Precipitation Concentration Research Articles

Tritium concentration in monthly precipitation in Okinawa and Kagoshima, southern Japan

Abstract This study aimed at understanding the background concentration level of tritium in precipitation in southern Japan, where precipitation samples were collected monthly in 2022 in Okinawa and Kagoshima. Tritium concentration in monthly precipitation collected in Okinawa since 2014 ranged from 0.05 to 0.39 Bq per L with the mean value (2015–2022) of 0.15 ± 0.06 Bq per L. That of Kagoshima since 2022 ranged from < 0.11 to 0.58 Bq per L with the mean value of 0.30 ± 0.13 Bq per L. Our observed values were summarized with reported values, from which a clear latitude effect for tritium concentration was found in precipitation of southern Japan.

Characteristics of tritium, stable isotopes and chemical components in monthly precipitation at Hiroshima, Japan

Abstract This article mainly discusses tritium concentrations in monthly precipitation at Hiroshima City during 2021. The tritium concentrations, which were measured with a low background liquid scintillation counter, fluctuated from 0.16 to 0.78 Bq L−1. Additionally, hydrogen and oxygen stable isotopes and ionic species were measured in order to characterize precipitation, and their trends including tritium concentrations were compared with data collected in other regions of Japan in previous studies. Although the results showed the characteristics of precipitation were similar to those observed in the other regions, the tritium concentrations were found to be contrary to behavior expected from the latitude effect and some of the observed ionic components were suggested to be continental in origin. Since these results are not common at other sites in Japan, the tritium concentration in the westernmost region of Honshu, including Hiroshima City, may be influenced by Asian continental influences.

Spatiotemporal variations of global precipitation concentration and potential links to flood-drought events in past 70 years

The evaluation of precipitation events is crucial for predicting severe droughts and floods, particularly in the context of global warming. This study presents a comprehensive spatiotemporal analysis of the global precipitation concentration index (CI) from 1950 to 2020, comparing CI with nine extreme precipitation indices to assess its applicability. The world map was divided into three distinct regions related to flood-drought events using the standardized precipitation index (SPI). Key findings include: 1) CI exhibited significant spatial heterogeneity, with elevated values in seasonally warm coastlines and arid regions. Seasonal changes in CI were not synchronized, particularly lower in Northern Hemisphere winters, while the Southern Hemisphere displayed minor seasonal variation, alongside a long-term growth trend in approximately 36.5 % of the global area. 2) CI's spatiotemporal variations helped delineating regional precipitation patterns, revealing a strong correlation (R2 = 0.87) with the extreme precipitation index R95c, but a negligible relationship with total precipitation (P) (R2 = −0.05). This facilitated the global partitioning into three regions: Humidity Amplification Region (I), Dryness Intensification Region (II), and Climate Fluctuation Region (III). 3) Flood-drought events were potentially linked to variations in CI and P, where In Region I experienced increased flood risk due to rising CI and P, Region II faced heightened drought risk from rising CI and declining P, and Region III showed opposing effects on floods due to reduced CI and increased P, with P variability significantly influencing flood frequency. 4) The main atmospheric circulation factors varied by region, typically including the Antarctic Oscillation (AAO), Atlantic Multi-decadal Oscillation (AMO) and Arctic Oscillation (AO), with AAO being most significant. This research underscores the intricate relationships between climatic indices and hydrological extremes, emphasizing the challenges posed by global warming, atmospheric circulation changes, and data uncertainties in assessing drought and flood disasters.

Spatial and Temporal Patterns of Rainfall Erosivity in Southern Africa in Extreme Wet and Dry Years

