Dust storms evolution in Taklimakan Desert and its correlation with climatic parameters

Based on the sand dust storms data and climatic data in 12 meteorological stations around sand dust storm originating areas of the Taklimakan Desert, we analyzed the trends of the number of dust storm days from 1960 to 2005 as well as their correlations with temperature, precipitation, wind speed and the number of days with mean wind speed 〉 5 m/s. The results show that the frequency of dust storm events in the Taklimakan region decreased with the elapse of time. Except Ruoqiang and Minfeng, in the other 10 meteorological stations, the frequency of dust storm events reduces, and in 4 meteorological stations of Kuqa, Korla, Kalpin and Hotan, the frequency of dust storm events distinctly decreases. The temperature has an increasing trend, while the average wind speed and the number of days with mean wind speed ≥ 5 m/s have decreasing trends. The correlation analysis between the number of days of dust storms and climatic parameters demonstrates that wind speed and the number of days with mean wind speed 〉 5 m/s have strong positive correlation with the number of days of dust storms, with the correlations coefficients being 0.743 and 0.720 （p〈0.01）, respectively, which indicates that strong wind is the direct factor resulting in sand dust storms. Whereas precipitation has significant negative correlation with the number of days of dust storms （p〈0.01）, and the prior annual precipitation has also negative correlation, which indicates that the prior precipitation restrains the occurrence of sand dust storms, but this restraining action is weaker than the same year＇s precipitation. Temperature has negative correlation with the number of dust storm days, with a correlations coefficient of -0.433 （p〈0.01）, which means that temperature change also has impacts on the occurrence of dust storm events in the Taklimakan region.

Effect of altitude and climatic parameters on shrub-meadow community composition and diversity in the dry valley region of the eastern Hengduan Mountains,China

Because of the“foehn effect”,deeply incised gorges of major rivers in the Hengduan Mountains(commonly called dry valleys)have semiarid or arid climate.Harsh environment and difficult access have so far obstructed the systematic inventory and documentation of the flora of these dry valleys.This is particularly problematic for efforts towards the conservation of endemic and valuable plant species.Therefore,102 shrub-meadow community survey plots were set up along four dry valleys in Ganzi prefecture,located in the eastern Hengduan Mountains,China.The compositions,richness,diversity of these communities were calculated and assessed using sample plot survey and phytosociological approach.Overall,244 plant species were recorded,consisting of subtropical(48.77%)and temperate(38.83%)species,47.13%of which were endemic to southwest China.Obvious differences in species composition and structure along the altitude gradient were observed.The variations of richness,diversity,and evenness followed a bimodal-hump shaped pattern with increasing altitude,with peak occurring at mid-level altitude(3501–4000 m)and valley occurring at 2501–3000 m altitude.The regions at 2501–3000 m altitudes were more sensitive to global climate change and biological interference,and were found to have the highest protection value.The impacts of altitude gradients and climatic parameters on the features of this shrubmeadow community were also evaluated using principal component and multiple linear stepwise regression analysis methods.Altitude and temperature-related variables were the most important drivers of both species richness and cover.Speciesα-diversity here only depended on the precipitation frequency.This founding could help to understand the impact of the very harsh environment and altitude gradient on plant-plant interactions in a variety of natural systems.

Influence of Climatic Parameters on Soybean Subjected to Low Nitrogen Application Rates in Southern Guinea Savanna of Oyo State, Nigeria

The variability in weather patterns consequent upon climate change constitutes a critical factor influencing soil N availability and the performance of crops. This paper aimed at evaluating the effects of climatic factors on soybean subjected to low N rates under rain-fed situation in the southern Guinea agro-ecology of Oyo State, Nigeria. A 2-year field experiment involving two soybean varieties (TGx1485-1D and TGx1448-2E) and five low rates of N fertilizer application (0, 5, 15, 25, 35 kg/ha) using Urea applied by banding 7 days after planting was arranged in a 5 × 2 split-plot with three replications. N rates constitute the main plot while variety constitutes the sub-plot. Parameters measured include dry shoot weight, shoot N accumulation, and grain yield. Data were subjected to GENSTAT statistical package for analysis, and means separated with Duncan Multiple Range Test (DMRT) at 5% level of probability. Climatic parameters of rainfall, temperature, and potential evapotranspiration data were collected from the surface observatory of the National meteorological agency (NIMET) and subjected to Excel package for computation and graphics. The dry shoot weight increases as N rate increases up to 25 kg/ha, but declines at 35 kg/ha application rate, however, TGx1448-2E produced a higher dry shoot weight (2.9 t/ha) than TGx1485-1D (2.8 t/ha). Low N rates had no significant effects on shoot N accumulation and there was no significant difference in varietal response. Low N rate did not affect grain yield, but the TGx1448-2E had a greater yield of 1.5 t/ha than TGx1485-1D (1.30 t/ha). Annual rainfall amounts were 935.5 mm and1475.8 mm in 2009 and 2010 respectively. Potential evapotranspiration (PET) values were 1676.5 mm and 1676.8 mm in 2009 and 2010 respectively. Temperature values range from 24˚C to 29.8˚C in both years and the mean monthly temperature for 2009 was 26.5˚C and 27.1˚C for 2010. The application of N fertilizer to soybean requires appropriate timing for effective use. Climatic parameters such as rainfall, temperature, and evapotranspiration have dire consideration for fertilizer use and efficiency.

