Precipitation Frequency Research Articles

Cause and Characteristics of Changes in Mesoscale Convective Systems within a Convection-Permitting Regional Climate Model

Abstract Changes in the frequency and intensity of mesoscale convective systems (MCS) are assessed using convection-permitting regional climate model simulations. We present a novel classification method that relates MCSs to the magnitude of their large-scale forcing environments to better understand the changing nature of MCSs across different forcing environments in a possible future climate scenario. Overall, the annual frequency, intensity, and amount of precipitation associated with MCSs are projected to increase for broad portions of the eastern conterminous United States (CONUS). Furthermore, changes in the characteristics of MCSs show larger, longer-lived, and faster MCSs particularly over the Midwest and Southeast. Seasonal examination of this response reveals a robust intensification of March–May (MAM) MCSs. The higher frequency of MAM MCSs are found to occur in weakly forced synoptic environments. Increased rainfall amounts are explained by an intensification of MCSs due to changing thermodynamics and enhanced lower-tropospheric moisture transport into the central CONUS by the North Atlantic Subtropical High. Differences in mid-latitude storm tracks are favorable towards the enhanced development of MCSs associated with strong baroclinic forcing within the Midwest and Northern Plains, but are only realized in the presence of strong changes in lower-tropospheric moisture transport. Overall, these results suggest a shift in the behavior of MAM MCSs that more closely corresponds to the behavior of June–August (JJA) MCSs in the historical climate. The increased occurrence of MCSs in weakly forced environments, particularly in MAM, deserves further investigation.

Evaluating Factors Affecting Flood Susceptibility in the Yangtze River Delta Using Machine Learning Methods

AbstractFloods are widespread and dangerous natural hazards worldwide. It is essential to grasp the causes of floods to mitigate their severe effects on people and society. The key drivers of flood susceptibility in rapidly urbanizing areas can vary depending on the specific context and require further investigation. This research developed an index system comprising 10 indicators associated with factors and environments that lead to disasters, and used machine learning methods to assess flood susceptibility. The core urban area of the Yangtze River Delta served as a case study. Four scenarios depicting separate and combined effects of climate change and human activity were evaluated using data from various periods, to measure the spatial variability in flood susceptibility. The findings demonstrate that the extreme gradient boosting model outperformed the decision tree, support vector machine, and stacked models in evaluating flood susceptibility. Both climate change and human activity were found to act as catalysts for flooding in the region. Areas with increasing susceptibility were mainly distributed to the northwest and southeast of Taihu Lake. Areas with increased flood susceptibility caused by climate change were significantly larger than those caused by human activity, indicating that climate change was the dominant factor influencing flood susceptibility in the region. By comparing the relationship between the indicators and flood susceptibility, the rising intensity and frequency of extreme precipitation as well as an increase in impervious surface areas were identified as important reasons of heightened flood susceptibility in the Yangtze River Delta region. This study emphasized the significance of formulating adaptive strategies to enhance flood control capabilities to cope with the changing environment.

Impact of flooding at the early reproductive growth stage on soybean yield and seed composition

AbstractFlooding stress is a growing threat to global soybean [Glycine max (L.) Merr.] production as the frequency and intensity of extreme precipitations are increasing due to climate change. Soybean is highly sensitive to flooding and substantial yield losses are observed due to a cascade of negative physiological responses induced by hypoxia. Hence, there is a pressing need for the development of flood‐tolerant genotypes. This study evaluated the grain yield and seed protein and oil content of 31 soybean genotypes over 2 years under both non‐flooding and flooding conditions, where flooding entailed a 4‐day partial water submergence during the early reproductive growth stages R1/R2. Mixed‐effects linear models were utilized to assess the impact of flood damage scores (FDSs, 1–4 scale) on observed phenotypes, as well as differences in observed phenotypes between tolerant, moderate, and susceptible genotypes across flooding and non‐flooding treatments. No significant impact of FDS was observed for seed protein and oil content. In addition, no significant differences in these phenotypes were observed between flooding and non‐flooding treatments across the various genotype categories. On average, for each unit increase in FDS, grain yield decreased by 432.7 kg ha−1 (17.4%). Tolerant genotypes experienced roughly 33% yield losses between flooding and non‐flooding treatments, while moderate and susceptible genotypes experienced 44% and 51% yield losses, respectively. The advancements in genomics and phenomics are promising for the identification and incorporation of novel flood‐tolerant alleles through plant breeding, potentially mitigating flooding‐induced yield losses across diverse environmental conditions.

