Analysis of Spatio-temporal Land Surface Temperature and Normalized Difference Vegetation Index Changes in the Andassa Watershed,Blue Nile Basin,Ethiopia

Analysis of the nexus between vegetation dynamics and climatic parameters like surface temperature is essential in environmental and ecological studies and for monitoring of the natural resources. This study explored the spatio-temporal distribution of land surface temperature (LST) and Normalized Difference Vegetation Index (NDVI) and the relationship between them in the Andassa watershed from 1986 to 2016 periods using Landsat data. Monthly average air temperature data of three meteorological sites were used for validating the results. The findings of the study showed that the LST of the Andassa watershed has increased during the study periods. Overall, average LST has been rising with an increasing rate of 0.081°C per year. Other results of this study also showed that there has been a dynamic change in vegetation cover of the watershed in all seasons. There was also a negative correlation between LST and NDVI in all the studied years. From this study we can understand that there has been degradation of vegetation and intensification of LST from 1986 to 2016.     

Soil Respiration Dynamics and Influencing Factors in Typical Steppe of Inner Mongolia under Long-term Nitrogen Addition

We investigated soil respiration (Rs) dynamics and influencing factors under different nitrogen (N) addition levels (0, 2, 4, 8, 16, 32 g m^-2 yr^-1) on typical grassland plots in Inner Mongolia. We measured soil respiration, temperature, moisture and nutrients. We found that N addition did not change dynamic characteristics of Rs; daily and seasonal dynamics followed a single peak curve. N addition reduced Rs during the growing season. Rs under N2, N4, N8, N16 and N32 treatments decreased by 24.00%, 21.93%, 23.49%, 30.78% and 28.20% in the growing season, respectively, compared to the N0 treatment. However, Rs in the non-growing season was not different across treatments. Rs was significantly positively correlated with soil temperature and moisture and these two factors accounted for 72%-97% and 74%-82% of variation in Rs, respectively. The soil respiration temperature sensitivity (Q₁₀) was between 2.27 and 4.16 and N addition reduced Q₁₀ except in the N8 treatment.     

Experimental study on a sampling technique based on a freeze-sediments valve for deep-sea microorganism

Deep-sea microorganism research has become a hot topic in life science, owing to its special value in high pressure and low temperature environment. If not kept the in-situ low temperature and high pressure, it will cause the microorganism to lose its activity and lead to a waste of resources. A freeze-sediments valve presents a promising solution to maintain the low temperature and high pressure in situ, improve the success rate of sampling and shorten the sampling time. A series of experiments were conducted to characterize the freeze-sediments valve, including the sampling time without leakage, adaptation to different sediments and the effect of the length, etc. Most important of all, temperature change of sediments is observed in all experiments. Experimental results indicated that the freeze-sediments valve was formed in 5 min without leakage and the freeze-sediments valve could be applied to different sediments without limitations on sediments type and mineral content. Considering the pressure retaining capacity and formation time of freeze-sediments valve, it was found that the freeze-sediments valve with a length of 60–80 mm has good pressure retaining capacity whose value was 41.3–48.4 Mpa and short formation time which was 5 min. Precooling the sampler could be used to shorten the sampling time in the actual environment application. This sampling technique can play a vital role in maintaining in-situ low temperature at −2 to −4 ° Celsius in all the experiments. According to the tests, the sampling technique based on the freeze-sediments valve had the potential to solve the problem of keeping in-situ temperature, low sampling success rate and long sampling time in the future.     

围海造陆条件下排海高温浓盐水对渤海湾温盐场分布影响模拟及预测

自2000年以来围绕渤海湾的围海工程剧增,致使工程区附近潮流场发生变化,进而影响排海高温浓盐水的时空分布特征。本文通过建立2000年和2015年两种不同岸线、地形条件下的三维数学模型对渤海湾沿岸3个电厂高温浓盐水表层排海问题进行模拟,研究结果表明,渤海湾的潮流场和高温浓盐水输移扩散特征在近十几年发生了较大变化:工程后,渤海湾平均盐度增大0.203,平均温度升高了0.105℃,同时曹妃甸附近海域浓盐水输移扩散速度明显增加。增大排放口流量至12.7 m3/s,湾内最高温度为26.46℃,较2015年最高温度增加了2.72℃。本文模型可准确模拟及预测排海废水盐度、温度分布特征,为合理布置水电联产设备排放口的位置提供理论基础。     

