TRMM卫星降水产品降尺度及其在湘江流域水文模拟中的应用

高时空分辨率降水数据对准确刻画区域降水时空变化特征、精准模拟区域生态和水文过程具有重要的现实意义。以湘江流域为例,在考虑地理、地形和植被等多重要素的基础上,建立了基于地理加权回归法（Geographic Weighted Regression,GWR）的热带降雨测量卫星（Tropical Rainfall Measuring Mission,TRMM）降水降尺度模型,并采用比例指数法反演得到星地融合日降水Ⅰ、Ⅱ、Ⅲ三种产品,用来驱动土壤和水评估模型（Soil and Water Assessment Tool,SWAT）,分析评估其在水文模拟中的应用潜力。结果表明：1）GWR降尺度后,在TRMM降水空间分辨率由0.25°提升至0.05°的同时,同气象站点观测月降水之间的决定系数（R2）平均提升了0.33,均方根误差（RMSE）平均降低了43.30 mm,平均相对偏差（Average Relative Error,ARE）平均降低了38.71%,表明该降尺度模型在湘江流域TRMM月降水降尺度研究中具有良好的适用性;2）与TRMM日降水量相比,星地融合日降水Ⅲ产品同气象站点观测日降水量的R2提高了0.81,RMSE降低了10.27 mm,ARE降低了0.11%,表明以气象站点观测日降水量作比例指数展布星地融合月降水是可行有效的;3）星地融合日降水Ⅲ产品在SWAT模型日、月径流模拟中的纳什效率系数最大,分别为0.79、0.93,相对误差最小,分别为0.12%、1.10%,水文模拟效果最优,可替代气象站点和TRMM卫星降水进行水文模拟。研究结果可为气象站点稀缺区域的高精度降水资料获取和高效水文模拟提供数据支撑和方法借鉴。     

2000—2020年福建省土地利用隐性转型的影响因素

[目的] 分析福建省县域土地利用转型主导驱动力与影响因素的时空特征,构建土地利用转型影响因素逻辑分析框架,为福建省土地利用高质量发展提供参考。[方法] 基于2000,2010和2020年福建省土地利用数据和社会经济数据,采用理论分析法、土地利用转移矩阵、综合指数法、地理加权回归（GWR）等方法,构建土地利用转型影响因素逻辑分析框架,并在此基础上对福建省土地利用转型主导驱动力、地类转移、土地利用隐性形态时空特征及影响因素进行系统分析。[结果] ①福建省各地区呈现出以发展型经济—生态安全驱动力为主要动力的发展趋势; ②2000—2020年福建省建设用地增加了2 897.45 km²,主要由耕地转换而来,其次为林地和草地;③福建省县域土地利用隐性形态呈现出“条带状”空间特征,形成福州—莆田—厦门高级形态区以及福鼎—闽侯—仙游—安溪—诏安和武夷山—南平—三明—龙岩中级形态区; ④交通密度和城市化是福建省土地利用转型的主要影响因素。[结论] 福建省应该加强内陆和沿海、偏远地区和市区之间的交通建设,实施新型城镇化战略,同时深入推进新时代山海协作,充分发挥福州、厦门等沿海城市的带动作用。     

Clinical and immunological response of lymphoma dogs following chemotherapy and irradiation

The effect of chemotherapy or chemotherapy followed by total body irradiation and autologous bone marrow transplantation on clinical status and lymphocyte function was evaluated in 79 dogs with spontaneous lymphoma. Advanced disease led to the early deaths of 28 dogs (35%), and 24 dogs (30%) administered chemotherapy had a mean survival time of 85 days (range 33–199 days). Survival for eight dogs receiving chemotherapy and BCG was comparable to that produced by chemotherapy only. Thirteen dogs administered chemotherapy followed by irradiation and autologous marrow grafts had survival times ranging from 59 to > 807 days (median 222 days) with median unmaintained clinical remission lasting 151 days. Most treated dogs experienced improvement in health, reduction in lymph node sizes and normalization of liver function. Lymphocyte reactivity to mitogens and streptolysin-O antigen by untreated and treated lymphoma dogs was depressed (P<0.05) compared to response of normal lymphocytes. Mixed leukocyte reactivity of irradiated lymphoma dogs was impaired in the first 150 days post-irradiation and returned to normal values after 150 days. Lymphocyte responses of irradiated normal dogs paralleled those seen in lymphoma dogs. Mixed leukocyte reactivity was not correlated with tumor rejection since eight of nine dogs evaluated following irradiation relapsed during a period of vigorous reactivity to allogeneic stimulating cells.     

