应用测井资料对四川碳酸盐岩的老井选层

本文提出了一整套对四川资料不全老井的测井解释方法。在定性划分渗透层定性判断气水层的基础上,本文首次提出石灰岩电极相对位概念,用它半定量划分气水层,效果良好,进而计算储层孔隙度、渗透率、油气水饱和度,定量地划分气水层,并确定出气水层分类评价标准。这套测井解释方法,对四川碳酸盐储层评价、老井选层挖潜是可信的,可行的。     

长江泥沙输移及三峡工程泥沙问题

长江泥沙主要来自上游金沙江和嘉陵江,来沙,来水分别占宜昌站７０％和４９％,地表侵蚀以水力侵蚀和重力侵蚀为主,但输沙量远小于侵蚀量。水土保持和兴建中小型水库的拦沙作用明显减少了进入河道的泥沙,与人类活动剧水土流失所带来的负效应部分相互抵消,经综合对比分析,从宏观上看宜昌站输水量多年平均值相对稳定,三峡水库为河道型水库,长６００ｋｍ,水库来水量大,含水量较小,水库采用“蓄清排浑”的调度运行方式可做到长     

北江飞来峡库区典型流域非点源污染特征分析及模拟

为分析飞来峡库区典型流域非点源污染状况,开展社岗流域场次降雨径流水量水质监测试验,采用平均浓度法估算非点源污染负荷量,分析各场次降雨径流污染物特征并计算非点源污染贡献率,应用SWAT模型进行径流及非点源污染模拟。结果表明,径流量对SS、BOD_5等浓度和负荷量均有较强的主导作用,TN、NH_3-N等初期冲刷效应较为明显,污染物负荷量与径流量的相关性好于污染物浓度与径流量的相关性,各污染物的非点源污染贡献率均达到80%以上。SWAT模型径流模拟的率定期和验证期效率系数分别为0.85和0.73,表明所构建的SWAT模型精度良好。在污染物率定期,雨强较大的典型日污染物模拟效果最佳,污染物相对误差基本在10%以内,而在雨强较小时,TN、TP等均出现较大程度的低估现象;在污染物验证期,由于该时期正处于春耕施肥期,TN、TP模拟结果相对误差较大,且均为低估。总体而言,SWAT模型模拟精度良好,基本能反映该流域非点源污染状况,可为飞来峡库区流域的非点源污染模拟与控制提供参考。     

边坡群稳定性耦联分析方法在三峡库区的应用

针对库区边坡群地质资料、水文信息等数据缺乏的问题,提出了一种库区边坡群稳定性分析方法。该方法首先利用遥感信息技术提取相关库区边坡群岩土整体特征;然后,依据某一个边坡变形破坏的实际监测数据,反算整个边坡群的详细地质信息数据;最后,使用库区边坡稳定性耦联分析模型,跟踪和预测边坡群各个边坡体的变形情况。将提出的方法应用于三峡库区一个典型边坡群的稳定性分析,结果表明预测的滑坡体位移大小和方向与野外观测结果基本吻合,预测的主要滑坡趋势轨迹方向与实际趋势形态吻合良好。     

Distribution of Botia superciliaris (Günther, 1892) ichthyoplankton in the upper mainstem of the Yangtze River preimpoundment and postimpoundment of the upstream dam cascade

Dam cascades have been a major threat to fishes of the upper Yangtze River. The remaining lotic river segment of the upper mainstem between the Xiangjiaba Dam (XJD) and the Three Gorges Reservoir (TGR) may serve as a critical refuge for endemic fishes. We investigated distribution of ichthyoplankton Botia superciliaris, an endemic species, at three sections preimpoundment and postimpoundment of the XJD, that is, Yibin (close to the XJD), Zhuyang (in the middle of the segment), and Mudong (in the tail of the TGR). Preimpoundment of the XJD, larvae occurred in all three sections, and their abundance tended to be highest at Yibin and lowest at Mudong. Postimpoundment, larval abundance dramatically decreased in all three sections. In particular, no larvae were found at Yibin, and larval abundance tended to be higher at Zhuyang than at Mudong. Eggs were collected from all three sections, and those from Zhuyang accounted for over 90% of the total. Initiation of egg occurrence at Yibin was approximately 1 month later than at Zhuyang and Mudong. Water temperature was lower, and water transparency was higher at Yibin than at Zhuyang and Mudong postimpoundment. We suggest that the decrease in abundance and delayed presence of ichthyoplankton at Yibin postimpoundment of the XJD reflected the impact of upstream dam discharge and that the lower abundance of ichthyoplankton at Mudong compared with Zhuyang both preimpoundment and postimpoundment of the XJD reflected the influence of inundation by the TGR. We recommend that the river sections around Zhuyang become high‐priority conservation areas     

