainability in Watershed Management: Spatiotemporal Assessment of Baseflow Alpha Factor in SWAT

The increasing frequency of extreme rainfall events poses significant challenges to sustainable water resource management, leading to severe natural disasters. To mitigate these challenges, understanding the hydrological characteristics of watersheds, especially baseflow, is critical for enhancing watershed resilience and supporting sustainable water quality and resource management. However, conventional watershed models often neglect the accurate simulation of baseflow recession. This study proposes a method for calculating and applying the alpha factor for each hydrologic response unit (HRU) in the Soil and Water Assessment Tool (SWAT), considering both temporal and spatial variability in baseflow. The study watershed has undergone significant development, increasing the need for effective water management strategies that promote long-term sustainability. The alpha factor was computed using BFlow2021, and its effectiveness was evaluated by comparing recession and baseflow estimates under different methods. The results indicate that incorporating monthly HRU-specific alpha factors significantly improves model predictions of recession characteristics, highlighting the need for a more spatially and temporally detailed approach in hydrological modeling. The proposed methodology can help clarify the connection between recession and baseflow and can be applied to ungauged stations, offering a valuable tool for sustainable watershed and water quality management.

ER-MACG: An Extreme Precipitation Forecasting Model Integrating Self-Attention Based on FY4A Satellite Data

Extreme precipitation events often present significant risks to human life and property, making their accurate prediction an essential focus of current research. Recent studies have primarily concentrated on exploring the formation mechanisms of extreme precipitation. Existing prediction methods do not adequately account for the combined terrain and atmospheric effects, resulting in shortcomings in extreme precipitation forecasting accuracy. Additionally, the satellite data resolution used in prior studies fails to precisely capture nuanced details of abrupt changes in extreme precipitation. To address these shortcomings, this study introduces an innovative approach for accurately predicting extreme precipitation: the multimodal attention ConvLSTM-GAN for extreme rainfall nowcasting (ER-MACG). This model employs high-resolution Fengyun-4A(FY4A) satellite precipitation products, as well as terrain and atmospheric datasets as inputs. The ER-MACG model enhances the ConvLSTM-GAN framework by optimizing the generator structure with an attention module to improve the focus on critical areas and time steps. This model can alleviate the problem of information loss in the spatial–temporal convolutional long short-term memory network (ConvLSTM) and, compared with the standard ConvLSTM-GAN model, can better handle the detailed changes in time and space in extreme precipitation events to achieve more refined predictions. The main findings include the following: (a) The ER-MACG model demonstrated significantly greater predictive accuracy and overall performance than other existing approaches. (b) The exclusive consideration of DEM and LPW data did not significantly enhance the ability to predict extreme precipitation events in Zhejiang Province. (c) The ER-MACG model significantly improved in identifying and predicting extreme precipitation events of different intensity levels.

Numerical Simulations of a Permeability Test on Non-Cohesive Soil Under an Increasing Water Level

With the intensification of global climate change, extreme rainfall events are occurring more frequently. Continuous rainfall causes the debris flow gully to collect a large amount of rainwater. Under the continuous increase in the water level, the water flow has enough power to carry plenty of loose solids, thus causing debris flow disasters. The intensity of the soil is reduced with the infiltration of rainwater, which is one of the key causes of the disaster. The rise in the water level affects the infiltration behavior. There have been few previous studies on infiltration under variable head. In order to understand the infiltration behavior of soils under the action of water level rises, this paper conducted an indoor permeability test on non-cohesive soil under the condition of an increasing water level. A numerical model was established using the finite element analysis software, Abaqus 6.14, and the pore pressure was increased intermittently to simulate the intermittent increase in the water level. Thereafter, the permeability coefficient and seepage length were changed to interpret the changes in the flow velocity and rate in the permeability test of the non-cohesive soil. The results showed that the finite element numerical simulation method could not reflect the particle movement process in the soil. The test could better reflect the through passage and void plugging phenomenon in soil; when the permeability coefficient alone changed, the velocity of the measuring point with higher velocity changed more violently with the permeability coefficient; when the length of soil seepage diameter was uniformly shortened, the velocity of water flow increased faster and faster.

