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- 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
- Retracted: A Personalized Recommendation Method for Short Drama Videos
Based on External Index Features
PubDate: Wed, 23 Aug 2023 07:07:31 +000
- Application of K-Nearest Neighbor Classification for Static Webcams
Visibility Observation
Abstract: Visibility observations and accurate forecasts are essential in meteorology, requiring a dense network of observation stations. This paper investigates image processing techniques for object detection and visibility determination using static cameras. It proposes a comprehensive method that includes image preprocessing, landmark identification, and visibility estimation, mirroring the observation process of professional meteorological observers. This study validates the visibility observation procedure using the k-nearest neighbors machine learning method across six locations, including four in the Czech Republic, one in the USA, and one in Germany. By comparing our results with professional observations, the paper demonstrates the suitability of the proposed method for operational application, particularly in foggy and low visibility conditions. This versatile method holds potential for adoption by meteorological services worldwide.
PubDate: Mon, 21 Aug 2023 11:05:00 +000
- Spatiotemporal Variability of Hot Days in Association with the Large-Scale
Atmospheric Drivers over Vietnam
Abstract: The severe heatwaves and hot spells in Vietnam were observed more frequently in intensity and duration due to global warming and climate change impacts. The hot days and extreme summer events make the weather harsh and significantly affect human health and the environment. This study presents the spatiotemporal distribution of the number of hot days (NHDs) in Vietnam. The variability of NHD in seven climate subregions is also examined in association with the large-scale drivers. The European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis version 5 (ERA5) data for the period 1981–2020 are used. Principal component analysis is also applied to the observed monthly NHD to obtain spatial patterns and time series. The results show that the NHD in the Highland and South subregions from March to May is linked with the subtropical high associated with decreased 500hPa-level vertical velocity (VV500). From May to June, the North and Central subregions seem related to deepening the Asiatic low and enhancement of southwest flows across the Indochina Peninsula. Finally, both increased southwest flows and decreased VV500 can partly contribute to the intensification of NHD in the North and Central subregions during July and August. The long trends of NHD are also examined. The results reveal that the increasing trends in NHD occur in most subregions, except for the Central Highland, and changing trends of NHD in June greatly contribute to the annual trend of NHD. Finally, the examinations with the El Niño-Southern Oscillation events show that NHD is significantly higher in El Niño events than in La Niña events in March and April for the Northwest, South Central, Central Highlands, and South, in May and June for all subregions, and in July and August for only the Red River Delta subregion. This suggests that ENSO can provide the potential for improving seasonal climate forecasts and mitigating natural disaster risks for the community.
PubDate: Thu, 17 Aug 2023 04:50:00 +000
- Missed Approach, a Safety-Critical Go-Around Procedure in Aviation:
Prediction Based on Machine Learning-Ensemble Imbalance Learning
Abstract: The final approach phase of an aircraft accounts for nearly half of all aviation incidents worldwide due to low-level wind shear, heavy downpours, runway excursions, and unsteady approaches. Adopting the missed approach (MAP) procedures may prevent a risky landing, which is usually executed in those situations, but it is safety-critical and a rare occurrence. This study employed machine learning-ensemble imbalance learning to predict MAPs under low-level wind shear conditions based on environmental and situational parameters. The models were developed using the 2017–2021 Hong Kong International Airport (HKIA) Pilot Reports (PIREPs). Initially, imbalance data were applied to machine learning models such as the random forest (RF), light gradient boosting machine (LGBM), and extreme gradient boosting (XGBoost), but these were unable to accurately predict the occurrence of MAPs. Then, these models were used as base estimators for ensemble imbalance learning methods, including the self-paced ensemble (SPE) framework, the balance cascade model, and the easy ensemble model. The SPE framework utilizing XGboost as the base estimator performed better than other frameworks in terms of recall, F1-score, balanced accuracy, and geometric mean. Afterwards, SHAP was utilized to interpret the SPE framework with XGboost as the base estimator. Results showed that low-level wind shear magnitude, runway orientation, and vertical location of low-level wind shear contributed most to MAPs. Runways 07C and 07R had the most MAPs. Most MAPs were initiated when low-level wind shear was within 500 feet of the ground. Strong tailwind triggered MAPs more than headwind. For aviation safety researchers and airport authorities, the framework proposed in this study is a valuable tool.
PubDate: Wed, 19 Jul 2023 07:17:01 +000
- Estimation of the Total Amount of Enhanced Rainfall for a Cloud Seeding
Experiment: Case Studies of Preventing Forest Fire, Drought, and Dust
Abstract: In this study, a method for verifying the effect of cloud seeding in the case of a mixture of natural and artificial rainfall bands was proposed, and its applicability to each experimental case was evaluated. Water resources that could be secured through cloud seeding were also quantified for the experiments on forest fire prevention, drought mitigation, and dust reduction in 2020. Data on numerical simulations, radar-derived rainfall, rain gauge-derived rainfall, and weather conditions were applied. Areas with seeding and nonseeding effects were classified according to the numerical simulation results and wind system, and enhanced rainfall was determined by comparing the changes in rainfall between the two areas. The amount of water resources was determined by considering the area of the seeding effect and rainfall density. As a result, 1.74 mm (4.75 million tons) of rainfall increased from the experiment on forest fire prevention, 0.84 mm (1.30 million tons) on drought mitigation, and 2.78 mm (24.44 million tons) on dust reduction. Thus, an average rainfall of 1.0 mm could be achieved through the experiment. These results helped verify the pure seeding effect and achieve the experimental purpose.
