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Advances in Meteorology
Journal Prestige (SJR): 0.48
Citation Impact (citeScore): 1
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  This is an Open Access Journal Open Access journal
ISSN (Print) 1687-9309 - ISSN (Online) 1687-9317
Published by Hindawi Homepage  [340 journals]
  • Information Entropy-Based Hybrid Models Improve the Accuracy of Reference
           Evapotranspiration Forecast

    • Abstract: Accurate forecasting of reference crop evapotranspiration (ET0) is vital for sustainable water resource management. In this study, four popularly used single models were selected to forecast ET0 values, including support vector regression, Bayesian linear regression, ridge regression, and lasso regression models, respectively. They all had advantages of low requirement of data input and good capability of data fitting. However, forecast errors inevitably existed in those forecasting models due to data noise or overfitting. In order to improve the forecast accuracy of models, hybrid models were proposed to integrate the advantages of the single models. Before the construction of hybrid models, each single model’s weight was determined based on two weight determination methods, namely, the variance reciprocal and information entropy weighting methods. To validate the accuracy of the proposed hybrid models, 1–30 d forecast data from January 2 to February 1, 2022, were used as a test set in Xinxiang, North China Plain. The results confirmed the feasibility of the information entropy-based hybrid model. In detail, the information entropy model generated the mean absolute percentage errors of 11.9% or a decrease by 48.9% compared to the single and variance reciprocal hybrid models. Moreover, the model generated a correlation coefficient of 0.90 for 1–30 d ET0 forecasting or an increase by 13.6% compared to other models. The standard deviation and the root mean square error of the information entropy model were 1.65 mm·d−1 and 0.61 mm·d−1 or had a decrease by 16.4% and 23.7%. The maximum precision and the F1 score were 0.9618 and 0.9742 for the information entropy model. It was concluded that the information entropy-based hybrid model had the best midterm (1–30 d) ET0 forecasting performance in the North China Plain.
      PubDate: Sat, 03 Feb 2024 04:35:01 +000
       
  • Frequentist and Bayesian Approaches in Modeling and Prediction of Extreme
           Rainfall Series: A Case Study from Southern Highlands Region of Tanzania

    • Abstract: This study focuses on modeling and predicting extreme rainfall based on data from the Southern Highlands region, the critical for rain-fed agriculture in Tanzania. Analyzing 31 years of annual maximum rainfall data spanning from 1990 to 2020, the Generalized Extreme Value (GEV) model proved to be the best for modeling extreme rainfall in all stations. Three estimation methods–L-moments, maximum likelihood estimation (MLE), and Bayesian Markov chain Monte Carlo (MCMC)–were employed to estimate GEV parameters and future return levels. The Bayesian MCMC approach demonstrated superior performance by incorporating noninformative priors to ensure that the prior information had minimal influence on the analysis, allowing the observed data to play a dominant role in shaping the posterior distribution. Furthermore, return levels for various future periods were estimated, providing guidance for flood protection measures and infrastructure design. Trend analysis using value, Kendall’s tau, and Sen’s slope indicated no statistically significant trends in rainfall patterns, although a weak positive trend in extreme rainfall events was observed, suggesting a gradual and modest increase over time. Overall, the study contributes valuable insights into extreme rainfall patterns and underscores the importance of L-moments in identifying the best fit distribution and Bayesian MCMC methodology for accurate parameter estimation and prediction, enabling effective measures and infrastructure planning in the region.
      PubDate: Tue, 30 Jan 2024 11:35:01 +000
       
  • Diurnal Variation Characteristics of Raindrop Size Distribution Observed
           by a Parsivel2 Disdrometer in the Ili River Valley