Soil erosivity is a key indicator of the effectiveness of precipitation acting on the land’s surface and is mainly controlled by event-scale and seasonal weather and climatic factors but is also influenced by the nature of the land’s surface, including relief and vegetation cover. The aim of this study is to examine spatial and temporal variations in soil erosivity across southern Africa using rainfall data for the period 2000–2023 and a gridded raster spatial modelling approach. The two wettest and driest years in the record (±>1.5 standard deviation of rainfall values) were identified, which were 2000 and 2006, and 2003 and 2019, respectively. Monthly rainfall values in these extreme wet/dry years were then analyzed for four rainfall regions (arid, semiarid, subhumid, humid), identified according to their annual rainfall totals. These data were then used to calculate Precipitation Concentration Index (PCI) values as an expression of rainfall seasonality, and the modified Fournier index (MFI) was used to quantify rainfall erosivity. The results show that there are significant differences in erosivity between the different climate regions based on rainfall seasonality and also their distinctive environmental settings. In turn, these reflect the synoptic climatic conditions in these regions, their different precipitation sources, and rainfall totals. The results of this study show that calculated MFI values at the national scale, which is the approach taken in most previous studies, cannot effectively describe or account for erosivity values that characterize different climatic regions at the sub-national scale. Furthermore, the mismatch between PCI and MFI spatial patterns across the region highlights that, under semiarid, and highly seasonal rainfall regimes, episodic rainfall events interspersed with periods of dryness result in significant variability in erosivity values that are unaccounted for by rainfall totals or seasonality alone. In these environments, flash floods and wind erosion result in regional-scale soil erosion and land degradation, but these processes and outcomes are not clear when considering MFI values alone. Fully evaluating spatial and temporal patterns of erosivity in their climatic and environmental contexts, as developed in this study, has implications for sediment and carbon exports, as well as identifying the major regions in which land degradation is an environmental and agricultural issue.

Gamma Dose Rate Measurements in Northern Spain: Influence of Local Meteorological Scenarios on Radiological “False Alarms” in a Real-Time Radiological Monitoring Network

The present study characterizes gamma dose rate (GDR) concentrations in Bilbao, located in the northern Iberian Peninsula, utilizing a comprehensive 10-min interval database spanning from 2009 to 2018. This station belongs to the radiological environmental monitoring of the Basque Country network. The daily average GDR was found to be 0.07624 ± 0.00004 µSv/h, with the daily 95th percentile averaging 0.08026 ± 0.00007 µSv/h throughout the entire period. Our analysis specifically addresses the impact of precipitation on GDR, revealing a higher correlation coefficient for daily 95th percentile values compared to daily averages. Additionally, the influence of the Galerna (GL) event, a natural meteorological phenomenon in this region, on GDR was investigated, noting that it can develop both with and without precipitation. Understanding the interaction between GDR and this meteorological scenario is vital for the development of more reliable radiological monitoring strategies and for safeguarding public health. For this purpose, 40 GL events were analyzed. The present results indicate that GDR values frequently exceed alarm levels when a GL is formed. These GDR peaks should be considered natural radiological events, necessitating the classification of such GDR peaks as false alarms within the radiological monitoring network. To explain them in detail, 10-min time series of precipitation and radon outdoor concentrations were analyzed. The results demonstrate that the GL event with precipitation is a meteorological scenario that can be associated with false alarms. The present analysis provides a distinct contrast in radon behavior under the same meteorological event in case of being developed with precipitation or without precipitation. The findings from this analysis are crucial for avoiding and understanding false radiological alarms triggered in the monitoring network, thereby enhancing the accuracy of radiological data interpretation and improving public safety measures.

District wise spatiotemporal analysis of precipitation trend during 1900-2022 in Bihar state, India

Precipitation over a region plays vital role in determining availability of water resource whereas study of spatiotemporal distribution of rainfall helps in managing precipitation associated risks like drought, flood etc. The present study is carried out in the state of Bihar, India using District-wise rainfall data of Bihar for period 1900–2022. Major findings of study indicate that CV and SD of monsoon season and annual rainfall are in a moderate range except for districts Begusarai and Jahanabad; however, CVs for pre-monsoon, winter, and post-monsoon seasons were high for all the districts. Innovative trend analysis of the rainfall indicates either no trend or a negative trend except in few districts, and in recent years the negative trend is observed in almost all the districts. To understand homogeneity in rainfall, the precipitation concentration index and its trend analysis performed through Mann-Kendall test. PCI analysis indicates irregularly distributed annual and uniformly distributed monsoon rainfall. High inhomogeneity in annual rainfall is due to the post-monsoon PCI contribution. PCI trend was found positive in the southern, extreme northern, and central parts of Bihar, whereas extreme west districts of Bihar like Aurangabad, Bhojpur, Buxar, Rohtas, and extreme eastern districts like Purnia, Katihar, Araria, and Supaul had negative trend.