FIO-ESM气候模式北太平洋海表温度和降水的季节性预报技巧评估

The seasonal prediction of sea surface temperature(SST) and precipitation in the North Pacific based on the hindcast results of The First Institute of Oceanography Earth System Model(FIO-ESM) is assessed in this study.The Ensemble Adjusted Kalman Filter assimilation scheme is used to generate initial conditions, which are shown to be reliable by comparison with the observations. Based on this comparison, we analyze the FIO-ESM 6-month hindcast results starting from each month of 1993–2013. The model exhibits high SST prediction skills over most of the North Pacific for two seasons in advance. Furthermore, it remains skillful at long lead times for midlatitudes. The reliable prediction of SST can transfer fairly well to precipitation prediction via air-sea interactions.The average skill of the North Pacific variability(NPV) index from 1 to 6 months lead is as high as 0.72(0.55) when El Ni?o-Southern Oscillation and NPV are in phase(out of phase) at initial conditions. The prediction skill of the NPV index of FIO-ESM is improved by 11.6%(23.6%) over the Climate Forecast System, Version 2. For seasonal dependence, the skill of FIO-ESM is higher than the skill of persistence prediction in the later period of prediction.     

上层海洋对台风"凯萨娜"(2009)的响应特征

本文利用多源卫星遥感数据和Argo浮标数据对2009年台风"凯萨娜"过后,南海上层海洋的物理和生态响应特征进行了分析。结果表明,"凯萨娜"引起的上升流流速最大可以达到1.6×10~(–3)m/s,台风过后,海表面温度(SST)下降显著,最大降温幅度可以达到6℃,海表面高度降低,先前存在的中尺度冷涡进一步加强。台风过后,沿着台风路径,叶绿素浓度升高,最大值可以达到2 mg/m~3以上,初级生产力升高到台风过境前的5倍。SST的最大降温中心与海面高度下降区域以及叶绿素浓度升高的区域一致。Argo数据表明台风诱发了强烈的垂向混合和艾克曼泵吸,不同位置处,垂向混合和艾克曼泵吸的强度不一样。通过混合和泵吸过程,台风可以把海洋内部的营养盐输送到海洋表层,对整个南海的物理和生态过程有重要影响。     

基于傅里叶级数的中国沿海地区Tm模型精化研究

本文采用中国沿海地区13个探空站2010~2014年实测地表温度T_s与平均温度T_m数据,利用傅里叶级数分析法精化中国沿海地区T_m模型,并将2015年探空站实测T_m数据与精化模型进行对比检验。结果表明,精化模型在T_m探测方面具有更高的计算精度,其计算大气可降水量的误差概率分布趋近于正态分布,具有较强的稳定性。     

基于共轭梯度搜索的病态问题处理方法

在最小二乘平差准则基础上,把病态平差问题转化为无约束的二次规划问题,并利用优化理论分析病态对平差解的影响。通过共轭梯度搜索算法在可行域中寻找最优步长因子,自动寻找最速下降方向,并给出迭代初值的设置方法。分析近似计算中病态问题与局部最优解的关系,讨论局部最优解的快速迭代方法,并通过实例验证算法的有效性,计算迭代的速度。由于整个过程没有对法方程系数矩阵进行求逆计算,该算法可用于处理大规模系数矩阵高病态的平差问题。     

基于GPT2w模型化加权平均温度反演可降水量

提出一种基于GPT2w模型化加权平均温度反演大气可降水量的方法,并分析附加系统偏差改正的模型化加权平均温度对可降水量的影响。结果表明,基于GPT2w模型化加权平均温度反演的大气可降水量的精度与基于Bevis公式计算的加权平均温度反演的大气可降水量的精度相当;对GPT2w模型化加权平均温度进行系统偏差改正后,大气可降水量的精度有一定改善,但改善率不到1%。     

Examining the Relationship Between Spatial Configurations of Urban Impervious Surfaces and Land Surface Temperature

The urban heat island(UHI) effect has significant effects on the quality of life and public health. Numerous studies have addressed the relationship between UHI and the increase in urban impervious surface area(ISA), but few of them have considered the impact of the spatial configuration of ISA on UHI. Land surface temperature(LST) may be affected not only by urban land cover, but also by neighboring land cover. The aim of this research was to investigate the effects of the abundance and spatial association of ISAs on LST. Taking Harbin City, China as an example, the impact of ISA spatial association on LST measurements was examined. The abundance of ISAs and the LST measurements were derived from Landsat Thematic Mapper(TM) imagery of 2000 and 2010, and the spatial association patterns of ISAs were calculated using the local Moran’s I index. The impacts of ISA abundance and spatial association on LST were examined using correlation analysis. The results suggested that LST has significant positive associations with both ISA abundance and the Moran’s I index of ISAs, indicating that both the abundance and spatial clustering of ISAs contribute to elevated values of LST. It was also found that LST is positively associated with clustering of high-ISA-percentage areas(i.e.,>50%) and negatively associated with clustering of low-ISA-percentage areas(i.e.,<25%). The results suggest that, in addition to the abundance of ISAs,their spatial association has a significant effect on UHIs.