Landslide Hazard Prediction Based on UAV Remote Sensing and Discrete Element Model Simulation—Case from the Zhuangguoyu Landslide in Northern China

Rainfall-triggered landslides generally pose a high risk due to their sudden initiation, massive impact force, and energy. It is, therefore, necessary to perform accurate and timely hazard prediction for these landslides. Most studies have focused on the hazard assessment and verification of landslides that have occurred, which were essentially back-analyses rather than predictions. To overcome this drawback, a framework aimed at forecasting landslide hazards by combining UAV remote sensing and numerical simulation was proposed in this study. A slow-moving landslide identified by SBAS-InSAR in Tianjin city of northern China was taken as a case study to clarify its application. A UAV with laser scanning techniques was utilized to obtain high-resolution topography data. Then, extreme rainfall with a given return period was determined based on the Gumbel distribution. The Particle Flow Code (PFC), a discrete element model, was also applied to simulate the runout process after slope failure under rainfall and earthquake scenarios. The results showed that the extreme rainfall for three continuous days in the study area was 151.5 mm (P = 5%), 184.6 mm (P = 2%), and 209.3 mm (P = 1%), respectively. Both extreme rainfall and earthquake scenarios could induce slope failure, and the failure probabilities revealed by a seepage–mechanic interaction simulation in Geostudio reached 82.9% (earthquake scenario) and 92.5% (extreme rainfall). The landslide hazard under a given scenario was assessed by kinetic indicators during the PFC simulation. The landslide runout analysis indicated that the landslide had a velocity of max 23.4 m/s under rainfall scenarios, whereas this reached 19.8 m/s under earthquake scenarios. In addition, a comparison regarding particle displacement also showed that the landslide hazard under rainfall scenarios was worse than that under earthquake scenarios. The modeling strategy incorporated spatial and temporal probabilities and runout hazard analyses, even though landslide hazard mapping was not actually achieved. The present framework can predict the areas threatened by landslides under specific scenarios, and holds substantial scientific reference value for effective landslide prevention and control strategies.

Rainfall Enhancement Downwind of Hills Due to Stationary Waves on the Melting Level and the Extreme Rainfall of December 2015 in the Lake District of Northwest England

This paper investigates how stationary gravity waves generated by flow over orography enhance rainfall, with particular attention to the role of induced waves in the melting level. The findings reveal a new mechanism by which gravity wave flow focuses precipitation, amplifying rainfall intensity downwind of hills. This mechanism, which depends on the differential velocities of rain and snow, offers fresh insights into how orographic effects can intensify rainfall. A two-dimensional diagnostic model based on linear gravity wave theory is used to investigate the record-breaking rainfall of December 2015 in the Lake District of northwest England. The pattern of ascent is shown to have a qualitatively good fit to that of the Met Office’s operational high-resolution UKV model averaged over 24 h, suggesting that orographically excited stationary waves were the principal cause of the rain. Precipitation trajectories imply that a persistent downstream elevated wave caused by the Isle of Man supported a spray of seeding ice particles directed towards the Lake District, and that these grew whilst suspended in strong upslope flow before being focused by the undulating melting-level into intense shafts of rain.

Spatial Aggregations of the Grey Field Slug Deroceras reticulatum Are Unstable Under Abnormally High Soil Moisture Conditions.

Deroceras reticulatum in arable fields display spatio-temporally stable slug patches that have been well documented under typical soil moisture conditions. The effect of abnormally high soil moisture on slug patch stability, however, is unknown. In this study, stepped gradient choice tests comparing soil moisture levels of 50-125% soil capacity showed slug preferences for levels in a range near to 125%. Activity became erratic, however, when given a choice of high moisture levels (125-370%), potentially because slugs searched for preferred conditions. Slug spatial aggregation was investigated in 21 commercial fields in 2023/24, a season of extreme rainfall, and then compared to years exhibiting typical rainfall (2015-2018). Slug patches occurred in 27.2% of assessment visits to fields during 2023/24 compared to 96.4% in typical years, suggesting weather conditions leading to abnormally high soil moisture are significantly associated with the breakdown of slug spatial aggregation behaviour. Random forest models identified the weather predictors (precipitation, relative humidity, temperature) with the highest impact on slug distribution and relative abundance, with the assessment date and region also related to relative abundance. However, a complex of environmental parameters affects soil moisture content, and no statistically significant effects of individual weather predictors emerged. The results are discussed in relation to slug behaviour in the context of their impact on targeted slug treatments.