    • Abstract: The diurnal variation characteristics of raindrop size distribution (RSD) in the Ili River Valley are investigated in this study, using the RSD data from May to September during 2020-2021 collected by a Parsivel2 disdrometer in Zhaosu. Significant diurnal variations (02–07, 08–13, 14–19, and 20-01 local standard time (LST)) of precipitation and RSD in Zhaosu are revealed during the rainy seasons. Precipitation mainly occurs in the late afternoon and early evening. A higher concentration of small raindrops is observed in the morning, whereas more mid-size and large raindrops are observed in the afternoon. The RSD exhibits diurnal differences between different rainfall rate classes; the diurnal difference of RSD is more pronounced in the case of high rainfall rates. Stratiform precipitation can occur at any time of the day, yet convective precipitation mainly occurs during the late afternoon and early evening. The RSD of stratiform rainfall shows a similar distribution over the four time periods. For convective rainfall, the concentration of small raindrops is the highest (lowest) over 02–07 (14–19) LST, while the highest (lowest) concentration of medium and large drops is observed over 14–19 (02–07) LST. Convective rain in the Ili River Valley over 14–19 LST can be characterized as the continental convective cluster, while in the rest time of the day, it is neither in the maritime cluster nor in the continental cluster. The empirical relationships between the radar reflectivity factor and rainfall rate (Z-R) for stratiform and convective rain types are also derived. The purpose of this study is to advance our understanding of precipitation microphysics in arid mountainous region.
      PubDate: Tue, 16 Jan 2024 09:20:02 +000
       
  • Identifying the Moisture Sources in Different Seasons for Abaya-Chamo
           Basin of Southern Ethiopia Using Lagrangian Particle Dispersion Model

    • Abstract: Understanding the sources of precipitation and their impacts is crucial for basin-wide water balance research. Previous research concentrated on the sources of moisture in Ethiopia. The southern part’s moisture sources, however, were not investigated. The primary objective of this study is to trace the source of atmospheric moisture in the Abaya-Chamo sub-basin of southern Ethiopia using numerical water vapor tracers like Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. Exploring the possible regions of atmospheric vapor roots and the path of moist air initiating rainfall that reaches the basin was feasible for the year 2018–2020. The anticyclone from the Arabian High, which is positioned in the Arabian and Mediterranean seas, was the primary source of moisture supply in the study area during the Belg (March to May) season, according to the back trajectory cluster analysis results. Additionally, the Indian Ocean adds moisture resulting from Mascarene highs brought by equatorial easterlies. Furthermore, during Kiremt (June to September), air masses from the Congo basin were the potential moisture source region for the study areas in combination with air masses originating from the Mascarene highs, located in the South Indian Ocean, and the St. Helena high, centered in the subtropical southern Atlantic Ocean. This study primarily focuses on the complex dynamics of atmospheric moisture sources around Abaya-Chamo sub-basin of southern Ethiopia, offering insight into seasonal fluctuations and contributing various components. These findings contribute to basin-specific water balance research by filling gaps in the previous studies.
      PubDate: Tue, 09 Jan 2024 09:05:00 +000
       
  • Air Temperature Modeling Based on Land Surface Factors by the Cubist
           Method (Case Study of Hamoun International Wetland)

    • Abstract: The drying up of Hamoun International Wetland (HIW) and the loss of vegetation in this area have led to an increase in ambient temperature. This research examines the changes in the surface of HIW and its role in air temperature (Tair) using data on land surface temperature (LST), vegetation, wind speed, and relative humidity. The Cubist regression model (CRM) is used to simulate the effects of land surface factors (LSFs) on Tair. Four microsites with different plant cover percentages were selected for this purpose. After data collection, 75% of the data were used for modeling and 25% of the data were used for model testing. The results showed that CRM has adequate performance for estimating Tair. The assessment of the relationship between land surface temperature (LST) and Tair at 2 meter height showed that there was a high correlation coefficient between 0.86 and 0.91 in the different microsites. The results of using CRM for estimating Tair showed that this model can estimate air temperature from independent parameters of LST, wind speed, vegetation percentage, and relative humidity with a correlation coefficient of 0.98. In this model, the LST, relative humidity, and vegetation percentage were entered with values of 100%, 93%, and 83% respectively. Wind speed was not included in the model because the measurements were constant and less than 4 m/s throughout the period (no changes).
      PubDate: Mon, 08 Jan 2024 09:50:02 +000
       
  • Five-Year Analysis of Lightning Activities in Different Climatic Regions
           of Sichuan Province, China

