Arid Climate Zones Research Articles

A study on the thermal comfort inside a flat under arid climate zone in Algeria

The use of naturally materials improves to reduce the life-cycle cost of building (initial cost plus lifetime energy cost). In this paper, looking for the thermal comfort inside an apartment located in an arid climate zone in Algeria, in Biskra city (35.56 N, 6.16 E) by comparing the use of two materials: the concrete (C) and the stabilized earth-concrete (SEC). The temperature variations through the walls are determinate and analyzed. In this study the wall is exposed to the maximum temperature in the two cases: transient and steady state. The mathematical formulation of the problem leads to a parabolic equation. The equation is resolved analytically and numerically by setting the appropriate boundary conditions. The resulting model applied for both materials (concrete, and SEC). The comparison of the results obtained, analytically and numerically, show that the SEC has the higher thermal resistance than concrete and the temperature difference between these two materials can reach about 5.5 °C in steady state. We have also performed the calculation of wall insulation dimensions by resolving a nonlinear system, which corresponds to the optimum thickness to ensure an adequate thermal comfort in side the flat.

Evaluation of Climate Suitability for Nature-Based Tourism (NBT) in Arid Regions of Isfahan Province (Iran)

This article applies the weather types method to assess the climate suitability for nature-based tourism (NBT) in the arid and hyper-arid climate zones of the province of Isfahan (Iran) based on bioclimatic criteria and the preferences of Iranian domestic tourists identified by means of a survey. To date, there are no climate potential assessments for the practice of nature tourism based on an analysis of climate preferences in the study area. According to the results, the distribution of favorable weather types in the study area between March and November during the period 1998–2017 showed that there is a low season in summer and two high seasons corresponding to autumn and spring. The highest frequencies of weather types conducive to NBT were recorded between the second half of September to the first half of November and between the second half of April until the end of May. The calendars resulting from application of the weather types method will serve as an efficient tool for providing tourists and the region’s main tourist stakeholders with information; in the case of the latter, they will be particularly useful for destination planning and activity scheduling.

Impact of Climate Change on Crop Yields: Insights from the Abruzzo Region, Central Italy

Climate change poses a major sustainability challenge, with potential far-reaching economic and social impacts, including fluctuations in agricultural yields and subsequent volatility in the availability and prices of essential food resources. In this study, we analyze the changes in climatic variables and agricultural yields for main crops cultivated in the Abruzzo Region (Central Italy), as well as their reciprocal correlation pattern in the time range, 1952–2014. Central Italy exhibits a well-developed agricultural sector and is characterized by a Mediterranean climate; given its proximity to the semi-arid and arid North African climate zone, such a region may be particularly vulnerable to climate change impacts. The analysis reveals an increase in drought intensity and persistence, starting from the 1980s, with a shift towards more temperate conditions in winter months and a transition to a warmer and more arid climate during the summer season. Moreover, the study identifies an overall increase in the correlation magnitude between crop yield fluctuations and climatic variables in the period 1983–2014 compared to that during the previous thirty-year time range. This finding can be interpreted as a change in the resilience (or increased vulnerability) of the agricultural production system to climate change. This work can be thus considered as an interesting case study in the Mediterranean region, offering valuable insights for constructing probabilistic models of fluctuations in agricultural production possibly induced by climate change.

Greenness and excess deaths from heat in 323 Latin American cities: Do associations vary according to climate zone or green space configuration?