Unravelling the complex interplay between daily and sub-daily rainfall extremes in different climates

Understanding short-duration intense rainfall is crucial for mitigating flash floods, landslides, soil erosion, and pollution incidents. Yet, most observations from rain gauges are only available at the daily resolution. We use the new Global Sub Daily Rainfall dataset to explore extreme rainfall at both daily and sub-daily durations worldwide. Employing Single Gauge Analysis (SGA) and pioneering global-scale Regional Frequency Analysis (RFA), we reveal for the first time how Generalized Extreme Value Distribution (GEV) parameters change across climates and data durations (1h, 3h, 6h, 24h, and daily). This marks the first-ever near-global-scale RFA, made possible by the development of an algorithm that automates RFA on observed rainfall datasets. We compare our results with GEV applied to a gridded rainfall reanalysis (ERA5). Our key findings are that: 1) using ERA5, return levels are significantly underestimated across all climates for 1h rainfall and across all data durations for gauges in the tropical climate region. Even when accounting for differences between point and areal estimates, the median 1h return level estimates are approximately 40% lower compared to RFA. We therefore strongly advise against the use of reanalysis gridded rainfall for studying these extremes. 2) While most gauges show similar return levels with RFA or SGA, some differ significantly, and either method may yield the highest values. Thus, we strongly recommend using both SGA and RFA simultaneously to estimate return levels for a robust risk assessment in flood infrastructure design. 3) The interaction between daily and sub-daily GEV shape parameters varies across climate regions, rendering a universal method for inferring sub-daily rainfall extremes from daily extremes (e.g., using Intensity-Duration-Frequency curves) impractical. Our research provides innovative methodological insights that warrant consideration in future studies on rainfall extremes. Our results not only benefit local stakeholders globally but also serve as a crucial validation tool for the rising number of convection-permitting climate model experiments conducted worldwide.

Community structure of tropics emerging from spatio-temporal variations in the Intertropical Convergence Zone dynamics

The Intertropical Convergence Zone (ITCZ) is a narrow tropical belt of deep convective clouds, intense precipitation, and monsoon circulations encircling the Earth. Complex interactions between the ITCZ and local geophysical dynamics result in high climate variability, making weather forecasting and prediction of extreme rainfall or drought events challenging. We unravel the complex spatio-temporal dynamics of the ITCZ and the resulting teleconnection patterns via a novel tropical climate classification achieved using complex network analysis and community detection. We reduce the high-dimensional complex ITCZ dynamics into a simple yet insightful community structure that classifies the tropics into seven regions representing distinct ITCZ dynamics. The two largest communities, encompassing landmasses over the Northern and Southern hemispheres, are associated with coherent seasonal ITCZ dynamics and have significant long-range connections. Temporal analysis of the community structure highlights that the tropical Pacific and Atlantic Oceans communities exhibit substantial variation on multidecadal scales. Further, these communities exhibit incoherent dynamics due to atmosphere-ocean interactions driven by equatorial and coastal oceanic upwelling.

