新一代GRACE重力卫星反演地球重力场的预期精度

基于低低卫卫跟踪模式,本文主要探讨利用动力学法融合精密轨道数据和星间测距或距离变率数据求解地球重力场的基本原理与方法,该方法既可对两颗低低跟踪卫星的初始状态误差进行有效校正,也可充分利用低轨卫星轨道所包含的低频重力场信息.为探讨适合我国国情的低低跟踪模式下的重力卫星指标,本文以不同星载设备精度指标的组合进行模拟计算,模拟结果显示:(1)把GRACE卫星的星间距离变率指标提高一个量级,其余指标保持与GRACE卫星设计指标一致时,可使地球重力场的精度获得同量级的提高;(2)若星间距离变率为1.0×10^-8 m·s^-1,轨道高度为300 km,加速度计精度为3.0×10^-10 m·s^-2,轨道精度为0.03 m, 星间距离100 km,与利用GRACE的设计指标反演出的重力场精度相比,可提高约121倍,并建议我国未来低低跟踪重力卫星计划参考此指标.     

新一代GRACE重力卫星反演地球重力场的预期精度

基于低低卫卫跟踪模式,本文主要探讨利用动力学法融合精密轨道数据和星间测距或距离变率数据求解地球重力场的基本原理与方法,该方法既可对两颗低低跟踪卫星的初始状态误差进行有效校正,也可充分利用低轨卫星轨道所包含的低频重力场信息.为探讨适合我国国情的低低跟踪模式下的重力卫星指标,本文以不同星载设备精度指标的组合进行模拟计算,模拟结果显示:(1)把GRACE卫星的星间距离变率指标提高一个量级,其余指标保持与GRACE卫星设计指标一致时,可使地球重力场的精度获得同量级的提高;(2)若星间距离变率为1.0×10-8 m·s-1,轨道高度为300 km,加速度计精度为3.0×10-10 m·s-2,轨道精度为0.03 m, 星间距离100 km,与利用GRACE的设计指标反演出的重力场精度相比,可提高约121倍,并建议我国未来低低跟踪重力卫星计划参考此指标.     

One-dimensional soil moisture profile retrieval by assimilation of near-surface observations: a comparison of retrieval algorithms

This paper investigates the ability to retrieve the true soil moisture and temperature profiles by assimilating near-surface soil moisture and surface temperature data into a soil moisture and heat transfer model. The direct insertion and Kalman filter assimilation schemes have been used most frequently in assimilation studies, but no comparisons of these schemes have been made. This study investigates which of these approaches is able to retrieve the soil moisture and temperature profiles the fastest, over what depth soil moisture observations are required, and the effect of update interval on profile retrieval. These questions are addressed by a desktop study using synthetic data. The study shows that the Kalman filter assimilation scheme is superior to the direct insertion assimilation scheme, with retrieval of the soil moisture profile being achieved in 12 h as compared to 8 days or more, depending on observation depth, for hourly observations. It was also found that profile retrieval could not be realised for direct insertion of the surface node alone, and that observation depth does not have a significant effect on profile retrieval time for the Kalman filter. The observation interval was found to be unimportant for profile retrieval with the Kalman filter when the forcing data is accurate, whilst for direct insertion the continuous Dirichlet boundary condition was required for an increasingly longer period of time. It was also found that the Kalman filter assimilation scheme was less susceptible to unstable updates if volumetric soil moisture was modelled as the dependent state rather than matric head, because the volumetric soil moisture state is more linear in the forecasting model.     

Regional scale spatial and temporal variability of soil moisture in a prairie region

Understanding the dynamics of spatial and temporal variability of soil moisture at the regional scale and daily interval, respectively, has important implications for remote sensing calibration and validation missions as well as environmental modelling applications. The spatial and temporal variability of soil moisture was investigated in an agriculturally dominated region using an in‐situ soil moisture network located in central Saskatchewan, Canada. The study site evaluated three depths (5, 20, 50 cm) through 139 days producing a high spatial and temporal resolution data set, which were analysed using statistical and geostatistical means. Processes affecting standard deviation at the 5‐cm depth were different from the 20‐cm and 50‐cm depths. Deeper soil measurements were well correlated through the field season. Further analysis demonstrated that lag time to maximum correlation between soil depths increased through the field season. Temporal autocorrelation was approximately twice as long at depth compared to surface soil moisture as measured by the e‐folding frequency. Spatial correlation was highest under wet conditions caused by uniform rainfall events with low coefficient of variation. Overall soil moisture spatial and temporal variability was explained well by rainfall events and antecedent soil moisture conditions throughout the Kenaston soil moisture network. It is expected that the results of this study will support future remote sensing calibration and validation missions, data assimilation, as well as hydrologic model parameterization for use in agricultural regions. Copyright © 2016 John Wiley & Sons, Ltd.     

