Annual Mean Wind Speed Research Articles

Wind-wave characteristics in the coastal area between Damietta and Port Said, Egypt

This study attempts to investigate the wind-wave characteristics in the region between Damietta and Port Said from 2011 to 2020 using the ERA5 data set for the considered area. Fourteen stations were used for data collection to represent the study area. The ERA5 data were compared with measured wave data from a directional wave buoy from February 1999 to February 2000. The following error statistics were obtained: bias = - 0.014 m and RMSE = 0.3 m. The ERA5 data were then used to create seasonal maps to simulate wind speed and direction, significant wave height (Hs), wave direction, and wave period. Results showed that the annual average Hs was 0.42 m, and the annual mean wind speed was 5.13 m/s. According to the Mann-Kendall test, the average Hs exhibited increasing trends of 0.0014 m/year and 0.0024 m/year with 70–80 % significance levels, respectively, in winter and autumn. In the spring, the Hs trend was 0.0013 m/year with an 80–90 % significance. During summer, this trend decreased to −0.00037 m/year, with a 70–80 % significance level. The average wind speed had increasing trends in winter, spring, and autumn of 0.009 m/s/year and 0.016 m/s/year with a 70–80 % level of significance and 0.0026 m/s/year with 80–90 % significance level, respectively. Meanwhile, the summer average wind speed showed a weak, insignificant trend of increase of as low as 0.0001 m/s/year. The study recommends using simulated wave characteristics to fill the gap in wave data along the Southeastern Mediterranean coast and using the southeast zone of the study area for economic activities.

Assessment of wind power potential and economic viability at Al-Hodeidah in Yemen: Supplying local communities with electricity using wind energy

Wind energy is a promising sector in the power generation industry because it is renewable and globally available. This study assessed the wind power potential and the economic viability of utilizing wind turbines at the chosen location to produce electricity. This work uses wind speed data collected during (2012–2014) at 10 m height. This research presents the maximum and mean values of measured wind speeds. We have analyzed annual, seasonal, and monthly variations of wind speed. The wind characteristics and wind power potential of the study site have been investigated using Weibull and Rayleigh distribution functions. The annual mean wind speed and Weibull parameters (shape and scale) have been determined to be 4.477 m/s, 2.494, and 5.046 m/s, respectively, according to the actual data collected over the three years. The annual mean power density and energy density have been computed to be 86.39 W/m2 and 757.226 kWh/m2 according to the actual data collected over the three years, and the predicted annual mean power density and energy density have been computed to be 86.492 W/m2 and 758.358 kWh/m2 using Weibull parameters and 164.372 W/m2 and 1441.2 kWh/m2 using Rayleigh distribution, respectively. Accordingly, the power density computed using the Weibull parameters was similar to the power density computed using real wind data, whereas the power density calculated by the Rayleigh function was higher. An economic analysis has been done to choose a suitable turbine for the study site. The results indicate that the wind turbine ZEFIR D14-Px-Ty is indeed a viable option for wind power generation in Al-Hodeidah. It has a relatively low cost of energy of $0.03/kWh and a high capacity factor of 0.428, indicating its efficiency in converting wind energy into electricity. Therefore, the selected location is recommended for some small stand-alone systems as well as small wind farms.

Wind resource assessment for turbine class identification in Bayanzhaganxiang, China

Abstract The wind resource assessment has been used effectively to identify the classification of wind turbines at a particular wind farm site. The current study used WAsP software and various statistical methods such as graphical, energy pattern factor, standard deviation, and Rayleigh distribution methods to find the Weibull parameters by evaluating the raw data collected from August 2005 until July 2006 at four (4) different heights of the meteorological mast station in Bayanzhaganxiang, China. The Weibull parameters were utilized to find the annual mean wind speed, probability density, and cumulative distribution functions of wind conditions at the reference heights of 70 m, 50 m, 30 m, and 10 m. The wind shear coefficient was 0.130 with an overall roughness factor of 0.0385 m, suggesting the site vicinity is an open country with no significant structures and vegetation. The results also showed that the post-processed output from WAsP and standard deviation method at the sensor’s height of 70 m have a correlation coefficient and confidence level of 0.99977 and above 95%, respectively. Based on the turbine classification from GL Wind 2003 and IEC 61400-1 Ed.2, it was found that the turbine class ideal for the site is class III wind turbines with an annual mean wind speed of 7.439 m/s at a hub height of 99 m. The measured wind power density at hub height was calculated according to IEC 61400-12-1, which yields 464.36 W/m2. The characteristic wind turbulence at 70 m high is IEC subclass B. Among the selected wind turbines, the net annual energy production with efficiency is 8,059.57 MWh/year using Avantis AV1010, with the highest capacity factor of 40.05%. It has been found that the lowest energy generation cost is US$ 0.0292/kWh for a period of 20 years.