Discrepancies in precipitation changes over the Southwest River Basin of China based on ISIMIP3b

Selecting appropriate global climate models (GCMs) is crucial for minimizing uncertainty in regional climate projections under future scenarios. Previous studies have predominantly assessed the modeling capability of GCMs for regional precipitation climatology and its long-term patterns based on annual and seasonal precipitation data. Building upon these, we primally evaluated the performance of five GCMs from phase 3b of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3b) in simulating precipitation concentration and its variations in the Southwest River Basin (SWRB) of China using the precipitation concentration index (PCI). The results indicate that: (1) The 5 GCMs generally capture the spatial distribution of annual average precipitation in the SWRB but significantly overestimate its magnitude, with a maximum regional average deviation of 207.80 mm. Furthermore, all models tend to overestimate the overall drying trend in the SWRB and show limited capability in simulating interdecadal variations of annual precipitation. (2) While the 5 GCMs reasonably simulate the spatial distribution of annual average PCI in the SWRB, they tend to overestimate its values, with a maximum regional average deviation of 1.54. Additionally, their simulation performance in capturing PCI trends and interdecadal variations is also limited. (3) The 5 GCMs tend to overestimate seasonal precipitation in the SWRB, with the best simulation performance for the distribution of autumn precipitation, followed by spring and summer, and the poorest for winter. Significant differences exist in the simulation performance of the models for seasonal precipitation proportions, which result in discrepancies in the models’ representation of PCI. Moreover, the models’ poor simulation performance of PCI trends is partly due to their inadequate modeling of trends in seasonal precipitation proportions. The findings will contribute to laying the foundation for meteorological hydrological research and water resource management in the SWRB.

Climate Profile Over Jeldu Woreda of West Shewa Zone, Oromia, Ethiopia

Identifying the climate profile of an area is essential for effective water resource management, agricultural planning and disaster preparedness. This study focuses on characterizing rainfall patterns and identifying the variability of Jeldu woreda in Oromia, Ethiopia. Previously and recently, little study tried to show the woreda profile, which did not bases on station data properly. Forty years (1981-2020) of stations’ rainfall and temperature data were utilized. The study area had natural forest, highland and mid land with varying topography. Based on the annual rainfall cycle, the woreda had bi-modal rainfall types, with the peak of the year being July. Statistical methods and python script were applied to identify the climate profile. The results show that main rainy seasonal rainfall starts on average from 12-14 June and ceases averagely around 10-15 October. During second rainy period, the rain starts from 25 February to 10 March. The mean annual rainfall of woreda is 1395.1 mm, and the average maximum temperature is 19.5°. Kiremt contributes the highest percent, which is more than 64 % of the annual total rainfall. The 1987 and 2015 were the driest years, and 1996 and 2010 is the wettest during kiremt, 1987 and 2010 the wettest, and 1999 and 2015 the driest during Belg. Seasonal rainfall had a regular to moderate precipitation concentration over woreda. Identifying the causes of unseasonal rainfall that happened during dry season over the study area can help to decrease weather hazard during the season.

Spatiotemporal characteristics of daily precipitation concentration in Iran

Spatiotemporal characteristics of daily precipitation concentration in Iran

Carbon sequestration through conservation tillage in sandy soils of arid and semi-arid climates: A meta-analysis

This meta-analysis assessed soil organic carbon (SOC) percent changes in sandy soils, transitioning from conventional tillage (CT) to conservational tillage (CST) in arid and semi-arid climates. High levels of SOC in sandy soils are difficult to attain especially when precipitation levels are very low, contributing to low biomass production, and increased decomposition of organic matter. While CT practices are known to reduce SOC through the breakdown of soil aggregates, accelerated decomposition of soil organic matter, and promote erosion, CST methods (i.e., mulch tillage, no tillage, reduced tillage, ridge tillage, etc.) offer the potential to preserve soil aggregates and increase SOC concentration. Analyzing 55 peer-reviewed publications in arid and semi-arid climates with ≥ 45 % sand content, this study compared SOC content between CST and CT over short- and long-term periods (349 paired observations). Results showed that CST increased SOC in sandy soils, with an estimated 12.74 ± 1.46 % increase. Specifically, reduced tillage (RdT), mulch tillage (MchT), and no tillage (NT) exhibited the highest increases of SOC by 18.94 ± 2.48 %, 11.45 ± 2.46 %, and 10.06 ± 2.46 %, respectively, compared to CT. Studies with durations of up to 15 years (n = 297) showed a progressive increase in SOC concentrations under CST; however, the long-term stability of the accrued carbon content in sandy soils of arid and semi-arid climates is still uncertain, as studies extending beyond 15 years (n = 52) did not demonstrate significant changes in SOC levels. CST significantly raised SOC concentrations in precipitation up to 600 mm, though no significant changes were observed for precipitation over 600 mm. In soils with over 56 % sand content, CST increased SOC by approximately 13 %. This study highlights both positive and limited impacts of CST practices for soil conservation and climate change mitigation, emphasizing their significance for both existing agricultural areas in arid regions and those in parts of the world where aridity is on the rise.

