Modelling the effect of changing precipitation inputs on deep soil water utilization

Forests in the Southeastern United States are predicted to experience future changes in seasonal patterns of precipitation inputs as well as more variable precipitation events. These climate change‐induced alterations could increase drought and lower soil water availability. Drought could alter rooting patterns and increase the importance of deep roots that access subsurface water resources. To address plant response to drought in both deep rooting and soil water utilization as well as soil drainage, we utilize a throughfall reduction experiment in a loblolly pine plantation of the Southeastern United States to calibrate and validate a hydrological model. The model was accurately calibrated against field measured soil moisture data under ambient rainfall and validated using 30% throughfall reduction data. Using this model, we then tested these scenarios: (a) evenly reduced precipitation; (b) less precipitation in summer, more in winter; (c) same total amount of precipitation with less frequent but heavier storms; and (d) shallower rooting depth under the above 3 scenarios. When less precipitation was received, drainage decreased proportionally much faster than evapotranspiration implying plants will acquire water first to the detriment of drainage. When precipitation was reduced by more than 30%, plants relied on stored soil water to satisfy evapotranspiration suggesting 30% may be a threshold that if sustained over the long term would deplete plant available soil water. Under the third scenario, evapotranspiration and drainage decreased, whereas surface run‐off increased. Changes in root biomass measured before and 4 years after the throughfall reduction experiment were not detected among treatments. Model simulations, however, indicated gains in evapotranspiration with deeper roots under evenly reduced precipitation and seasonal precipitation redistribution scenarios but not when precipitation frequency was adjusted. Deep soil and deep rooting can provide an important buffer capacity when precipitation alone cannot satisfy the evapotranspirational demand of forests. How this buffering capacity will persist in the face of changing precipitation inputs, however, will depend less on seasonal redistribution than on the magnitude of reductions and changes in rainfall frequency.     

鄱阳湖湿地典型植被群落地下水—土壤—植被—大气系统界面水分通量及水源组成

地下水-土壤-植被-大气系统(GSPAC)界面水分传输是湿地生态水文过程研究的关键.本文选取鄱阳湖湿地高位滩地的2种典型植被群落:茵陈蒿(Artemisia capillaris)和芦苇(Phragmites australis)群落为研究对象,运用HYDRUS-1D垂向一维数值模拟,量化了湿地GSPAC系统界面水分通量,阐明了典型丰水年(2012年)和枯水年(2013年)鄱阳湖湿地植被群落的蒸腾用水规律和水源组成.结果表明:(1)茵陈蒿和芦苇群落土壤-大气界面的年降水入渗量为1570~1600 mm,主要集中在雨季4-6月,占年总量的60%;植物-大气界面的年蒸腾总量分别为346~470 mm和926~1057 mm,其中7-8月植被生长旺季最大,占年总量的40%~46%;地下水-根区土壤界面的向上补给水量受不同水文年水位变化的影响显著,地下水年补给量分别为15~513 mm和277~616 mm,主要发生在蒸散发作用强烈和地下水埋深较浅的时段.(2)植被蒸腾用水分为生长初期(4-6月)和生长旺季(7-10月)2个阶段,丰水年植被的整个生长期蒸腾用水充足,枯水年植被生长旺季的蒸腾用水受到严重水分胁迫,实际蒸腾量仅为潜在蒸腾量的一半左右.(3)不同水文年湿地植被生长旺季的水源贡献不同:丰水年茵陈蒿群落以地下水补给为主,芦苇群落以湖水和地下水补给为主;枯水年茵陈蒿群落以降水和前期土壤水储量为主,芦苇群落以地下水补给为主.本研究结果有助于揭示湿地植被的水分利用策略,为阐明湖泊水情变化与植被演替的作用机理提供参考依据.     

高分异花岗岩浆可能是华南花岗岩型铀矿床主要铀源——来自诸广山南体花岗岩锆石铀含量的证据

锆石的U-Pb测年、Hf和O同位素及稀土、微量元素的研究与应用已获得诸多进展,但锆石中铀含量所蕴藏的地质意义却较少被关注。华南花岗岩型铀矿床的铀源一直存在争议,不同观点认为其分别来自早期已固结地质体、分异岩浆、地幔柱或热点以及U、Th、K富集圈。为尝试利用锆石中的铀含量来追索铀源,本文通过搜集诸广山南体花岗岩锆石U-Pb同位素测年文献,掌握了该花岗岩中14个岩体、37件样品、3种岩性,共467个锆石定年数据。通过数据分析发现印支期(253Ma、244Ma)和燕山期(139Ma、124Ma)具高分异特征的4件酸性岩脉(小岩体)样品中锆石的铀含量明显高于同期岩体。依据铀矿床中高分异酸性岩脉(小岩体)侵位期、基性岩脉侵位期、铀成矿早期(140~90Ma)三者的良好对应关系,结合这一锆石铀含量指示,初步认为华南花岗岩型铀矿床中铀可能主要来自高分异花岗岩浆;推测花岗岩型铀成矿可能属壳幔混合作用结果,即铀源来自地壳分异岩浆,成矿流体和矿化剂主要来自地幔,而成矿空间受断裂系统控制。岩体锆石铀含量或可在铀源丰度、矿床品位判别等方面发挥积极作用。     

