用NOAA卫星可见光和红外资料估算甘肃省东部农田区土壤湿度

应用NOAA卫星AVHRR通道1、2计算的植被指数和通道4的亮温、农业气象试验站观测的土壤湿度，并结合气象卫星完成的土地覆盖分类等资料，建立了由植被指数和亮温估算甘肃省东部农田区土壤湿度的方程。结果表明，农田土壤湿度与植被指数和亮温间均存在一定相关关系；用植被指数和亮温可以估计土壤湿度情况，并对干旱进行监测。     

河南省土壤湿度年变化规律

利用河南省土壤墒情预报模型中建立的河南省台站土壤墒情数据库资料,对不同类型土壤湿度进行分析,从而确定不同类型土壤湿度差异、地下水对土壤湿度的影响及河南省土壤湿度的年变化规律.     

河南省土壤湿度年变化规律

利用河南省土壤墒情预报模型中建立的河南省台站土壤墒情数据库资料，对不同类型土壤湿度进行分析，从而确定不同类型土壤湿度差异、地下水对土壤湿度的影响及河南省土壤湿度的年变化规律。     

黄河源区土壤湿度时空分布的模拟研究

利用1961-2010年普林斯顿大学每3 h一次、1°×1°的大气强迫场数据驱动公用陆面模式CLM4.0(Common Land Model,version4.0)对黄河源区土壤湿度的时空分布进行了模拟试验,合理优化了CLM 4.0中土壤有机质和土壤质地属性参数,将模拟结果与荷兰自由大学AM SR-E土壤湿度产品进行了对比分析,并利用玛曲土壤湿度观测站点的观测数据对模拟结果进行了验证。结果表明,CLM4.0模式能较好的模拟黄河源区土壤湿度的空间分布及变化趋势,在优化陆面有机质和土壤质地数据参数后,模拟的土壤湿度空间分布更合理,但CLM4.0模拟的土壤湿度比地面观测值和AMSR-E土壤湿度产品的土壤湿度偏低。     

WRF模式中土壤湿度对位势高度模拟影响的敏感性分析

利用WRFV3.6中尺度预报模式对我国东部地区2003年5、6、7月下旬天气过程进行模拟,研究分析了土壤湿度对位势高度的影响。结果表明:(1)对流层中低层位势高度对土壤湿度有较强的敏感性,且各月的位势高度随土壤湿度的改变都有一致的变化规律,但相对于5、6月,7月高温过程中位势高度对土壤湿度更为敏感。土壤湿度的增加(减少)会导致750~500 h Pa位势高度减小(增大)、850 h Pa以下气压层位势高度增大(减小);(2)不同土壤湿度试验模拟的不同地表热通量可直接影响气温变化,在静力平衡和质量守恒条件下可进一步影响不同高度的气压,并最终导致位势高度的差异;(3)位势高度白天受土壤湿度的影响程度最大。白天热通量传输旺盛,在干(湿)的土壤湿度条件下,地表温度增温(降温)幅度大,850 h Pa位势高度减小(增大)、500 h Pa位势高度增大(减小)更为明显;夜间情况相反。     

2008年的湛江土壤湿度特征

对湛江地面气象观测站2008年0～50 cm土壤湿度、降水及蒸发皿蒸发资料进行了分析。结果表明,湛江土壤湿度的垂直分布形态为垂直均匀型;按土壤湿度随时间的变化规律,可将其划分为春季相对稳定期、夏季增墒期和秋季迅速下降期3个时段。对0～10 cm、10～30 cm与30～50 cm土层土壤湿度进行回归分析,表明土壤湿度与降水量、蒸发皿蒸发量存在线性关系,除春季30～50 cm土壤湿度的预报值明显偏低外,其余回归方程的预报结果均较好。同一土壤类型、不同时段,或同一时段、不同的土壤层次,拟合的方程不同,反映出土壤湿度时间和空间分布的复杂性。     