熵值密切值法在陆浑水库营养状况评估中的应用

为了掌握陆浑水库的富营养化情况,采用熵值密切值法结合湖库富营养化评估标准,对其2009-2015年的营养状况进行评估。结果表明:该水库为轻度富营养化,主要表现为总氮超标;将该方法与线性插值评分法相比较,两者存在差异,线性插值法的主观性较大且没有考虑各指标对评估结果的影响,而熵值密切值法能够减少主观性,还能确定影响水质的主要因素,使富营养化状况的评估结果更符合实际。因此,熵值密切值法可以作为评估水体水质的常用方法。     

Research Progress on the Application of Google Earth Engine in Geoscience and Environmental Sciences

With the rapid development and large integration of global informatization and industrialization since the 21st century,the Internet of things and cloud\|computing have emerged.The world has entered an era of big data.There are a huge amount geographical and remote sensing data generated every day in the field of geoscience,environmental science and related disciplines.However,the traditional approaches for storing,managing and analyzing massive data on the local platform,which take up lots of resources,time and energy,have been unable to meet the needs of the current researches.Google Earth Engine(GEE) cloud platform is powered by Google’s cloud infrastructure,and it combines a large number of geospatial datasets and satellite imagery,in which the datasets could be processing,analyzing as well as visualizing on a global scale.Meanwhile,it uses Google’s powerful computational capabilities to analyze and process a variety of environmental and social issues including climate change,vegetation degradation,food security and water resource shortages.Firstly,an introduction of GEE cloud platform has been given.Secondly,recent researches that using GEE cloud platform were reviewed.Thirdly,GEE cloud platform and MODIS land cover type data were used to analyze spatio\|temporal changes patterns of major land use and land cover type in Three Gorges Reservoir in the period of 2002~2013.The results indicate the largest changes occurring in forest lands,shrub grasslands and croplands.Finally,after a rough calculation,GEE cloud platform is superior to the traditional approaches in terms of both cost and economic efficiency,improving the overall efficiency by more than 90%.GEE cloud platform could not only provide powerful support to experts in the field of geosciences and remote sensing,but also offer valuable help to researchers in related disciplines.GEE cloud platform is an excellent tool for scientific research in geosciences,environment sciences and related disciplines.     

兼顾四大家鱼产卵需求的梯级水电站生态调度北大核心CSCD

为实现流域梯级水电站水资源高效利用,以金沙江下游溪洛渡-向家坝梯级水库生态调度为例,通过已有研究成果以及实测数据推求四大家鱼产卵所需的适宜生态流量,构建梯级水库长短期耦合双层优化模型,上层模型采用非支配排序遗传算法(NSGA-Ⅱ)求解,下层模型采用遗传算法求解,进而提出兼顾四大家鱼产卵流量需求的梯级水库日调度方案。结果表明:与常规调度相比,当以促进四大家鱼产卵为主要目标时,调度期内(5—6月)梯级水库发电量减小1.91亿kW·h,减幅为1.76%;同时生态流量契合度增大0.123,有效洪水上涨作用期累计可达12 d,可较好地促进四大家鱼产卵。研究成果可为制定面向下游河道四大家鱼产卵的梯级水库生态调度方案提供参考依据。     

雨强和土地利用对豫西南山区氮磷流失的影响

为了掌握雨强和土地利用方式对丹江口水库水源区养分流失的影响,通过室内人工模拟降雨试验,对从豫西南山区采集的5种常见土地利用类型的表层土壤,开展了6种雨强下TN(总氮)、TP(总磷)的流失规律研究。结果表明:1地表产流中,雨强对TN的平均含量影响显著,雨强大于1.0 mm/min时才会对TP的含量产生影响;不同用地产生的泥沙中,雨强大于等于3.0 mm/min时才会对TN、TP的含量产生显著影响。2土地类型对TN、TP的流失量也有极大的影响,乔木林、灌草地因其具有较坡耕地、梯田与荒草地更好的土壤结构,TN、TP的流失量相对较少,但前两者之间与后三者之间的差异不大。3对径流中TN、TP的含量与雨强作回归分析,除乔木林地和灌草地略有不同外,其他各用地类均为对数拟合的效果最差,多项式拟合的决定系数较高;不同用地产沙中TN、TP含量与雨强的关系,均以多项式拟合的效果为最好。     

多河流顶托情况下水库下游水位计算方法探讨

因受下游岷江、横江涨水顶托的影响,向家坝水库出库流量与下游水位的关系较为复杂,对水文计算结果和水库调度决策产生了直接影响。以2014年7月~2015年9月向家坝水库下游水位、出库流量、横江水文站和高场水文站流量为研究数据,利用顶托法和BP神经网络模型对横江、岷江的顶托影响进行了分析。结果表明,BP神经网络法检验期最大误差0.32 m,平均误差0.11 m;顶托法最大误差0.74 m,平均误差0.40 m;顶托量大于1m时,BP神经网络法平均误差0.09 m,顶托法平均误差0.42 m。BP神经网络模型计算精度较高,尤其是在顶托量较大时计算结果较好,可用于生产实际。