    • Abstract: Sichuan is a high-incidence area of thunderstorm activity in China. Based on the data of the total lightning location system from 2018 to 2022, the total lightning, cloud-to-ground (CG) lightning, and intracloud (IC) lightning activity regularity for the Sichuan province (SC) and its three climate subregions: Sichuan Basin (SB), Panxi district (PD), and West Sichuan Plateau (WSP) are analyzed, and the influences of different climate and topography conditions on lightning activities are also discussed. The results show that (1) for the whole province, the annual mean value of total lightning is about 850 thousand. The SB has the most lightning occurrences, and the WSP has the largest IC and +CG proportion. The southwest of PD, the north-center of PD, and the southeast of SB are the three high-value centers of lightning density. (2) For SB, the better thermodynamic and moisture conditions account for the most lightning occurrences. For PD, the lightning distribution is attributed to the joint effect of specific meteorological conditions and mountainous topography. For WSP, the convections are weak and shallow, which lead to high IC and +CG proportion. (3) The IC lightning mainly occurs below 12 km, and the IC height is much lower on WSP. The spatial and seasonal variation of IC height corresponds well to the cloud base height (CBH) and cloud top height (CTH). (4) The seasonal lightning frequency distribution in the three regions is similar, but the diurnal variation is quite different. The lightning activity mainly occurs between 1400 and 2200 LT on WSP but shows obvious nocturnal in SB. (5) Most CG intensity concentrates in the range below 50 kA, and IC concentrates in the range below 75 kA.
      PubDate: Sat, 16 Dec 2023 08:20:00 +000
       
  • Perspective on the Era of Global Boiling: A Future beyond Global Warming

    • Abstract: As the unpredictable nature of the Earth’s climate persists, the scholarly attention dedicated to climate research has undergone a notable transition, shifting its emphasis from the conventional notion of global warming to a greater disconcerting occurrence commonly referred to as “global boiling.” The present article endeavors to elucidate the scientific evidence that posits a discernible alteration in climate patterns, specifically towards an exacerbation of extreme heat events. Furthermore, this study aims to delve into the various factors that are believed to be instrumental in precipitating this noteworthy phenomenon. Furthermore, we engage in a comprehensive examination of the potential ramifications on ecological systems, human communities, and the imperative necessity for proactive measures aimed at both mitigating and adapting to these challenges. This paper endeavors to elucidate the potential issues presented by the period of global boiling through a thorough examination of existing research and data. Furthermore, it seeks to underscore the significance of concerted efforts to effectively tackle this pressing matter.
      PubDate: Sat, 09 Dec 2023 07:50:01 +000
       
  • Climatic Characteristics of Heavy Snowfall and the Water Vapor Transport
           Characteristics in Typical Snowfall Events in Hunan Province of China

    • Abstract: Due to the unique topography and geographical location, severe snowfall is the main disastrous weather in winter in the Hunan Province of China. Based on the daily precipitation data in Hunan Province from 1961 to 2021, the regional heavy snowfall processes are classified by using the synoptic diagnostic method. In addition, the water vapor transport characteristics of typical heavy snowfall processes are analyzed by the hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) air mass backward trajectory model. Then, the responses of the differences in water vapor transport to heavy snowfall under different weather situations are discussed. The results show that the spatial distribution of climatic mean heavy snowfall days in Hunan Province is extremely uneven, and the heavy snowfall days decrease from north to south, with the most in the Dongting Lake area and the least in the Nanling Mountains. In the past decades, snowstorms mainly occur in local areas, and there are fewer widespread snowstorms. The frequency of heavy snowfall days generally shows a decreasing trend, with three peaks all appearing before 1990. After the 2010s, the number of days and stations of heavy snowfall decreased noticeably, and so did the number of regional heavy snowfall processes. This result indicates that global warming has remarkable effects on the snowstorm events in Hunan Province. Heavy snowfall mainly occurs from December to February, and peaks from mid-January to early February. Over the past 61 years, more than 50% of heavy snowstorm events occurred after 2000. According to the main weather systems affecting regional heavy snowfall processes, these weather processes in Hunan Province can be classified into three categories: southern branch trough (SBT) type, blocking high collapse (BHC) type, and stepped trough type. Among them, the SBT type accounts for more than 60% of the heavy snowfall events in Hunan. In terms of the SBT type and the stepped trough type, the water vapor from the high-latitude inland and low-latitude sea surface accounts for a comparable proportion, each accounting for nearly 50%. For the SBT type, the proportion of the water vapor from warm-humid airflows is slightly higher than that from cold-humid airflows. However, in terms of the stepped trough type, the water vapor transported by cold-humid airflows from the north contributes more than that by warm-humid airflows. For the BHC type, the specific humidity and the water vapor from the high-latitude inland contribute 70% of heavy snowfall processes. In addition, the contribution of the two southwesterly water vapor channels to heavy snowfall processes is small. The water vapor sources differ remarkably for different heavy snowfall types, but all of them are dominated by the water vapor transport in the middle and lower troposphere, which is the main reason why the formation of snowfall areas under different weather types is obviously different.
      PubDate: Thu, 07 Dec 2023 11:05:01 +000
       