Green vegetation may protect against heat-related death by improving thermal comfort. Few studies have investigated associations of green vegetation with heat-related mortality in Latin America or whether associations are modified by the spatial configuration of green vegetation. We used data from 323 Latin American cities and meta-regression models to estimate associations between city-level greenness, quantified using population-weighted normalized difference vegetation index values and modeled as three-level categorical terms, and excess deaths from heat (heat excess death fractions [heat EDFs]). Models were adjusted for city-level fine particulate matter concentration (PM2.5), social environment, and country group. In addition to estimating overall associations, we derived estimates of association stratified by green space clustering by including an interaction term between a green space clustering measure (dichotomized at the median of the distribution) and the three-level greenness variable. We stratified analyses by climate zone (arid vs. temperate and tropical combined). Among the 79 arid climate zone cities, those with moderate and high greenness levels had modestly lower heat EDFs compared to cities with the lowest greenness, although protective associations were more substantial in cities with moderate versus high greenness levels and confidence intervals (CI) crossed the null (Beta: −0.41, 95% CI: −1.06, 0.25; Beta −0.23, 95% CI: −0.95, 0.49, respectively). In 244 non-arid climate zone cities, associations were approximately null. We did not observe evidence of effect modification by green space clustering. Our results suggest that greenness may offer modest protection against heat-related mortality in arid climate zone Latin American cities.

Evaluating the Primary Macronutrients and their Correlations with pH, Electrical conductivity, Organic Carbon and Soil Nutrient index in the Arid and Semi-Arid Climatic Zones of Anantapur District, Andhra Pradesh, India

In the year 2022, the study was carried at Agricultural Research Station, Anantapur aimed to assess macronutrient levels and their correlation with soil physico chemical properties in the arid and semi-arid regions of Anantapur district, Andhra Pradesh. A total 42 samples were collected from farmers field under different land use. The soil analysis revealed a pH range of 6.53 to 8.94, with a mean pH of 7.89 and a majority of samples exhibiting alkaline properties. Soil electrical conductivity (EC) varied from 0.05 to 0.83 dS/m, with a mean EC of 0.22 dS/m, and most samples falling within the non-saline and very low salinity categories. Organic carbon content ranged from 0.07% to 1.15% with a mean of 0.38%, and a significant proportion of samples displayed low organic carbon levels. Available nitrogen content ranged from 50.4 to 264.6 kg/ha (mean: 139.44 kg/ha), mostly below the critical threshold. Phosphorus availability was medium (range: 16.1 to 44 kg/ha, mean: 29.97 kg/ha), while available potassium content varied widely (range: 15.68 to 311 kg/ha, mean: 129.65 kg/ha), with a substantial proportion of samples indicating low levels. The nutrient index categorized nitrogen and potassium as low in fertility and phosphorus as medium, reflecting values of 0.71, 1.37, and 1.09 (L, M, L) respectively. Correlations showed EC negatively related to pH (-0.056), while organic carbon positively correlated with EC (0.288*), and nitrogen displayed positive correlations with both organic carbon (0.283*) and pH (0.257). Phosphorus exhibited a positive correlation with organic carbon (0.224), and potassium displayed robust positive correlations with EC (0.592**) and moderate positive correlations with organic carbon (0.392**), nitrogen (0.311*), and a slight negative correlation with pH (-0.253). This result helps to understand nutrient status in the arid and semi-arid areas of Anantapur and recommended to incorporate compost, crop residues and green manure crops and use cover crops as live mulching helps to increase organic carbon, nitrogen and potassium and it also decrease nitrogen losses through volatilization.

Analysis of Vegetation Dynamics and Driving Mechanisms on the Qinghai-Tibet Plateau in the Context of Climate Change