Mathematical Differential Analysis of Atlantic Ocean Wind to Electrical Energy Generation in Lekki Peninsular Lagos Nigeria

This article is a pragmatic approach on the evaluation of wind energy production and it’s estimation in the coastal area of Lagos Nigeria. The work focuses on the accessibility of wind energy production in Lagos Nigeria through analyzing wind data in the look up tables using proficient probability function. Here, three approaches are itemized; Analysis of sets of actual time series data, theoretical Weibull probability function using seven numerical methods and the comparison of theory and the analysis. Two important parameters are used in this analysis are the Weibull shape factor “k” and Weibull scale factor “c”. Theory involves the calculation using seven popular methods of moments (MM), standard deviation method (STDM) or empirical method (EM), maximum likelihood method (MLM), modified maximum likelihood method (MMLM), second modified maximum likelihood method (SMMLM), graphical method (GM) or least mean square method (LSM), energy pattern factor method (EPFM). The performance of the numerical methods has been tested by five methods; RMSE, X2, IA, MAPE and RRMSE. The results expatiate on Actual and theoretical technique being used to find out wind energy conversion per 1 km2. In this paper, a differential performance method for accuracy check has been proposed as an error indicator between the wind energy calculated by theoretical Weibull function and the one by actual time-series data. The wind speed data was measured from January 2018 to December 2021 in the Lekki peninsular area of Lagos State. The suitability values for these parametric; shape and scale parameters of Weibull distribution are determined in selecting the best location for the installation the wind turbine generators. The measured annual mean wind speed and mean wind power are 10.11 ms-1 and 10.4 KWm-2, respectively.

Investigations of the spatial and climate characteristics of natural pure chestnut (Castanea sativa Mill.) forests: A case of Zonguldak Regional Directorate of Forestry

Chestnuts with high economic and socio-cultural value (Castanea sativa Mill.) must determine forests' spatial and climatic characteristics to improve them, increase fruit yield, and effectively combat diseases and pests. This study aimed to determine the spatial and climatic attributes of pure chestnut forests spreading within the borders of the Zonguldak Forestry Regional Directorate. A total of six criteria were used for spatial and climatic analyses: total precipitation (mm/year), annual mean temperature (C°), wind speed (m/s), altitude (m), slope (%), and aspect (°). Climate values were calculated as maximum, minimum, and average values, respectively; 1 619.25, 866.95, and 1 024.07 mm/year for total precipitation (mm/year), 13, 6 and 11.76 C° for annual mean temperature (C°), 6.62, 0.22 and 2.46 m/s for wind speed (m/s). The spatial characteristics were calculated as 1 221.68, 33.24, and 300.23 m for the height (m), 32.43%, 7.34%, and 18.63% for the slope (%) and finally, 338.63°, 18.18° and 184.18° for the angle (°), respectively, in terms of maximum, minimum and average values. Bartın Forest Management Directorate (FMD) ranks first with 57.18% of the natural chestnut forests spread the most in the study area, while Zonguldak FMD ranks second with 21.45% and Ulus FMD ranks third with 17.13%. The results obtained from the study based on the location and climate will contribute to the selection of the most suitable place for the new chestnut forest afforestation studies to be established and increase the percentage of success.