PubDate: Fri, 14 Jul 2023 11:50:01 +000
- Modelling the Impacts of the Changing Climate on Streamflow in Didesa
Catchment, Abay Basin, Ethiopia
Abstract: The Didesa catchment, which is the second largest subbasin of the Abay basin, significantly contributes to the Blue Nile’s outflow. Understanding the dynamics of water availability under the changing climate in such a basin assists in the proper planning of land use and other development activities. This study presents changes in climatic elements such as rainfall, temperature, and evapotranspiration using observation data and regional climate models (RCMs) under two representative concentration pathways (RCPs) for three future periods. We use a calibrated hydrological model to further assess climate change’s effects on streamflow. We select three RCMs and their ensemble’s mean by evaluating their performance with respect to observations. We apply the modified Mann–Kendall test to detect trends in each dataset. The result shows that annual mean maximum and minimum temperatures increase in the catchment for the 2021–2040, 2041–2070, and 2071–2100 periods as compared to baseline (1989–2018) under both RCP4.5 and RCP8.5 scenarios. Annual mean maximum temperature and potential evapotranspiration experienced a significant decreasing trend during the year from 1989 to 2018. Furthermore, there was an increasing trend in annual rainfall from 1989 to 2018, which could be related to the cooling of sea surface temperature over the equatorial Pacific. We detect an increasing trend in temperature in both scenarios and all periods; however, no clear trend pattern is found in rainfall. The result from hydrological model simulations reveals that the mean monthly streamflow slightly increases in the winter season while it decreases during the main rainy season. Further study of detailed weather systems, which affect the subbasin’s climate, is recommended.
PubDate: Wed, 12 Jul 2023 13:05:00 +000
- Interdecadal Variation of Spring Extreme High-Temperature Events in the
Western Tianshan Mountains and Its Relationship with the Tropical SST
Abstract: This study performed an observational analysis to examine the interdecadal variation in the frequency of extreme high-temperature events (EHEs) during spring over the western Tianshan mountain, China, which were characterized by relatively fewer (more) EHEs during 1983–1996 (2000–2015). A composite analysis indicated that the interdecadal increase in EHEs is closely related to a deep dynamic anomalous Iranian high. Under the control of this high system, the water vapor content decreased over the western Tianshan mountains, and atmospheric circulation was dominated by a descending motion. Both were attributed to the decreased cloud cover, inducing a cloud-forced net solar radiation increase. The short-wave radiation flux and sensible heat flux reaching the surface increased, and the net surface heat flux increased cumulatively, which was conducive to the surface temperature increase and EHE occurrence. The anomalous Iranian high responsible for ECEs occurrence was related to the air-sea interaction over the Atlantic and Indo-Pacific. The latitudinal sea surface temperature (SST) difference between the tropical western Pacific and the western Indian Ocean directly strengthens the Walker circulation and thus enhances the Iranian high. In addition, the anomalous Iranian high was affected by the atmospheric wave trains at middle latitude, which was triggered by the warm anomaly of the Atlantic SST.
PubDate: Tue, 20 Jun 2023 06:35:01 +000
- Analysis of Climate Variability and Trends for Climate-Resilient Maize
Farming System in Major Agroecology Zones of Ethiopia
Abstract: Maize is one of the most important cereal food crops, and it can be grown all year in various agroecological zones. However, its vegetative growth and yield are susceptible to rainfall and temperature variability. As a result, the analysis of rainfall and temperature variability and trend was urgently needed in maize-growing agroecology zones to restructure the production system. The aim of the study was to examine rainfall and temperature variability and trends for developing a climate-resilient maize farming system in major agroecology zones in northwest Ethiopia. The study was implemented in low productive agroecology zones (LPZ), medium productive agroecology zones (MPZ), and high productive agroecology zones (HPZ) of northwest Ethiopia using daily time series climate data during the period 1987–2018. The coefficient of variation (CV), precipitation concentration index (PCI), rainfall anomaly index (RAI), and standardized precipitation (SPI) were applied to examine rainfall variability. Mann–Kendall’s and Sen’s slope estimator trend tests were used to detecting the statistical significance of changes in rainfall and temperature. Statistically significant increasing trends for annual maximum and minimum temperatures were recorded for all maize-producing agroecology zones. The mean annual temperature has exhibited a significant warming trend of 0.12 to 0.54°C per decade. The average annual rainfall has decreased by 38 to 67 mm per decade in all maize agroecology zones. Our research also showed that droughts now happen every one to three years; even consecutive droughts were seen in 2009, 2010, and 2011. For this reason, it could be required to develop a system of climate-resilient maize farming to address the issues of both global warming and the sub-Saharan countries that make up our study area. Climate-resilient maize agronomic activities have been determined by analyzing the onset, length of the growth period (LGP), and cessation date. Accordingly, the lower and upper quartiles of the date of onset of rainfall were in a range of May 9 to June 2, respectively; the length of the growth period (LGP) during the rainy season ranges from 97 to 232 days, and the cessation date of rainfall was November 1. Therefore, the short- to long-maturing maize varieties can be planted from May 9 to June 2 and can begin to be harvested in the first week of November under the current climatic circumstances.
PubDate: Mon, 19 Jun 2023 09:05:00 +000
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