The Qinghai-Tibet Plateau (TP) is susceptible to climate change and human activities, which brought about drastic alterations in vegetation on the plateau. However, the trends and driving mechanisms of vegetation changes remain unclear. Therefore, the normalized difference vegetation index (NDVI) was used to analyze the spatiotemporal distribution of vegetation and the consistency of dynamic trends in the TP from 2000 to 2020 in this study. The independent contributions and interactive factors of natural and human activities on vegetation changes were investigated through the Geodetector model. The drivers of vegetation under different dry–wet zones and precipitation gradients were quantitatively separated, and the internal mechanisms of vegetation changes were discussed from multiple perspectives. The results showed that from 2000 to 2020, the NDVI had an overall increasing trend, with an increasing rate of 0.0027 a−1, and the spatial pattern was different, increasing gradually from the northwest to the southeast. Consistent improvement occurred in the central and southeastern parts of the TP, while the western and northern parts consistently deteriorated. The annual mean precipitation had the greatest explanatory power for vegetation changes (0.781). The explanatory power of the integrated effects between two factors was greater than that of individual factors. The integrated effects between annual mean precipitation and other driving factors had the strongest explanatory power on vegetation variations. The driving mechanisms of vegetation dynamics varied among different dry–wet zones, and the vegetation growth was more sensitive to the response of precipitation in arid and semi-arid climate zones. This study enhances our understanding of the intrinsic mechanisms of vegetation changes on the plateau, which can provide a reference for ecological conservation, and has implications for further prediction and assessment of vegetation ecosystem stability.

Developing a satellite-based combined land degradation index for monitoring environmental change: A case study in Tana-Beles watershed, Upper Blue Nile, Ethiopia

Land degradation is one of the most pressing environmental challenges due to its effect on the people and ecosystem. Early detection of land degradation could help to avoid further deterioration and work on reversing the trend. This would require an integrated approach combining indicators such as vegetation condition, soil health (i.e., soil salinity), and soil exposure (i.e., albedo) to characterize land degradation comprehensively. Therefore, this study aims at developing a satellite image-based combined land degradation index to monitor the spatial extent of land degradation in the Upper Blue Nile Basin, Ethiopia. In addition, the study also evaluates the environmental change associated with land degradation across the Tana-Beles watershed. The combined land degradation index (CLDI) was developed by integrating multiple indicators, including normalized vegetation Index (NDVI), salinity index (SI), and albedo, for two periods spanning between 1990 and 2000 (1990s) and 2010 to 2020 (2010s). The individual indicators were combined with Principal Component Analysis (PCA) by assigning weights to the individual indicators. The analysis indicated that, in the 2010s, the vegetation condition deteriorated over 98.5% of the study area, while soil salinity and albedo increased by 82% and 97% in the 1990s, respectively. The combined land degradation index developed with the PCA indicated in the 2020s a fraction of the study area ∼2% is extremely degraded, while 18%, 43%, 27%, and 10% of the site is strongly, moderately, slightly, and not degraded, respectively. Monitoring land degradation is important for countries like Ethiopia, where most of the county are occupied by semi-arid and arid climate zones.

Contributions of natural and anthropogenic factors to historical changes in vegetation cover and its future projections in the Yellow River basin, China

AbstractThe fragile ecology of the Yellow River basin (YRB) is vulnerable to global warming and human activities. As the second largest river in China, the YRB covers the subhumid, semi‐arid, arid and alpine climate zones, with considerably spatial differences in vegetation cover (VC), thus demonstrating substantial variations in response to climate change. In this study, characteristics of VC variations in different climate zones of the YRB during 1982–2019 were analysed, impacts of climate change and human activities on VC variations were identified, and possible VC variations in the future were projected under different climate scenarios. Results show that NDVI in the YRB significantly increased at a rate of 2.3 × 10−3 year−1 from 1982 to 2019, especially in the middle reaches of the YRB. Temperature is the main driver of VC variations in the source region of the YRB (alpine zone), wherein the warming trend contributes to the greening. The internal variability of VC is also influenced by temperature, with a marginal effect of precipitation minus evaporation (P − E). In the middle and lower reaches of the YRB consisting of arid, semi‐arid, and subhumid zones, VC benefited only from the warming trend and had a less relationship with the internal variability of temperature, whereas the internal variability of VC was influenced by P − E and had a weaker relationship with the P − E trend. Contributions of climate change to VC variations in the source region and middle and lower reaches of the YRB are approximately 62%, 42%, and 27%, respectively. Human activities considerably impact the ecological environment but mostly play a positive role. In the future, VC will increase in the YRB, especially under the SSP5‐8.5 scenario, and VC reveals the most prominent changes in the lower reaches. This study suggests that climate change might be favourable to VC in YRB, and emphasizes that human activities should adapt to climate change in an orderly manner.