Influence of Climate and Socio-Economic Factors on the Spatio-Temporal Variability of Soil Organic Matter： A Case Study of Central Heilongjiang Province,China

For the scientific management of farmland, it is significant to understand the spatio-temporal variability of soil organic matter and to study the influences of related factors. Using geostatistical theory, GIS spatial analysis, trend analysis and a Geographically Weighted Regression （GWR） model, this study analyzed the response of soil organic matter to climate and socio-economic factors in central Heilongjiang Province during the past 25 years. Second soil survey data of China for 1979-1985, 2005 field sampling data, climate observations and socio-economic data for 1980-2005 were analyzed. First, soil organic matter in 2005 was spatially interpolated using the Co-Kriging method along with auxiliary data sets of soil type and pH. The spatio-temporal variability was then studied by comparison with the 1980s second soil census data. Next, the temporal trends in climate and socio-economic factors over the past 25 years were investigated. Finally, we examined the variation of the response of soil organic matter to climate and socio-economic factors using the GWR model spatially and temporally. The model showed that 53.82% area of the organic matter content remained constant and 29.39% has decreased during the past 25 years. The impact of precipitation on organic matter content is mainly negative, with increasing absolute values of the regression coefficient. The absolute value of regression coefficient of annual average temperature has decreased, and more areas are now under its negative effects. In addition, the areas of positive regression coefficient of annual sunshine hours have northward shifted, with the increasing absolute value of positive coefficient and decreasing absolute value of negative coefficient. The areas of positive regression coefficient of mechanized farming as a socio-economic factor have westward shifted, with the increasing absolute value of negative coefficient and decreasing absolute value of positive coefficient. The area of regions with the positive regression coefficient of irrigatio     

基于GWR的新疆农机总动力空间异质性分析

从县域空间尺度出发,利用空间分析方法和地理加权回归模型,研究了2003-2012年新疆县域农机总动力空间聚集特征及其影响因素的空间异质性。结果表明:新疆县域农机总动力存在明显的空间异质性和空间聚集分布模式,空间分布表现出在经向上北疆大、南疆小,纬向上西部大、东部小;总体由天山南北两侧向外递减的趋势,中部为高高聚集区,南部为低低聚集区。近10年来,新疆县域农机总动力存在一定的空间正相关特性,且空间相关性随着时间整体呈现下降趋势。新疆县域农机总动力的影响因素存在明显的空间异质性。新疆西北部区域农机总动力对耕地经营规模比较敏感,西南部区域对政府财政投入比较敏感,而中南部区域对农民收入比较敏感。在不同区域第一产业人数对农机总动力的作用表现出正反两种相关关系,并且负相关关系有南移的趋势。根据农机总动力影响因素的空间分异特征,可以更好地解释分析农业机械总动力的发展变化规律,推动农业机械化事业更加科学、快速发展。     

Spatial modeling of the carbon stock of forest trees in Heilongjiang Province,China

Heilongjiang province is the largest forest zone in China and the forest coverage rate is 46%. Forests of Heilongjiang province play an important role in the forest ecosystem of China. In this study we investi- gated the spatial distribution of forest carbon storage in Heilongjiang province using 3083 plots sampled in 2010. We attempted to fit two global models, ordinary least squares model （OLS）, linear mixed model （LMM）, and a local model, geographically weighted regression model （GWR）, to the relationship between forest carbon content and stand, environment, and climate factors. Five predictors significantly affected forest carbon storage and spatial distribution, viz. average diameter of stand （DBH）, number of trees per hectare （TPH）, elevation （Elev）, slope （Slope） and the product of precipitation and temperature （Rain Temp）. The GWR model outperformed the two global models in both model fitting and prediction because it successfully reduced both spatial auto- correlation and heterogeneity in model residuals. More importantly, the GWR model provided localized model coefficients for each location in the study area, which allowed us to evaluate the influences of local stand conditions and topographic features on tree and stand growth, and forest carbon stock. It also helped us to better understand the impacts of silvi- cultural and management activities on the amount and changes of forest carbon storage across the province. The detailed information can be readily incorporated with the mapping ability of GIS software to provide excellent tools for assessing the distribution and dynamics of the for- est-carbon stock in the next few years.     

