Environmental variables related to wheat yields in the semiarid pampa region of Argentina

The current paper analyses various environmental parameters in relation to wheat yields in Bordenave, Province of Buenos Aires, Argentina. The variables used are: precipitation (ppt), maximum (Tx) and minimum temperature (Tmi) as well as those obtained by applying the Palmer model. Decadic and phenological scales are used for data corresponding to the period 1977–2000. The stepwise method is used to obtain a multi-variate equation to calculate yield taking into account environmental variables only. For a five variates model the coefficient of determination, R² equals 95.79% and the standard error of estimation is 129.0 kg ha⁻¹. In the sample yields, the incidence of total variability for thermal variables is 42.7% and for hydrological variables, 53%. The value and sign of the correlation coefficients were analysed throughout the cultivation cycle. The α coefficient is mainly responsible for yield variance during tillering and stem elongation. There is good correlation with the values of Palmer's Drought Severity Index (PDSI) for the flowering and grain filling stages.     

Spatiotemporal estimation of snow depth using point data from snow stakes,digital terrain models,and satellite data

Snow availability in Alpine catchments plays an important role in water resources management. In this paper, we propose a method for an optimal estimation of snow depth (areal extension and thickness) in Alpine systems from point data and satellite observations by using significant explanatory variables deduced from a digital terrain model. It is intended to be a parsimonious approach that may complement physical‐based methodologies. Different techniques (multiple regression, multicriteria analysis, and kriging) are integrated to address the following issues: We identify the explanatory variables that could be helpful on the basis of a critical review of the scientific literature. We study the relationship between ground observations and explanatory variables using a systematic procedure for a complete multiple regression analysis. Multiple regression models are calibrated combining all suggested model structures and explanatory variables. We also propose an evaluation of the models (using indices to analyze the goodness of fit) and select the best approaches (models and variables) on the basis of multicriteria analysis. Estimation of the snow depth is performed with the selected regression models. The residual estimation is improved by applying kriging in cases with spatial correlation. The final estimate is obtained by combining regression and kriging results, and constraining the snow domain in accordance with satellite data. The method is illustrated using the case study of the Sierra Nevada mountain range (Southern Spain). A cross‐validation experiment has confirmed the efficiency of the proposed procedure. Finally, although it is not the scope of this work, the snow depth is used to asses a first estimation of snow water equivalent resources.     

Quantifying the impact of landscape changes on hydrological variables in the alpine and cold region using hydrological model and remote sensing data

Quantifying the impact of landscape on hydrological variables is essential for the sustainable development of water resources. Understanding how landscape changes influence hydrological variables will greatly enhance the understanding of hydrological processes. Important vegetation parameters are considered in this study by using remote sensing data and VIC-CAS model to analyse the impact of landscape changes on hydrology in upper reaches of the Shule River Basin (URSLB). The results show there are differences in the runoff generation of landscape both in space and time. With increasing altitude, the runoff yields increased, with approximately 79.9% of the total runoff generated in the high mountains (4200–5900 m), and mainly consumed in the mid-low mountain region. Glacier landscape produced the largest runoff yields (24.9% of the total runoff), followed by low-coverage grassland (LG; 22.5%), alpine cold desert (AL; 19.6%), mid-coverage grassland (MG; 15.6%), bare land (12.5%), high-coverage grassland (HG; 4.5%) and shrubbery (0.4%). The relative capacity of runoff generation by landscapes, from high to low, was the glaciers, AL, LG, HG, MG, shrubbery and bare land. Furthermore, changes in landscapes cause hydrological variables changes, including evapotranspiration, runoff and baseflow. The study revealed that HG, MG, and bare land have a positive impact on evapotranspiration and a negative impact on runoff and baseflow, whereas AL and LG have a positive impact on runoff and baseflow and a negative impact on evapotranspiration. In contrast, glaciers have a positive impact on runoff. After the simulation in four vegetation scenarios, we concluded that the runoff regulation ability of grassland is greater than that of bare land. The grassland landscape is essential since it reduced the flood peak and conserved the soil and water.     

辽宁地区不同降水云系雨滴谱参数及其特征量研究

利用位于辽宁省沈阳市和辽阳市的Parsivel（Particle Size and Velocity）激光雨滴谱仪观测到的雨滴谱资料按照积雨云降水系统、积层混合云降水系统和层状云降水系统分析雨滴谱特征量和谱参数的平均特征及随时间的演变特征。结果表明:Gamma分布拟合谱参数N₀和λ按照层状云、积层混合云和积雨云的顺序减小,谱参数μ按照层状云、积层混合云和积雨云的顺序增大;直径小于1 mm的降水粒子对数浓度的贡献最大,直径大于1 mm的降水粒子对雷达反射率的贡献最大;M-P分布的谱参数N₀与雨强I具有幂函数关系,并且随着雨强I的增大而增大,谱参数λ与雨强I具有指数函数关系,随着雨强I的增大而减小。     