Seasonal activity and phoresy rates of Nitidulid beetles (Coleoptera: Nitidulidae) captured in stands with oak wilt infections in northern Michigan, USA.

Nitidulid beetles (Coleoptera: Nitidulidae), the overland vectors of the Bretziella fagacearum fungus that causes oak wilt, were monitored in infection centers in Quercus rubra stands in northern Michigan, USA using baited, wind-oriented traps for 2 years. First nitidulid captures, accounting for <1.5% of total annual captures, occurred in late April in both years (<50 cumulative degree days [DDs]; base 10°C). A total of 1,188 and 735 beetles representing 19 and 18 species were captured in 2018 (18 traps; 3 sites) and 2019 (16 traps; 4 sites), respectively. Ground traps (1.5 m high) captured more beetles and species than traps on canopy branches of red oaks. Most nitidulids (81-86%) were captured in May and June, but frequent precipitation and cool spring temperatures extended activity into early July in 2019. In 2018, 336 beetles representing 12 species were screened for B. fagacearum spores, but only 20 beetles from 4 species bore viable spores. Mycelial mats on red oaks killed were 4-fold more abundant in 2019 than in 2018. Of the 225 beetles screened in 2019, 56 beetles representing 6 species had viable spores. Nearly all (96%) spore-bearing beetles in both years were captured in late May or June. In bimonthly xylem samples collected from healthy trees, large earlywood vessels, presumably more vulnerable to infection than latewood, were present from May to June in 2018 and until early July in 2019. Results are consistent with mid-May to mid-July high-risk periods designated in current state and regional guidelines for oak wilt management.

Light Precipitation rather than Total Precipitation Determines Aerosol Wet Removal.

Precipitation scavenging is an important sink for both organic and inorganic pollutants in the atmosphere. However, there has been controversy over the relative importance of different precipitation characteristics (i.e., the precipitation amount, intensity, frequency, and duration) in the removal of air pollutants. It is critical to reach a consensus on which precipitation characteristics are most significant for aerosol wet removal. In this study, the analysis of multisource in situ observations of aerosol wet deposition worldwide indicates that the precipitation frequency plays a first-order role in scavenging aerosols. This is because large amounts of air pollutants are efficiently removed at precipitation initiation. As the duration and amount of precipitation increase, the scavenging efficiency decreases sharply. Consequently, it is featured that light precipitation, due to its high frequency of occurrence, rather than total precipitation, determines climatological aerosol wet deposition. To further confirm this, a state-of-the-art global climate model with convection resolved is modified to reduce the light precipitation frequency in both stratiform and convective cloud regimes. Results show widespread increases in aerosol optical depth (AOD) due to the reduced wet deposition. The spatial distribution of aerosol wet deposition changes resembles that of changes in the light precipitation frequency rather than that of total precipitation changes. These findings are consistent with those from the Coupled Model Intercomparison Project Phase 6 multimodel simulations, which show that the intermodel uncertainty in simulated AOD correlates more strongly with the uncertainty in light precipitation frequency than with that in total precipitation.

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.