Evaluation of Multi-Utility Models with Municipal Solid Waste Combustion as the Primary Source under Specific Geographical and Operating Conditions

Developments in waste incineration technology in terms of efficient fuel preparation, combustion, and emissions reduction, as well as the growing needs of the community in terms of electricity, water, and air conditioning loads, are the prime motive for this study. This study presents a novel approach, in which three models of the fluidized bed combustion of municipal waste for simultaneous power generation, freshwater production, and district cooling are analyzed for their energy and exergy performance. The three simultaneously evaluated utility models are different configurations of a fluidized bed combustion system with Rankine cycle power generation, cooling with a vapor absorption refrigeration system, and fresh water production using multiple effect desalination. The output from the turbine, cooling system, and desalination system is determined using the Engineering Equations Solver for different boiler operating pressures. Energy and exergy analysis data for different pressures are used to identify the best configuration. Two variants of the absorption cooling system, namely, single effect and double effect, are considered. The variants of the multiple-effect desalination are the three-stage and five-stage methods. Input parameters used in this study are municipal solid waste generation and composition data collected for an urban community in an arid climate zone with high demand for electric power, cooling, and fresh water. Model 2, which contains two turbines with the reheating and cooling systems connected to a high-pressure turbine and water desalination connected to a low-pressure turbine, gave the best overall performance. Significant savings in terms of the replacement of conventional energy were observed from these waste conversion plants with greater benefits in arid weather conditions. The results obtained by different models under different operating criteria constitute a guideline for municipal planners for the selection of appropriate waste utilization technology, as well as the appropriate operations.

Connecting material degradation and power loss of PV modules using advanced statistical methodology

The presented work focuses on the influence of different stress combinations (in climate-specific ageing tests) on polymer material degradation and electrical power loss of PV modules and how these data can be linked using advanced statistical methods. The statistical analysis is divided into three parts: in the first part, the direct effects of the climatic stresses on the electrical performance are assessed. The second part aims to correlate the electrical degradation behaviour with changes in the encapsulation material, and the third part does the same for the backsheet material of the modules.Time series of electrical data are extracted from IV curves and correlated with changes in material parameters obtained from fluorescence spectroscopy of the encapsulant as well as infrared spectroscopy and colour measurements of the backsheet. The data set analyzed consists of measurements that were taken throughout accelerated ageing experiments simulating stressors typically found for arid, tropical, moderate, and alpine climate zones. Significant correlations were found between the material changes - as indicated by the spectral measurements - and the electrical power loss of the test modules in the course of climate-specific ageing.The results of the analysis provide valuable input for possible measures of preventive maintenance based on condition monitoring of single modules. The methodology developed in this paper allows for an estimation of the power output for modules with identical design and bill of materials using simple, non-destructive measurement methods. The results from accelerated lab tests and the correlations determined from them can be used to predict the electrical power output of installed PV modules. As a result, deviations in module performance can be detected at an early stage and preventive maintenance measures in PV systems can be carried out.

Selection of Ideal MSW Incineration and Utilization Technology Routes Using MCDA for Different Waste Utilization Scenarios and Variable Conditions

Liability to prevent the consequences of an unhealthy situation due to accumulating toxic and hazardous emissions caused by open dumping of municipal solid waste with increasing urbanization has necessitated a renewed thinking on waste disposal. Grate-fired incineration systems were adopted by urban management in the past and present, but with criticism due to the formation of airborne emissions. Improved combustion methods like fluidized beds are now propagated because of current requirements like efficient energy recovery potential, stricter emission norms, adaptability with urban growth, adaptability to co-firing with other waste like biomass, edible oil wastes or industrial effluent, and integration with conventional energy generation. Such a comprehensive and futuristic approach is more sustainable for the community. A multi-criteria decision-making tool is used to identify the best technology option between grate combustion and fluidized bed combustion for disposing and energy recovery from waste. A total of 10 different collection and disposal options involving two combustion methods, namely, grate combustion and fluidized bed method, are considered. Utilization of the energy is done for three end uses, namely, power generation, water distillation, and district cooling. Two different regions in an arid climate zone are considered for this study under two types of scenarios, namely with recycling and without recycling. The different options are prioritized based on their overall ranking using five major performance factors.