线性相关“可传递性”的几何意义

从几何角度对随机变量显著相关可传递性质进行了分析,得出了样本容量为n(n≥3)随机变量显著相关可传递性质的概率的几何表达式。用Monte-Carlo方法验证了几何分析的合理性,并对此问题展开了一定的讨论。     

西北太平洋气候和海洋环境变化对柔鱼空间分布的影响

Ommastrephes bartramii is an ecologically dependent species and has great commercial values among the AsiaPacific countries. This squid widely inhabits the North Pacific, one of the most dynamic marine environments in the world, subjecting to multi-scale climatic events such as the Pacific Decadal Oscillation(PDO). Commercial fishery data from the Chinese squid-jigging fleets during 1995–2011 are used to evaluate the influences of climatic and oceanic environmental variations on the spatial distribution of O. bartramii. Significant interannual and seasonal variability are observed in the longitudinal and latitudinal gravity centers(LONG and LATG) of fishing ground of O. bartramii. The LATG mainly occurred in the waters with the suitable ranges of environmental variables estimated by the generalized additive model. The apparent north-south spatial shift in the annual LATG appeares to be associated with the PDO phenomenon and is closely related to the sea surface temperature(SST)and sea surface height(SSH) on the fishing ground, whereas the mixed layer depth(MLD) might contribute limited impacts to the distribution pattern of O. bartramii. The warm PDO regimes tend to yield cold SST and low SSH, resulting in a southward shift of LATG, while the cold PDO phases provid warm SST and elevated SSH,resulting in a northward shift of LATG. A regression model is developed to help understand and predict the fishing ground distributions of O. bartramii and improve the fishery management.     

Coastal aquaculture,mangrove deforestation and blue carbon emissions: Is REDD+ a solution?

Globally, coastal aquaculture particularly shrimp farming has been under huge criticism because of its environmental impacts including devastating effects on mangrove forests. However, mangroves are ecologically and economically important forests, and the most carbon-rich forests in the tropics that provide a wide range of ecosystem services and biodiversity conservation. Carbon emissions are likely to have been the dominant cause of climate change and blue carbon emissions are being critically augmented through mangrove deforestation. Because of mangrove deforestation, different climatic variables including coastal flooding, cyclone, drought, rainfall, salinity, sea-level rise, and sea surface temperature have dramatic effects on coastal aquaculture. Mangrove forests have been instrumental in augmenting resilience to climate change. The “Reducing Emissions from Deforestation and forest Degradation (REDD)” program can help to restore mangroves which in turn increases options for adaptation to climate change. However, technical and financial assistance with institutional support are needed to implement REDD+.     

On the period changes of RR Lyrae variables in the globular cluster M3

O – C diagrams of 78 RR Lyrae variables of the years 1963 to 1978 were used to investigate their comparability with the diagrams of 1895 to 1963. Only 5% have been proved to show a fundamentally deviating trend of the period change.     

An Automatic Method for Detecting Transients and Variable Sources in AST3 Survey Based on Image Subtraction and Random Forest

AST3-2 (the second Antarctic Survey Telescope) is located in Antarctic Dome A, the loftiest ice dome on the Antarctic Plateau. It produces a huge amount of observational data which require a more efficient data reduction program to be developed. Also the data transmission in Antarctica is much difficult, thus it is necessary to perform data reduction and detect variable and transient sources remotely and automatically in Antarctica, but this attempt is restricted by the unsatisfactory performance of the low power consumption computer in Antarctica. For realizing this purpose, to develop a new method based on the existing image subtraction method and random forest algorithm, taking the AST3-2 2016 dataset as the test sample, becomes an alternative choice. This method performs image subtraction on the dataset, then applies the principle component analysis to extract the features of residual images. Random forest is used as a machine learning classifier, and in the test a recall rate of 97% is resulted for the positive sample. Our work has verified the feasibility and accuracy of this method, and finally found out a batch of candidates for variable stars in the AST3-2 2016 dataset.     

Formation of Carbon-Enhanced Metal-Poor RR Lyrae Stars

Carbon-enhanced metal-poor (CEMP) stars are considered to be related to the first generation of stars, and responsible for the chemical evolution of the early Galaxy. More than half of them are in binaries, and could be explained by the binary evolution, but the formation channel of them is still not fully understood. Among the hundreds of CEMP stars, there are nine CEMP RR Lyrae stars identified, and at least seven of which are very likely not binaries. The usual binary star evolution channel is difficult to produce such a single star, particularly that of carbon enrichment. One way in which such a single star might be produced is the merger of a helium white dwarf with a Hertzsprung gap (HG) star. We use a stellar evolution program to calculate the models of the merger remnants, and find that the models can reproduce the observed distribution of these CEMP single RR Lyrae stars in terms of surface temperature, gravity, and carbon abundance. Hence, it is extremely possible that the helium white dwarf and HG star merger model is one of the formation channels of the metal-poor carbon-rich RR Lyrae stars.