卫星被动微波遥感土壤湿度研究进展

土壤湿度是控制陆地和大气间水分和能量交换过程的重要变量,而被动微波遥感是众多监测土壤湿度技术中最有效的手段之一。文中概述了被动微波反演土壤湿度的物理原理,重点介绍了被动微波反演土壤湿度的主要模型。在对不同模型进行比较分析后,基于不同传感器类型分别列举了当今发展较完善的3个典型算法：①Njoku和Li基于AMSR的多通道同时反演土壤湿度、土壤温度、植被含水量的方法;②Owe等基于SMMR利用极化差异指数同时反演土壤湿度和植被光学厚度2个参数的方法;③Wen等基于SSM／I同时反演土壤湿度和土壤温度的方法。对被动微波遥感土壤湿度研究中目前所存在的问题和发展前景进行了一些探讨。     

初值对中国东部初夏土壤湿度可预报性影响

土壤湿度是影响天气和气候非常重要的因子之一,但目前针对土壤湿度可预报性的研究报道相对较少。该文在对BCC_CSM模式进行了适合的陆面初始化的条件下,设计了两组在中国东部地区采用不同土壤湿度初值的回报试验研究该地区土壤湿度的可预报性及初值对其可预报性影响问题。试验结果表明:BCC_CSM模式在真实的外场强迫下可以模拟出相对合理的土壤湿度;土壤湿度的可预报性在表层约为3候,随着深度的增加,土壤湿度的可预报性持续时间增加,在中层预报性甚至能达到月尺度以上;初值对于土壤湿度的预报存在影响,在表层影响时间约为2~3候,影响时间随着深度增加;浅层土壤湿度受降水的影响较大,浅层土壤湿度变化滞后降水变化约1~2 d,中层土壤湿度变化与降水变化存在5 d左右的滞后关系。     

春季华南土壤湿度异常与中国夏季降水的可能联系

基于ERA40（ECMWF）1958—2001年土壤湿度再分析资料和中国541站降水资料,通过观测分析揭示了华南春季土壤湿度异常与中国夏季降水的联系及其可能的物理过程。结果表明,春季华南土壤湿度与夏季华南（长江流域及其以北地区）降水呈正（负）相关;春季华南土壤湿度负（正）异常,夏季华南降水异常偏少（多）,而长江以北地区降水则偏多（少）。通过对春季华南土壤湿度异常年份对应的环流异常特征的诊断分析发现：土壤湿度负异常年,西太平洋副高位置明显偏西,华南地区对应异常的下沉运动和水汽辐散,导致该地区降水偏少;而长江中下游地区对应异常的上升运动和水汽通量的辐合,降水偏多;土壤湿度正异常年的情况大致相反。进一步的分析表明,春季华南土壤湿度与同期长江中下游及以北地区土壤湿度存在明显的负相关关系。春季华南土壤湿度负（正）异常年的同期华北到长江中下游区域土壤湿度为正（负）异常,将导致南部区域的地表温度异常升高（降低）,北部地表温度异常偏低（偏高）,并通过改变地表对大气的加热,引起夏季大气环流的异常,最终造成夏季降水异常。     

脉冲降雨—蒸发对土壤湿度影响的动力机制分析

土壤湿度直接作用于植被的呼吸、蒸腾和各种化学反应, 是生态气象学的重要的环境因素。降雨和蒸发作为土壤湿度的两个重要扰动因子, 对其进行定性的理论分析及定量的数值分析具有重要意义, 尤其在半干旱和干旱地区。蒸发对土壤湿度具有持续的耗散作用, 可以视为连续的, 而降雨作为湿度补充作用具有脉冲效应。为阐释降雨和蒸发对土壤湿度的影响, 该文通过建立脉冲降雨—蒸发对土壤湿度响应模型, 并对其进行定性分析, 得到此微分方程系统的周期解的充分条件。在此基础上, 通过Forcal对方程参数拟合, 并以Maple作为数值分析工具, 对此系统进行数值模拟。      