空间分辨率和空间差分精度对水平网格性能的影响

为了说明在物理空间中空间差分精度和空间分辨率对Arakawa A-D网格性能的影响程度,将线性浅水方程组采用二阶中央差和四阶中央差格距分别取100 km、10 km和1 km在Arakawa A-D网格上进行离散和120 min的模拟预报,模拟结果和解析解进行比较,结果表明：当网格距是1 km时,不论采用二阶中央差分方案还是四阶中央差分方案,重力惯性波在4种网格上模拟的结果相同,即变量配置的影响是可以忽略的;当网格距增加到10 km或100 km时,不同的变量配置模拟的结果是不同的.但是,不论对二阶中央差还是四阶中央差,当网格距相同的情况下,在C网格上模拟得到的结果均方根误差最小,在D网格上模拟得到的结果均方根误差最大.同时也表明：当差分精度从二阶增加到四阶后,模拟结果的误差并非一致减少,并和群速相对误差的变化一致,因此对于初值为混合波的多变量方程组而言,增加差分精度不能改进模拟的结果.并且缩小网格距比增加差分精度对减少在Arakawa A-D 网格上离散产生的误差更明显.     

新疆艾比湖湿地土壤水盐空间变异性分析

为揭示艾比湖湿地土壤退化程度空间分布特征,在离艾比湖湖滨5~15 km,绕湖一周160 km范围内,以湖心质点为中心,将艾比湖划分为东北、东南、西南、西北4个区域,采用传统统计学和地统计学相结合的方法对表层(0~20 cm)土壤盐分、含水量与p H的空间分异特征进行研究.结果表明:绕湖一周不同区域的土壤盐分均属中等变异强度;土壤含水量在西北部属强变异性,而东北、东南和西南部均属中等变异强度;土壤p H在不同区域内均属弱变异强度.绕湖一周除西北部土壤盐分的半方差理论模型较符合球状模型外,其它区域土壤盐分、含水量和p H均符合高斯模型;受结构性因素影响,不同区域土壤盐分、含水量和p H均具有较强的空间相关性;西南部土壤盐分、含水量和p H的Moran's I系数比其它区域的波动大,表明空间相关性较强.艾比湖湿地常年大风、干旱、缺水及沙化盐化的自然因素与引水围堰、种植耐盐碱植物的人为活动造成了采样区表层土壤盐分、含水量和p H的空间分布多呈现不规则条带状格局.艾比湖湿地土壤以盐土为主,重度盐化土次之,土壤盐渍化日益严重.     

土壤含水量空间尺度分布非均匀对中尺度脉动量和通量的影响研究

本文采用RAMS（Regional Atmospheric Modeling System）模式模拟研究了没有背景风的情况下,土壤湿度非均匀分布的长度尺度分别为40km逐渐减小到2km时,地表通量的分布和大气边界层的响应.运用二维傅里叶变换,分析了地表通量、中尺度脉动量和中尺度通量的二维幅度谱分布,初步探讨大尺度模式中非均匀地表条件下的边界层参数化问题.分析结果显示各试验的地表水、热通量和中尺度脉动量的幅度谱的极大值都出现在与各自非均匀尺度相对应的波数处,当有不同尺度的非均匀斑块共存时,最大的非均匀尺度占主导.但是中尺度水、热通量的结果有所不同,除了在与各自非均匀尺度相对应的波数处有峰值之外,在其他波数还有多个峰值.这些结果表明地表水、热通量的空间分布尺度与非均匀尺度之间存在较好的对应关系,而中尺度水、热通量与非均匀尺度的关系并不明显,说明地表水、热通量的网格平均值的代表性较好,但是不能反映次网格脉动的影响,而中尺度通量的网格平均值的代表性较差.     