Designing and Modelling of Wind Turbine Simulation Using MATLAB / SIMULINK

Abstract: The renewable energy, Malaysia hopes to lessen its reliance on oil and gas. Wind energy is one of the more promising renewable sources of electricity. Malaysia is situated in an area with low wind speeds, with an annual mean wind speed of 1.2- 4.1 m/s, close to the equator. The northeast monsoon and the southwest monsoon are the two monsoonseasons that Malaysia experiences. While wind speeds during the northeast monsoon can approach 15 m/s, they can only reach 7 m/s during the southwest monsoon season. The potential for wind energy in Sarawak, Malaysia, has only receiveda limited amount of research and study. In this article, MATLAB/SIMULINK will be used for modelling and simulation of numerous wind energy conversion systems (WESC) utilizing different generators operating under the same conditionsin order to analysis the generators' efficiency. Although ultimately all of them are equally efficient, PMSG has shown tobe more efficient than SCIG and DFIG at lower wind speeds. SCIG, DFIG, and PMSG efficiency are, respectively, 66.25%, 69.38%, and 71.88% at the rated wind speed.

Feasibility of wind power generation for the reduction of power costs in residential buildings

Introduction: The installation of wind power generators on buildings located in areas with regular winds may be a suitable investment in a renewable power source. Brazil has a high eolic potential, where the annual mean wind speed may reach over eight meters per second. This case study is aimed to assess the economic feasibility of the installation of small wind power plants in urban areas. Methods: This work evaluates a project for the installation of a vertical axis wind turbine in three buildings (15-, 22-, and 26-store) including the following stages: 1) Installation of a real-time power meter in the 15-store unit; 2) Demand analysis of the 26-store building´s power consumption; 3) Winds survey along the coast of the State of Ceará; 4) Analysis of the wind turbines available in the market; 5) Simulation aimed to choose the system. Results: Vertical wind power generators offer better conditions of use in urban areas. The turnover time was established to be between four and six years in the three studied units. Conclusions: The installation of a wind power generator on buildings in regions with an adequate eolic regimen reaches a financial return of the investment before the end of the equipment´s lifespan.

Site suitability analysis of wind energy resources in different regions of Algeria’s southwestern highland

This paper presents a site suitability analysis for a 20 MW wind farm project in western Algeria’s highlands. The aim is to improve the quality of the electricity grid’s service and increase Algeria’s renewable energy utilization. The wind potential of three regions, Mecheria, El Kheiter, and Naâma, was evaluated using the Weibull function and the wind atlas analysis and application program (WAsP) with a ten-year database (2011-2021) at 10 m hub height. The assessment encompassed a comprehensive analysis of various wind resource parameters, including mean wind speed, prevailing direction, and power densities. In comparison to other sites, the Mecheria region has the best wind potential, with a mean annual wind speed of 6.31 m/s, a power density of 283 W/m2, and Weibull parameters A = 7.1 m/s and k = 2.02. These promising results prompted us to design a wind farm in this region using Power Wind 90/2000 kW turbine technology facing the predominant wind directions of the site, producing 103.91 GWh of total annual gross energy produced (gross AEP) and 103.75 GWh of total annual net energy produced (net AEP). Finally, it appears that the wind resources in the selected region are well-suited for electricity generation, offering a promising opportunity to reduce the country's dependence on fossil fuels.

Spatiotemporal distribution and environmental influences of severe fever with thrombocytopenia syndrome in Shandong Province, China

BackgroundSevere fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease discovered in China in 2009. The purpose of this study was to describe the spatiotemporal distribution of SFTS and to identify its environmental influencing factors and potential high-risk areas in Shandong Province, China.MethodsData on the SFTS incidence from 2010 to 2021 were collected. Spatiotemporal scan statistics were used to identify the time and area of SFTS clustering. The maximum entropy (MaxEnt) model was used to analyse environmental influences and predict high-risk areas.ResultsFrom 2010 to 2021, a total of 5705 cases of SFTS were reported in Shandong. The number of SFTS cases increased yearly, with a peak incidence from April to October each year. Spatiotemporal scan statistics showed the existence of one most likely cluster and two secondary likely clusters in Shandong. The most likely cluster was in the eastern region, from May to October 2021. The first secondary cluster was in the central region, from May to October 2021. The second secondary cluster was in the southeastern region, from May to September 2020. The MaxEnt model showed that the mean annual wind speed, NDVI, cattle density and annual cumulative precipitation were the key factors influencing the occurrence of SFTS. The predicted risk map showed that the area of high prevalence was 28,120 km2, accounting for 18.05% of the total area of the province.ConclusionsThe spatiotemporal distribution of SFTS was heterogeneous and influenced by multidimensional environmental factors. This should be considered as a basis for delineating SFTS risk areas and developing SFTS prevention and control measures.