Ex-sulfates in the Atmosphere of Barnaul: Assessment and Sources of Еmission

The methodology for calculating the content of ex-sulfates (sulfates of anthropogenic origin) in the ground layer of the atmosphere for the flat territory of the Altai Territory is substantiated. The assessment was made of the content of ex-sulfates in precipitation falling on the territory of the city of Barnaul, as well as the analysis of the percentage of ex-sulfates in the total volume of sulfates in the surface layer of the city’s atmosphere over the past eight years (2014–2022). A correlation analysis of the weighted average seasonal values of ex-SO42– concentration in precipitation and air temperature was carried out. Their high negative correlation in the cold season of the year is shown. It was noted that no significant relationship was found for the warm period, which indicates a direct dependence of the concentration of ex-sulfates on the volume of coal combustion during the heating season.

Vertical structures of typhoon cloud microphysical and radiative features associated with the precipitation type over the western North Pacific

Intensity of different precipitation types (convective, stratiform and shallow) and associated cloud vertical microphysical and radiative heating features are analyzed considering typhoon development, maturity, and decaying stages over the western North Pacific using the CloudSat Tropical Cyclone and China Meteorological Administration tropical cyclone best-track datasets from 2 June 2006 to 31 December 2015. At all three stages, the convective precipitation intensity, approximately twice that of stratiform precipitation, is the highest and peaks at development stage. The strongest stratiform precipitation occurs at typhoon maturity stage. Shallow precipitation is the weakest throughout the typhoon lifespan. Although the cloud microphysical parameters (radar reflectivity, cloud ice particle number concentration and effective radius) of both convective and stratiform precipitation tend to increase with precipitation intensity, convective precipitation contains more ice water of larger sizes in upper layers than stratiform precipitation. Unlike convective and stratiform precipitation, dominated by cold clouds, shallow precipitation is dominated by warm clouds with weak vertical contrast in the radiative distribution but strong radiation nearby 5 km. Our results show that cloud ice particle number concentration is important not only in precipitation intensity enhancement but also in determining the shortwave radiative heating center vertical location. More and larger ice particles in convective precipitation profiles result in stronger or comparable shortwave radiative heating than those in stratiform precipitation profiles, while the longwave radiative cooling rates in convective and stratiform precipitation profiles exhibit very similar features, likely attributable to similar infrared radiation levels due to comparable temperatures in these profiles.

Identification of potential landslide in Jianzha county based on InSAR and deep learning

Landslide disasters have characteristics of frequent occurrence, widespread impact, and high destructiveness, posing serious threats to human lives, property, and the ecological environment. Timely and accurate early identification of landslides remains an urgent issue within the disaster prevention field. This study focuses on Jianzha County, Qinghai Province, integrating PS-InSAR, SBAS-InSAR, and optical remote-sensing techniques to delineate potential landslide-prone areas. Utilizing Google Earth imagery and existing landslide datasets, potential landslide points were identified through a deep learning model. Results indicate the following: (1) In Jianzha County, the variation trend of the average surface velocity monitored by PS-InSAR and SBAS-InSAR technology is consistent, and the deformation monitoring results are reliable. (2) Utilizing the deep learning model, 56 potential landslide points were identified, comprising 39 high-risk points and 17 medium-risk points. By integrating the spatial distribution data of historical geological disaster points, 10 out of 13 previously occurred landslide disaster points were found to be located at the identified high-risk landslide points, achieving a detection accuracy of 76.92%. (3) The spatial distribution of landslide points exhibits clustering, with slopes ranging from 10° to 40°, elevations between 15 and 30 m, and slope orientations predominantly toward the northeast. (4) Landslide formation is correlated with seasonal precipitation concentrations and temperature fluctuations. This method can provide a crucial basis for large-scale surface deformation monitoring and early identification of landslide risks.

