Frequency Of Precipitation Events Research Articles

Evaluation and Comparison of Five Long-Term Precipitation Datasets in the Hang-Jia-Hu Plain of Eastern China

This study analyzed the applicability of five long-term precipitation datasets in the Hang-Jia-Hu Plain of eastern China based on meteorological observation data. The accuracy of each dataset at different time scales (yearly, monthly) was analyzed. Besides, their spatiotemporal distributions and differences in precipitation event frequency were also compared. The results indicate that the high-resolution (1 day, 1 km) and long-term (1961–2019) gridded dataset for temperature and precipitation across China (HRLT) exhibited the best overall performance at the annual scale, while the long-term, gauge-based gridded precipitation dataset for the Chinese mainland (CHM_PRE) performed the best at the monthly scale. The dataset of monthly precipitation with a resolution of 1 km in China from 1960 to 2020 (HHU) and the China Meteorological Forcing Dataset (CMFD) tend to overestimate the local precipitation amounts, while the other three products are characterized by an underestimation. The mean result of the five datasets indicates a slight, statistically insignificant rise in precipitation, by 4.19 mm annually, with an overall multi-year average of 1303.28 mm. The analysis of the five datasets successfully captures the spatial precipitation patterns across the Hang-Jia-Hu Plain, characterized by higher precipitation levels in the southwest and lower in the northeast. Although the interannual variability displays general consistency, there are significant discrepancies in the interannual growth rates and the spatial distribution of significance across different regions. This study can provide a reference for the accuracy of precipitation data in the fields of hydrology, meteorology, agriculture, and ecology, facilitating the analysis of uncertainties in related research.

Response of chlorophyll-a to rainfall event in the basin of the South China sea: Statistical analysis

Chlorophyll-a (Chl-a) is an essential ecological indicator, and affected by processes such as typhoons, mesoscale eddies, and Rossby waves. However, the impact of more frequent and widespread precipitation events on Chl-a seems to be overlooked. This study utilized remote sensing data and reanalysis data to investigate the response of Chl-a to 240 precipitation events in the central South China Sea from 2005 to 2019. The results indicate that precipitation events have a significant impact on Chl-a concentration. Following a precipitation event in 2019, the Chl-a concentration in the affected area increased by approximately 0.22 mg m−³ from the 3rd to the 7th day. The reasons for the increase in Chl-a concentration were the vertical mixing induced by wind stirring and the upwelling caused by wind stress curl, which transported nutrients to the euphotic zone, lowering the sea surface temperature and triggering a proliferation of phytoplankton. Additionally, dissolved nutrients in precipitation provided a nutrient source for Chl-a growth. The contributions of nutrient supply, wind speed, and wind stress curl to the increase in Chl-a concentration during precipitation events were 18%, 37%, and 45%, respectively. Precipitation events enhanced marine primary productivity, playing a crucial role in deepening our understanding of ocean-atmosphere interactions and their impact on marine ecosystem.

The observed evolution of sub‐daily to multi‐day heavy precipitation in Switzerland

AbstractHeavy precipitation is a major natural hazard in the Alps. Understanding the possible changes due to climate change is a prerequisite for effective climate adaptation and protection. In this study, we revisit the long‐term (1901–2023) evolution of daily and multi‐day heavy precipitation intensity and frequency, discuss trends for sub‐daily to multi‐day events in the recent period 1981–2023 and investigate elevation dependencies in the complex topography of Switzerland. We analyze station measurements from MeteoSwiss' dense operational network covering the whole country and a wide range of elevation levels. We find that daily maximum precipitation and the frequency of precipitation events exceeding the 99th all‐day percentile have increased since 1901 with a peak in the 1980s and decreases thereafter. For the recent period 1981–2023, positive trends in summer heavy precipitation intensity are detected for short (10‐min to 3‐h) events, but no changes are found for the frequency of these moderate extreme events. For longer (1‐ to 5‐day) events on the other hand, decreases in intensity and frequency are found, especially for the winter half‐year. We hypothesize that the opposing trends on the centennial (1901–2023) vs. decadal (1981–2023) time scales are caused by the interaction between thermodynamics, reflecting the primary influence of human‐induced climate change, and the internal variability of atmospheric dynamics. Moreover, we observe a small negative elevation dependency of the daily long‐term trends up to 2300 m above sea level. For the 1981–2023 trends, no strong elevation dependencies are found for sub‐daily events. For daily events, we find small opposing negative summer and positive winter elevation dependencies for both intensities and frequencies. The reason for these tendencies remains unclear. Our results underscore the need to further investigate the interplay between climate change, internal variability of large‐scale dynamics and elevation to better understand heavy precipitation variability in the complex Alpine terrain.