An observational study on the microclimate and soil thermal regimes under solar photovoltaic arrays

The high demand for low-carbon energy sources to mitigate climate change has prompted a rapid increase in ground-mounted solar parks. The implementation of photovoltaic (PV) significantly impacted the local climate and ecosystem, which are both poorly understood. To investigate the effects of a typical solar park on the Gobi ecological system, local microclimate and soil thermal regimes were measured year-round under and between PV arrays, at an applied solar park sited in Xinjiang, China. Our results demonstrated their seasonal and diurnal changes. Under solar PV arrays, the mean annual net radiation and wind speed decreased by 92.68 % and 50.53 % respectively. In contrast, PV panels caused an increase of the rear sides air by 10.12 % with 0.87 °C. South-facing PV panels reduced wind speed with the prevailing northerly wind below. In addition, the relative humidity rapidly decreased when snow covered the ground, but slightly increased from April to September. We found the soil under PV panels was cooler and tended to be a sink of energy during spring and summer whereas was more often a source during autumn and winter compared with the soil between PV panels. Observed data developed the understanding of the energy processes of solar parks in Gobi ecosystems and provided evidence to support the sustainable management of the solar park.

Temporal assessment of wind energy resource in algerian highlands regions

The aim of this paper is to investigate the monthly, seasonal and annual variation of the wind resource in term of wind energy potential using the wind speed data collected in the last decade for the meteorological stations at four locations situated in Algerian highlands from East to West boundaries. After the study of temporal Weibull parameters, the vertical extrapolation of Weibull parameters and mean wind speed at a height of 50 m was made and the analysis of annual energy efficiency, seasonal and monthly was made for wind energy conversion systems of 600 kW rated capacity. The results show that M’Sila has the highest wind potential among the studied sites with the annual mean wind speed V = 4 m/s and the annual wind energy production equal to 2.4GWh/year, while Kasr Chellala gives a good potential too but affected by an important rate of zero wind frequency. The study gives the regions situated in Algerian highland with an average wind potential, where the better wind energy potential is given in coldest months.

Surface soil pollen and climate in northern Xinjiang, China: Implications for paleoclimate reconstruction

Quantitative paleoclimate reconstruction based on surface soil pollen and climate records on a regional scale is widely applied as a standard approach to investigating past climate changes. This study aimed to explore the quantitative relationships between the distribution of pollen taxa in 438 pollen samples from surface soils at altitudes ranging from 190 m to 3235 m and 19 environmental parameters in northern Xinjiang, China, using ordination and correlation analyses. The results showed that mean annual precipitation (MAP), precipitation in July, mean annual temperature (MAT), mean temperature in July and mean annual wind speed were the main environmental factors influencing the surface pollen distribution. Pinus pollen dispersal and transportation were primarily affected by spring wind, suggesting that exotic pollen played an essential role in surface pollen distribution. Picea pollen, with a percentage and concentration of over 30% and 30000 grains/g, was distributed at sites with MAT and MAP of −5.1–1.2 °C and 212–459 mm, respectively. Artemisia and Chenopodiaceae, with percentages and concentrations of over 50% and 50,000 grains/g and 50% and 10,000 grains/g, respectively, were mainly distributed at sites with MAT and MAP of −1.9–9.1 °C and 145–416 mm, and −3.2–8.9 °C and 129–449 mm, respectively, providing an accurate reference for paleoclimates reconstruction. This study presents information regarding the relationship between surface soil pollen and climate records, which may contribute to the reconstruction of historical climates, promote ecological development in Xinjiang, China, and assist in predicting future climate trends.

Sustainable restoration in sandy lands is challenged by the divergent responses of grassland functional types to disturbance

The effective management of regional grassland degradation requires a deep understanding of how different grassland types respond to climate change and human disturbance over different spatial and temporal scales. This study examined the dynamics and underlying driving mechanisms of various grassland types in the Otindag Sandy Land over 36 years (1986–2021) by integrating time-series Landsat images and extensive climate and socio-environmental data. We found that the dynamics and driving factors of different grassland types varied significantly. In low-cover grasslands, the area of desert grassland fluctuated with a decreasing trend after the breakpoint, while that of sandy grassland increased continuously without any breakpoints. In high-cover grasslands, the area of typical grassland only increased after the breakpoint, whereas that of temperate savanna grasslands first decreased and then increased insignificantly. Additionally, the desert grassland area was significantly negatively correlated with annual precipitation (AP), whereas the sandy grassland area was negatively correlated with annual mean wind speed (AMWS). The dynamics of typical grasslands were jointly influenced by climate and human factors, with temperate savannas mainly driven by the AP and AMWS. Moreover, high-cover grasslands were more sensitive to human activities than low-cover grasslands, and the most significant driving factors changed before and after the breakpoint in the area dynamics. These findings provide valuable insights into the monitoring and conservation of sandy land ecosystems.