PyMTRD: A Python package for calculating the metrics of temporal rainfall distribution

Temporal rainfall distribution facilitates the understanding of rainfall patterns at various time scales, extreme events, and corresponding water resources implications. Researchers have developed various metrics of temporal rainfall distribution but there exist no easy-to-use software packages for calculating these metrics. To address this gap, we developed the PyMTRD package, which can be conveniently used to calculate the metrics of temporal rainfall distribution and conduct rainfall pattern analysis. The metrics calculated in the package included rainfall intensity, rainfall frequency, consecutive dry days, Gini index, unranked Gini index, wet-day Gini index, precipitation concentration index, dimensionless seasonality index, and seasonality index. This paper documented our Python software development, which included the architecture design, the Application Programming Interfaces design and algorithms for calculating each metric, and also the point and global scale applications.

Stable isotopic characterization and sources of ammonium in wet deposition at the Danjiangkou Reservoir

Ammonium nitrogen (NHx) in atmospheric wet deposition is a primary external nitrogen source for reservoir-type water sources, and identifying its sources is crucial for controlling nitrogen pollution in water. This study aimed to examine the NHx characteristics and sources in wet deposition in the Xichuan reservoir area of the Danjiangkou Reservoir, the South-to-North Water Diversion Project (SNWDP) water source, from September 2019 to August 2020. Major inorganic nitrogen concentrations in precipitation and ammonia-nitrogen isotope (δ15N–NH4+) values were measured, and the sources were analyzed using the Bayesian mixing model. The results showed that NH4+ was the main inorganic nitrogen form in wet deposition, with a concentration ranged of 0.16–5.26 mg L−1. The δ15N–NH4+ values ranged from −14.7‰ to +6.3‰, with significant isotopic effects from agricultural sources and climatic conditions, showing higher values in summer and lower values in winter. The release of ammonia (NH3) from agricultural sources was the primary NHx source (58.0%–76.6%), with fertilizer application being the most significant contributor (32.1%–46.6%). Seasonally, the relative contribution of agricultural sources to NH4+ in wet deposition was higher in autumn and winter. Spatially, agricultural activities significantly impacted atmospheric nitrogen accumulation across the reservoir area. This study quantified NHx sources in wet deposition, providing isotopic evidence and scientific references for nitrogen control measures and atmospheric nitrogen cycling studies.

A method to describe attenuation of river contamination under peak flows: Can the public water supply from Paraopeba River finally return after the Brumadinho dam disaster?

Tailings dams' disasters begin a stage of river water contamination with no endpoint at first sight. But when the river was formerly used for public water supply and the use was suspended as consequence of a dam break, a time window for safe suspension lift must be anticipated to help water managers. The purpose of this study was to seek for that moment in the case of Brumadinho dam disaster which occurred in 2019 and injected millions of cubic meters of iron- and manganese-rich tailings into the Paraopeba River, leading to the suspension of public water supply to Belo Horizonte metropolitan region with this resource, until now. To accomplish the proposed goal, an assemblage of artificial intelligence and socio-economic development models were used to anticipate precipitation, river discharge and metal concentrations (iron, manganese) until 2033. Then, the ratios of metal concentrations between impacted and non-impacted sites were determined and values representing extreme events of river discharge were selected for further assessment. A ratio ≈1 generally indicates a similarity between impacted and non-impacted areas or, put another way, a return of impacted areas to a pre-rupture condition. Moreover, when the ratio is estimated under the influence of peak flows, then a value of ≈1 indicates a return to pre-rupture conditions under the most unfavorable hydrologic regimes, thus a safe return. So, the extreme ratios were plotted against time and fitted to a straight line with intercept-x representing the requested safe time. The results pointed to 6.57 years after the accident, while using iron as contaminant indicator, or 8.71 years when manganese was considered. Despite of being a relatively low-risk timeframe, the suspension lift should be implemented in phases and monitored for precaution of potential sporadic contamination events, while dredging of the tailings from impacted areas should continue and be accelerated.