  • Comparison of the Visibility Grading Forecast Method Based on
           Meteorological Factors and Environmental Factors

    • Abstract: The main visibility forecast factors were identified with the support of data from routine meteorological observations from the Mianyang Airport and the Mianyang Environmental Monitoring Station from 2015 to 2018, and a visibility grading forecast model was established and tested by dint of the multiple linear regression and the KNN algorithm based on big data mining technology, and the variation characteristics of visibility in winter at the Mianyang Airport were studied. The results showed that (1) in addition to having a significant positive correlation with wind speed, the visibility in winter at the Mianyang Airport has a significant negative correlation with relative humidity, dew point temperature, AQI, PM2.5 concentration, PM10 concentration, and CO, and it has the strongest correlation with relative humidity, and the correlation coefficient is −0.76. (2) The multivariate linear regression model and the KNN model were adopted for grading forecasting experiments on visibility, and the results revealed that both models could be used for visibility grading forecasts. The multiple regression model secures an accuracy of over 70% for forecasts of level 1–5 visibility. In terms of the KNN model, the forecast accuracy is the best when K = 3 or K = 5, notably for level-2, level-4, and level-5 visibility. The forecast accuracy rate is 100% for level-2 visibility, but the forecast for level-1 visibility is poor. (3) The minimum value of the average daily visibility of the Mianyang Airport in winter appeared at 09 : 00 and the maximum value appeared at 17 : 00. The level-1 visibility occurred and developed before 09 : 00 and faded and vanished between 08 : 00 and 15 : 00.
      PubDate: Tue, 21 Nov 2023 09:35:01 +000
       
  • The Synergic Effects of Climate Variability on Rainfall Distribution over
           Hare Catchment of Ethiopia

    • Abstract: Climate analysis at relevant time scales is important for water resources management, agricultural planning, flood risk assessment, ecological modeling, and climate change adaptation. This study analyzes the spatiotemporal variability of climate on rainfall distribution for the Hare catchment of Ethiopia. Numerous hydroclimatic variables and scenarios were developed to assess the pattern of rainfall during different seasons. The average annual precipitation varies between −37.3%, +33.1%, and −38.2%, +61.2%, for RCP 4.5 and RCP 8.5, respectively. The anticipated declines in mean seasonal rainfall changes for the Bega and Belg seasons range from −69.6% to 88.4% and from −60.6% to 15.2% for RCP 4.5 and RCP 8.5, respectively. Climate models predict that the average periodic precipitation considered for the Kiremt season will vary from −12.1% to 1.33%. The Belg, Kiremt, and Bega seasons will likely see a 28.2%, 12.2%, and 22.6% drop in mean seasonal precipitation, respectively. The decrease in stream flow accompanied by the aforementioned climate scenarios (RCP 4.5 and RCP 8.5) can be as high as 19.6% and 6.7%, respectively. Also, the amount of discharge will reduce in the near future because of a substantial reduction in rainfall and a rise in evapotranspiration in the catchment. This decline in stream flow has its own effect on the future availability of water resources. The research finding is vital to environmental protection authority, decision makers, and scientific community to undertake climate change adaption techniques for rain scare areas. A program combined with multi-RCMs to evaluate climate change effects on hydrometeorology generated a novel approach to this research with appropriate adaptation mechanisms.
      PubDate: Mon, 06 Nov 2023 04:20:00 +000
       