Insights and Challenges for the Development of Mosquito Control Action Thresholds Using Historical Mosquito Surveillance and Climate Datasets.

Strategies to advance action threshold development can benefit both civilian and military vector control operations. The Anastasia Mosquito Control District (AMCD) has curated an extensive record database of surveillance programs and operational control activities in St. Johns County, Florida, since 2004. A thorough exploratory data analysis was performed on historical mosquito surveillance and county-wide climate data to identify climate predictors that could be used in constructing proactive threshold models for initiating control of Aedes, Culex, and Anopheles vector mosquitoes. Species counts pulled from Centers for Disease Control and Prevention (CDC) light trap (2004-2019) and BG trap (2014-2019) collection records and climate parameters of temperature (minimum, maximum, average), rainfall, and relative humidity were used in two iterations of generalized linear models. Climate readings were incorporated into models 1) in the form of continuous measurements, or 2) for categorization into number of "hot," "wet," or "humid" days by exceedance of selected biological index threshold values. Models were validated with tests of residual error, comparison of model effects, and predictive capability on testing data from the two recent surveillance seasons 2020 and 2021. Two iterations of negative binomial regression models were constructed for 6 species groups: container Aedes (Ae. aegypti, Ae. albopictus), standing water Culex (Cx. nigripalpus, Cx. quinquefasciatus), floodwater Aedes (Ae. atlanticus, Ae. infirmatus), salt-marsh Aedes (Ae. taeniorhyncus, Ae. sollicitans), swamp water Anopheles (An. crucians), and a combined Total Mosquitoes group. Final significant climate predictors varied substantially between species groups. Validation of models with testing data displayed limited predictive abilities of both model iterations. The most significant climate predictors for floodwater Aedes, the dominant and operationally influential species group in the county, were either total precipitation or frequency of precipitation events (number of "wet" days) at two to four weeks before trap collection week. Challenges hindering the construction of threshold models were discussed. Insights gained from these models provide initial feedback for streamlining the AMCD mosquito control program and analytical recommendations for future modelling efforts of interested mosquito control programs, in addition to generalized guidance for deployed armed forces personnel with needs of mosquito control but lacking active surveillance programs.

A 131-year evidence of more extreme and higher total amount of hourly precipitation in Hong Kong

Based on the observations of hourly precipitation for 131 years from Hong Kong Observatory Headquarters, this study examined the long-term changes in the characteristics of hourly precipitation extremes in terms of intensity, total precipitation amount, duration, and frequency. Results show that the hourly precipitation extremes have significantly intensified by 29%–38% from 1885 to 2022. The 131-year observations evidence that the more extreme the hourly precipitation is (i.e. higher percentiles), the faster the increasing rate it has. Specifically, the magnitudes of hourly precipitation with the 95th, 97.5th, 99th, and 99.9th percentiles increased by rates of 0.03 mm, 0.05 mm, 0.07 mm, and 0.12 mm per year, respectively. Through the secular trend analysis, we found that only the maximum intensity of extreme precipitation events (i.e. events with maximum intensity exceeding the 95th percentiles) shows a significant increasing trend during 1885–2022, while the trends in the total precipitation amount, duration, and mean intensity are not significant. However, by comparing the percentile bin values between three sub-periods of the 131-year record, we found a significant rise over time in the total precipitation amount, mean intensity, and maximum intensity of extreme precipitation events with different intensities (i.e. 95th, 96th, 97th, 98th, and 99th percentiles), while the change in the duration is not significant. The analysis of the frequency of precipitation events shows significant increases in the proportion of extreme precipitation events during 1885–2022. The observations of 53 stations across Hong Kong from 1986 to 2022 show significant intensification and increasing frequency in the hourly precipitation extremes in most areas of Hong Kong. Meanwhile, the precipitation duration shows a decreasing tendency, which may explain the insignificant changes in the total precipitation amount. These findings provide important insights into the longer-term variations in the characteristics of hourly precipitation extremes.