Predictive Model for Flood – Induced Collapse Phenomenon in Residual Soils of Northern Edo, Nigeria

Residual soils are in the category of questionable soils which have been experienced in the arid and semi-arid climatic zones of the world. The conditions in these zones favour the development of most unsafe collapsible soils. At their dry natural state, they possess awesome stiffness and high apparent shear strength, however upon flooding, may demonstrate a remarkable reduction in volume, consequently deteriorate in strength and collapse. In this research, the collapse phenomenon of residual soil collected from three locations in Auchi, Northern Edo, Nigeria has been investigated on undisturbed specimens by utilizing single Oedometer test. The results obtained from Oedometer tests were utilized to form the database to develop the Artificial Neural Network model for the prediction of collapse potential induced by flood. The influences of flood, flooding pressure, void ratio, dry density and porosity on soil collapse have been investigated. Six input parameters (i.e. Flooding Pressure, Initial void ratio, Initial water content, Initial dry density, Liquid limit and Initial porosity) are considered to have the most noteworthy influences on the degree of collapse and have been utilized as the model’s inputs while the model output will be the equivalent collapse potential. The proposed network was developed using Microsoft Visual Studio 2010 and the MS.NET Framework 4.0 and source codes were written in C-Sharp (C#). A supervised learning was utilized to train the Back Propagation feed forward multi-layer ANN algorithm with the momentum coefficient and learning rate as its parameters. The prediction performance of the Artificial Neural Network model was assessed by utilizing the primary statistical criterion proposed by Shahin, et al., [1] such as the coefficient of correlation, R2, and the root mean square error, RMSE. The model outcomes demonstrated that it has the aptitude to predict the collapse potential from single Oedometer test in residual soil samples with a good degree of precision with coefficient of correlation, R2 = 0.856 and root mean square error, RMSE = 166.199.

Driving role of climatic and socioenvironmental factors on human brucellosis in China: machine-learning-based predictive analyses

BackgroundBrucellosis is a common zoonotic infectious disease in China. This study aimed to investigate the incidence trends of brucellosis in China, construct an optimal prediction model, and analyze the driving role of climatic factors for human brucellosis.MethodsUsing brucellosis incidence, and the socioeconomic and climatic data for 2014–2020 in China, we performed spatiotemporal analyses and calculated correlations with brucellosis incidence in China, developed and compared a series of regression and Seasonal Autoregressive Integrated Moving Average X (SARIMAX) models for brucellosis prediction based on socioeconomic and climatic data, and analyzed the relationship between extreme weather conditions and brucellosis incidence using copula models.ResultsIn total, 327,456 brucellosis cases were reported in China in 2014–2020 (monthly average of 3898 cases). The incidence of brucellosis was distinctly seasonal, with a high incidence in spring and summer and an average annual peak in May. The incidence rate was highest in the northern regions’ arid and continental climatic zones (1.88 and 0.47 per million people, respectively) and lowest in the tropics (0.003 per million people). The incidence of brucellosis showed opposite trends of decrease and increase in northern and southern China, respectively, with an overall severe epidemic in northern China. Most regression models using socioeconomic and climatic data cannot predict brucellosis incidence. The SARIMAX model was suitable for brucellosis prediction. There were significant negative correlations between the proportion of extreme weather values for both high sunshine and high humidity and the incidence of brucellosis as follows: high sunshine, r = −0.59 and −0.69 in arid and temperate zones; high humidity, r = −0.62, −0.64, and −0.65 in arid, temperate, and tropical zones.ConclusionsSignificant seasonal and climatic zone differences were observed for brucellosis incidence in China. Sunlight, humidity, and wind speed significantly influenced brucellosis. The SARIMAX model performed better for brucellosis prediction than did the regression model. Notably, high sunshine and humidity values in extreme weather conditions negatively affect brucellosis. Brucellosis should be managed according to the “One Health” concept.

