Structure in fluctuations of large-scale soil moisture climate due to external random forcing and internal feedbacks

Large-scale fields of soil moisture are forced by atmospheric precipitation and radiative forcing. When these forcing factors are themselves influenced by surface and soil moisture processes, the result is a nonlinear land-atmosphere system with inherent feedback mechanisms that may strongly modulate variability in climate. Given such feedbacks, simple randomness in the forcing factors may be manifested as a complex statistical signature in the surface hydrology. In this paper, we investigate the impacts of non-Gaussian and colored-noise on the probability distribution of soil moisture resulting from the statistical-dynamical land-atmosphere interaction model of Rodriguez-Iturbe et al. (1991). Persistence of hydroclimatologic anomalies as characterized by the correlation time scale of soil moisture is discussed.     

Sensitivity of soil moisture field evolution to rainfall forcing

In this paper the temporal behaviour of soil moisture is modelled and statistically characterized by use of the zero‐dimensional model for soil moisture dynamics and the rectangular pulses Poisson process model for rainfall forcing. The mean, covariance and spectral density function of soil moisture (both instantaneous and locally averaged cases) are analytically derived to evaluate its sensitivity to the model parameters. Finally, the probability density function of soil moisture is derived to evaluate the effect of rainfall forcing. All the model parameters used have been tuned to the Monsoon '90 data. Results can be summarized as follows. (1) Only the soil moisture model parameters (η and nZ_r) are found to affect the autocorrelation function in a distinguishable manner. On the other hand, both the rainfall model parameter (θ) and the effective soil depth (nZ_r) are found to be of impact to the soil moisture spectrum. However, as the smoothing (or damping) effect of soil is so dominant, about ±20% variation of one parameter seems not to affect significantly the second‐order statistics of soil moisture. (2) More difference can be found by applying a longer averaging time, which is found to obviously decrease the variance but increase the correlation even though no overlapping between neighbouring soil moisture data was allowed. (3) Among rainfall model parameters, the arrival rate (λ) was found to be most important for the soil moisture evolution. When increasing the arrival rate of rainfall, the histogram of soil moisture shifts its peak to a certain value as well as becomes more concentrated around the peak. However, by decreasing the arrival rate of rainfall, a much smaller (almost to zero) mean value of soil moisture was estimated, even though the total volume of rainfall remained constant. This indicates that desertification may take place without decreasing the total volume of rainfall. Copyright © 2005 John Wiley & Sons, Ltd.     

A stochastic differential equation approach to soil moisture

The dynamics of water within the unsaturated root zone of the soil are represented by a pair of stochastic differential equations (SDE's), one representing the so-called surplus state of the moisture and the other the deficit condition. The inputs to the model are the climatically controlled random infiltration events and evapotranspiration which are modeled as a compound Poisson process and a Wiener (Brownian motion) process, respectively.The solutions to these SDE's are not in close-form but sample functions are obtained by numerical integration. The moment properties of the soil moisture evolution process have also been derived analytically including the mean, variance, covariance and autocorrelation functions.To illustrate the model, climatic parameters representing the surplus and deficit cases and properties of clay loam soil have been used to numerically derived the corresponding sample functions. With proper selection of all the parameters, physically realistic sample trajectories can be obtained for the model.     

Potential of soil moisture observations in flood modelling: Estimating initial conditions and correcting rainfall

Rainfall runoff (RR) models are fundamental tools for reducing flood hazards. Although several studies have highlighted the potential of soil moisture (SM) observations to improve flood modelling, much research has still to be done for fully exploiting the evident connection between SM and runoff. As a way of example, improving the quality of forcing data, i.e. rainfall observations, may have a great benefit in flood simulation. Such data are the main hydrological forcing of classical RR models but may suffer from poor quality and record interruption issues. This study explores the potential of using SM observations to improve rainfall observations and set a reliable initial wetness condition of the catchment for improving the capability in flood modelling. In particular, a RR model, which incorporates SM for its initialization, and an algorithm for rainfall estimation from SM observations are coupled using a simple integration method. The study carried out at the Valescure experimental catchment (France) demonstrates the high information content retained by SM for RR transformation, thus giving new possibilities for improving hydrological applications. Results show that an appropriate configuration of the two models allows obtaining improvement in flood simulation up to 15% in mean and 34% in median Nash Sutcliffe performances as well as a reduction of the median error in volume and on peak discharge of about 30% and 15%, respectively.     

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.     