Spatial and Temporal Variability Characteristics and Driving Factors of Extreme Precipitation in the Wei River Basin

As global climate change intensifies, the global atmospheric circulation process is undergoing significant changes, and the local water vapor pattern has also changed. This study takes the Wei River Basin as the research area. Firstly, an evaluation index system for extreme precipitation was established, and the time-series characteristics of the magnitude, frequency, and duration of extreme precipitation were analyzed. Statistical methods were used to analyze the non-consistency in time-series changes in extreme precipitation indicators. Using spatial heterogeneity analysis methods, the spatial variation differences in extreme precipitation in the Wei River Basin were identified. This study selected the El Niño-Southern Oscillation (ENSO) index, global land-ocean temperature index (LOTI), and land surface temperature (LST) index to quantitatively evaluate the impact of climate change on regional extreme precipitation and analyzed the correlation between temperature and extreme precipitation, identifying the key driving factors of extreme precipitation changes. The conclusions of this study are as follows: (1) The southern region of the Wei River Basin experiences more frequent and intense precipitation events, while the northern region experiences relatively few. (2) From 1981 to 2021, the intensity, frequency, and duration of precipitation events in the Wei River Basin gradually increased, with the most significant increase in extreme precipitation in the Guanzhong Plain. (3) Global climate change has an important impact on precipitation events in the Wei River Basin. The increase in the ENSO, LOTI, and LST indices may indicate an increase in the probability of drought and flood events in the Wei River Basin. The relationships between extreme precipitation and temperature present a peak structure. This conclusion is helpful to better understand the impact of climate change on extreme precipitation in the Wei River Basin and provides some support for the response to extreme meteorological events under the background of future climate change.

Evaluation of GSMaP Version 8 Precipitation Products on an Hourly Timescale over Mainland China

A thorough evaluation of the recently released Global Satellite Mapping of Precipitation (GSMaP) is critical for both end-users and algorithm developers. In this study, six products from three versions of GSMaP version 8, including real time (NOW-R and NOW-C), near real time (NRT-R and NRT-C), and post-real time (MVK-R and MVK-C), are systematically and quantitatively evaluated based on time-by-time observations from 2167 stations in mainland China. Among each version, both products with and without gauge correction are adopted to detect the gauge correction effect. Error quantification is carried out on an hourly timescale. Three common statistical indices (i.e., correlation coefficient (CC), relative bias (RB), and root mean square error (RMSE)) and three event detection capability indices (i.e., probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI)) were adopted to analyze the inversion errors in precipitation amount and precipitation event frequency across the various products. Additionally, in this study, we examine the dependence of GSMaP errors on rainfall intensity and elevation. The following main results can be concluded: (1) MVK-C exhibits the best ability to retrieve rainfall on the hourly timescale, with higher CC values (0.31 in XJ to 0.47 in SC), smaller RMSE values (0.14 mm/h in XJ to 0.99 mm/h in SC), and lower RB values (−4.78% in XJ to 16.03% in NC). (2) Among these three versions, the gauge correction procedure plays a crucial role in reducing errors, especially in the post-real-time version. After being corrected, MVK-C demonstrates an obvious CC value improvement (>0.3 on the hourly timescale) in various sub-regions, increasing the percentage of sites with CC values above 0.5 from 0.03% (MVK-R) to 28.47% (MVK-C). (3) GSMaP products generally exhibit error dependencies on precipitation intensity and elevation, particularly in areas with drastic elevation changes (such as 1200–1500 m and 3000–3300 m), where the accuracy of satellite precipitation estimates is significantly affected. (4) CC values decreased with an increasing rainfall intensity; RB and RMSE values increased with an increasing rainfall intensity. The results of this study may be helpful for algorithm developers and end-users and provide a scientific reference for different hydrological applications and disaster risk reduction.

Investigating the long-term response of plateau vegetation productivity to extreme climate: insights from a case study in Qinghai Province, China.

