Precipitation Patterns Research Articles

PreciDBPN: A customized deep learning approach for hourly precipitation downscaling in eastern China

Long-term series of high-resolution gridded precipitation datasets are essential for hydrological and meteorological research. Producing high-resolution precipitation data from regional models demands substantial computational resources and labor. Global Reanalyses offer long-term coverage and effectively capture annual and seasonal precipitation patterns. However, they have inadequate resolution and frequently have difficulties depicting extreme conditions. This study proposes an efficient and accurate approach for generating long-term series of high spatial and temporal resolution precipitation. It is achieved by leveraging deep learning techniques to integrate the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) global climate reanalysis (ERA5, 0.25°, hourly) data with high-resolution precipitation fusion datasets. Considering the heavy-tailed distribution of precipitation, we developed the PreciDBPN model structure, which combines a classification network with a super-resolution network and incorporates physically relevant indices into the model's input. We trained and evaluated the PreciDBPN and baseline models in eastern China using the China Meteorological Administration Land Data Assimilation System (CLDAS) precipitation dataset (0.0625°, hourly, 2017–2022). When compared to baseline methods and ERA5, our model excels in multiple metrics and provides a more precise representation of relative rainfall frequency. Independent verification was performed using station observations during the period of 2010–2015 when CLDAS data were unavailable. During this verification, the PreciDBPN demonstrated exceptional performance and greater robustness compared to the baseline models. Because our method can efficiently downscale precipitation and bias-correct reanalysis data using minimal computational resources, it can be used to generate high-resolution precipitation datasets (0.0625°, hourly) from 1979 to 2022 while correcting for heavy precipitation underestimations in reanalysis data.

Precipitation-induced biomass enhancement and differential allocation in Inner Mongolia's herbaceous and shrub communities

Changes in precipitation patterns induced by global climate change have profound implications for the structure and function of grassland ecosystems. However, the relationship between plant diversity and ecosystem function across different grassland types, particularly those with varying plant compositions and dominant species, remains inadequately understood. To address this knowledge gap, a five-year experimental manipulation of precipitation was conducted within herbaceous and shrub communities in the desert grasslands of Inner Mongolia. We found that increased precipitation significantly enhances aboveground biomass (AGB), belowground biomass (BGB), and community total biomass (CTB) in both herbaceous and shrub communities. In herbaceous communities, increased precipitation led to a disproportionate increase in both aboveground and belowground biomass, supporting the optimal allocation hypothesis. Structural equation modeling (SEM) further elucidated that precipitation regulates AGB and CTB through species richness and functional traits in herbaceous communities. In shrub communities, precipitation influences AGB, BGB, and CTB by affecting species richness and soil water content. This study highlights the critical role of precipitation in shaping biomass dynamics and allocation strategies within herbaceous and shrub communities in desert steppe of Inner Mongolia. These findings provide essential insights into the potential responses of desert grassland ecosystems to ongoing climate change.

Precipitation patterns strongly affect vertical migration and methylation of mercury in legacy contaminated sites

Legacy-contaminated sites act as significant sources of mercury (Hg) to their surrounding surface and underground environments. Intensified extreme precipitation is posing great threats to the environment and human health by changing the fate of pollutants, yet little is known about its effect on the vertical migration and methylation of Hg in contaminated sites. Here, we applied a range of simulated extreme precipitation patterns (frequency and intensity) to column leaching assays with soils collected near a contaminated site. We observed that precipitation with high frequency but low intensity resulted in more vertical migration of Hg through the soil profile than that with low frequency but high intensity. The majority (> 90%) of leached Hg was prone to migrate vertically within the top 10 cm of the soil profile. Furthermore, rainfall stimulated microbial Hg methylation, as demonstrated by enhanced production of methylmercury (MeHg) in both simulated and field-contaminated soils. We identified specific microbial taxa including Geobacteraceae, Desulfuromonadaceae, Syntrophaceae, Oscillospiraceae, and Methanomicrobiaceae as key predictors of MeHg production, which differed from those typically observed in overlying water of croplands. Particularly, the relative abundance of these dominant Hg methylators significantly increased during rainfall-induced leaching compared to that of the control, suggesting the crucial yet previously overlooked impacts of increased precipitation events on the process of microbial Hg methylation in industry-contaminated sites. Given the rising incidences of extreme precipitation events worldwide due to climate change, this study highlights the significance of assessing Hg mobility and microbial transformation in legacy contaminated sites.