Land Subsidence Susceptibility Mapping Using Interferometric Synthetic Aperture Radar (InSAR) and Machine Learning Models in a Semiarid Region of Iran

Most published studies identify groundwater extraction as the leading cause of land subsidence (LS). However, the causes of LS are not only attributable to groundwater extraction. Other land-use practices can also affect the occurrence of LS. In this study, radar interferometric techniques and machine learning (ML) models were used for the prediction, susceptibility zoning, and prioritization of influential variables in the occurrence of LS in the Bakhtegan basin. The LS rate was characterized by applying an interferometric synthetic aperture radar (InSAR). The recursive feature elimination (RFE) method was used to detect and select the dominant combination of indicators to prepare an LS susceptibility map. Three ML models, including random forest (RF), k-nearest neighbors (KNN), and classification and regression trees (CART), were used to develop predictive models. All three models had acceptable performance. Among the ML models, the RF model performed the best (i.e., Nash–Sutcliffe efficiency, Kling–Gupta efficiency, correlation coefficient, and percent bias metrics of 0.76, 0.78, 0.88, and 0.70 for validating phase, respectively). The analysis conducted on all three ML model outputs showed that high and very high LS susceptibility classes were located on or near irrigated agricultural land. The results indicate that the leading cause of land LS in the study region is not due to groundwater withdrawals. Instead, the distance from dams and the proximity to anticlines, faults, and mines are the most important identifiers of LS susceptibility. Additionally, the highest probability of LS susceptibility was found at distances less than 18 km from synclines, 6 to 13 km from anticlines, 23 km from dams, and distances less than 20 to more than 144 km from mines. The validated methods presented in this study are reproducible, transferrable, and recommended for mapping LS susceptibility in semiarid and arid climate zones with similar environmental conditions.

Characterizing soiling losses for photovoltaic systems in dry climates: A case study in Cyprus

Ensuring optimal performance of solar photovoltaic (PV) systems requires the extensive assessment and understanding of losses of different origin that affect these installations. Soiling is a key loss factor influencing the performance of PV systems, particularly in arid and dry climatic regions, and its thorough knowledge and modelling aspects including the seasonal evolution is challenging for the early stages of energy prospecting for PV power plants. The purpose of this study is to address this fundamental challenge by evaluating the loss of soiling and the performance of six soiling models based on both physical and machine learning (ML) approaches. Specifically, the case study is a soiling test-bench experimental apparatus installed at the outdoor test facility of the University of Cyprus in Nicosia, Cyprus. The climatic conditions of the site represent a dry climate with high PV potential due to high irradiation levels. The obtained results reported soiling rates ranging from 0.039%/day to 0.535%/day depending on the season and the presence of dust episodes. The average yield daily and monthly soiling losses were 1.9% and 2.4% over a 2-year period, respectively. Furthermore, the comparative analysis of the different soiling models illustrated that the physical models achieved slightly better performance than the ML models with root mean square error (RMSE) of 1.16% and 0.83% for daily and monthly losses, respectively. Finally, the findings provide evidence and useful information on the performance and limitations of the different soiling models for fielded PV systems located in arid and dry climatic zones.