Over the past three decades, there has been a significant global climate change characterized by an increase in the intensity and frequency of extreme climate events. The vegetation status in Qinghai Province has undergone substantial changes, which are more pronounced than other regions in the Qinghai-Tibet Plateau. However, a clear understanding of the response characteristics of plateau vegetation to extreme climate events is currently lacking. In this study, we investigated the response of net primary productivity (NPP) to different forms of extreme climate events across regions characterized by varying levels of aridity and elevation gradients. Specifically, we observed a significant increase in NPP in relatively arid regions. Our findings indicate that, in relatively arid regions, single episodes of high-intensity precipitation have a pronounced positive effect (higher correlation) on NPP. Furthermore, in high-elevation regions (4000-6000m), both the intensity and frequency of precipitation events are crucial factors for the increase in regional NPP. However, continuous precipitation can have significant negative impacts on certain areas within relatively wet regions. Regarding temperature, a reduction in the number of frost days within a year has been shown to lead to a significant increase in NPP in arid regions. This reduction allows vegetation growth rate to increase in regions where it was limited by low temperatures. Vegetation conditions in drought-poor regions are expected to continue to improve as extreme precipitation intensifies and extreme low-temperature events decrease.

Effects of extra-extreme precipitation variability on multi-year cumulative nitrous oxide emission in a semiarid grassland

High temporal precipitation variability, characterized by less frequent but larger-magnitude precipitation events, is increasing. However, how precipitation variability affects N2O emissions and the underlying mechanisms remain unclear, especially at multiple-year scales. We conducted a 3-year manipulative experiment in which the same long-term mean growing season precipitation total was repackaged into events of inversely varying magnitude and frequency to simulate four levels of precipitation variability (extra-extreme, extreme, medium, and normal) in a semiarid grassland. Cumulative N2O emissions was the smallest under the extra-extreme precipitation variability scenario (6 very large rainfall events), 26% less than emission under the other three treatments (10, 16, and 24 rainfall events) over the three growing seasons. However, this difference was almost entirely due to three sampling events in the third year. Plant community (biomass and biodiversity), soil abiotic properties (water, dissolved organic carbon and pH), soil microbial biomass (carbon, nitrogen and the ratio), and soil functional genes (archaeal and bacterial amoA, nirS, nirK, narG, and nosZ) explained 61% of the variation in N2O emissions in response to the precipitation variability. Structural equation modelling indicated that the precipitation variability had direct positive effects, and indirect negative effects via soil abiotic properties, on soil functional genes that ultimately had positive effects on N2O emissions. The reduction in N2O emission in the extra-extreme precipitation variability scenario in the third year was due to low levels of soil water content, soil pH, aboveground biomass, and microbial biomass carbon simultaneously. Our results suggest that at the multi-year timescale, semiarid grasslands may have negative feedbacks to future precipitation regimes with higher variability.

Long short‐term memory model for predicting groundwater level in Alabama

AbstractGroundwater serves as a primary source of public‐water and agricultural supply in many areas of Alabama, in particular during drought periods. Long‐term climatic models for the southeastern United States indicate that the region will be subjected to more intense and more frequent precipitation events, with no overall change in the amount of precipitation, resulting in increased runoff and reduced aquifer recharge. Reliable prediction of groundwater levels would be beneficial to water resources decision makers and stakeholders especially for time‐sensitive decisions. This paper uses a compound application of continuous wavelet transform (CWT) analysis and long short‐term memory (LSTM) framework to address the major question with regards to groundwater level: “how long does it take for groundwater to respond to major precipitation events and what is the magnitude of the response?” CWT analysis is used to answer the “how long” part in this question, while the LSTM is used to answer the “what is the magnitude” part of the question. The insights from CWT analysis related to the short‐term and long‐term response in groundwater level were used to set the parameters of the LSTM model. The LSTM model uses daily groundwater levels, precipitation, and maximum/minimum temperatures as input data. The model was able to provide predictions within a 95% confidence interval of actual groundwater levels. The findings of this study suggest a workflow for groundwater level forecasting in the wells of Alabama given a minimum amount of easy‐to‐measure and widely available data.