CATALOG OF LANDSLIDES OF THE CITY OF ZENICE WITH SPECIFIC CAUSES

As part of its activities, the Federal Institute of Geology carries out mapping of active landslides in the territory of the Federation of Bosnia and Herzegovina, and my task is to map instability in the area of the City of Zenica. A very large number of landslides were registered during the catastrophic rainfall that occurred in 2014. Following these phenomena and certain records, detailed mapping of unstable slopes and records of unstable surfaces was started. The cadastre is defined in such a way that it displays all input data with location polygons in GIS. Not in the field, quite recognizable causes were observed, which are always a combination of bad geological and hydrogeological conditions with a significant anthropogenic factor. The slope of the terrain is also very unfavourable, and gravity also played a significant role in the instability of these slopes. Almost all registered landslides have unregulated water (surface, underground, waste). The frequent occurrence of earthquakes on this section also significantly threatened the stability of slopes and buildings, and on the spot, the consequences of cracking and shearing of buildings were evident as a result of the earthquake. A special review will be given to the activation of mudflows during extensive rainfall in this area, which is not rare. A significant share in the formation of mudflows belongs to heavy precipitation, which over time formed the so-called "watersheds" where water was transported from higher to lower parts of the slope. However, during the climate changes, there was a significant increase in the amount and frequency of precipitation, which carried the surface cover and rock blocks that were moved down the steep slopes. Road approaches to the villages, as well as a significant number of buildings, made artificial dams and cut cuts that were a natural way of draining water. So you have a significant number of buildings built exactly on these "watersheds" that represent the dam. In the newly created situation, by changing the natural paths of movement of surface and underground water, it would be necessary to regulate the water below and around the road strip, as well as the drainage of water around buildings. Only after the water has been drained, certain geotechnical procedures can be carried out to stabilize the slope and to permanently rehabilitate it, so that the inhabitants of these villages do not constantly fear during rainfall.

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.

Anthropogenic influence on precipitation in Aotearoa New Zealand with differing circulation types

This study quantifies the influences of anthropogenic forcing to date on precipitation over Aotearoa New Zealand (ANZ). Large ensembles of simulations from the weather@home regional climate model experiments are analysed under two scenarios, a natural (NAT) or counter-factual scenario which excludes human-induced changes to the climate system and an anthropogenic (ANT) or factual scenario. The impacts of anthropogenic forcing on precipitation are analysed in the context of large-scale circulation types characterized using an existing Self Organizing Map classification. The combined effect of both thermodynamics and dynamics are compared with values expected from the Clausius–Clapeyron (C–C) relation. Changes in the precipitation intensity due to greenhouse gas-forced temperature rise are lower than the expected C–C value. However extreme precipitation changes approach the C–C value for some circulation types. Specifically westerly flows enhance precipitation change across ANZ relative to the C–C rate, particularly over the West Coast. Conversely, northwesterly flows reduce the change over the North Island relative to the C–C value. Moreover, the wet day frequency generally reduces in the ANT scenario relative to NAT, reductions are largest on the West Coast of the South Island for westerly flows. Additionally, the frequency of days with extreme precipitation rises over ANZ for most circulation patterns, except in Northland and for northwesterly flows. This underscores the combined influence of dynamics and thermodynamics in shaping both precipitation intensity and frequency patterns across ANZ.

Comparison of Climatology of Precipitation Diurnal Cycle in Kalimantan Deduced from Rain Gauge and IMERG Data

Abstract The diurnal cycle is repetitive pattern of changes in weather factors on land and oceans due to the Earth rotation. Kalimantan Island as the largest island in the Maritime Islands region has a complex rainfall pattern that is influenced by local factors and global circulation. The research was conducted to find out the climatological comparison of the diurnal cycle of rain in Kalimantan based on rain gauge data and Integrated Multi-SatellitE Retrievals for GPM Version 07 (IMERG V07) data for the period 2016 - 2022. Data validation using rain gauge data to validate and calibrate IMERG satellite data, improving the accuracy and reliability of satellite rainfall estimates. The diurnal cycle is evaluated through a Precipitation Amount (PA), Precipitation Frequency (PF), and Precipitation Intensity (PI) every hour. The results show that the maximum rainfall occurs in the afternoon and evening along the coast, and in the middle of the night and morning on the Kalimantan plains. Kalimantan has high rainfall intensity values at short rainfall durations, i.e. rain lasting less than 3 hours. This is expected to improve the quality of hydrometeorological disaster mitigation in Kalimantan.

Integrating heterogeneous information for modeling non-stationarity of extreme precipitation in the Yangtze River Basin