Assessment of Meteorological Drought under the Climate Change in the Kabul River Basin, Afghanistan

Kabul River Basin is one of the most significant river basins in Afghanistan from a socio-economic perspective. Since the country is located in an arid climate zone with drastically varying climatic behavior, an effective assessment of meteorological drought is very essential to managing the limited availability of water resources. For this endeavor, the outputs of three general circulation models under two representative concentration pathways (RCP 4.5 and RCP 8.5) were used against the baseline period of 1961–1980. Different bias correction methods were applied, and the results show that the delta change method, quantile mapping method, and empirical quantile mapping all performed better for the precipitation, maximum temperature, and minimum temperature datasets, respectively. The ERA5-Land datasets and WorldClim Version 2 are used to validate the bias-corrected precipitation and temperature datasets, respectively, to determine their dependability, and the results were found to be promising. Standardized Precipitation Index (SPI), Reconnaissance Drought Index (RDI), Deciles Index (DI), and New Drought Index (NDI) were used to assess the drought condition in the past and forecast for the future periods of the 2020s, 2050s, and 2080s. The spatial distribution of assessed drought indices was mapped using the inverse distance weighting (IDW) method. Our results revealed that moderate to extreme droughts are consistent across the entire basin. This might be because the projected annual precipitation in the river basin shows a decline of 53–65% up to the end of this century (2100), and the average annual temperature is projected to increase by 1.8 °C, 3.5 °C, and 4.8 °C, respectively, for the three future periods of the 2020s, 2050s, and 2080s. Furthermore, the results show that the drought estimated by SPI and RDI for future climate scenarios is almost the same, whereas NDI estimates frequent drought events after the 2050s. However, for moderate drought, RDI, which includes the effects of evapotranspiration, was found to be far greater than SPI under both scenarios, and NDI considering temperature and precipitation also estimates a larger number of drought years, strengthening the possibility of its occurrence in the basin. A regional comparison of drought also indicates a decrease in precipitation in future periods, predominantly in high altitudes.

Comparison of the Grid and Off-Grid Hybrid Power Systems for Application in University Buildings in Nigeria

The Nigerian Universities rely on weak and unreliable fossil-based electric grids with diesel engine generators (DEG) as a backup. However, there is a potential to light up the campuses using power systems derived from primary renewable power systems (RPS) like wind turbine (WT) and solar photovoltaic (PV), that can be on or off-grid to improve the energy mix and duration reliably. This study presents the comparative analysis of the optimal hybrid grid and off-grid systems (OGS & OOGS) for serving the demand load of university buildings in four climatic regions of Nigeria. HOMER Pro is used to design and select the systems based on minimal net present cost (NPC) and cost of electricity (COE). The impact of a minimal renewable fraction of 95% on the optimal system architecture (OSA) and COE is studied for both grid and off-grid modes. Also, sensitivity analysis of the impact of key variables on performance for the sites is carried out. It is found that the OGS in the four regions is PV/Converter (Conv), while for the OOGS, it is PV/WT/DEG/battery (BB)/Conv except in Port Harcourt (PH), where it is PV/DEG/BB/Conv. The COE for the OGS in the Savana and monsoon climes of Enugu and PH are 10 and 19% more than that in the warm-semi arid climate zones of Maiduguri and Kano, which is approximately 0.09 $/kWh. The COE ($/kWh) for the OOGS is 0.21 in Maiduguri, 0.245 in Kano, 0.275 in Enugu and 0.338 in PH. An obligatory 95% RF changes the architecture and increases COE in all the locations except Maiduguri, with a slightly improved COE but higher NPC like other locations. It is established that the suggested hybrid system is beneficial and feasible for supplying more reliable and clean energy to educational buildings in different Nigerian locations.