Estimating US farmers' speed of climate change adaptation: the case of subsurface tile drainage

PurposeClimate change is expected to cause larger and more frequent precipitation events in key agricultural regions of the United States, damaging crops and soils. Subsurface tile drainage is an important technology for mitigating the risks of a wetter climate in crop production. In this study, the authors examine how quickly farmers adapt to increased precipitation by investing in drainage technology.Design/methodology/approachUsing farm-level data from the 2018 Agricultural Resource Management Survey (ARMS) of soybean producers, the authors construct a drainage adoption timeline based on when the operator began farming their land and when tile drainage was installed, if at all. The authors examine both the initial investment decision and the speed with which drainage is installed by adopters. A Heckman-style Poisson regression is used to model the count nature of adoption speed (measured in years taken to install tile drainage) and to correct for potential sample-selection bias.FindingsThe authors find that local precipitation is not a significant determinant of the drainage investment decision but may be highly influential in the timing of adoption among drainage users. Farms exposed to crop-damaging levels of precipitation install tile drainage faster than those with low to moderate levels of rainfall. Estimates of farm adaptation speeds are heterogeneous across farm and operator characteristics, most notably land tenure status.Originality/valueUnderstanding how US farmers adapt to extreme weather through technology adoption is key to predicting the long-term impacts of climate change on America's food system. This study extends the existing climate adaptation literature by focusing on the speed of adoption of an important and increasingly common climate-mitigating technology – subsurface tile drainage.

Precipitation and Adolescent Respiratory Health in the Northeast United States.

Rationale: With more frequent and intense precipitation events across the globe due to a changing climate, there is a need to understand the relationship between precipitation and respiratory health. Precipitation may trigger asthma exacerbations, but little is known about how precipitation affects lung function and airway inflammation in early adolescents. Objectives: To determine if short-term precipitation exposure is associated with lung function and airway inflammation in early adolescents and if ever having a diagnosis of asthma modifies associations of precipitation with lung function and airway inflammation. Methods: In a prospective prebirth cohort, Project Viva, that included 1,019 early adolescents born in the northeastern United States, we evaluated associations of 1-, 2-, 3-, and 7-day moving averages of precipitation in the preceding week and forced expiratory volume in 1 second, forced vital capacity, and fractional exhaled nitric oxide (FeNO) using linear regression. We used log-transformed FeNO with effect estimates presented as percentage change. We adjusted for maternal education and household income at enrollment; any smoking in the home in early adolescence; child sex, race/ethnicity, and ever asthma diagnosis; and age, height, weight, date, and season (as sine and cosine functions of visit date) at the early adolescent visit and moving averages for mean daily temperature (same time window as exposure). Results: In fully adjusted linear models, 3- and 7-day moving averages for precipitation were positively associated with FeNO but not lung function. Every 2-mm increase in the 7-day moving average for precipitation was associated with a 4.0% (95% confidence interval, 1.1, 6.9) higher FeNO. There was evidence of effect modification by asthma status: Precipitation was associated with lower forced vital capacity and higher FeNO among adolescents with asthma. We also found that outdoor aeroallergen sensitization (immunoglobulin E against common ragweed, oak, ryegrass, or silver birch) modified associations of precipitation with FeNO, with higher FeNO in sensitized adolescents compared with nonsensitized adolescents. The associations of precipitation with FeNO were not explained by relative humidity or air pollution exposure. Conclusions: We found that greater short-term precipitation may trigger airway inflammation in adolescents, particularly among those with asthma.

Rainfall Intensity Duration Frequency Curve Statistical Analysis and Modeling for Patna, Bihar

Using data from 41 years in Patna, India, the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981–2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall, the historical rainfall data set for Patna, India, during a 41-year period (1981–2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 hours and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval. Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall. Originality and Value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.

Rainfall Intensity Duration Frequency Curve Statistical Analysis and Modeling for Patna, Bihar

Using data from 41 years in Patna, India, the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981–2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall, the historical rainfall data set for Patna, India, during a 41-year period (1981–2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 hours and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval. Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall. Originality and Value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.