Relationships between Precipitation and Elevation in the Southeastern Tibetan Plateau during the Active Phase of the Indian Monsoon

The precipitation gradient (PG) is a crucial parameter for watershed hydrological models. Analysis of daily precipitation and elevation data from 30 stations in the southeastern Tibetan Plateau (SETP) during the active phase of the Indian monsoon reveals distinct patterns. Below 3000 m, precipitation generally decreases with increasing altitude. Between 3000 and 4000 m, precipitation patterns are more complex; in western regions, precipitation increases with elevation, whereas in eastern regions, it decreases. Above 4000 m, up to the highest observation point of 4841 m, precipitation continues to decrease with elevation, with a more pronounced decline beyond a critical height. In the SETP, PGs for LYR and NYR are positive, at 11.3 ± 2.7 mm/100 m and 17.3 ± 3.8 mm/100 m, respectively. Conversely, PLZB exhibits a negative PG of −22.3 ± 4.2 mm/100 m. The Yarlung Zangbo River (YLZBR) water vapor channel plays a significant role in these PGs, with the direction and flux of water vapor potentially influencing both the direction and magnitude of the PG. Additional factors such as precipitation intensity, the number of precipitation days, precipitation frequency, and station selection also significantly impact the PG. Notable correlations between elevation and variables such as the number of precipitation days, non-precipitation days, and precipitation intensity. The precipitation intensity gradients (PIGs) are 0.06 ± 0.02 mm/d/100 m, 0.11 ± 0.04 mm/d/100 m, and −0.18 ± 0.04 mm/d/100 m for the three catchments, respectively. Future research should incorporate remote sensing data and expand site networks, particularly in regions above 5000 m, to enhance the accuracy of precipitation–elevation relationship assessments, providing more reliable data for water resource simulation and disaster warning.

An ensemble approach to downscale climatological variables via screening of clustered ANNs

ABSTRACT The statistical downscaling of global circulation models presents a significant challenge in selecting appropriate input variables from a vast pool of predictors. To address the issue, we developed ensemble approach based on the Combining Multiple Clusters via Similarity Graph (COMUSA), which integrates k-means and self-organizing maps (SOMs) methods with the mutual information (MI)-random sampling approach. This innovative feature extraction technique demonstrated a 21% improvement in the classification efficacy of large-scale climatic variables. When comparing feature extraction methods, the combination of MI-random sampling and ensemble clustering yielded more accurate results than SOM clustering alone. The most efficient artificial neural networks (ANNs)-based downscaling model was employed to project near- and mid-future precipitation and temperature (2025–2035, 2035–2045), revealing varied outcomes under different scenarios (SSP3-7.0 and SSP5-8.5). Under SSP3-7.0 and SSP5-8.5, annual mean precipitation values are projected to decrease by 2–3 and by 4–5%. Also, projected annual mean temperature values indicate an increase in 21-27 and 29-35% under SSP3-7.0 and SSP5-8.5 scenarios. Integrating COMUSA ensemble clustering with MI-random sampling enhances the estimation accuracy of the ANN downscaling model, contributing to accurate projections of future precipitation and temperature values.

Analysis of Pre-monsoon and Post-monsoon Seasonal Rainfall Trends by Mann-Kendall and Sen’s Slope Estimator Test in Himachal Pradesh, India