Closed-loop randomized kinodynamic path planning for an autonomous underwater vehicle

A randomized kinodynamic path planning algorithm based on the incremental sampling-based method is proposed here as the state-of-the-art in this field applicable in an autonomous underwater vehicle. Designing a feasible path for this vehicle from an initial position and velocity to a target position and velocity in three-dimensional spaces by considering the kinematic constraints such as obstacles avoidance and dynamic constraints such as hard bounds and non-holonomic characteristic of AUV are the main motivation of this research. For this purpose, a closed-loop rapidly-exploring random tree (CL-RRT) algorithm is presented. This CL-RRT consists of three tightly coupled components: a RRT algorithm, three fuzzy proportional-derivative controllers for heading and diving control and a six degree-of-freedom nonlinear AUV model. The branches of CL-RRT are expanded in the configuration space by considering the kinodynamic constraints of AUV. The feasibility of each branch and random offspring vertex in the CL-RRT is checked against the mentioned constraints of AUV. Next, if the planned branch is feasible by the AUV, then the control signals and related vertex are recorded through the path planner to design the final path. This proposed algorithm is implemented on a single board computer (SBC) through the xPC Target and then four test-cases are designed in 3D space. The results of the processor-in-the-loop tests are compared by the conventional RRT and indicate that the proposed CL-RRT not only in a rapid manner plans an initial path, but also the planned path is feasible by the AUV.     

A-FPN算法及其在遥感图像船舶检测中的应用

光学遥感图像船舶检测主要面临两个挑战:光学遥感图像背景复杂,船舶检测易受海浪、云雾及陆地建筑等多方面干扰;遥感图像分辨率低,船舶目标小,对于其分类与定位带来很大困难;针对上述问题,在FPN的基础上,提出一种融入显著性特征的卷积神经网络模型A-FPN (Attention-Based Feature Pyramid Networks)。首先,利用卷积提取图像特征金字塔;然后,利用顶层金字塔逐级构建显著特征层,抑制背景信息,通过金字塔顶层的细粒度特征提高浅层特征的表达能力,构建自上而下的多级显著特征映射结构;最后利用Softmax分类器进行多层级船舶检测。A-FPN模型利用显著性机制引导不同感受下的特征进行融合,提高了模型的分辨能力,对遥感图像处理领域具有重要应用价值。实验阶段,利用公开的遥感目标检测数据集NWPU VHR-10中的船舶样本进行测试,准确率为92.8%,表明A-FPN模型适用于遥感图像船舶检测。     

长江口邻近海域夏季底栖纤毛虫的多样性与群落结构特点

采用Ludox-QPS方法,研究了2011年8月采自长江口邻近海域9个站位沉积物中纤毛虫的群落结构及分布特点,并结合沉积环境进行综合分析。结果表明,表层8cm沉积物中底栖纤毛虫的平均丰度为(2782±1493)cells/10cm2,生物量为(10.06±6.41)μgC/10cm2。长江口海域北部站位的丰度和生物量呈从近岸向外海增加,南部的站位呈现相反的分布趋势。在垂直分布上, 62%的底栖纤毛虫分布在表层2cm, 12%分布在5—8 cm。本研究共检获纤毛虫106种,隶属于15纲/亚纲, 24目, 69属,前口纲在丰度及生物量上均为最优势类群(丰度占45.5%,生物量占56.4%),核残迹纲在生物量上居第二位。就食性来看,肉食性纤毛虫物种数最多(44种),其丰度和生物量所占比例也最高(40.3%,66.8%),但在长江口外站位(M1站),菌食性纤毛虫为最优势摄食类群。分析表明,研究海域底栖纤毛虫群落结构与底层水盐度最相关。聚类分析结果显示,由于较多量的伪钟虫属(Pseudovorticella)和原领毛虫属(Prototrachelocerca)种类出现导致长江口北部L1站的纤毛虫群落结构不同于其他站位。本研究所获底栖纤毛虫的丰度和生物量较东海离岸海域已有研究结果均高,表层8cm沉积物中纤毛虫的丰度约是上层30m水柱中浮游纤毛虫的116倍,生物量约是后者的150倍。基于目前黄东海有关底栖纤毛虫物种多样性的已有报道,其多样性在长江口离岸海域高于近岸潮间带,且在离岸海域东海低于黄海。     