Development of yield forecast model using multiple regression analysis and impact of climatic parameters on spring wheat

Understanding the impacts of climate change in agriculture is important to ensure optimal and continuous crop production.The agricultural sector plays a significant role in the economy of Upper Midwestern states in the USA,especially that of North Dakota(ND).Spring wheat contributes most of the wheat production in ND,which is a major producer of wheat in the USA.This study focuses on assessing possible impacts of three climate variables on spring wheat yield in ND by building a regression model.Eighty-five years of field data were collected and the trend of average minimum temperature along with average maximum temperature,average precipitation,and spring wheat yield was analyzed using Mann-Kendall test.The study area was divided into 9 divisions based on physical locations.The minimum temperature plays an important role in the region as it impacts the physiological development of the crops.Increasing trend was noticed for 6 divisions for average minimum temperature and average precipitation during growing season.Northeast and Southeast division showed the strongest increasing trend for average minimum temperature and average precipitation,respectively.East-central division had the most decreasing trend for average maximum temperature.A significant relationship was established between spring wheat yield and climatic parameters as the p-value is lower than 0.05 level which rejects the null hypothesis.The regression model was tested for forecasting accuracy.The percentage deviation of error for the model is approximately±30%in most of the years.

Spatio-Temporal Variability of Rainfall and Temperature and Their Effects on Pasture Variability over East Africa: Implication on the Cattle Grazing Areas

Understanding the spatiotemporal variability of climatic parameters and their effects on pasture variability is vital for pasture management interventions over East Africa. The present study aims to assess the spatial-temporal variability of rainfall, temperature and Normalized Difference Vegetation Index (NDVI) (which is being used to assess pasture quality and productivity) over the region, between the period of 1982 and 2019. This study used annual mean values for rainfall, temperature and NDVI which were calculated for the period mentioned above. NDVI was derived from National Oceanic and Atmospheric Administration (NOAA) Global Area Cover (GAC) (NOAA-07-GAC) data. The rainfall data was acquired from the Climate Hazards Group Infrared Precipitation with Station (CHIRPS) while temperature is ERA5 reanalysis data sourced from the European Centre for Medium-Range Weather Forecasts (ECMWF). The study utilized the empirical orthogonal function (EOF) to identify patterns and dominant relationships between the climate variables. The correlation was calculated between rainfall, temperature and NDVI to assess the relationship among them. A non-parametric Mann-Kendall trends test was used to determine whether annual precipitation, temperature and NDVI had statistically increasing or decreasing trend. Results revealed a positive correlation between rainfall and NDVI while a negative correlation between NDVI and temperature. Positive correlation between rainfall and NDVI indicates that pasture health (quality and productivity), will improve accordingly. A negative correlation between temperature and NDVI indicates that pasture health will decrease with increase in temperature while improving with decreasing temperature. Outcome from this study suggests that changes in climatic variables influence the distribution of pasture in East Africa’s cattle grazing areas. The study hence recommends prioritisation of climatic (weather) information during pasture management over East Africa.

Meteorological impacts on evapotranspiration in different climatic zones of Pakistan

Arid regions are highly vulnerable and sensitive to drought. The crops cultivated in arid zones are at high risk due to the high evapotranspiration and water demands. This study analyzed the changes in seasonal and annual evapotranspiration（ET） during 1951–2016 at 50 meteorological stations located in the extremely arid, arid, and semi-arid zones of Pakistan using the Penman Monteith（PM） method. The results show that ET is highly sensitive and positively correlated to temperature, solar radiation, and wind speed whereas vapor pressure is negatively correlated to ET. The study also identifies the relationship of ET with the meteorological parameters in different climatic zones of Pakistan. The significant trend analysis of precipitation and temperature（maximum and minimum） are conducted at 95% confidence level to determine the behaviors of these parameters in the extremely arid, arid, and semi-arid zones. The mean annual precipitation and annual mean maximum temperature significantly increased by 0.828 mm/a and 0.014℃/a in the arid and extremely arid zones, respectively. The annual mean minimum temperature increased by 0.017℃/a in the extremely arid zone and 0.019℃/a in the arid zone, whereas a significant decrease of 0.007℃/a was observed in the semi-arid zone. This study provides probabilistic future scenarios that would be helpful for policy-makers, agriculturists to plan effective irrigation measures towards the sustainable development in Pakistan.