The purpose of the current study is to determine the trends and patterns of precipitation occurring in the periods before and after the monsoon season at 12 meteorological stations located in the twelve districts of the Himachal Pradesh region. The timeframe of the analysis is from 1991 to 2020. For this study, we used the Mann-Kendall test and estimator Sen's slope estimator test. Precipitation is an essential climatic factor that can act as a signal of climate change. In order to understand how climate change may affect water resource planning and management, it is crucial to understand rainfall pattern. The results indicate that there is a growing tendency in the pre-monsoon season at some sites, while some stations are seeing a declining trend. Keylong has the most significant falling trend, with a Sen slope of -4.154mm whereas Manali had the highest increasing trend at Sen's Slope (1.471mm). In contrast, the analysis of post-monsoon season data reveals an upward tendency in all studied regions, except for Dharamshala, where Sen's slope (-0.042mm) shows a slight downward trend whereas Manali had the highest increasing trend with the value of 1.771mm. According to the research, agricultural and horticultural sectors in Himachal Pradesh are at risk from heavy rainfall and landslides during the periods before and after the monsoon season, respectively, if the patterns continue to rise. Conversely, a decrease in these patterns might result in water scarcity. To tackle adverse climate changes, the general public must embrace adaptation measures based on long-term series data.

The combined link of the Indian Ocean dipole and ENSO with the North Atlantic-European circulation during early boreal winter in reanalysis and the ECMWF-SEAS5 hindcast

Abstract During early boreal winter, the extra-tropical atmospheric circulation is influenced by Rossby waves propagating from the Indian Ocean towards the North Atlantic-European (NAE) regions, resulting in a North Atlantic Oscillation (NAO)-like pattern. The mechanisms driving these teleconnections are not well understood and are crucial for improving model skills. This study investigates these mechanisms using the ERA5 dataset and tests the predictive capabilities of the ECMWF-SEAS5 hindcast, exploring potential reasons for a weak model response. Linear regression methods are employed to examine the extra-tropical links with the Indian Ocean dipole (IOD), both in isolation and in combination with the El Niño-Southern Oscillation (ENSO). Our findings demonstrate a connection between October IOD sea surface temperature anomalies and December Indian Ocean precipitation patterns. Furthermore, a correlation between the October IOD and December NAO time series suggests a link between the IOD and NAE circulation. The early winter European response to a positive IOD is characterized by a north-south precipitation dipole and a large positive surface air temperature anomaly. Positive feedback from transient eddy forcing reinforces the wavenumber-3-like propagation across extra-tropical regions, with ENSO playing a minor role compared to the IOD. This phenomenon is particularly evident in regions such as the North Pacific and North Atlantic, where wave energy propagation is intensified. Although SEAS5 replicates the NAO response, its magnitude is significantly weaker. The model struggles to simulate the delayed rainfall dipole response to the IOD accurately and shows structural discrepancies compared to reanalysis data.

Multi-proxy reconstruction of climate changes from the Medieval Climate Anomaly to the Little Ice Age in Southeast China

Exploring climate fluctuations across geological eras provides crucial historical context for addressing contemporary global climate challenges. This research leverages multi-proxies records analytical techniques, including pollen and charcoal analysis, X-ray fluorescence (XRF), and assessments of humification degree and loss on ignition (LOI), alongside paleoclimate simulations based on an alpine peat bog in Jiangxi Province, Southeast China. The study aims to provide a comprehensive review of vegetation evolution, climatic shifts, East Asian summer monsoon (EASM) intensity, and the potential drivers behind EASM behavior over the past two millennia. Our findings reveal a significant transition in precipitation and temperature patterns from the Medieval Climate Anomaly (MCA, 960–1420 CE) to the Little Ice Age (LIA, 1420–1850 CE). A distinct pattern emerges, highlighting particularly wet periods occurring between 960–1050 CE, 1190–1250 CE, and 1350–1400 CE, as well as significant droughts during 1430–1550 CE, 1590–1650 CE, 1680–1720 CE, and 1750–1800 CE. When compared to our temperature reconstruction series and precipitation data, a clear correlation emerges—wet phases align with warmer period characteristic of the MCA. Based on our simulation results, we suggest that ENSO oscillations and the Indo-Pacific warm pool played a substantial role in driving climate dynamics from the MCA to the LIA.

Multimodel regional frequency analysis of CMIP extreme precipitation

Abstract A recurrent question in climate risk analysis is determining how climate change will affect
heavy precipitation patterns.
Dividing the globe into homogeneous sub-regions should improve the modeling of heavy precipitation by inferring common regional distributional parameters.
In addition, biases due to model errors in global climate models (GCMs) should be considered to understand the climate response to diffrent forcing effects.
Within this context, we propose an efficient clustering algorithm that, compared to classical regional frequency analysis (RFA) techniques, is covariate-free and accounts for dependence.
It is based on a new non-parametric dissimilarity that combines both the RFA constraint and the pairwise dependence.
We derive asymptotic properties of our dissimilarity estimator, and we interpret it for generalized extreme value distributed pairs.