Numerical Design of an Off-Grid Wind Energy Systems for Small Scale Residential Power Supply

The socio-economic developments of all nations depend duly on electrical energy supply. Nigeria as a country is faced with challenges of poor electric power supply due to the challenges from the power holding company of Nigeria (PHCN) which have left many households and isolated areas with no access to electricity. The fact that fossil fuel generators contributes to climate changes, renewable energy such as wind could be the best alternative. This paper presents the numerical design of an off-grid wind energy systems for powering load demand of two bedrooms apartment. The load demand of two bedroom flats were estimated and sizes of wind turbine, storage batteries and inverter with respect to typical load demand were numerically estimated. The performance of three 1 KW horizontal axis wind turbines of the same capacity but from different manufacturers namely; Bergey’s (BWC XL.1), Wind energy (WE.7) and Hummer wind energy (H3.1) were numerically tested using the average monthly wind speed data of Aliero Kebbi state, Nigeria. The result shows that integrating 1 kW wind turbine with 6 pieces of 200 Ah batteries on 24 V system and 1 kVA inverter would be feasible for 505 W (4.37 kWh/day) load demand with two days autonomy. The annual mean energy generated by the BWC XL.1, WE.7 and H3.1 using annual mean wind speed 5.03 m/s were 1782 kWh/yr, 1451kWh/yr and 1909 kWh/yr, attaining efficiencies of 44.45 % 33.22 % and 33.22 % respectively. Based on these results, BWC XL.1 could be best alternative wind turbine for the location because of its optimum efficiency. In addition, its energy generation of 1782 kWh/yr is 11.72% greater than 1595 kWh/year, the typical residential household load demand.

Development of novel optimized deep learning algorithms for wildfire modeling: A case study of Maui, Hawai‘i

To address the growing global concern regarding increased wildfire occurrences and their widespread socio-ecological impacts, cost-effective and practical approaches must be urgently identified to accurately predict the probability of wildfire incidents. The objective of this study was to develop deep learning models to estimate the likelihood of wildfire incidents and compare the predictive capability of the methods. To this end, the group method of data handling (GMDH) and convolutional neural network (CNN) algorithms were coupled with the biogeography-based optimization (BBO) and ant colony optimization (ACO) algorithms to enhance the predictive performance of the models. Overall, 1745 historical wildfires were identified in the island of Maui, Hawai‘i. Among them, 1221 events (70%) were randomly selected to generate the models, and the remaining 524 (30%) were used for validation. The frequency ratio, information gain ratio (IGR), and variance inflation factor methods were used to select the optimal number of predictor variables for model development. 13 influencing factors: elevation, aspect, slope, plan curvature, slope length, valley depth, topographic wetness index, mean annual wind speed, mean annual air temperature, mean monthly rainfall, distance to the road, distance to the river, and normalized difference vegetation index were selected to generate the proposed models and detect fire-prone areas. The IGR values indicated that the key parameters for predicting wildfire susceptibility were the distance to the road, mean annual air temperature, elevation, and slope. Finally, the area under the receiver operating characteristic curve (AUROC) was computed to verify the reliability and accuracy of the wildfire susceptibility maps. Both the optimization algorithms enhanced the performances of the GMDH and CNN models. The ACO most notably enhanced the CNN performance compared with the models (AUROCTraining=0.889 and AUROCTesting=0.885). The findings demonstrated the potential of coupled models in overcoming the limitations of individual models for mapping fire-susceptible areas and analyzing the multifactorial aspects that lead to wildfire occurrence. Overall, the proposed frameworks can be used to predict the spatio-temporal patterns of wildfire occurrence, which are of significance for land management, wildfire prevention, and mitigation of wildfire consequences.