  • Study on O3 Variations in Nanjing and the Surrounding Source Analysis

    • Abstract: To understand the transport patterns and major sources of ozone (O3) in Nanjing, this study carried out the 48-hour backward trajectories of air masses in Nanjing from March 2021 to March 2022, based on the HYSPLIT backward trajectory model driven by GDAS global reanalysis data. The primary transmission routes and putative source locations of O3 pollution in Nanjing were determined through the integration of trajectory clustering analysis, potential source contribution function (PSCF), and concentration-weighted trajectory (CWT) analysis with meteorological data and O3 concentration data. The results showed that the high O3 concentrations and exceedance rates in Nanjing were in late spring and early summer, with the highest in June. The diurnal variation of O3 concentrations in all seasons exhibited a single peak with a maximum from 13:00 to 16:00. The southeasterly flow passing through Zhenjiang, Changzhou, Wuxi, Suzhou, and Shanghai dominated the O3 pollution in Nanjing. The PSCF and CWT presented a high consistency of O3 potential sources in Nanjing. Zhenjiang, Ma’anshan, Changzhou, Wuxi, Suzhou, and Huzhou were identified as the main potential source regions of O3 pollution in Nanjing. This study provides accurate theoretical references for regional joint prevention and control of O3 pollution in Nanjing.
      PubDate: Fri, 20 Oct 2023 08:20:01 +000
       
  • Objective Identification Method of Cold-Front Precipitation in Winter Half
           Years over East Asia

    • Abstract: Cold front is an important weather system that produces precipitation in East Asia. Under the background of global warming, extreme precipitation caused by cold fronts presents a significant increasing trend. Hence, it is very important to quantify the cold-front precipitation that may cause great damages. In this study, an objective identification method is proposed for cold-front precipitation, which can objectively identify the precipitation area affected by cold fronts. Then, the climatological characteristics and trends of cold-front precipitation over East Asia in the winter half years from 1989 to 2018 are investigated by using the ERA-5 reanalysis dataset. Based on the dataset of cold fronts and frontal zones, this method automatically distinguishes the precipitation area affected by cold fronts to quantitatively estimate cold-front precipitation. The results show that this identification method can well describe cold-front activities and associated precipitation during an extreme cold wave event that occurred in southern China in January 2016. In the past 30 years, cold fronts have significantly contributed to the precipitation in East Asia in winter half years. The areas with the maximum cold-front precipitation and maximum contribution rate of cold-front precipitation to total precipitation are located in the North Pacific storm track, cold-front precipitation exceeds 700 mm, and the contribution of cold-front precipitation to total precipitation exceeds 60%. In addition, the contribution rates of cold-front precipitation are also relatively large in the midlatitudes of East Asia, especially in North China and Northeast China, where cold-front precipitation accounts for 50%–60% of total precipitation. In East Asia, the total precipitation in autumn is greater than that in winter; however, cold-front precipitation and its contribution rate in winter are significantly more and larger than those in autumn. As the cold-frontal activity is more frequent and intense in winter, the rainfall in winter depends more on cold fronts. In the past 30 years, the trends of cold-front precipitation and total precipitation are consistent in most parts of East Asia, indicating that cold-front precipitation makes a great contribution to the trend of total precipitation in winter half years.
      PubDate: Thu, 19 Oct 2023 08:20:01 +000
       
  • Long-Term Rainfall Variability and Trends for Climate Risk Management in
           the Summer Monsoon Region of Southeast Asia

    • Abstract: This study presents an analysis of long-term rainfall variability and trends in the summer monsoon region of Southeast Asia, encompassing Lao People’s Democratic Republic (Lao PDR), Thailand, Vietnam, Cambodia, and Myanmar, as well as their respective river basins. Utilizing Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS) having a spatial resolution of 5 km spanning from 1981 to 2021, rainfall variability and trends were examined. Data preprocessing and geospatial analysis were conducted using R-Studio and ArcGIS software. The Mann–Kendall (MK) test and Sen’s slope estimator were employed for annual and seasonal rainfall trend analysis. Myanmar exhibited the highest average annual rainfall of 2137 mm during the study period, while Thailand had the lowest (1641 mm). Over the past four decades, the Peninsula Malaysian Basin experienced the highest average annual rainfall (2691 mm), whereas the Chao Praya Basin recorded the lowest (1311 mm). Increasing trends in rainfall were observed across all five countries and nine major river basins. Vietnam displayed the highest annual rainfall trend of 5.63 mm/year, while Lao PDR exhibited the lowest trend (3.16 mm/year). Among the river basins, the Chao Phraya Basin demonstrated the maximum annual rainfall trend (11.21 mm/year), while the Peninsula Malaysia Basin had the minimum trend (1.21 mm/year). These findings could significantly contribute to climate change monitoring in the region and can aid policymakers in sectors such as agriculture, urban planning, and disaster management.
      PubDate: Mon, 16 Oct 2023 11:05:01 +000
       