As an application, we cluster annual daily precipitation maxima of 16 GCMs from the coupled model intercomparison project.
We combine the climatologically consistent subregions identified for all GCMs.
This improves the spatial clusters coherence and outperforms methods either based on margins or on dependence.
Finally, by comparing the natural forcings partition with the one with all forcings, we assess the impact of anthropogenic forcing on precipitation extreme patterns.

Understanding tropical cyclone persistence over land: A case study of Cyclone Gulab

Abstract Tropical Cyclone (TC) Gulab originated in the Bay of Bengal during the post-monsoon season of 2021. Following an unusual westward trajectory, TC Gulab made landfall on the east coast of India, traversed central India, and reintensified over the Arabian Sea into TC Shaheen. This study employed an online Eulerian water vapor tracer (WVT) tool embedded within the Weather Research and Forecasting (WRF) model to investigate the role of land in terms of soil moisture and Land Use Land Cover (LULC) in persistance of TC Gulab over central India. We quantified the contributions of sub-regions and dominant LULC types within the Indian subcontinent to the precipitation associated with TC Gulab. Our findings indicate that evapotranspiration across the entire Indian subcontinent contributed approximately 30% to the post-landfall precipitation associated with TC Gulab. The highest contribution originated from the northwestern region, followed by the northeast. Croplands, the dominant LULC type, were the primary contributors, followed by forests. Despite changing the dominant land cover from cropland to forest, the low-pressure system persisted over land while altering the spatial patterns of precipitation. Sensitivity experiments demonstrated a linear decrease in precipitation originating from land with decreasing soil moisture (SM). Notably, there is a disproportionate decline in total precipitation with decreasing SM. Further analysis separating atmospheric vapor sources into land and ocean contributions using WVT revealed a compensatory mechanism. The contribution of oceanic vapor to atmospheric vapor over land increased when SM decreased due to increase in the latent heat over ocean. However, the total atmospheric vapor remained low, ultimately leading to a lower total precipitation. Although SM plays a role, our results highlight the importance of oceanic and atmospheric processes in determining TC persistence over land. This study offers valuable insights into the dynamics of land-atmosphere interactions during low-pressure systems of TCs.

Effects of simulated precipitation changes on soil respiration:Progress and prospects.

Soil respiration, the main pathway for transferring terrestrial carbon pool to atmospheric carbon pool, is profoundly affected by the intensification in global precipitation variability in the context of climate change. Nowadays, variable controlling methods and field manipulation experiments are two main methods widely used to investigate the effects of simulated precipitation changes on soil respiration. Yet, due to the heterogeneity of soil properties, vegetation types, and the magnitude of precipitation change, there is substantial inconsistency in the conclusions of simulated precipitation change effects on soil respiration. Here, we analyzed data from domestic and foreign literature, and examined the effects of simulated precipitation change on soil respiration. Firstly, we described the response pattern of soil respiration to soil moisture fluctuation and pointed out that the magnitude and direction of the response of soil respiration to simulated precipitation change depended on whether soil moisture was optimally conditioned at different precipitation treatments. Second, we summarized the response patterns of soil respiration to symmetric increase and decrease in precipitation, which mainly included symmetric and asymmetric responses (positive and negative asymmetric). Meanwhile, the adaptation of plants and soil microorganisms to drought stress and soil oxygen limitation, as well as the reduction of organic substrates, were the main mechanisms accounting for the shifts of soil respiration response patterns to simulated precipitation change from symmetric to asymmetric responses. Third, we identified a significant effect of ambient climate on soil respiration in response to precipitation treatments as increasing duration of the experimental treatments. In addition, cumulative or buffering effects of ambient climatic conditions on precipitation treatment could affect the sensitivity of soil respiration along precipitation gradient by altering hydrothermal conditions. Finally, to accurately assess the implications of precipitation changes on soil carbon balance processes, we proposed three aspects of future precipitation effects on soil respiration for attention: 1) focusing on the phenomenon of "threshold effects" in the asymmetric response of soil respiration along precipitation gradients; 2) distinguishing the intrinsic mechanisms of autotrophic and heterotrophic components in soil respiration in response to precipitation changes; and 3) focusing on the impacts of intensified precipitation variability on soil respiration in the context of future climate extremes. In conclusion, with the intensified variability in global precipitation patterns, clarifying the response mechanism of soil respiration to precipitation changes is of great significance for accurately predicting and evaluating the alterations of soil carbon cycle processes and carbon balance in the context of global changes.