Analysis of Spatial-Temporal Changes and Driving Forces of Desertification in the Mu Us Sandy Land from 1991 to 2021

Desertification is one of the most critical environmental and socioeconomic issues in the world today. Located in the transitional region between the desert and the Loess Plateau, the Mu Us Sandy Land is one of the nine most environmentally sensitive areas in the world. Remote sensing provides an effective technical method for desertification monitoring. In order to analyze the spatiotemporal distribution of desertification in the Mu Us Sandy Land from 1991 to 2021, the “MSAVI-Albedo” model was employed to extract desertification data in 1991, 2002, 2009 and 2021. The clustering characteristics of desertification were analyzed based on Moran’s I statistic. Subsequently, the driving forces in desertification changes were investigated using a geographical detector to analyze the influence of soil, meteorology, and topography on desertification. Additionally, the impact of meteorological and human factors on desertification change in the Mu Us Sandy Land was assessed. From 1991 to 2021, the degree of desertification of the Mu Us Sandy Land showed an overall decreasing trend, and the percentage of land classified as undergoing extremely severe, severe, moderate and mild desertification was improved by 86.11%, 81.82%, 52.5% and 37.42%, respectively. The proportion of land classified as undergoing extremely severe desertification decreased from 29.22% to 5.62%, and the proportion of land undergoing no desertification increased from 4.16% to 18.33%. At the same time, the desertification center shifted westward, and the desertification distribution showed a clustering trend. It is known that different factors affect the formation and distribution of desertification in the Mu Us Sandy Land in the following order: soil, meteorology, and topography. Over the past 30 years, the mean annual temperature and annual precipitation increased at rates of 0.01871 °C/a and 1.0374 mm/a, respectively, while the mean annual wind speed decreased at a rate of 0.00945 m/s·a. These changes provided more favorable natural conditions for vegetation growth and sand fixation. Human factors, such as economic development, agriculture and animal husbandry practices, and the policy of returning farmland to forest (grassland) also had a significant impact on the desertification process, leading to a year-by-year improvement in the ecological environment of the Mu Us Sandy Land.

Landscape and Marine Environmental Factors Jointly Regulate the Intertidal Species Richness and Community Structure in the Islands of South Korea

Most studies on island biogeography have focused on the terrestrial taxa; however, there are few studies on the drivers of diversity and community structure of intertidal organisms on islands. We evaluated the effect of landscape and marine environmental factors on the species richness (SR), functional diversity (FD), and community structure (SES.MFD) of intertidal invertebrates among the overall, inhabited, and uninhabited islands. Using the data on the intertidal organisms from 78 islands in South Korea, we implemented variable selection and piecewise structural equation modeling to determine the causal relationships between the SR, FD, and SES.MFD with four landscapes (i.e., island area, coastline length, distance from the mainland, and structural connectivity) and three marine environment factors (i.e., mean annual sea surface temperature variation, wind speed, and evapotranspiration). The coastline length had a positive effect on the SR and SES.MFD in the overall islands including inhabited and uninhabited islands. The SR and FD were negatively affected by the variation in sea surface temperature. The relative importance of the landscape and marine environmental factors differed between the inhabited and uninhabited islands. That is, the inhabited islands were mainly affected by the coastline length, whereas uninhabited islands were regulated by marine environmental factors. Our results demonstrated that the biotic factors of the island intertidal ecosystems are controlled by the interactions between the biotic and various environmental factors. Moreover, the results emphasize that the water stress on the intertidal organisms due to climate change may lead to a loss of biodiversity and functional clustering.