Comparison of soil moisture products from microwave remote sensing,land model,and reanalysis using global ground observations

High-quality soil moisture (SM) datasets are in great demand for climate, hydrology, and other fields, but detailed evaluation of SM products from various sources is scarce. Thus, using 670 SM stations worldwide, we evaluated and compared SM products from microwave remote sensing [Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) (C- and X-bands) and European Space Agency's Climate Change Initiative (ESA CCI)], land surface model [Global Land Data Assimilation System (GLDAS)], and reanalysis data [ECMWF Re-Analysis-Interim (ERA-Interim) and National Centers for Environmental Prediction (NCEP)] under different time scales and various climates and land covers. We find that: (a) ESA CCI and GLDAS have the closest values to the in situ SM on the annual scale, whereas others overestimate the SM; ERA-Interim (averaged R = 0.58) and ESA CCI (averaged R = 0.54) correlate best with the in situ data, while GLDAS performs worst. (b) Overall, the deviations of each product vary in seasons. ESA CCI and ERA-Interim products are closer to the in situ SM at seasonal scales, and AMSR-E and NCEP perform worst in December–February and June–August, respectively. (c) Except for NCEP and ERA-Interim, others can well reflect the intermonthly variation of the in situ SM. (d) Under various climates and land covers, AMSR-E products are less effective in cold climates, whereas GLDAS and NCEP products perform poorly in arid or temperate and dry climates. Moreover, the Bias and R of each SM product differ obviously under different forest types, especially the AMSR-E products. In summary, SM from ESA CCI is the best, followed by ERA-Interim product, and precipitation is an important auxiliary data for selecting high-quality SM stations and improving the accuracy of SM from GLDAS. These results can provide a reference for improving the accuracy of the above SM products.     

Analysis of the contributions of topographic,soil, and vegetation features on the spatial distributions of surface soil moisture in a steep natural forested headwater catchment

Surface soil moisture has been extensively studied for various land uses and landforms. Although many studies have reported potential factors that control surface soil moisture over space or time, the findings have not always been consistent, indicating a need for identification of the main factors. This study focused on the static controls of topographic, soil, and vegetation features on surface soil moisture in a steep natural forested headwater catchment consisting of three hillslope units of a gully area, side slope, and valley‐head slope. Using a simple correlation analysis to investigate the effects of the static factors on surface soil moisture at depths of 0–20 cm at 470 points in 13 surveys, we addressed the characteristics of surface soil moisture and its main controlling factors. The results indicated that the mean of surface soil moisture was in the decreasing order of gully area > valley‐head slope > side slope. The relationship between the mean and standard deviation of surface soil moisture showed a convex‐upward shape in the headwater catchment, a negative curvilinear shape in the gully area, and positive curvilinear shapes at the side and valley‐head slopes. At the headwater catchment and valley‐head slope, positive contributions of soil porosity and negative contributions of slope gradient and saturated hydraulic conductivity were the main controlling factors of surface soil moisture under wetter conditions, whereas positive contributions of topographic wetness index and negative contributions of vegetation density were the main controlling factors of surface soil moisture under drier conditions. At the side slope underlain by fractured bedrocks, only saturated hydraulic conductivity and vegetation density were observed to be the controlling factors. Surface soil moisture in the gully area was mainly affected by runoff rather than were static features. Thus, using hillslope units is effective for approximately estimating the hydrological behaviours of surface moisture on a larger scale, whereas dependency between the main static factors and moisture conditions is helpful for estimating the spatial distributions of surface moisture on a smaller scale.     

Temporal and spatial features of the soil moisture in boreal spring in eastern China

Soil moisture data of 45 years from European Center for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) and the in situ observational data are used to study the temporal and spatial characteristics of the soil moisture in boreal spring in the area to the east of 100°E in China. Results show that ERA-40 soil moisture well reproduces the temporal and spatial features of observations. ERA-40 data capture the spatial pattern that the soils in Northeast China and Southwest China are wetter than those...     

A case study of spatial heterogeneity of soil moisture in the Loess Plateau, western China： A geostatistical approach

Soil moisture distribution shows highly variation both spatially and temporally. This study assesses the spatial heterogeneity of soil moisture on a hill-slope scale in the Loess Plateau in West China by using a geostatistical approach. Soil moisture was measured by time-domain reflectometry （TDR） in 313 samples. Two kinds of sampling scales were used （2 × 2 m and 20 ×20 m） at two soil layers （0-30 cm and 30-60 cm）. The general characteristics of soil moisture were analyzed by a classical statistics method, and the spatial heterogeneity of soil moisture was analyzed using a geostatistical approach. The results showed that the spherical model is the best-fit model to simulate soil moisture on the experimental hill-slope. The parameters of this model indicated that the spatial dependence of soil moisture in the selected hill-slope was moderate. Even the 2 × 2 m sampling scale was too coarse to show the detailed spatial variances of soil moisture in this area. The dependent distance increased from 27.4 m to 494.16 m as the sampling scale became coarse （from 2× 2 m to 20 ×20 m）. A map of soil moisture was generated by using original soil moisture data and interpolated values determined by the Kriging method. The average soil moisture （area weighted） in the different layers of soil was calculated on the basis of this map （10.94% for the 0-30 cm soil layer, 11.88% for the 30-60 cm soil layer）. This average soil moisture is lower than the corresponding average effective soil moisture, which suggests that the soil moisture is not sufficient to support vegetation in this area.     