Assessment of grassland carrying capacity drivers and evaluation of pasture-livestock balance: A case study of Xinjiang, China

Evaluating the spatiotemporal variations and driving mechanisms of carrying capacity (CC) is critical for optimizing grassland resource utilization and promoting sustainable development in grassland animal husbandry. This study focuses on Xinjiang, an arid and semi-arid region significantly impacted by environmental changes and human activities. To capture the dynamics of grassland CC, we employed Theil-Sen trend analysis and the Mann-Kendall test to assess spatiotemporal trends. The innovative use of the Geodetector method allowed for a detailed quantification of the influence of various environmental and human-related factors on grassland CC. Additionally, we incorporated livestock data to evaluate the overgrazing conditions of the grassland ecosystem. Our findings demonstrate that: (1) Grassland CC has shown a pronounced overall upward trajectory over the last twenty years, with notable inter-annual fluctuations and significant spatial variations, particularly between the northern and southern regions. (2) The spatial distribution of grassland CC is primarily influenced by precipitation patterns and population density, with key determining factors varying across different types of grasslands. Except for alpine steppe and alpine desert, the spatial distribution of grassland CC was primarily influenced by two-factor interactions, surpassing the impacts of single-factor effects. (3) The overgrazing rate has generally declined, peaking in 2014 and showing the mildest condition in 2017. In comparison, the overgrazing situation in Northern Xinjiang is relatively more favorable, whereas the southern and eastern regions necessitates more immediate and comprehensive ecological restoration and management measures. In summary, this study provides important scientific evidence for prioritizing grassland protection and planning for the sustainable animal husbandry development in arid and semi-arid regions.

Responses of greenhouse gas emissions to increased precipitation events in different ecosystems: A meta-analysis

Changes in precipitation patterns induced by climate change exacerbate the phenomenon by stimulating carbon (C) and nitrogen (N) processes in terrestrial ecosystems, leading to increased emissions of soil greenhouse gases (GHGs). However, the response of soil GHG fluxes across various global ecosystems under conditions of increased precipitation (IP) and extreme precipitation (EP) remains unclear. This study conducted a meta-analysis to examine the effects of IP and EP on soil GHG fluxes, synthesizing data from 49 published studies worldwide, and explored potential influencing factors. The results indicated that (1) IP significantly elevated CO2 emissions by 10.2 % and N2O emissions by 61.7 %, and did not impact CH4 emissions. EP treatment significantly increased N2O emissions by 61.8 %, CO2 emissions by 13.3 %, and CH4 emissions by 3.2 %. (2) Under IP treatment, significant differences in soil GHGs among various ecosystems were observed (P < 0.01). Among the three analyzed ecosystems (grassland, forest, and farmland), the response ratios of CO2 (30.4 %), N2O (61.8 %), and soil respiration (37.5 %, excluding plant respiration) were highest in the forest ecosystem and lowest in the grassland. (3) The response of CO2 flux to precipitation was most affected by soil dissolved organic C (P < 0.001) and microbial biomass C (P < 0.001). The key factor affecting the change in N2O flux was NH4+-N (P < 0.001). These findings provide a new perspective for understanding the effects of IP on soil C and N cycles in the context of global climate change.

Heavy rainfalls in Poland and their hyetographs.

