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.     

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.     

A land surface soil moisture data assimilation framework in consideration of the model subgrid-scale heterogeneity and soil water thawing and freezing

The Ensemble Kalman Filter (EnKF) is well known and widely used in land data assimilation for its high precision and simple operation. The land surface models used as the forecast operator in a land data assimilation system are usually designed to consider the model subgrid-heterogeneity and soil water thawing and freezing. To neglect their effects could lead to some errors in soil moisture assimilation. The dual EnKF method is employed in soil moisture data assimilation to build a soil moisture data as- similation framework based on the NCAR Community Land Model version 2.0 (CLM 2.0) in considera- tion of the effects of the model subgrid-heterogeneity and soil water thawing and freezing: Liquid volumetric soil moisture content in a given fraction is assimilated through the state filter process, while solid volumetric soil moisture content in the same fraction and solid/liquid volumetric soil moisture in the other fractions are optimized by the parameter filter. Preliminary experiments show that this dual EnKF-based assimilation framework can assimilate soil moisture more effectively and precisely than the usual EnKF-based assimilation framework without considering the model subgrid-scale heteroge- neity and soil water thawing and freezing. With the improvement of soil moisture simulation, the soil temperature-simulated precision can be also improved to some extent.     

Assimilation of streamflow and in situ soil moisture data into operational distributed hydrologic models: Effects of uncertainties in the data and initial model soil moisture states

We assess the potential of updating soil moisture states of a distributed hydrologic model by assimilating streamflow and in situ soil moisture data for high-resolution analysis and prediction of streamflow and soil moisture. The model used is the gridded Sacramento (SAC) and kinematic-wave routing models of the National Weather Service (NWS) Hydrology Laboratory’s Research Distributed Hydrologic Model (HL-RDHM) operating at an hourly time step. The data assimilation (DA) technique used is variational assimilation (VAR). Assimilating streamflow and soil moisture data into distributed hydrologic models is new and particularly challenging due to the large degrees of freedom associated with the inverse problem. This paper reports findings from the first phase of the research in which we assume, among others, perfectly known hydrometeorological forcing. The motivation for the simplification is to reduce the complexity of the problem in favour of improved understanding and easier interpretation even if it may compromise the goodness of the results. To assess the potential, two types of experiments, synthetic and real-world, were carried out for Eldon (ELDO2), a 795-km² headwater catchment located near the Oklahoma (OK) and Arkansas (AR) border in the U.S. The synthetic experiment assesses the upper bound of the performance of the assimilation procedure under the idealized conditions of no structural or parametric errors in the models, a full dynamic range and no microscale variability in the in situ observations of soil moisture, and perfectly known univariate statistics of the observational errors. The results show that assimilating in situ soil moisture data in addition to streamflow data significantly improves analysis and prediction of soil moisture and streamflow, and that assimilating streamflow observations at interior locations in addition to those at the outlet improves analysis and prediction of soil moisture within the drainage areas of the interior stream gauges and of streamflow at downstream cells along the channel network. To assess performance under more realistic conditions, but still under the assumption of perfectly known hydrometeorological forcing to allow comparisons with the synthetic experiment, an exploratory real-world experiment was carried out in which all other assumptions were lifted. The results show that, expectedly, assimilating interior flows in addition to outlet flow improves analysis as well as prediction of streamflow at stream gauge locations, but that assimilating in situ soil moisture data in addition to streamflow data provides little improvement in streamflow analysis and prediction though it reduces systematic biases in soil moisture simulation.     

Improving hydrologic predictions of a catchment model via assimilation of surface soil moisture