Rainfall intensity duration frequency curve statistical analysis and modeling for Patna, Bihar

Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on aweekly, seasonal, and annual basis (19812020). First, utilizing the intensity-duration-frequency (IDF) curve and therelationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period(19812020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhousegas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. Onestrategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal,normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and returntimes of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall andrecurrence interval. Findings:Based on findings, the Gumbel approach produced the highest intensity values, whereas the otherapproaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell duringthe monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall,92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that theyearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examinerainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence intervalmathematical correlations were also developed. Further regression analysis revealed that short wave irrigation,wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall. Originality and value:The results of the rainfall IDF curves can provide useful information to policymakers inmaking appropriate decisions in managing and minimizing floods in the study area

Understanding precipitation characteristics of Afghanistan at provincial scale

This study examines the pattern and trend of seasonal and annual precipitation along with extreme precipitation events in a data scare, south Asian country, Afghanistan. Seven extreme precipitation indices were considered based on the intensity, duration, and frequency of precipitation events. The study revealed that the precipitation pattern of Afghanistan is unevenly distributed at seasonal and yearly scales. Southern and southwestern provinces remain significantly dry whereas, the northern and northeastern provinces receive comparatively higher precipitation. Spring and winter seasons bring about 80% of yearly precipitation in Afghanistan. However, a notable declining precipitation trend was observed in these two seasons. An increasing trend in precipitation was observed for the summer and autumn seasons, however; these seasons are the lean periods for precipitation. A declining annual precipitation trend was also revealed in many provinces of Afghanistan. Analysis of extreme precipitation indices reveals a general drier condition in Afghanistan. Large spatial variability was found in precipitation indices. In many provinces of Afghanistan, significantly declining trends were observed in intensity-based (Rx1-day, RX5-day, SDII, and R95p) and frequency-based (R10) precipitation indices. The duration-based precipitation indices (CDD and CWD) also infer a general drier climatic condition in Afghanistan. This study will assist the agriculture and allied sectors to take well-planned adaptive measures in dealing with the changing patterns of precipitation, and additionally, facilitate future studies for Afghanistan.

Exploring the impact of landscape changes on runoff under climate change and urban development: Implications for landscape ecological engineering in the Yangmei River Basin

The increase in impervious surfaces caused by urbanization and the frequency of precipitation events resulting from climate change significantly impact the hydrological and geomorphological processes of urban watersheds. Landscape changes caused by urban development may affect surface/groundwater generation and circulation processes. In-depth research on the relationship between landscape changes and runoff is a precondition of urban watershed restoration and stormwater management. Taking the Yangmei River Basin as the study area, the storm water management model (SWMM), landscape index, correlation analysis, and the random forest importance measure were integrated to simulate the runoff process under urban development and climate change scenarios and analyze the direction and contribution of landscape composition and configuration to runoff changes. Results indicated that the runoff change rate relative to the reference period under the RCP 4.5 and RCP 8.5 scenarios gradually decreased as the return period increased. Landscape composition and configuration were significantly correlated with runoff change. Specifically, woodland could reduce runoff, while farmland and water bodies positively impacted runoff changes. The higher fragmented landscape pattern was likely to have altered the runoff, while landscape patterns of a larger size, higher concentrations, and a declining richness typically reduced runoff. The random forest importance evaluation demonstrated the significant contributions of Patch Density, Shannon's Diversity Index, and Large Path Index to runoff. By exploring the potential relationship between landscape changes and runoff, urban planners can better understand the runoff process and find feasible ecological landscape optimization schemes, thus strengthening the sustainable management of water resources and enhancing the resilience of cities to climate change.

Responses of grassland ecosystem carbon fluxes to precipitation and their environmental factors in the Badain Jaran Desert.