Analysing the Precipitation Concentration of the Highland and Lowland Zones of Ri Bhoi District, Meghalaya

Analysing the Precipitation Concentration of the Highland and Lowland Zones of Ri Bhoi District, Meghalaya

Influence of large scale climate drivers on hydro-climate variability in Dwangwa River Basin, Malawi

Large-scale climate processes such as the Indian Ocean Dipole (IOD) have significant roles in modulating rainfall and hydrological systems. Understanding such processes can inform adaptation measures for climate change and variability, as well as water resource management and planning. This study investigated the impact of the Indian Ocean Dipole (IOD) on rainfall and discharge variability in the Dwangwa River Basin (DRB) in Malawi, a key inflow to Lake Malawi. Specifically, the study analysed annual rainfall variability trends from 1985 to 2015 using the Coefficient of Variation (CV) and the annual Precipitation Concentration Index (PCI). The significance and direction of rainfall and discharge trends were quantified using the Mann–Kendall trend test at α = 0.05 significance level. To evaluate the association between rainfall and IOD, the Pearson product moment used three IOD phases: positive, negative, and neutral. Simple linear regression was utilised to check the response of the river during the concerned IOD phases. The study found CVs between 20 and 30%, typical of climates with moderate monthly rainfall variability. The PCI ranged from 20 to 30%, suggesting a strongly seasonal and highly variable temporal intra-annual rainfall distribution in the DRB. Moreover, the Mann–Kendall test statistics showed insignificant decrease in annual rainfall trends. Further, the findings demonstrated an insignificant negative correlation between rainfall and positive IOD, with rainfall increases associated with negative IOD, whereas positive IOD is associated with decreased river discharge. Consequently, El Niño and a positive IOD could cause DRB to have low water availability. Therefore, the study demonstrates that rainfall is experiencing a decreasing trend, which is driven by large-scale mechanics.

Analysis of Rainfall Variability Using Precipitation Concentration Index for a Catchment

The PCI is a critical metric to assess the distribution and concentration of precipitation over a specified period. By analyzing historical rainfall data, this research aims to identify trends and patterns in precipitation concentration, shedding light on potential implications for water resource management and hydrological systems. The methodology involves calculating the PCI for different temporal scales and regions, allowing for a comprehensive assessment of rainfall variability. The findings contribute to a deeper understanding of precipitation dynamics, facilitating informed decision-making in climate-sensitive sectors. There are simple rainfall measures that can be used to provide information on variability and, therefore, on the climate state. These mainly include annual and monthly rainfall data and averages, which can be used to assess rainfall seasonality, variability, and the frequency of extreme events.

Establishing resilience-targeted prediction models of rainfall for transportation infrastructures for three demonstration regions in China

Rainstorm is one of the global meteorological disasters that threaten the safety of transportation infrastructure and the connectivity of transportation system. Aiming to support the resilience assessment of transportation infrastructure in three representative regions: Sichuan–Chongqing, Yangtze River Delta, and Beijing-Tianjin-Hebei-Shandong, rainfall data over 40 years in the three regions are collected, and the temporal distribution of rainfall are analyzed. Prediction equations of rainfall are established. For the purpose of this, the probabilistic density function (PDF) is assigned to the rainfall by fitting the frequency distribution histogram. Using the assigned PDF, the rainfall data are transformed into standard normal space where regression of prediction equations is performed and the prediction accuracy is tested. The results show that: (1) The frequency of rainfall in the three regions follows a lognormal distribution based on which the prediction equations of rainfall can be established in standard normal space. The error of regression shows no remarkable dependence on self-variables, and the significance analysis indicates that the equations proposed in this paper are plausible for predicting rainfalls for the three regions. (2) The Yangtze River Delta region has a higher risk of rainstorm disaster compared to the other two regions according to the frequency of rainfall and the return period of precipitation concentration. (3) Over the period of 1980–2021, the Sichuan–Chongqing region witnessed an increase in yearly rainfall but a decrease in rainstorm disasters, whereas the other two regions experienced a consistent rise in both metrics.