Evaluation of MSWX Bias-Corrected Meteorological Forcing Datasets in China

Near-surface meteorological forcing (NSMF) datasets, mixed observations, and model forecasts are widely used in global climate change and sustainable development studies. For practical purposes, it is important to evaluate NSMF datasets, especially those released latest, and determine their strengths and limitations. In this study, we evaluate the performance of Multi-Source Weather (MSWX) in China over the period of 1979–2016. For comparison, ECMWF Reanalysis version 5 (ERA5), China Meteorological Forcing Dataset (CMFD) and Princeton Global Forcing (PGF) dataset are also evaluated to determine the strengths and weaknesses of MSWX. The following variables are compared with observations over 2400 stations: 2 m air temperature (T2m), 2 m daily maximum air temperature (Tmax), 2 m daily minimum air temperature (Tmin), precipitation (P), and 10 m wind speed (V10). The evaluation is conducted in terms of climatology, inter-annual variations and seasonal cycles. Results show that MSWX reasonably reproduces the spatial pattern of T2m with root-mean-square errors (RMSEs) below 1.12 °C and spatial correlations above 0.97, but underestimates Tmax and overestimates Tmin, with biases ranging from −2.0 °C to 2.0 °C, especially over the North China and Northeast China. Compared with ERA5 and PGF, MSWX can better simulate the inter-annual variations of surface air temperature with high spatial correlations (>0.97) but shows higher RMSEs than PGF. For precipitation, MSWX accurately captures the primary features of precipitation, including significant characteristics or patterns of the precipitation climatology and inter-annual variation. Its inter-annual variation shows low RMSEs ranging from 0.55 mm/day to 0.8 mm/day, compared to ERA5 and PGF. However, regions with abundant precipitation exhibit higher biases. Because the biased Global Wind Atlas (GWA3.1) is used for the wind bias correction of MSWX, MSWX significantly overestimates the annual mean wind speed, but it is consistently well-correlated with observations, with RMSEs less than 1.5 m/s and spatial correlations greater than 0.6 over the period of 1979–2016. This study reveals both the advantages and disadvantages of MSWX, and indicates the need for research into climate change and sustainable development in East Asia.

Effects of conservation practices on global wind erosion control: Evidence from experimental data

AbstractWind erosion represents one of the major land degradation forms that significantly threatens sustainable development across the globe. A series of conservation practices have been widely developed and tested in order to effectively reduce wind erosion rates in non‐irrigated dry and semi‐dry lands. However, a comprehensive investigation regarding the efficacy of conservation practices and potential impacting factors on erosion control at the global scale has not been undertaken. In this study, a synthesis was conducted, based on data from global experimental plots, to comprehensively examine the efficacy of varying conservation practices, and their effectiveness at reducing erosion, in different geographical regions, under varying climatic conditions, and with different soil properties. The results showed that conservation practices were able to reduce wind erosion rates by over 52% globally. Straw use and soil stabilizer were the most effective measures in wind erosion control, which could reduce erosion rates by over 70%. Wind erosion control efficacy of conservation practices differed among climatic zones, continents, and countries. Notably, the lowest efficacy was observed in dry climatic zones. Erosion control efficacy highly depended on climatic conditions, such as mean annual precipitation (MAP), mean annual temperature (MAT), wind speed, and soil properties, such as water content and clay content. We further observed a positive correlation between wind erosion reduction rates and straw mulching rates, suggesting a straw mulching rate of at least 40% was necessary in order to reach a potential erosion reduction rate of 70%. To sum up, this study illustrates that the overall efficacy of conservation practices in wind erosion control is highly effective at the global scale, but the specific effects vary among different regions, and depend on conservation practice type, climatic condition, and soil property. The lower erosion reduction rate of dry regions at high erosion risk suggests the high erosion control pressure, and more effective measures should be considered and developed. The results of this study are, importantly, complementary to existing wind erosion control guidelines.

An approach to assess offshore wind power potential using bathymetry and near-hub-height reanalysis data

Offshore wind power is emerging option in wind industry due to relatively consistent wind behavior over ocean. This study aims to access offshore wind resource employing a novel approach using bathymetry data, in-situ measured and 5th generation European Reanalysis (ERA-5) wind data. The measured wind data for four near-coast locations was used in Exclusive Economic Zone (EEZ) of Pakistan. The percentage bias for wind speed and wind direction of ERA-5 data compared to measured data for coastline location is −0.2% and −0.9% respectively, which shows suitability of ERA-5 data for offshore resource assessment with acceptable accuracy. Annual mean wind speed and wind power density of the EEZ is 6.1 m/s and 263 W/m2 respectively. The northeastern part of the EEZ near coastline of Sindh province is most suitable for offshore wind farms. An offshore wind farm site suitable for monopile foundation having wind power class as Fair was selected. The performance of more than 400 commercial wind turbines was evaluated and a 4.5 MW turbine having highest net capacity factor (NCF) was selected. The capacity factor of suggested wind farm having installed capacity of 540 MW is computed as 55% which can avoid 1.45 million tons in CO2eq emissions annually.