This paper examines the potential for improving Soil and Water Assessment Tool (SWAT) hydrologic predictions of root-zone soil moisture, evapotranspiration, and stream flow within the 341 km² Cobb Creek Watershed in southwestern Oklahoma through the assimilation of surface soil moisture observations using an Ensemble Kalman filter (EnKF). In a series of synthetic twin experiments assimilating surface soil moisture is shown to effectively update SWAT upper-layer soil moisture predictions and provide moderate improvement to lower layer soil moisture and evapotranspiration estimates. However, insufficient SWAT-predicted vertical coupling results in limited updating of deep soil moisture, regardless of the SWAT parameterization chosen for root-water extraction. Likewise, a real data assimilation experiment using ground-based soil moisture observations has only limited success in updating upper-layer soil moisture and is generally unsuccessful in enhancing SWAT stream flow predictions. Comparisons against ground-based observations suggest that SWAT significantly under-predicts the magnitude of vertical soil water coupling at the site, and this lack of coupling impedes the ability of the EnKF to effectively update deep soil moisture, groundwater flow and surface runoff. The failed attempt to improve stream flow prediction is also attributed to the inability of the EnKF to correct for existing biases in SWAT-predicted stream flow components.     

Sequential assimilation of soil moisture and streamflow data in a conceptual rainfall–runoff model

Soil moisture is a key hydrological variable in flood forecasting: it largely influences the partition of rain between runoff and infiltration and thus controls the flow at the outlet of a catchment. The methodology developed in this paper aims at improving the commonly used hydrological tools in an operational forecasting context by introducing soil moisture data into streamflow modelling. A sequential assimilation procedure, based on an extended Kalman filter, is developed and coupled with a lumped conceptual rainfall–runoff model. It updates the internal states of the model (soil and routing reservoirs) by assimilating daily soil moisture and streamflow data in order to better fit these external observations. We present in this paper the results obtained on the Serein, a Seine sub-catchment (France), during a period of about 2 years and using Time Domain Reflectivity probe soil moisture measurements from 0–10 to 0–100 cm and stream gauged data. Streamflow prediction is improved by assimilation of both soil moisture and streamflow individually and by coupled assimilation. Assimilation of soil moisture data is particularly effective during flood events while assimilation of streamflow data is more effective for low flows. Combined assimilation is therefore more adequate on the entire forecasting period. Finally, we discuss the adequacy of this methodology coupled with Remote Sensing data.     

Towards the estimation root-zone soil moisture via the simultaneous assimilation of thermal and microwave soil moisture retrievals

The upcoming deployment of satellite-based microwave sensors designed specifically to retrieve surface soil moisture represents an important milestone in efforts to develop hydrologic applications for remote sensing observations. However, typical measurement depths of microwave-based soil moisture retrievals are generally considered too shallow (top 2–5 cm of the soil column) for many important water cycle and agricultural applications. Recent work has demonstrated that thermal remote sensing estimates of surface radiometric temperature provide a complementary source of land surface information that can be used to define a robust proxy for root-zone (top 1 m of the soil column) soil moisture availability. In this analysis, we examine the potential benefits of simultaneously assimilating both microwave-based surface soil moisture retrievals and thermal infrared-based root-zone soil moisture estimates into a soil water balance model using a series of synthetic twin data assimilation experiments conducted at the USDA Optimizing Production Inputs for Economic and Environmental Enhancements (OPE³) site. Results from these experiments illustrate that, relative to a baseline case of assimilating only surface soil moisture retrievals, the assimilation of both root- and surface-zone soil moisture estimates reduces the root-mean-square difference between estimated and true root-zone soil moisture by 50% to 35% (assuming instantaneous root-zone soil moisture retrievals are obtained at an accuracy of between 0.020 and 0.030 m³ m⁻³). Most significantly, improvements in root-zone soil moisture accuracy are seen even for cases in which root-zone soil moisture retrievals are assumed to be relatively inaccurate (i.e. retrievals errors of up to 0.070 m³ m⁻³) or limited to only very sparse sampling (i.e. one instantaneous measurement every eight days). Preliminary real data results demonstrate a clear increase in the R² correlation coefficient with ground-based root-zone observations (from 0.51 to 0.73) upon assimilation of actual surface soil moisture and tower-based thermal infrared temperature observations made at the OPE³ study site.     