Extreme precipitation exhibits non-stationary characteristics due to climate change and land use/cover change in recent years. In the literature, probability distributions with varying parameters associated with covariates have been applied to describe the non-stationarity of extreme precipitation. However, there is no consensus on the optimal distributions and the number and type of covariates for constructing non-stationary models. This study comprehensively investigates and determines the optimal distribution models, the type of covariates, and the number of covariates to best characterize the non-stationarity of extreme precipitation, using the Yangtze River Basin as the study area. The time series of four indices representing extreme precipitation of different durations and intensities are fitted by stationary and non-stationary Generalized Additive Models for Location, Scale, and Shape. The models are constructed using seven distributions and four types (time, global climate, local climate, and land cover) of covariates. Each model incorporates one to three covariates simultaneously. The results show that the most preferred distribution is the Generalized Extreme Value distribution for very wet-day precipitation, while the Log-Normal distribution is more suitable for other extreme precipitation indices. For the type of covariates, when using a single covariate, the proportion of stations using a global climate covariate as the best covariate (22.3 %–33.2 % of 193 stations) is close to that using a local climate covariate as the best covariate (22.3 %–27.5 %). The proportion of stations using land cover as the best covariate is much lower (7.3 %–10.9 %), while that using time as the best covariate is the lowest (1.6 %–4.1 %). When using multiple covariates, the models with both global and local climate covariates are mostly selected (8.3 % to 17.6 %) for all extreme precipitation indices. For the number of covariates, one or two covariates are enough to describe the non-stationary behaviors of all extreme precipitation indices. This study emphasizes the necessity to use non-stationary models for extreme precipitation frequency analysis with careful consideration and selection of distributions and covariates.

Development and in situ application of actively heated fiber Bragg grating cable for soil water content measurement

The actively heated fiber-optic (AHFO) technology has emerged as a frontier and hotspot in soil water content measurement, offering advantages such as easy installation, large-scale distributed measurement capability, and resistance to electromagnetic interference. However, current AHFO water content sensors fail to simultaneously achieve high precision, applicability for deep soil, and automated real-time monitoring, thereby limiting their development and application. Therefore, this study introduces a novel actively heated fiber Bragg grating (AH-FBG) cable. Laboratory tests were conducted to assess the heating uniformity of the AH-FBG cable and to establish the temperature characteristic value (Tt) – soil water content (θ) calibration formula for water content measurement. Subsequently, AH-FBG cables were deployed for in situ soil water content monitoring in a test pit on the Loess Plateau. Through two-year monitoring data verified the accuracy of the AH-FBG cable and elucidated the spatiotemporal distribution of in situ loess water content. Laboratory results demonstrated superior heating uniformity of AH-FBG cable, with a Tt standard deviation of approximately 0.3 °C. In the field, the AH-FBG cable exhibited excellent performance in soil water content measurement, achieving a high accuracy of 0.023 cm3/cm3. Further analysis revealed that the θ fluctuation predominantly occurred within a 10 m depth from the soil surface, with an overall upward trend over the two-year monitoring period; the response of shallow θ to precipitation was significant but exhibited increasing hysteresis with depth; frequent precipitation significantly enhanced water infiltration depth. This study provides technical guidance for high-precision, quasi-distributed, automated and real-time water content measurement of deep soil.

Diurnal variation of convective precipitation during the flood season in Fujian Province, China

Utilizing hourly observational data from automatic weather stations across Fujian Province from 2008 to 2020, this study defined and classified concepts and thresholds for convective precipitation, short-duration convective precipitation, long-duration convective precipitation, localized heavy precipitation, and widespread heavy precipitation. We explored the fundamental diurnal climatic characteristics of convective precipitation during the flood season in Fujian Province’s complex terrain. The results indicate the following. 1) Convective precipitation during the flood season exhibits distinct diurnal variation, with a significant spatial distribution along mountainous and coastal terrains. 2) The frequency of precipitation during the pre-flood season is higher and its intensity is lower than in the post-flood season. Due to differing primary weather systems influencing each period, resulting in substantial differences in the spatial distribution of precipitation frequency and intensity. 3) Both short-duration and localized precipitation show pronounced afternoon peaks in their diurnal patterns, predominantly occurring on windward slopes, highlighting mechanisms of afternoon moist convection and orographic lifting, whereas long-duration and widespread precipitation lack diurnal variation, reflecting the influence of large-scale circulation. These results will help forecasters better understand the unique precipitation characteristics of Fujian Province.

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.