Modeling crop yield potential of Eastern Anatolia by using geographically weighted regression

Wheat is a very important cereal crop in Eastern Anatolia in terms of acreage and production. It is a staple food and plays an important role in people's livelihood. Drought often occurs in the region and it dramatically influences wheat yield. The yield also depends upon prevailing climatic conditions, rainfall, temperature and humidity. Crop-modeling studies to forecast crop yield are important not only for people now but for planning studies and precautionary measures for the future. Traditional decision support systems based on crop simulation models are normally site-specific. In order to address the effect of spatial variability of weather variables on crop production, we modeled wheat yield potential on certain climatic conditions by using Geographically Weighted Regression and Geographical Information Systems in Eastern Anatolia in Turkey.     

重庆市县域农村贫困化空间分异及影响因素探析

以重庆市为研究区,使用重庆市涉及扶贫开发任务的33个区县2014—2018年县域农村贫困数据,采用探索性空间数据分析(ESDA),刻画农村贫困化空间关联格局,构建影响县域农村贫困化空间分异的指标体系,并运用地理加权回归模型对县域农村贫困化的空间分异影响因素进行回归分析和探讨.研究结果表明:①总体来看,研究时段内重庆市县域农村贫困化热点区域整体空间格局稳定,高值区主要分布于巫溪、城口、酉阳和彭水等渝东北和渝东南区域的相关区县,而主城区和渝西地区的区县贫困发生率相对较低,呈现显著的空间差异性;从时间序列上来看,研究时段内各区县农村贫困发生率明显呈现下降态势,区县间的差距在缩小;②重庆市2014-2018年全局Moran’s I指数均达到显著的空间正相关,县域农村贫困化呈现出明显的空间集聚现象;③通过GWR模型进行回归分析发现,县域农村贫困化空间分异影响因素中,海拔高度、人均耕地资源面积、 25°以上耕地面积比重和区位水平、未通客运班车村比重、农村居民人均纯收入、文盲人口比、因残致贫人口比重等8个显著的解释变量对县域农村贫困化起到了直接或间接的影响,且各影响因素在不同区域上表现出的影响力不尽相同.     

基于地块尺度的永久基本农田非粮化特征、驱动与管控

掌握永久基本农田非粮生产地块特征、空间集聚及驱动因子可为优化作物种植空间布局、完善耕地精细化管控策略提供参考依据。基于广西壮族自治区第三次全国国土调查数据,分析全区永久基本农田非粮生产现状、类型以及地块特征,采用探索性空间数据分析方法、最小二乘法、地理加权回归模型识别永久基本农田非粮生产空间集聚特征、驱动因子及其作用效应的空间差异。结果表明：全区永久基本农田非粮生产地块主要分布在中部、西部、北部,非粮生产类型以果树和乔木为主,占非粮生产面积的88.85%;非粮生产地块的耕地质量、灌溉条件低于全区平均水平,但集中分布在地势平坦的区域,田面坡度小于等于6°的地块占77.86%;非粮生产比例在县级尺度上呈现显著空间正自相关性,H-H型主要分布在中部和西南部,L-L型主要分布在东部和西北部,H-L型集中分布在西部;田块规模、人均永久基本农田面积、农村居民人均可支配收入、各县到省会城市距离与县域非粮生产比例呈正相关,耕地质量、灌溉保证率与县域非粮生产比例呈负相关,不同驱动因子对非粮生产的影响存在显著的空间差异性。未来应从优化作物布局、加强质量建设、监管属性特征、完善保护补偿等方面加强全区永久基本...     

Inhibition of in vitro lymphocyte blastogenesis by chicken alpha-fetoprotein

Chicken alpha-fetoprotein (ch-AFP), purified from fetal chicken serum and embryo extracts, respectively, was examined for its immunomodulatory effect in vitro. Significant (P less than 0.05) suppression of the allogeneic mixed lymphocyte reaction (MLR) was observed, when these preparations were added to one-way mixed lymphocyte cultures (MLC) in quantities of 62.5-1000 micrograms/ml. Suppression of the MLR was depending on the presence of ch-AFP for at least 16 h after initiation of the MLC, suggesting that this fetal protein was acting mainly in the early phase of lymphoblastogenesis. Serum of chicken embryos (12th and 15th day of incubation), day-old chickens, and of 10-week-old chickens of four different inbred lines were also found to exert suppression of the MLR. From these data, it is hypothesized that ch-AFP plays an immunoregulatory role by maintaining a certain stage of self tolerance during differentiation of the avian immune system.