The role of landscape morphology on soil moisture variability in semi-arid ecosystems

Previous studies on semi-arid ecosystems have shown high values of soil moisture variability (SMV) primarily induced by the combined effects of non-uniform precipitation, incoming solar radiation, and soil and vegetation properties. However, the relative impact of these various factors on SMV has been difficult to evaluate due to limited availability of field data. In addition, only a limited number of studies have analysed the role of landscape morphology on SMV. Here we use numerical simulations of a simple hydrological model, the Bucket Grassland Model, to systematically analyse the effect of each contributing factor on SMV on two different landscape morphologies. The two different landform morphologies represent landscapes dominated respectively by either diffusive erosion or fluvial erosion processes. We conducted various simulations driven by a stochastically generated 100-year climate time series, which is long enough to capture climatic fluctuations, in order to understand the effect of various soil moisture controlling factors on the spatiotemporal SMV. Our modelling results show that the fluvial dominated landscapes promote higher spatial SMV than the diffusive dominated ones. Further, the role of landform morphology on SMV is more pronounced in regions where the spatial variability of incoming solar radiation and precipitation is high.     

A mathematical model of soil moisture spatial distribution on the hill slopes of the Loess Plateau

Based on important factors that affect soil moisture spatial distribution, such as the slope gradients, land use, vegetation cover, and surface water diffusion characteristics together with field measurements of soil moisture data obtained from the surface soil under different land use structures, a soil moisture spatial distribution model was established. The diffusion degree coefficient of surface water for different vegetations was estimated from soil moisture values obtained from field measurements. The model can be solved using the finite unit method. The soil moisture spatial distribution on the hill slopes in the Loess Plateau were simulated by the model. A comparison of the simulated values with measurement data shows that the model is a good fit.     

Spatial variability in soil moisture as predicted from airborne thematic mapper (ATM) data

In the first part of this project, the extent to which moisture content of alluvial soils could be predicted from imagery derived from an airborne thematic mapper (ATM) was investigated. From sampling done on the same day as the flight, it was found that digital numbers derived from the thermal channel (waveband 11) were strongly correlated with gravimetric moisture content. From sampling three fields of contrasting land cover, the relationship between waveband 11 values and moisture content was found to be independent of land cover type. Spatial variation in waveband 11 values and thus moisture content were related to palaeochannel patterns on the alluvial land. This was investigated by deriving variograms for long transects from each of the three investigated fields. The range and sills of the variograms are shown to express the nature and pattern of palaeochannels. By the application of such geostatistical techniques, high resolution imagery can thus be used to quantify palaeochannel characteristics on alluvial land.     

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.     

Factors affecting soil moisture spatial variability for a humid forest hillslope

Soil moisture is an important variable in explaining hydrological processes at hillslope scale. The distribution of soil moisture along a hillslope is related to the spatial distribution of the soil properties, the topography, the soil depth, and the vegetation. In order to investigate the factors affecting soil moisture, various environmental data were collected from a humid forest hillslope in this study. Several factors (the wetness index; the contributing area; the local slope; the soil depth; the composition of sand, silt, and clay; the scaling parameter; the hydraulic conductivity; the tree diameter at breast height; and the total weighted basal area) were evaluated for their effect on soil moisture and its distribution over the hillslope at depths of 10, 30, and 60 cm. Both linear correlation analysis and empirical orthogonal function analysis indicated that the soil texture was a dominant factor in soil moisture distribution. The impact of soil hydraulic conductivity was important for all soil moisture ranges at a depth of 30 cm, but those at 10 and 60 cm were limited to very wet and dry conditions, respectively. The relationships of the various factors with the spatial variability of soil moisture indicated the existence of a threshold soil moisture that is related to the composition of the soil and the factors related to the distribution of water in the study area.     

Spatial soil moisture scaling structure during Soil Moisture Experiment 2005

Soil moisture state and variability control many hydrological and ecological processes as well as exchanges of energy and water between the land surface and the atmosphere. However, its state and variability are poorly understood at spatial scales larger than the fields (i.e. 1 km²) as well as the ability to extrapolate field scale to larger spatial scales. This study investigates soil moisture profiles, their spatial organization, and physical drivers of variability within the Walnut Creek watershed, Iowa, during Soil Moisture Experiment 2005 and relates the watershed scale findings to previous field‐scale results. For all depths, the watershed soil moisture variability was negatively correlated with the watershed mean soil moisture and followed an exponential relationship that was nearly identical to that for field scales. This relationship differed during drying and wetting. While the overall time stability characteristics were improved with observation depth, the relatively wet and dry locations were consistent for all depths. The most time stable locations, capturing the mean soil moisture of the watershed within ± 0·9% volumetric soil moisture, were typically found on hill slopes regardless of vegetation type. These mild slope locations consistently preserve the time stability patterns from field to watershed scales. Soil properties also appear to impact stability but the findings are sensitive to local variations that may not be well defined by existing soil maps. Copyright © 2010 John Wiley & Sons, Ltd.     