  • Retracted: Deep Learning-Based English-Chinese Translation Research

    • PubDate: Wed, 11 Oct 2023 07:14:32 +000
       
  • Spatiotemporal Variability and Trends in Rainfall and Temperature in South
           Ethiopia: Implications for Climate Change Adaptations in Rural Communities
           

    • Abstract: Climate change is an environmental challenge for rural communities that rely heavily on rainwater-based agriculture. The main goal of this study is to investigate spatiotemporal variability and trends in rainfall and temperature in southern Ethiopia. Extreme temperature and rainfall indices were computed using the ClimPACT2 software. The detection and quantification of trends in rainfall and temperature extremes were analyzed using a nonparametric modified Mann–Kendall (MMK) test and Sen’s slope estimator. Results indicated that the mean annual rainfall has a declining trend at Boditi School and Mayokote stations with a statistically significant amount at magnitudes of 0.02 mm and 0.04 mm, respectively. The highest average monthly rainfall in the catchment was observed in the months of April, May, June, July, and August up to maximum rainfall of 117.50 mm, 177.43 mm, and 228.84 mm in Bilate Tena, Boditi, and Mayakote stations, respectively. On a seasonal scale, rainfall in Bilate Tena station was highly variable in all months, ranging from 49.54% to 126.92%, and three seasons except spring which showed moderate variation at 40.65%. In addition, the three locations over the catchment exhibited varied drought signs such as severe (1.28 
      PubDate: Mon, 25 Sep 2023 08:05:01 +000
       
  • Spatial and Temporal Analysis of Rainfall Variability and Trends for
           Improved Climate Risk Management in Kayonza District, Eastern Rwanda

    • Abstract: The variability, intensity, and distribution of rainfall have drawn a lot of interest globally and especially in nations where rainfed agriculture is the norm. This article uses rainfall data from the Rwanda Meteorology Agency for the years 1981 to 2021 to delineate and analyze rainfall variability and trends in the Kayonza District. The time series were grouped using the K-means clustering technique based on computed Euclidean distance, the total within-cluster sum of squares, and the elbow plot technique to determine the optimal number of clusters. The coefficient of variation measures was employed to analyze rainfall variability, while Sen’s slope and the Mann–Kendall (MK) test were used, respectively, to find trends and changes in magnitude. The results indicated four near homogeneous zones named region one to four. The dry seasons indicated higher variability compared to rainy seasons and annual rainfall total with a variability of 128–142% over the southeastern part during June to August (JJA) season, while a variability of 16–48% was observed over most of the district during both annual and rainy seasons. It was further noted that the areas in the central part of the Kayonza District indicated a significant increasing trend at a significance level of 95% and above during January to February (JF), September to December (SOND), and on annual basis, while March to May (MAM) and JJA season exhibited no significant trend. The findings of this study are essential for creating adequate mitigation strategies to lessen climate change’s effects on agriculture as well as other socioeconomic sectors.
      PubDate: Tue, 19 Sep 2023 05:50:01 +000
       
  • Retracted: Evaluation Model of Eco-Environmental Economic Benefit Based on
           the Fuzzy Algorithm

    • PubDate: Thu, 14 Sep 2023 07:10:06 +000
       
  • Retracted: Interaction Design of Educational App Based on Collaborative
           Filtering Recommendation

    • PubDate: Thu, 14 Sep 2023 07:10:05 +000
       
  • Retracted: Research on the Design of Public Space in Urban Renewal Based
           on Multicriteria Cluster Decision-Making

    • PubDate: Thu, 14 Sep 2023 07:10:03 +000
       
  • Retracted: Detection Algorithm of Tennis Serve Mistakes Based on Feature
           Point Trajectory

    • PubDate: Thu, 14 Sep 2023 07:10:01 +000
       
  • Retracted: Nitrogen Inversion Model in a Wetland Environment Based on the
           Canopy Reflectance of Emergent Plants

    • PubDate: Thu, 14 Sep 2023 07:10:00 +000
       
  • Retracted: Research on Tourism Resource Evaluation Based on Artificial
           Intelligence Neural Network Model