How water, temperature, and seismicity control the preconditioning of massive rock slope failure (Hochvogel)

Abstract. The anticipation of massive rock slope failures is a key mitigation strategy in a changing climate and environment requiring a precise understanding of pre-failure process dynamics. Here we exploit &gt;4 years of multi-method high-resolution monitoring data from a large rock slope instability close to failure. To quantify and understand the effect of possible drivers (water from rain and snowmelt, internal rock fracturing, and earthquakes), we correlate slope displacements with environmental data, local seismic recordings, and earthquake catalogues. During the snowmelt phase, displacements are controlled by meltwater infiltration with high correlation and a time lag of 4–9 d. During the snow-free summer, rainfall induces accelerations with a time lag of 1–16 h for up to several days without a minimum activation rain sum threshold. Rock fracturing, linked to temperature and freeze–thaw cycles, is predominantly near the surface and unrelated to displacement rates. A classic Newmark analysis of recent and historic earthquakes indicates a low potential for immediate triggering of a major failure at the case site, unless it is already very close to failure. Seismic topographic amplification of the peak ground velocity (PGV) at the summit ranges from a factor of 2–11 and is spatially heterogeneous, indicating a high criticality of the slope. The presented in-depth monitoring data analysis enables a comprehensive rockfall driver evaluation and indicates where future climatic changes, e.g. in precipitation intensity and frequency, may alter the preconditioning of major rock slope failures.

Impacts of moisture transport on extreme precipitation in the Central Plains Urban Agglomeration, China

In recent decades, the Central Plains Urban Agglomeration of China (CPUA) has faced recurring extreme precipitation events (EPEs), leading to severe floods, endangering residents, and causing significant property damage. This study examines the spatiotemporal patterns of summer EPEs in the CPUA from 1961 to 2022. We used the Hybrid Single-Particle Lagrangian Integrated Trajectory model to trace the water vapor trajectories associated with these events, identifying atmospheric circulations linked to various moisture sources. Summer EPEs in the CPUA have become more frequent and intense. Urban regions typically experience stronger EPEs, while mountainous regions encounter more frequent but milder precipitation. The moisture contributing to these events comes from sources including Eurasia (9.94 %), the northern and southern Western North Pacific (48.39 %), and the Bay of Bengal and South China Sea (41.67 %). Notably, contributions from Eurasia and the northern Western North Pacific have increased, whereas those from the Bay of Bengal and the South China Sea have decreased. Events driven by Western North Pacific moisture have stronger impacts on urban areas, influenced by abnormal anticyclonic patterns and the development of the Huang-Huai cyclone, which triggers intense convective activity over the CPUA. The strengthening of the Western North Pacific subtropical high promotes the transport of warm air, which merges with colder inland air, leading to extreme precipitation.

From trees to rain: enhancement of cloud glaciation and precipitation by pollen

Abstract The ability of pollen to enable the glaciation of supercooled liquid water has been demonstrated in laboratory studies; however, the potential large-scale effect of plants and pollen on clouds, precipitation and climate is pressing knowledge to better understand and project clouds in the current and future climate. Combining ground-based measurements of pollen concentrations and satellite observations of cloud properties within the United States, we show that enhanced pollen concentrations during springtime lead to an increase in cloud ice fraction of up to 0.1 in the temperature regime where pollen are considered to act as INP (−15 ∘ C and −25 ∘ C ). We further establish the link from the pollen-induced increase in cloud ice to a higher precipitation frequency. In light of anthropogenic climate change, the extended and strengthened pollen season and future alterations in biodiversity can introduce a localized climate forcing and a modification of the precipitation frequency and intensity.

Season-dependent climate sensitivity of the surface runoff of major rivers in Changbai Mountain

Due to the sensitivity of runoff to changes in seasonal snowpack, the changes in surface runoff within high-latitude and high-elevation areas are more complex than in other areas. However, this sensitivity is often overlooked when correlating changes in runoff with meteorological variables. Here, we analyze changes in surface runoff during snowmelt and snowless periods and their sensitivity to climate change from 1960 to 2019 in the upstream watersheds of the Songhua, Yalu, and Tumen Rivers in Changbai Mountain. Most runoff indices, except for low flow in the Yalu River, exhibited decreasing trends during snowmelt and snowless periods. Precipitation was the primary variable responsible for surface runoff during snowless and snowmelt periods. Notably, rainfall replenished all runoff indices during snowless periods, particularly under high flow conditions. The average and high flows were also primarily supplemented by rainfall during snowmelt periods. However, low flow was positively correlated with snowmelt in the Songhua and Tumen Rivers and with temperature in the Songhua, Yalu and Tumen Rivers during snowmelt periods. Additionally, extreme runoff events were closely associated with extreme precipitation. These findings demonstrate the seasonal and index-dependent climate sensitivity of surface runoff within Changbai Mountain, offering insights into the surface runoff response mechanism to climate change in high-altitude mountainous areas. Under future climate change conditions, frequent and intense extreme precipitation events may increase the risk of flooding, owing to the high sensitivity of high flows to precipitation, posing a serious threat to regional ecological security.