In the light of observed variability in precipitation patterns, there is a growing need for comprehensive data mining of regularly updated rainfall recording databases. Therefore, an analysis of heavy rainfall and hyetographs was conducted using a 30-year high-resolution dataset from 100 rain gauges across Poland, covering 31 646 rainfall events. Distributions of rainfall depths, durations, and intensities were explored, and maxima were compared to global records. Spatial analysis revealed significant variations in the frequency, depths, and durations of extreme rainfall across different regions. Cluster analysis determined model hyetographs for each station. The likelihood of regions belonging to clusters with three to five model hyetographs was assessed using Indicator Kriging. Findings underscore the importance of using local, characteristics rainfalls in hydrodynamic modelling of drainage systems and future rainfall scenarios. These results provide a foundational step towards understanding and monitoring the impacts of climate change on rainfall characteristics, especially extremes, in future decades.

Estimating future climate change impacts on human mortality and crop yields via air pollution

Future climate change may bring local benefits or penalties to surface air pollution, resulting from changing temperature, precipitation, and transport patterns, as well as changes in climate-sensitive natural precursor emissions. Here, we estimate the climate penalties and benefits at the end of this century with regard to surface ozone and fine particulate matter (PM[Formula: see text]; excluding dust and smoke) using a one-way offline coupling between a general circulation model and a global 3-D chemical-transport model. We archive meteorology for the present day (2005 to 2014) and end of this century (2090 to 2099) for seven future scenarios developed for Phase 6 of the Coupled Model Intercomparison Project. The model isolates the impact of forecasted anthropogenic precursor emission changes versus that of climate-only driven changes on surface ozone and PM[Formula: see text] for scenarios ranging from extreme mitigation to extreme warming. We then relate these changes to impacts on human mortality and crop production. We find ozone penalties over nearly all land areas with increasing warming. We find net benefits due to climate-driven changes in PM[Formula: see text] in the Northern Extratropics, but net penalties in the Tropics and Southern Hemisphere, where most population growth is forecast for the coming century.

Seasonal Variability and Hydrological Patterns Influence the Long-Term Trends of Nutrient Loads in the River Po

This study investigates the long-term trends (1992–2022) of nitrogen and phosphorus loadings exported by the River Po to the Adriatic Sea, to better analyse how changes in hydrology are affecting the timing and magnitude of river nutrient loads. We used 30 years of monitoring data in order to (a) identify the main temporal patterns and their interactions at a decadal, annual and seasonal scale, (b) estimate precipitation effects on load formation and evaluate whether and to which extent the hydrological regime affects nutrient export across the years and (c) analyse the nutrient export regime at a monthly scale and the main transport dynamic of N and P chemical species (hydrological vs. biogeochemical control). The long-term analysis shows a general decrease of both P and N loadings, but the trends are different between the elements and their chemical species, as well as undergoing different seasonal variations. We found a statistically significant relationships between precipitation and loads, which demonstrates that precipitation patterns drive the exported load at the intra- and interannual time scales considered in this study. Precipitation-induced load trends trigger seasonal changes in nutrient deliveries to the sea, peaking in spring and autumn. The nitrogen decrease is mainly concentrated in the summer dry period, while total phosphorus diminishes mainly in spring and autumn. This mismatch of N and P results in variable molar N:P ratios within the year. The effects of extreme drought and flood events, along with the progressive decrease in the snowmelt contribution to water fluxes, are expected to exacerbate the variability in the N and P loadings, which in turn is expected to perturbate the biodiversity, food webs and trophic state of the Northern Adriatic Sea.

The Main Controlling Factors of the Development and Extinction of ' Carbonate Factory '

Building upon the profound foundation of previous research on carbonate factories, this paper systematically organizes the classification system of carbonate factories and delves into the primary controlling factors of their demise, thereby providing a more precise portrayal of the diverse facets and intricate evolution of carbonate factories throughout geological history. Based on carbonate rock types, sedimentary environment characteristics, the dominant agents in the construction process, deposition and precipitation patterns, as well as oceanographic conditions, the carbonate factories are categorized into five major groups, each of which is comprehensively described. Furthermore, this paper summarizes six key controlling factors: fluctuations in nutrient levels, anoxic events, tectonic activities, massive inputs of terrestrial materials, mass extinctions of organisms, and significant changes in sea level. It delves into how these factors, acting either independently or synergistically, lead to the demise of carbonate factories, elaborating on the corresponding processes and mechanisms in detail.