Comparison of SMOS,modelled and in situ long-term soil moisture series in the northwest of Spain

ABSTRACT

This work aimed to evaluate the capability of modelled vs in situ soil moisture observations in the northwest of Spain for a period of four years (2010–2013) in order to validate the SMOS L2 product. Comparisons were performed for a set of representative stations of the Soil Moisture Measurement Stations network of the University of Salamanca (REMEDHUS) at both point and area scales. The SMOS series showed good correlation with the modelled series, better than that obtained with the in situ observations (0.77 vs 0.68 average correlation coefficients). However, some underestimation or overestimation of the SMOS series, related to the soil characteristics, was observed with respect to both the in situ and the modelled series. The SMOS data normalization produced a notable improvement in the results, highlighting the capability of the modelled data to validate the SMOS soil moisture series. This research provides a solid foundation for the future validation of SMOS at large scales, overcoming the spatial representativeness issues arising from the use of in situ point measurements.

Editor M.C. Acreman; Associate editor N. Verhoest     

考虑降雨空间变化的随机产汇流模型

引入两个负指数型差值函数,估计降雨量的概率分布,以此描述流域降雨空间变异性问题.将降雨量空间统计分布与垂向混合产流模型耦合进行产流量计算,即对地表径流,采用超渗产流模式,根据降雨与土壤下渗能力的联合分布推求其空间分布;对地面以下径流,采用蓄满产流模式,以地表渗入量的均值作为输入,进行简化处理以提高其实用性;最终推导出总产流量概率分布函数计算公式.将流域概化成一个线性水库,并根据随机微分方程理论,推导任一计算时段洪水流量的概率分布,从而构建了一个完整的随机产汇流模型.以淮河支流黄泥庄流域为例进行应用研究,结果表明,该模型可提供洪水过程的概率预报,可用于防洪风险分析,若以概率分布的期望值作为确定性预报,亦具有较高精度.     

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.     

Modelling the spatial variation in soil moisture at the landscape scale: an application to five areas of ecological interest in the UK

A simple conceptual hydrological model that explicitly includes the lateral movement of soil water and operates efficiently at the landscape scale is outlined. It is applied to five areas of ecological interest in the UK to provide distributed mean monthly soil moisture on a 50 m grid. As the model's driving variables—daily rainfall and potential evapotranspiration—are assumed constant over each of the tracts of land, the variability in soil moisture is due to different soil types and to topographic effects. Box plots of the mean monthly simulated soil moisture clearly show the spread of values occasioned by modelling the lateral water movement down the hillslope. The general magnitude of the results are compared with published data wherever possible and there is some discussion of the form of the curve used in the model to describe the attenuation of evapotranspiration with decreasing soil moisture. Copyright © 2000 John Wiley & Sons, Ltd.     

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...     

Spatial patterns and temporal stability of soil moisture across a range of scales in a semi‐arid environment

The antecedent soil moisture status of a catchment is an important factor in hydrological modelling. Traditional Hortonian infiltration models assume that the initial moisture content is constant across the whole catchment, despite the fact that even in small catchments antecedent soil moisture exhibits tremendous spatial heterogeneity. Spatial patterns of soil water distribution across three transects (two in a burnt area and one in an unburnt area) in a semi‐arid area were studied. At the transect scale, when the factors affecting soil moisture were limited to topographical position or local topography, spatial patterns showed time stability, but when other factors, such as vegetation, were taken into account, the spatial patterns became time unstable. At the point scale, and in the same areas, topographical position was the main factor controlling time stability. Scale dependence of time stability was studied and local topography and vegetation presence were observed to play an important role for the correlation between consecutive measures depending on the scale. Copyright © 2000 John Wiley & Sons, Ltd.