基于神经网络算法的Sentinel-1和Sentinel-2遥感数据联合反演土壤湿度研究

以西班牙萨拉曼卡地区为研究区域,联合Sentinel-1后向散射系数和入射角信息、Sentinel-2光学数据提取的植被指数以及地面实测数据,构建了BP神经网络土壤湿度反演模型,并将该模型应用于试验区土壤湿度反演。结果表明:1)基于Sentinel-1卫星VV和VH极化雷达后向散射系数、雷达入射角和Sentinel-2植被指数数据构建的BP神经网络土壤湿度反演模型,能够实现对该地区土壤湿度高精度反演;2)在光学与微波数据联合反演植被覆盖区土壤湿度中,Sentinel-2的NDVI、NDWI1和NDWI2指数都可以用于削弱植被对土壤湿度反演的影响,但基于SWRI1波段的NDWI1能够获得更高精度的土壤湿度反演结果(RMSE为0.049 cm~3/cm~3,ubRMSE为0.048 cm~3/cm~3,Bias为0.008 cm~3/cm~3,r为0.681);3)相比于Sentinel-1 VH极化模式,Sentinel-1 VV极化模式在土壤湿度中表现出更大优势,说明Sentinel-1 VV极化模式更适用于土壤湿度反演。     

脉冲降雨—蒸发对土壤湿度影响的动力机制分析

土壤湿度直接作用于植被的呼吸、蒸腾和各种化学反应,是生态气象学的重要的环境因素。降雨和蒸发作为土壤湿度的两个重要扰动因子,对其进行定性的理论分析及定量的数值分析具有重要意义,尤其在半干旱和干旱地区。蒸发对土壤湿度具有持续的耗散作用,可以视为连续的,而降雨作为湿度补充作用具有脉冲效应。为阐释降雨和蒸发对土壤湿度的影响,该文通过建立脉冲降雨—蒸发对土壤湿度响应模型,并对其进行定性分析,得到此微分方程系统的周期解的充分条件。在此基础上,通过Forcal对方程参数拟合,并以Maple作为数值分析工具,对此系统进行数值模拟。     

Sensitivity of an ecosystem model to hydrology and temperature

We tested the sensitivity of a dynamic ecosystem model (LPJ-GUESS) to the representation of soil moisture and soil temperature and to uncertainties in the prediction of precipitation and air temperature. We linked the ecosystem model with an advanced hydrological model (JULES) and used its soil moisture and soil temperature as input into the ecosystem model. We analysed these sensitivities along a latitudinal gradient in northern Russia. Differences in soil temperature and soil moisture had only little influence on the vegetation carbon fluxes, whereas the soil carbon fluxes were very sensitive to the JULES soil estimations. The sensitivity changed with latitude, showing stronger influence in the more northern grid cell. The sensitivity of modelled responses of both soil carbon fluxes and vegetation carbon fluxes to uncertainties in soil temperature were high, as both soil and vegetation carbon fluxes were strongly impacted. In contrast, uncertainties in the estimation of the amount of precipitation had little influence on the soil or vegetation carbon fluxes. The high sensitivity of soil respiration to soil temperature and moisture suggests that we should strive for a better understanding and representation of soil processes in ecosystem models to improve the reliability of predictions of future ecosystem changes.     

Surface radar-derived convective rainfall associations with Midwest US land surface conditions in summer seasons 1999 and 2000

Previous research has suggested that spatial heterogeneities in soil moisture and/or vegetation cover promote the development of convective clouds. We examine the intensity of convective precipitation for the Midwest US Corn Belt in the summers of 1999 and 2000, which had contrasting synoptic circulation, atmospheric humidity, and soil moisture conditions. For days when synoptic scale atmospheric forcing is weak, we calculate a convective severity index (CSI) based on radar reflectivity composite values. Our results suggest that boundaries between soil types, and cropland and forest vegetation types in the western portion of the Corn Belt, enhance the development of convective precipitation. In the eastern part of the Corn Belt, less convection occurs, but we find a positive correlation between the intensity of convection and soil moisture conditions. Our results also demonstrate that the CSI is a simple yet effective technique for identifying where deep convection occurs relative to lighter precipitation.     