Evaluation of the Antecedent Saturation and Rainfall Conditions on the Slope Failure Mechanism Triggered by Rainfalls

The stability analysis of rainfall-induced slope failures considers a number of factors including the characteristics of the rainfall, vegetation, geometry of the slope, unsaturated soil characteristics, infiltration capacity, and saturation degree variations. Amongst all these factors, this study aims to investigate the effects of the antecedent rainfall and saturation conditions. A numerical modeling study was conducted using finite difference code software on a representative slope geometry with two different soil types. Two scenarios were followed: The first involved the application of three different rainfall intensities for varying initial saturation levels between 40% and 60%, representing the antecedent saturation conditions. The second scenario involved modeling successive rainfalls for a typical initial saturation degree of 50%. The impact of antecedent rainfall was assessed by determining the time required for failure during the application of a main extreme rainfall after a preceding rainfall of varying durations. Consequently, a zone of susceptible time for failure was suggested for use as a criterion in hazard management, allowing for the tracking of rainfall and its duration through the proposed chart for potential failures. Once the anticipated critical rainfall intensities have been determined through a meteorological analysis, a risk assessment for a specific slope can be conducted using the proposed practical procedure. Accordingly, a control mechanism may be established to detect the potential for a natural hazard. Furthermore, the proposed procedure was applied to a case study, whose modeling insights were in harmony with the real conditions of the slope failure. Thus, this demonstrated the significance of the antecedent conditions in modeling landslides triggered by rainfalls.

Multi-Approaches for Flash Flooding Hazard Assessment of Rabigh Area, Makkah Province, Saudi Arabia: Insights from Geospatial Analysis

Flash flood hazard assessment is a critical component of disaster risk management, particularly in regions vulnerable to extreme rainfall and climatic events. This study focuses on evaluating the flash flood susceptibility of the Rabigh area, located along the Red Sea coast in Makkah province, Saudi Arabia. Using advanced GIS tools and a spatial multi-criteria analysis approach, the research integrates a variety of datasets, including remotely sensed satellite data, the SRTM Digital Elevation Model (DEM), and topographic indices. The main goal was to produce detailed flood susceptibility maps based on the morphometric characteristics of the region’s drainage basins. These basins were delineated and assessed for their flood vulnerability using three distinct modeling techniques, each highlighting different aspects of flood behavior. The results show that the northern basin (Dulaidila) and the central basins (Rabigh, Algud, and Al Nuaibeaa) exhibit the highest flood risk, with significant susceptibility also observed in the southern basins (Ofoq and Saabar). Other basins in the region display moderate susceptibility levels. A key aspect of this analysis was the overlay of the integrated flood susceptibility map with the Topographic Position Index (TPI), a crucial topographic indicator, which helped refine the understanding of flood-prone areas by linking basin morphometry with in-situ topographic features. This study’s comprehensive approach offers valuable insights that can be applied to other coastal regions where hydrological and climatic data are scarce, contributing to more effective flood risk mitigation and strategic planning.

Assessment of the influence of climatic factors on the state of terrestrial ecosystems in the Northwestern region of Russia

The article analyzes the change of climatic conditions in the Northwest of Russia, including the characteristics of dangerous hydrometeorological events (cold and heat waves, strong winds, extreme rainfall, snowfall, ice-frost deposits, hail) and slow climatic changes (increase in the number of days with the transition of air temperature through 0°С, coastal abrasion) in connection with their negative impact on terrestrial ecosystems. It was found that the influence of meteorological and climatic factors on terrestrial ecosystems is most pronounced in the northern part of the studied region, especially on the coast of the Barents Sea. Towards the south, the values of all indicators gradually decrease, and their structure changes. In the northern part of the study area (Murmansk and Arkhangelsk oblasts, the Nenets Autonomous Okrug), phenomena associated with strong winds and intensive ice-frost deposition, which contribute to the formation of an ice crust on the Earth’s surface, prevail. As one moves away from the coast, severe frost is observed more often (Komi Republic). In the center and south of the region, heavy rainfall, severe frost, and intense heat are the most frequent, resulting in a high fire hazard. The study carried out the ranking of the subjects of the Northwestern Federal District according to the degree of intensity of this process. Comprehensive assessments of the negative impact of changing climatic conditions on terrestrial ecosystems can be used to make decisions on the development of a strategy for environmental security of the regions of the Russian Federation.

FuXi-Extreme: Improving extreme rainfall and wind forecasts with diffusion model

FuXi-Extreme: Improving extreme rainfall and wind forecasts with diffusion model