Drought Monitoring and Assessment of Climate Parameters Variability in Koutiala and San Districts, Mali

In Mali, the annual temperature, rainfall, and evapotranspiration are high variables. Their distributions are unevenly spread from north to south. Climate change strengthens to increase air temperature and evapotranspiration. It also increases the intense rainstorms and the risk of drought associated heat waves. Drought is considered a natural disaster among all hydrologic extremes. It causes severe damage to the environment, agriculture, and livelihoods relying on water resources. The present study evaluated the variation of drought indices from 1989 to 2019 in Koutiala and San districts, respectively. Therefore, the Standardized Precipitation Evapotranspiration Index (SPEI) was applied. Hence, the Mann-Kendall (MK) test was used and for 12-month time-scales. Trend analysis of monthly precipitation, temperature, and evapotranspiration has been done by using the MK test. Based on the analysis result, the climate of the Koutiala and San districts has been classified as moderate to severe drought category. However, this result clearly shows SPEI pattern changes in both districts. The monthly precipitation showed a significant decreasing trend in Koutiala and San districts. In comparison, the monthly temperature and evapotranspiration displayed an increasing trend in both districts.

Climate Impact on the Productivity of Sugarcane Varieties in Ferke1 Industrial Plantations

This study assesses the climate impact on the productivity of five sugarcane varieties (R579, SP711406, M2593/92, M1400/86, and SP701006) in the industrial plantations of Ferké 1 sugar complex. It is a contribution to research efforts aimed at increasing the productivity of sugarcane varieties in the sugar fields. Also to support agricultural development and guarantee the income of planters. The sugarcane production data are from 2013 to 2017. Climatological data are measured and calculated continuously daily at the production site. In addition, the CMIP-5 (Coupled Model Intercomparison Project) climate database at 1º × 1º horizontal resolution was used for the predictability of crop yields of the 5 sugarcane varieties in the near future (2021-2050) and far future (2056-2075) to improve the quality of climate services to producers. The statistical methodological approach by multiple linear regression associated with the significativity test shows important and significant coefficients of determination (R2 > 0.90) between the yields of sugarcane varieties with certain climatic parameters such as minimum and maximum temperatures, insolation, global solar radiation, and potential evapotranspiration. The impact of rainfall has not been directly evaluated because the linear models do not explicitly show sensitivities to this parameter and the total water requirements for the plot are completely assured by irrigation. The future climate projections analyzed only from extreme thermal parameters (Tmax and Tmin) highlight their strong sensitivities with yields from an idealized model. In this model, we have assumed that the water supply needed by sugarcane is always met by irrigation on different plots. Moreover, linear models do not evolve fast enough in time and changes due to external environmental constraints are not too important at the plot scale. The projected thermic parameters can thus constitute a limiting factor for the producibility of sugarcane varieties either by excess or by default. In addition, the linear models used allowed us to observe the behavior of yields with respect to observed past climatic conditions. However, for future yields, there is no way to know if these regressions have the ability to predict them since they are based on projected weather conditions (i.e. CMIP5 data) marked by uncertainties. Additionally, none of the regression equations have been tested against independent observations.

The Influence of Weather and Climate Variability on Groundwater Quality in Zanzibar