    • PubDate: Thu, 14 Sep 2023 07:09:58 +000
       
  • Retracted: Automatic Capture Processing Method of Basketball Shooting
           Trajectory Based on Background Elimination Technology

    • PubDate: Thu, 14 Sep 2023 07:09:56 +000
       
  • Assessing the Return Periods and Hydroclimatic Parameters for Rainwater
           Drainage in the Coastal City of Cotonou in Benin under Climate Variability
           

    • Abstract: Cotonou, the economic capital of Benin, is suffering from the impacts of climate change, particularly evident through recurrent floods. To effectively manage these floods and address this issue, it is crucial to have a deep understanding of return periods and hydroclimatic parameters (such as intensity-duration-frequency (IDF) curves and related coefficients), which are essential for designing stormwater drainage structures. Determining return periods and these parameters requires statistical analysis of extreme events, and this analysis needs to be regularly updated in response to climate change. The objective of this study was to determine the necessary return periods and hydroclimatic parameters to improve stormwater drainage systems in the city and its surroundings areas. This required annual maximum precipitation series of 1, 2, 3, 6, 12, and 24 h for 20 years length (1999–2018) as well as flood record data. The intensity series, derived by dividing the amount of rainfall by its duration, was adjusted using Gumbel’s law. IDF curves were constructed based on Montana and Talbot models, and their coefficients were determined according to the corresponding return periods. In 2010, which witnessed devastating floods in the country, the return period for the most intense rainfall events was 40 years, followed by 2013 with a return period of 13.4 years. Consequently, the commonly used 10-year return period for the design of stormwater drainage structures in Cotonou is insufficient. The Talbot model produced the lowest mean square errors for each quantile series and coefficients of determination closest to one, indicating that the parameters obtained from this model are well suited for designing hydraulic structures in Cotonou. The hydroclimatic parameters presented in this study will contribute to the improved design of hydraulic structures in the city of Cotonou.
      PubDate: Sat, 09 Sep 2023 06:35:01 +000
       
  • Monitoring and Control of Particulate Matter in Urban Area in
           Douala-Cameroon Town

    • Abstract: This study focused on the content of fine particle air pollution in the city of Douala. Several studies have analyzed pollution problems due to road traffic in Douala, Cameroon. Particle concentration levels are higher in heavy traffic than in light traffic. The population’s exposure to air pollution in cities is higher near roads. Several studies have analyzed pollution problems due to road traffic in Douala, Cameroon. In this city, the traffic density at the intersections is indeed higher. Thus, the question is as follows: Are these traffic areas hotspots of increased PM exposure levels' To determine it, four particle size fractions (PM10, PM2.5, PM5, and PM1) were collected using an “OC300 Gas and Dust Particle Laser Detector” for three months at different traffic locations (roundabouts or/and crossroads). Statistical analysis of the data shows very high concentrations at most measurement sites. PM concentrations at the different measurement sites are around 35.69-68.08 µg m−3 for PM1, 50.72-99.13 µg m−3 for PM2.5, 54.11-111.22 µg m−3 for PM5, and 57.97-119.25 µg m−3 for PM10. Exceedances of WHO daily guidelines for PM2.5 (45 µg m−3) and PM10 (15 µg m−3) were found during the measurement campaign, indicating that crossroads are the pollution hotspots in urban areas. Occupation of the roadsides for various economic activities (painting, restaurants, donut shops, etc.) is common in Cameroon, increasing health risks for people working around the roadside. Thus, crossroad locations are areas where the level of exposure to PMx is the highest on road traffics.
      PubDate: Fri, 08 Sep 2023 05:05:01 +000
       
  • Copula-Based Joint Flood Frequency Analysis: The Case of Guder River,
           Upper Blue Nile Basin, Ethiopia