The urban heat island and thermal heat stress correlate with climate dynamics and energy budget variations in multiple urban environments

Rising temperatures that occur in cities resulted in the urban heat island (UHI) which could also amplify the thermal heat stress, leading to climate-related human mortality. Our study investigated the UHI effect and thermal heat stress of heat index (HI) in the U.S. cities of arid, continental, temperate, and tropical climate zones. A long-term simulation of the UHI and HI was conducted using Community Land Model version 5.0 from 1990 to 2014. The relationship between UHI and HI with climate and energy flux variables are analyzed with divided study periods (1990–2000 and 2001–2014) for the urban impact assessment. Higher sensible heat was reported, particularly in the arid and tropical cities. Further, temperate climate showed increasing annual trends for UHI and ΔHI (urban-rural difference of HIs). The later period (2001–2014) demonstrated greater UHI and ΔHI trends, implying a notable change in climate and urban expansion. The UHI, ranging from 0.73 °C to 2.07 °C, was more intense compared with ΔHI, at 0.14 °C to 1.23 °C. The UHI was positively correlated with temperatures in hot cities, while precipitation is the main driver in the continental city. For ΔHI, the relationship with temperatures were dominant in all climate zones.

Quality Control and Homogeneity Analysis of Precipitation Time Series in the Climatic Region of Iraq

Non-climatic reasons, such as station replacement and changing the measurement device and calculation method, may make climate data unrepresentative of the actual variation of the regional climate. Data quality control and homogenization tests for climate data are critical. Thus, this study aims to evaluate the quality and homogeneity level of precipitation time series in arid and semi-arid climatic zones and specify the breakpoint in the datasets. The proposed methodology has been used to conduct arid and semi-arid representative case studies comprising 40 annual precipitation series for Iraq between 1979 and 2014. A Box-Cox transformation has been used to adjust the non-normally distributed datasets. Outliers have been censored by truncating extreme values. The results of the outliers indicate that they may be caused more by climate variability than by human-induced reasons. Homogeneity adjustments have been developed by applying these four homogeneity tests: Pettitt’s test, the Standard Normal Homogeneity method, Buishand’s test, and von Neumann’s check. Approximately 40% of the series (i.e., 16 stations out of 40) were homogeneous. Each homogeneity test was evaluated separately, and non-homogeneous stations were identified. Then, the series was classified into three groups that were assigned the labels “useful”, “doubtful”, and “suspect”. The results indicated that twenty-one stations were associated with the class ‘suspect’, three of the stations belonged to the class ‘doubtful’, and sixteen locations were within the class ‘useful’. Furthermore, the data analysis indicated no influence of the outliers on the results of the homogeneity tests. Accordingly, the study recommends further research on homogeneity tests that can be applied without considering outlier tests for similar case studies.

Trends, Cycles, and Spatial Distribution of the Precipitation, Potential Evapotranspiration and Aridity Index in Xinjiang, China

Xinjiang is a typical continental arid climate zone and ecologically fragile zone. Drought has caused damage to the local social economy, agricultural production, and the ecological environment. However, the study of drought is more difficult due to the complex topography and the lack of monitoring information. In this paper, based on the meteorological data of 94 meteorological stations in Xinjiang from 1961 to 2020, we used the precipitation and potential evapotranspiration (ET0) to calculate the aridity index (AI); the Mann–Kendall test, Morlet wavelet analysis, and Kriging interpolation were used to identify the trend, period, and spatial distribution. The results showed that (1) the average change rate of the precipitation in Xinjiang was 8.58 mm/10a, the average change rate of the ET0 was −14.84 mm/10a, and the average change rate of the AI was −1.94/10a; (2) the periods of precipitation, ET0, and AI in Xinjiang were 39, 29, and 14 years, respectively, and the abrupt changes occurred in 1986, 1971, and 1987, respectively; (3) The Moran index of precipitation and temperature are 0.41 and 0.33, respectively, indicating that precipitation and temperature in Xinjiang are positively correlated in spatial distribution and have spatial clustering characteristics; and the z-values are both greater than 2.58 (p < 0.01), indicating that the spatial autocorrelation of precipitation and temperature in Xinjiang is significant. This study can provide a reference for the diagnosis of the meteorological drought mechanism and the coping with climate change in Xinjiang.