Climate change and variability have been inducing a broad spectrum of impacts on the environment and natural resources including groundwater resources. The study aimed at assessing the influence of weather, climate variability, and changes on the quality of groundwater resources in Zanzibar. The study used the climate datasets including rainfall (RF), Maximum and Minimum Temperature (Tmax and Tmin), the records acquired from Tanzania Meteorological Authority (TMA) Zanzibar office for 30 (1989-2019) and 10 (2010-2019) years periods. Also, the Zanzibar Water Authority (ZAWA) monthly records of Total Dissolved Solids (TDS), Electrical Conductivity (EC), and Ground Water Temperature (GWT) were used. Interpolation techniques were used for controlling outliers and missing datasets. Indeed, correlation, trend, and time series analyses were used to show the relationship between climate and water quality parameters. However, simple statistical analyses including mean, percentage changes, and contributions to the annual and seasonal mean were calculated. Moreover, t and paired t-tests were used to show the significant changes in the mean of the variables for two defined periods of 2011-2015 and 2016-2020 at p ≤ 0.05. Results revealed that seasonal variability of groundwater quality from March to May (MAM) has shown a significant change in trends ranging from 0.1 to 2.8 mm/L/yr, 0.1 to 2.8 μS/cm/yr, and 0.1 to 2.0℃/yr for TDS, EC, and GWT, respectively. The changes in climate parameters were 0.1 to 2.4 mm/yr, 0.2 to 1.3℃/yr and 0.1 to 2.5℃/yr in RF, Tmax, and Tmin, respectively. From October to December (OND) changes in groundwater parameters ranged from 0.2 to 2.5 mm/L/yr 0.1 to 2.9 μS/cm/yr, and 0.1 to 2.1℃/yr for TDS, EC, and GWT, whereas RF, Tmax, and Tmin changed from 0.3 to 1.8 mm/yr, 0.2 to 1.9℃/yr and 0.2 to 2.0℃/yr, respectively. Moreover, the study has shown strong correlations between climate and water quality parameters in MAM and OND. Besides, the paired correlation has shown significant changes in all parameters except the rainfall. Conclusively, the study has shown a strong influence of climate variability on the quality of groundwater in Zanzibar, and calls for more studies to extrapolate these results throughout Tanzania.

Application of Machine Learning Methods on Climate Data and Commercial Microwave Link Attenuations for Estimating Meteorological Visibility in Dusty Condition

Accurately measuring meteorological visibility is an important factor in road, sea, rail, and air transportation safety, especially under visibility-reducing weather events. This paper deals with the application of Machine Learning methods to estimate meteorological visibility in dusty conditions, from the power levels of commercial microwave links and weather data including temperature, dew point, wind speed, wind direction, and atmospheric pressure. Three well-known Machine Learning methods are investigated: Decision Trees, Random Forest, and Support Vector Machines. The correlation coefficient and the mean square error, between the visibility distances estimated by Machine Learning methods and those provided by Burkina Faso weather services are computed. Except for the SVM method, all the other methods give a correlation coefficient greater than 0.90. The Random Forest method presents the best result both in terms of correlation coefficient (0.97) and means square error (0.60). For this last method, the best variables that explain the model are selected by evaluating the weight of each variable in the model. The best performance is obtained by considering the attenuation of the microwave signal and the dew point.

Input database related uncertainty of Biome-BGCMuSo agroenvironmental model outputs

Gridded model assessments require at least one climatic and one soil database for carrying out the simulations.There are several parallel soil and climate database development projects that provide sufficient,albeit considerably different,observation based input data for crop model based impact studies.The input database related uncertainty of the Biome-BGCMuSo agro-environmental model outputs was investigated using three and four different gridded climatic and soil databases,respectively covering an area of nearly 100.000 km2 with 1104 grid cells.Spatial,temporal,climate and soil database selection related variances were calculated and compared for four model outputs obtained from 30-year-long simulations.The choice of the input database introduced model output variability that was comparable to the variability the year-to-year change of the weather or the spatial heterogeneity of the soil causes.Input database selection could be a decisive factor in carbon sequestration related studies as the soil carbon stock change estimates may either suggest that the simulated ecosystem is a carbon sink or to the contrary a carbon source on the long run.Careful evaluation of the input database quality seems to be an inevitable and highly relevant step towards more realistic plant production and carbon balance simulations.

Ammonia and greenhouse gas distribution in a dairy barn during warm periods

This research aimed to quantify concentrations of ammonia(NH3),carbon dioxide(CO_(2))and methane(CH_(4)),estimate emissions,and analyze the factors influencing them during warm periods in an open dairy barn equipped with two cooling systems in a Mediterranean climate zone.Gas distribution within the barn was observed to vary both vertically and horizontally,with the highest gas concentrations observed in the central area of the barn.NH_(3),CH_(4)and CO_(2)ranged in 1.7–7.4,7–18,560–724μg·g^(–1),respectively.Natural ventilation through openings and the operation of cooling systems induced changes in indoor microclimate conditions,influencing cow behavior and,consequently,gas production.Gas concentrations were the highest at air velocities below 0.5 m·s^(–1).The highest concentration of NH_(3)was observed when the temperature-humidity index(THI)was>72 and≤78;and CO_(2)and CH_(4)concentrations were the highest with THI≥72 and decreased with THI≤72.NH_(3)concentrations when barn management included three daily milkings were higher than those measured when barn management was based on two daily milkings,and lower for CH_(4)and CO_(2).NH_(3)and CH_(4)emissions were the highest during barn cleaning,while the lowest NH_(3)emissions occurred during activity of the cows(i.e.,feeding,walking).