Spatiotemporal variation and teleconnections of extreme precipitation in the Upper Indus Basin: insights for natural hazard assessment

ABSTRACT The study investigates trends and teleconnections of extreme precipitation events in the Upper Indus Basin (UIB) within the Indian region, part of the crucial geo-ecological Hindu Kush Himalayan Mountain system. Utilizing high-resolution (10 km × 10 km) HAR data from 2002 to 2013, we analyzed 11 indices established by the Expert Team on Climate Change Detection and Indices (ETCCDI) to observe variations in extreme precipitation at the monthly, seasonal, and annual time scale. The result shows an increase in dry and wet extreme precipitation frequency and intensity, increasing monsoon precipitation and a shift of winter precipitation, increasing continuous dry days. Using the APHRODITE daily (0.25° × 0.25°) data from 1952 to 2014, continuous wavelet transform and wavelet coherence analysis indicate significant periodicities and correlations with large-scale climate anomalies such as the El Niño-Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO). Wavelet coherence analysis reveals that monthly extreme precipitation events significantly correlate with ENSO at a 3–5 years periodicity, while annual extremes show significant coherence with NAO at 8–10 years and over 16 years periodicities. The study highlights the impact of global climate change on regional precipitation and the need for adaptive water management policies to mitigate flood risks during the rainy season and address water scarcity in the dry season.

A framework for natural resource management with geospatial machine learning: a case study of the 2021 Almora forest fires

BackgroundWildfires have a substantial impact on air quality and ecosystems by releasing greenhouse gases (GHGs), trace gases, and aerosols into the atmosphere. These wildfires produce both light-absorbing and merely scattering aerosols that can act as cloud condensation nuclei, altering cloud reflectivity, cloud lifetime, and precipitation frequency. Uttarakhand province in India experiences frequent wildfires that affect its protected ecosystems. Thus, a natural resource management system is needed in this region to assess the impact of wildfire hazards on land and atmosphere. We conducted an analysis of a severe fire event that occurred between January and April 2021 in the Kumaun region of Uttarakhand, by utilizing open-source geospatial data. Near-real-time satellite observations of pre- and post-fire conditions within the study area were used to detect changes in land and atmosphere. Supervised machine learning algorithm was also implemented to estimate burned above ground biomass (AGB) to monitor biomass stock.ResultsThe study found that 21.75% of the total burned area burned with moderate to high severity, resulting in a decreased Soil Adjusted Vegetation Index value (> 0.3), a reduced Normalized Differential Moisture Index value (> 0.4), and a lowered Normalized Differential Vegetation Index (> 0.5). The AGB estimate demonstrated a significant simple determination (r2 = 0.001702) and probability (P < 2.2 10−16), along with a positive correlation (r ≤ 0.24) with vegetation and soil indices. The algorithm predicted that 17.56 tonnes of biomass per hectare burned in the Kumaun forests. This fire incident resulted in increased emissions of carbon dioxide (CO2; ~ 0.8 10−4 kg carbon h−1), methane (CH4; ~ 200 10−9 mol fraction in dry air), carbon monoxide (CO; 2000 1015 molecules cm−2 total column), and formaldehyde (HCHO; 3500 1013 molecules cm−2 total column), along with increased aerosol optical thickness (varying from 0.2 to 0.5).ConclusionsWe believe that our proposed operational framework for managing natural resources and assessing the impact of natural hazards can be used to efficiently monitor near-real-time forest-fire-caused changes in land and atmosphere. This method makes use of openly accessible geospatial data that can be employed for several objectives, including monitoring carbon stocks, greenhouse gas emissions, criterion air pollution, and radiative forcing of the climate, among many others. Our proposed framework will assist policymakers and the scientific community in mitigating climate change problems and in developing adaptation policies.