Climate changes at mine closure: a systematic review

This systematic review addresses the implications of climate change on mine closure processes, aiming to identify, analyze, and synthesize recent literature on how climate change influences the rehabilitation of mined areas, environmental risks, the safety of remaining structures, and the long-term sustainability of closure projects. Through a methodology of selecting scientific articles, articles published between 2020 and 2024 found in the Capes/CAfe database were analyzed. The analysis covered the climatic impacts on increasing temperatures and changes in precipitation patterns in relation to soil erosion, water management, and revegetation in mine closure processes. The results highlight challenges and opportunities arising from extreme weather events, which compromise the stability of mine structures, and the noted absence of Emergency Action Plans for dealing with extreme events, which hinders decision-making and the implementation of control measures. Recommendations include the development of adaptive practices and the incorporation of advanced environmental monitoring to mitigate risks and promote sustainability in mine closure, as well as being essential to increase environmental resilience.

Spatial and temporal analysis and trends of extreme precipitation over the Mississippi River Basin, USA during 1988–2017

Study regionMississippi River Basin. Study focusUsing daily precipitation records of 769 meteorological stations over the Mississippi River Basin (MRB), the spatial-temporal variability and trend of nine extreme precipitation indices were estimated and statistically assessed using the Mann-Kendall test. Factors likely to influence the spatial pattern and trends of precipitation extremes indices were also checked. New hydrological insights for the regionThe spatial pattern of the extreme precipitation indices exhibits a southeast to Northwest dipole, with the maximum values recorded over the southeastern part of the domain (exception being for Consecutive Dry Days, CDD which shows otherwise) driven by the southerly moisture transport toward the southeast. The spatial pattern of the extreme precipitation is controlled by the topography. The results also show that, on average, almost all the indices (except CDD) exhibit an increasing trend. The total wet day precipitation exhibits a significant increasing trend. Spatially, most of the significant increasing (decreasing) trends of the extreme's precipitation-except CDD- are located over the Upper (South) MRB where there is a significant sign toward cooling (warming) conditions. This supports the view that changing climate towards warming (cooling) conditions is significantly affecting precipitations extremes over the MRB. The relationships between large-scale teleconnections and extreme precipitation show that Pacific North America significantly increases (decreases) frequency and intensity indices over the Northwest (southeast) MRB, whereas the Pacific Decadal Oscillation does increase the frequency and intensity indices over the southeast. El Niño Southern Oscillation significantly increases the frequency and intensity indices over the entire MRB, with consequences to infrastructure failures, increasing vulnerable populations, risk zones and relocations populations.

Diversity pattern of Symplocos tree species in China under climate change scenarios: Toward conservation planning

Symplocos, a genus rich in endemic and keystone species in China's subtropical forests, plays a vital role in maintaining biodiversity. Climate change impacts on Symplocos trees affect ecosystem composition, dynamics, and functions. This study delves into the potential ramifications of climate change on the distribution and richness of Symplocos species across China under different climatic conditions. A comprehensive analysis was conducted using occurrence records of 29 species of Symplocos, focusing on habitat suitability modeling and spatial analysis, to demonstrate the possible effects of climate change on the distribution and diversity patterns of Symplocos species in China. Environmental data, including bioclimatic variables, were collected, and the future climate scenarios were projected using the combined average of the two global climate models. Four modeling algorithms were applied in an ensemble species distribution models to assess the distribution of Symplocos species and decipher the pattern of species richness and endemism. The results indicate high accuracy and performance of the models with a mean AUC of 0.94 and TSS of 0.77, particularly the random forest. The analysis identified key bioclimatic variables influencing Symplocos species distribution, with precipitation patterns (precipitation of driest month and precipitation of warmest quarter) and temperature variability (temperature mean diurnal range) playing significant roles. Predictions suggest a reduction in Symplocos species richness over the next six decades, particularly in southern and southwestern China, with potential gains in other regions. Overall, this study highlights the vulnerability of Symplocos species to climate change and underscores the importance of proactive conservation efforts and forest plantations to mitigate future loss in Symplocos species diversity.