Controls of surface soil moisture spatial patterns and their temporal stability in a semi‐arid steppe

Temporal stability of soil moisture spatial patterns has important implications for optimal soil and water management and effective field monitoring. The aim of this study was to investigate the temporal stability of soil moisture spatial patterns over four plots of 105 m × 135 m in grid size with different grazing intensities in a semi‐arid steppe in China. We also examined whether a time‐stable location can be identified from causative factors (i.e. soil, vegetation, and topography). At each plot, surface soil moisture (0–6 cm) was measured about biweekly from 2004 to 2006 using 100 points in each grid. Possible controls of soil moisture, including soil texture, organic carbon, bulk density, vegetation coverage, and topographic indices, were determined at the same grid points. The results showed that the spatial patterns of soil moisture were considerably stable over the 3‐y monitoring period. Soil moisture under wet conditions (averaged volumetric moisture contents > 20%) was more stable than that under dry ( ) or moist ( ) conditions. The best representative point for the whole field identified in each plot was accurate in representing the field mean moisture over time (R² ≥ 0·97; p < 0·0001). The degree of temporal persistence varied with grazing intensity, which was partly related to grazing‐induced differences in soil and vegetation properties. The correlation analysis showed that soil properties, and to a lesser extent vegetation and topographic properties, were important in controlling the temporal stability of soil moisture spatial patterns in this relatively flat grassland. Response surface regression analysis was used to quantitatively identify representative monitoring locations a priori from available soil‐plant parameters. This allows appropriate selection of monitoring locations and enhances efficiency in managing soil and water resources in semi‐arid environments. Copyright © 2010 John Wiley & Sons, Ltd.     

Role of time variant and invariant terms on soil moisture spatio-temporal variability in a subhumid coastal wetland

The knowledge of soil moisture spatio-temporal variability is highly relevant for water resources management. This paper reports an analysis of the spatial–temporal variability of soil moisture data for a small to medium-scale soil-sensors network in a coastal wetland of southwestern Spain. Measurements were taken from five sites located in the Doñana National Park over the time-period of one hydrological year from September 2017 to September 2018. The total area of the soil-sensors network shows an extension about 25 × 3 km. Soil moisture data was separated into time invariant (the temporal mean of the whole period at each site) and time-variant terms (the deviations of soil moisture from the mean, or anomalies). The time-invariant component was generally the main contributor to the total spatial variance of soil moisture and it was mostly controlled by the groundwater levels in the area. Nevertheless, the time variant terms have a huge effect on soil moisture variability in very dry states. Characteristic convex time-dependent patterns for this field site were found between spatially averaged soil moisture and its variability. This information could be used for the up and downscaling of soil moisture from satellite data. Those patterns of relation between spatial mean and variability of soil moisture were only affected by heavy rainfalls giving rise to hysteretic behaviour. This study shows that even though groundwater level is a time-variant variable, it significantly affects soil moisture's time-variant but also time-invariant terms due to the different average groundwater level depths at the different sites.     

Effect of spatial resolution of satellite images on estimating the greenness and evapotranspiration of urban green spaces

Urban green spaces (UGS), like most managed land covers, are getting progressively affected by water scarcity and drought. Preserving, restoring and expanding UGS require sustainable management of green and blue water resources to fulfil evapotranspiration (ET) demand for green plant cover. The heterogeneity of UGS with high variation in their microclimates and irrigation practices builds up the complexity of ET estimation. In oversized UGS, areas too large to be measured with in situ ET methods, remote sensing (RS) approaches of ET measurement have the potential to estimate the actual ET. Often in situ approaches are not feasible or too expensive. We studied the effects of spatial resolution using different satellite images, with high-, medium- and coarse-spatial resolutions, on the greenness and ET of UGS using Vegetation Indices (VIs) and VI-based ET, over a 780-ha urban park in Adelaide, Australia. We validated ET with the ground-based ET method of Soil Water Balance. Three sets of imagery from WorldView2, Landsat and MODIS, and three VIs including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Enhanced Vegetation Index 2 (EVI2), were used to assess long-term changes of VIs and ET calculated from the different imagery acquired for this study (2011–2018). We found high correspondence between ET-MODIS and ET-Landsat (R² > 0.99 for all VIs). Landsat-VIs captured the seasonal changes of greenness better than MODIS-VIs. We used artificial neural network (ANN) to relate the RS-ET and ground data, and ET-MODIS (EVI2) showed the highest correlation (R² = 0.95 and MSE =0.01 for validation). We found a strong relationship between RS-ET and in situ measurements, even though it was not explicable by simple regressions; black box models helped us to explore their correlation. The methodology used in this research makes a strong case for the value of remote sensing in estimating and managing ET of green spaces in water-limited cities.     