吕宋和台湾岛以东黑潮季节与年际变化规律的对比分析

黑潮是北太平洋副热带环流系统的一支重要的西边界流。前人对不同流段黑潮的季节和年际变化进行了诸多研究,然而基于不同数据所得结论仍存在差异,尤其是不同模式计算所得流量差别很大,而且以往研究往往着眼于某一流段,对不同流段黑潮变化之间的异同及其原因涉及较少。本文基于卫星高度计数据,评估了OFES(Ocean generalcir culation model For the Earth Simulator)和HYCOM(Hybrid Coordinate Ocean Model)两个模式对吕宋岛和台湾岛以东黑潮季节与年际变化的模拟能力,进而对两个海域黑潮变化的异同及其物理机制进行了分析。结果表明:HYCOM模式对黑潮季节变化的模拟较好,而OFES模式对黑潮年际变化的模拟较好。吕宋岛以东黑潮和台湾岛以东黑潮在季节与年际尺度上的变化规律均不相同,且受不同动力过程控制。吕宋岛以东黑潮呈现冬春季强而秋季弱的变化规律,主要受北赤道流分叉南北移动的影响;而台湾岛以东黑潮呈现夏季强冬季弱的变化特点,主要受该海区反气旋涡与气旋涡相对数目的季节变化影响。在年际尺度上,吕宋岛以东黑潮与北赤道流分叉及风应力旋度呈负相关,当风应力旋度超前于流量4个月时相关系数达到了-0.56;而台湾岛以东黑潮的流量变化则受制于副热带逆流区涡动能的变化,且滞后于涡动能9个月时达到最大正相关,相关系数为0.44。本研究对于深入理解不同流段黑潮的多尺度变异规律及其对邻近海区环流与气候的影响具有重要意义,同时对于黑潮研究的数值模式选取具有重要参考价值。     

基于高分辨率风场的海洋近惯性能通量计算——时空特征及其影响因素

基于高分辨率CFSR(climate forecast system reanalysis)风场资料、气候态海洋混合层厚度资料和卫星高度计海面高度异常资料,本文估计了大气风场向全球海洋混合层的近惯性能通量和近惯性能量输入功率,并探究了混合层厚度、风场时间分辨率、经验衰减系数和中尺度涡旋涡度对近惯性能通量和能量输入功率的影响。浮标实测风场和流速表明,本文所用的风场和阻尼平板模型可用于估计风场向全球海洋的近惯性能通量。本文计算得到的大气向全球海洋输入近惯性能量的功率为0.56TW(1TW=10~(12)W),其中北半球贡献0.22TW,南半球贡献0.34TW。在时间上,风场的近惯性能通量呈现各个半球冬季最强、夏季最弱的特征,这和西风带风场的季节变化有关。在空间上,近惯性能通量的高值海域为南、北半球西风带海洋,尤其是南大洋。混合层厚度和风场空间不均匀性使得西风带近惯性能通量呈现纬向变化,即海盆西部强于海盆东部。风场时间分辨率对近惯性能通量的估计至关重要,低时间分辨率风场对近惯性能通量的低估达到13%—30%。阻尼平板模型中的经验衰减系数对近惯性能通量估计的影响不超过5%。中尺度涡旋涡度仅改变近惯性能通量的空间分布,而对全球近惯性能量输入功率的影响可以忽略。     

北太平洋副热带模态水盐度的年代际变化

副热带模态水(Subtropical Mode Water;STMW)在气候变化中起着重要作用。本文利用全球高分辨率数值模拟结果,研究了北太平洋STMW核心层盐度(Core Layer Salinity;CLS)的年代际变化及其物理机制。结果表明,CLS存在显著的年代际变化,其空间分布则与背景流场分布特征有关。侵蚀区CLS滞后生成区CLS约1~2年,这主要是海流平流输运引起的。生成区内,STMW的季节循环一般可分为生成期(12-4月)、隔离期(5-6月)和侵蚀期(7-11月),生成期混合层盐度(Mixed Layer Salinity;MLS)决定着隔离期和侵蚀期的CLS,而MLS年代际变化则主要由同太平洋年代际涛动存在负相关性的海表面淡水通量的变化引起。