    • Abstract: The univariate analysis of hydrological extremes is a well-established practice in developing countries such as Ethiopia. However, for the design of hydrological and hydraulic systems, it is essential to have a thorough understanding of flood event characteristics, including volumes, peaks, time of occurrence, and duration. This study utilizes copula functions for bivariate modeling of flood peak and volume characteristics, examining the performance of four Archimedean copulas in the Guder basin located in Ethiopia from 1987 to 2017. Flood peak and volume were extracted using the theory of runs for analysis of their joint characteristics with the truncation level chosen as equal to the lowest annual maximum event. Univariate distributions with the best fitness on both variables were determined, and results showed that gamma and GEV-fitted flood peaks and lognormal-fitted flood volumes are the most suitable. Four Archimedean copulas were evaluated, and the Gumbel-Hougaard copula was found to be the best fit for the data based on graphical and measurable tests. Bivariate probability and return period were computed in “AND” and “OR” states. The joint return period for flood peak (97.49 m3/s) and volume (77.35 m3/s) was found to be 15 years in the “AND” state and approximately 4 years in the “OR” state. The study also evaluates univariate and conditional return periods, comparing them with the primary one. The copula method was an effective method for distributing marginal variables, highlighting its potential as a valuable tool in flood management.
      PubDate: Thu, 07 Sep 2023 07:05:01 +000
       
  • Modes of Atmospheric Energetics Based on HadGEM3-GC3.1-LL Simulations in
           the Framework of CMIP6

    • Abstract: In this study, the focus is on investigating how different climate scenarios, as they have been adopted in Phase 6 of the Coupled Model Intercomparison Project (CMIP6), can lead to different regimes in the energetics components in Lorenz’s energy cycle, hence impacting the “working rate” of the climate system, which is considered as a “heat engine.” The four energy forms on which this investigation is based on are the zonal and eddy components of the available potential and kinetic energies. The permissible correspondingly considered transformations between these forms of energy are also studied. Generation of available potential energy and dissipation of kinetic energy complete the Lorenz energy cycle that is adopted here. In the CMIP6 approach, the results of different climate change analyses were collected in a matrix defined by two dimensions: climate exposure as characterized by a radiative forcing or temperature level and socioeconomic development as classified by the pathways, known as Shared Socioeconomic Pathways (SSPs). The basis of the calculations in this study is the climatic projection produced by the HadGEM3-GC3.1-LL climatic model in the period from 2015 to 2100. In this respect, the results are presented in terms of time projections of the energetics components under different SSPs. The results have shown that the different SSPs yield diverse energetics regimes, consequently impacting on Lorenz energy cycle and, hence, a “working rate” of the climate system based on the components of this cycle. In this respect, Lorenz energy cycle projections are presented, under different SSPs. The results are also contrasted to the calculations for the historical period 1929 to 2014 as this is simulated by the same climatic model.
      PubDate: Sat, 02 Sep 2023 05:20:01 +000
       
  • Assessing Variability and Trends of Rainfall and Temperature for the
           District of Musanze in Rwanda

    • Abstract: Variability in rainfall and temperature results in different impacts on agricultural practices. Assessesment of variability and trend of rainfall and temperature for the district of Musanze in Rwanda was conducted using six meteorological stations for a period of 37 years, ranging from 1981 to 2018, and data were obtained from Rwanda Meteorology Agency. Musanze district is located in highland areas of Rwanda, understanding the variability and trend in rainfall and temperature is paramount to increase the uptake of climate information and support strategic orientation. The Mann–Kendall nonparametric test and modified Mann–Kendall were used to assess the trend in rainfall and temperature, whereas Sen’s slope estimator was used to assess the magnitude of change. The results from both methods showed much similarity and consistency. The assessment of variability and trend in rainfall and temperature in Musanza district indicated that increasing temperature and decreasing rainfall trends gave an indication of changes in variability and trend in rainfall and temperature. The annual pattern revealed a substantial downward tendency of −25.7% for Nyange, the only station with constant decreasing trend over all seasons, DJF, −61.4%, SON, −12.2%, JJA, −40.3%, and MAM, −4.35. Temperature analysis for both maximum and minimum indicated increasing trend which was signal for constant warming up in the area. The results from coefficient of variation indicated a high disparity in rainfall variation from June to August which ranged between 51 and 74%, and other seasons changes were moderate.
      PubDate: Fri, 25 Aug 2023 11:50:00 +000
       
  • Retracted: Study on Meteorological Disaster Monitoring of Field Fruit
           Industry by Remote Sensing Data

    • PubDate: Wed, 23 Aug 2023 07:07:34 +000
       
  • Retracted: Research on the Optimization of Agricultural Industry Structure
           Based on Genetic Algorithm

    • PubDate: Wed, 23 Aug 2023 07:07:33 +000
       
 
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School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
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Tel: +00 44 (0)131 4513762
 


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