Impact of Land Surface Heterogeneity on Mesoscale Atmospheric Dispersion

Prior numerical modelling studies show that atmospheric dispersion is sensitive to surface heterogeneities, but past studies do not consider the impact of a realistic distribution of surface heterogeneities on mesoscale atmospheric dispersion. While these focussed on dispersion in the convective boundary layer, the present work also considers dispersion in the nocturnal boundary layer and above. Using a Lagrangian particle dispersion model (LPDM) coupled to the Eulerian Regional Atmospheric Modeling System (RAMS), the impact of topographic, vegetation, and soil moisture heterogeneities on daytime and nighttime atmospheric dispersion is examined. In addition, the sensitivity to the use of Moderate Resolution Imaging Spectroradiometer (MODIS)-derived spatial distributions of vegetation characteristics on atmospheric dispersion is also studied. The impact of vegetation and terrain heterogeneities on atmospheric dispersion is strongly modulated by soil moisture, with the nature of dispersion switching from non-Gaussian to near-Gaussian behaviour for wetter soils (fraction of saturation soil moisture content exceeding 40%). For drier soil moisture conditions, vegetation heterogeneity produces differential heating and the formation of mesoscale circulation patterns that are primarily responsible for non-Gaussian dispersion patterns. Nighttime dispersion is very sensitive to topographic, vegetation, soil moisture, and soil type heterogeneity and is distinctly non-Gaussian for heterogeneous land-surface conditions. Sensitivity studies show that soil type and vegetation heterogeneities have the most dramatic impact on atmospheric dispersion. To provide more skilful dispersion calculations, we recommend the utilisation of satellite-derived vegetation characteristics coupled with data assimilation techniques that constrain soil-vegetation-atmosphere transfer (SVAT) models to generate realistic spatial distributions of surface energy fluxes.     

基于遥感与GIS集成的土壤墒情监测服务系统

遥感与GIS集成土壤墒情监测服务系统运行于Windows平台,基于遥感与GIS集成技术,利用热惯量法、植被缺水指数法、植被温度条件指数法和单时相资料回归法等多种模式计算土壤含水量。在遥感监测与墒情分析中考虑背景地理信息的影响,提高了遥感墒情监测的精度和服务水平。该系统具有多种墒情模式计算、图像显示、图像叠加、统计分析、模板处理和输出等功能。应用该系统得出的2005年4月河南省遥感墒情分布图和干旱面积与实测结果基本一致。     

A nonlinear coupled soil moisture-vegetation model

Based on the physical analysis that the soil moisture and vegetation depend mainly on the precipitation and evaporation as well as the growth, decay and consumption of vegetation a nonlinear dynamic coupled system of soil moisture-vegetation is established. Using this model, the stabilities of the steady states of vegetation are analyzed. This paper focuses on the research of the vegetation catastrophe point which represents the transition between aridness and wetness to a great extent. It is shown that the catastrophe point of steady states of vegetation depends mainly on the rainfall P and saturation value v0, which is selected to balance the growth and decay of vegetation. In addition, when the consumption of vegetation remains constant, the analytic solution of the vegetation equation is obtained.     

基于MODIS数据的作物苗期干旱监测方法

针对苗期低植被覆盖情况,在原有的归一化植被指数基础上,增加了土壤调节植被指数,将其与条件温度指数结合,通过与地面实测土壤相对湿度进行相关分析,分别建立作物苗期的干旱监测模型,并与热惯量方法和供水植被指数方法进行对比分析,初步得出辽宁省范围内作物苗期进行大范围干旱遥感监测的最适宜模型为土壤调节植被指数一温度模型.