Effect of spatial variability and seasonality in soil moisture on drainage thresholds and fluxes in a conceptual hydrological model

This paper uses soil moisture data from 17 recording sensors within the 50 km² Mahurangi catchment in New Zealand to determine how measured variability in soil moisture affects simulations of drainage in a typical lumped conceptual model. The data show that variability smoothes the simulated field capacity threshold such that a proportion of the catchment contributes to drainage even when mean soil moisture content is well below field capacity. Spatial variability in soil moisture controls by extension the catchment drainage behaviour: the resulting smoothed shape of the catchment‐scale drainage function is demonstrated and is also determined theoretically under simplifying assumptions. The smoothing effect increases the total simulated discharge by 130%. The analysis explains previous findings that different drainage equations are required at point scale versus catchment scale in the Mahurangi. The spatial variability and hence the emergent drainage behaviour are found to vary with season, suggesting that time‐varying parameters would be warranted to simulate drainage. Copyright © 2012 John Wiley & Sons, Ltd.     

Evaluation of a surface energy balance method based on optical and thermal satellite imagery to estimate root‐zone soil moisture

Various remote‐sensing methods are available to estimate soil moisture, but few address the fine spatial resolutions (e.g. 30‐m grid cells) and root‐zone depth requirements of agricultural and other similar applications. One approach that has been previously proposed to estimate fine‐resolution soil moisture is to first estimate the evaporative fraction from an energy balance that is inferred from optical and thermal remote‐sensing images [e.g. using the Remote Sensing of Evapotranspiration (ReSET) algorithm] and then estimate soil moisture through an empirical relationship to evaporative fraction. A similar approach has also been proposed to estimate the degree of saturation. The primary objective of this study is to evaluate these methods for estimating soil moisture and degree of saturation, particularly for a semi‐arid grassland with relatively dry conditions. Soil moisture was monitored at 28 field locations in south‐eastern Colorado with herbaceous vegetation during the summer months of 3 years. In situ soil moisture and degree of saturation observations are compared with estimates calculated from Landsat imagery using the ReSET algorithm. The in situ observations suggest that the empirical relationships with evaporative fraction that have been proposed in previous studies typically provide overestimates of soil moisture and degree of saturation in this region. However, calibrated functions produce estimates with an accuracy that may be adequate for various applications. The estimates produced by this approach are more reliable for degree of saturation than for soil moisture, and the method is more successful at identifying temporal variability than spatial variability in degree of saturation for this region. Copyright © 2015 John Wiley & Sons, Ltd.     

Analysis of spatial distribution and multi-year trend of the remotely sensed soil moisture on the Tibetan Plateau

Long-term highly accurate surface soil moisture data of TP(Tibetan Plateau)are important to the research of Asian monsoon and global atmospheric circulation.However,due to the sparse in-situ networks,the lack of soil moisture observations has seriously hindered the progress of climate change researches of TP.Based on the Dual-Channel soil moisture retrieval algorithm and the satellite observation data of AMSR-E(Advanced Microwave Scanning Radiometer for EOS),we have produced the surface soil moisture data of TP from 2003 to 2010 and analyzed the seasonal characteristic of the soil moisture spatial distribution and its multi-year changing trend in area of TP.Compared to the in-situ observations,the accuracy of the soil moisture retrieved by the proposed algorithm is evaluated.The evaluation result shows that the new soil moisture product has a better accuracy in the TP region than the official product of AMSR-E.The spatial distribution of the annual mean values of soil moisture and the seasonal variations of the monthly-averaged soil moisture are analyzed.The results show that the soil moisture variations in space and time are consistent with the precipitation distribution and the water vapor transmission path in TP.Based on the new soil moisture product,we also analyzed the spatial distribution of the changing trend of multi-year soil moisture in TP.From the comparisons with the precipitation changing trend obtained from the meteorological observation sites in TP,we found that the spatial pattern of the changing trend of soil moisture coincides with the precipitation as a whole.