Studying the effects of precipitation on carbon exchange in grassland ecosystems is critical for revealing the mechanisms of the carbon cycle. In this study, the eddy covariance (EC) technique was used to monitor the carbon fluxes in a grassland ecosystem in the Badain Jaran Desert (BJD) during the growing season from 2018 to 2020. The responses of net ecosystem CO2 exchange (NEE), ecosystem respiration (Reco), and gross primary productivity (GPP) to precipitation were analysed, as well as the effects of environmental factors on carbon fluxes at half-hour and daily scales. The results showed that (1) during the growing seasons in 2019 and 2020, the grassland ecosystem in a lake basin in the BJD was a net CO2 sink, and the cumulative NEE was - 91.9 and - 79.2g C m-2, respectively. The greater the total precipitation in the growing season, the stronger the carbon sequestration capacity of a grassland ecosystem. (2) The precipitation intensity, frequency, and timing significantly affected the carbon fluxes in the ecosystem. Isolated minor precipitation events did not trigger obvious NEE, GPP, and Reco pulses. However, large precipitation events or continuous minor precipitation events over several days caused delayed high assimilation; in addition, the greater the precipitation intensity, the greater the carbon flux pulse and carbon assimilation. The timing and frequency of precipitation events had more important effects on carbon exchange than total precipitation. Droughts create a shift in grasslands, causing them to move from being a carbon sink to a carbon source. (3) Correlation analysis showed that NEE was significantly negatively correlated with photosynthetically active radiation (PAR). On the half-hour scale, Reco and GPP were significantly positively correlated with soil temperature at 5 cm deep (Ts5) and PAR, respectively. However, they were strongly correlated with air temperature (Ta), soil surface temperature (Ts) and (Ts5)on the daily scale. The correlations between daily NEE, Reco, GPP, and precipitation varied across years and seasons. Due to warming and humidification in northwest China, precipitation events will have a greater impact on the carbon sequestration capacity of the BJD. The results are vital for predicting the possible effects of climate change on the carbon cycle.

Validation of Precipitation Events in ERA5 to Gauge Observations during Warm Seasons over Eastern China

AbstractPrecipitation events should be characterized using data with high temporal resolution, such as hourly precipitation. Event-based evaluation can provide more information than the traditional equal-time-interval method by considering precipitation intermittency. This study focuses on the performance of hourly gauge observations and ERA5 products based on precipitation events in eastern China during 1979–2015. The annual frequency, duration, amount, and intensity of precipitation events are compared, and the statistics of precipitation events with different durations are also evaluated. Results show that ERA5 estimated more annual precipitation events and longer duration compared to the gauge observations, with relative deviation values of 48.75% and 49.22% at the national scale. Precipitation intensity and amount estimated by ERA5 based on precipitation events were less than those obtained from gauge observations, and the discrepancies in low-latitude regions were greater than those in high-latitude areas. The frequency of precipitation events decreased exponentially with duration for both ERA5 and gauge observations, but generally the value for the former was larger than for the latter. The statistics related to precipitation events showed smaller trends for ERA5 than for gauge observations, i.e., −0.13 h decade−1and −0.17 mm decade−1for the trends of duration and amount in ERA5, which contrasts with 0.03 h decade−1and 0.14 mm decade−1for gauge observations, respectively. These results can provide a reference for improving the parameterization scheme of the precipitation triggering mechanism in the process of model simulation.

Geo-Hydrological Events and Temporal Trends in CAPE and TCWV over the Main Cities Facing the Mediterranean Sea in the Period 1979–2018

The Mediterranean region is regarded as the meeting point between Europe, Africa and the Middle East. Due to favourable climatic conditions, many civilizations have flourished here. Approximately, about half a billion people live in the Mediterranean region, which provides a key passage for trading between Europe and Asia. Belonging to the middle latitude zone, this region experiences high meteorological variability that is mostly induced by contrasting hot and cold air masses that generally come from the west. Due to such phenomenon, this region is subject to frequent intensive precipitation events. Besides, in this complex physiographic and orographic region, human activities have contributed to enhance the geo-hydrologic risk. Further, in terms of climate change, the Mediterranean is a hot spot, probably exposing it to future damaging events. In this framework, this research focuses on the analysis of precipitation related events recorded in the EM–DAT disasters database for the period 1979–2018. An increasing trend emerges in both event records and related deaths. Then a possible linkage with two meteorological variables was investigated. Significant trends were studied for CAPE (Convective Available Potential Energy) and TCWV (Total Column Water Vapor) data, as monthly means in 100 km2 cells for 18 major cities facing the Mediterranean Sea. The Mann–Kendall trend test, Sen’s slope estimation and the Hurst exponent estimation for the investigation of persistency in time series were applied. The research provides new evidence and quantification for the increasing trend of climate related disasters at the Mediterranean scale: recorded events in 1999–2018 are about four times the ones in 1979–1998. Besides, it relates this rise with the trend of two meteorological variables associated with high intensity precipitation events, which shows a statistically significative increasing trend in many of the analysed cities facing the Mediterranean Sea.