Spatial variation in solutional denudation and soil moisture over a hillslope hollow

A spatial pattern of relative solutional denudation is described for a hillslope hollow and adjacent spurs at Bicknoller Combe, Somerset. The pattern was obtained from a network of micro-weight loss rock tablets emplaced in the soil. The results show that the hollow is the main locus of solutional denudation. The soil moisture distribution over the hollow indicates that it is a transmission zone for acid soil water percolating from the adjacent spurs to the saturated wedge at the base of the hollow. The wetter acid soils in the hollow are responsible for the relatively higher solutional denudation taking place in the hollow.     

Assessment of drought vulnerability based on the soil moisture PDF

This paper studies the statistics of the soil moisture condition and its monthly variation for the purpose of evaluating drought vulnerability. A zero-dimensional soil moisture dynamics model with the rainfall forcing by the rectangular pulses Poisson process model are used to simulate the soil moisture time series for three sites in Korea: Seoul, Daegu, and Jeonju. These sites are located in the central, south-eastern, and south-western parts of the Korean Peninsular, respectively. The model parameters are estimated on a monthly basis using hourly rainfall data and monthly potential evaporation rates obtained by the Penmann method. The resulting soil moisture simulations are summarized on a monthly basis. In brief, the conclusions of our study are as follows. (1) Strong seasonality is observed in the simulations of soil moisture. The soil moisture mean is less than 0.5 during the dry spring season (March, April, and June), but other months exceed the 0.5 value. (2) The spring season is characterized by a low mean value, a high standard deviation and a positive skewness of the soil moisture content. On the other hand, the wet season is characterized by a high mean value, low standard deviation, and negative skewness of the soil moisture content. Thus, in the spring season, much drier soil moisture conditions are apparent due to the higher variability and positive skewness of the soil moisture probability density function (PDF), which also indicates more vulnerability to severe drought occurrence. (3) Seoul, Daegue, and Jeonju show very similar overall trends of soil moisture variation; however, Daegue shows the least soil moisture contents all through the year, which implies that the south-eastern part of the Korean Peninsula is most vulnerable to drought. On the other hand, the central part and the south-western part of the Korean peninsula are found to be less vulnerable to the risk of drought. The conclusions of the study are in agreement with the climatology of the Korean Peninsula.     

Capturing critical behaviour in soil moisture spatio-temporal dynamics

A physical system is subject to a phase transition process when it shows a discontinuous change of a macroscopic feature of the system under a continuous change of a system’s state variable.     

洞庭湖洲滩土壤种子库对土壤水分变化的响应

由于三峡大坝及上游水库群的运行,长江中下游水域水文节律随之发生了改变,导致洞庭湖枯水期提前,进而影响洞庭湖洲滩植被及其土壤种子库的分布格局.本研究在洞庭湖4个自然保护区内选取共11个典型洲滩湿地,沿由水到陆方向根据植被类型将洲滩分为泥沙洲滩、泥沙—湖草洲滩过渡带、湖草洲滩、湖草—南荻洲滩过渡带、南荻洲滩5种洲滩类型.通过样带—样方法调查和采样,并结合湿润和水淹两种条件下的土壤种子库萌发实验,分析了土壤水分变化对洲滩种子库萌发特征的影响及土壤种子库与地表植被的关系.结果显示:①土壤含水量沿水到陆方向由泥沙洲滩向南荻洲滩递减;②不同类型洲滩土壤种子库密度没有显著差异;③温室萌发实验中,水淹条件下土壤种子库物种丰富度和种子库密度显著降低,东洞庭湖自然保护区土壤种子库物种丰富度和种子库密度较高;④地表植被物种丰富度高于土壤种子库,泥沙洲滩土壤种子库与地表植被物种组成的Jaccard相似性指数最低.此外,虉草(Phalaris arundinacea)、芦苇(Phragmites communis)、南荻(Miscanthus sacchariflorus)等只在地表植被中存在,而陌上菜(Lin...     

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.     

Internal soil moisture and piezometric responses to rainfall-induced shallow slope failures

Better knowledge regarding internal soil moisture and piezometric responses in the process of rainfall-induced shallow slope failures is the key to an effective prediction of the landslide and/or debris flow initiation. To this end, internal soil moisture and piezometric response of 0.7-m-deep, 1.5-m-wide, 1.7-m-high, and 3.94-m-long semi-infinite sandy slopes rested on a bi-linear impermeable bedrock were explored using a chute test facility with artificial rainfall applications. The internal response time defined by the inflection point of the soil moisture and piezometric response curves obtained along the soil–bedrock interface were closely related to some critical failure states, such as the slope toe failure and extensive slope failures. It was also found that the response times obtained at the point of abrupt bedrock slope decrease can be used as indicators for the initiation of rainfall-induced shallow slope failures. An investigation of spatial distributions of soil water content, ω (or degrees of saturation, S_r), in the slope at critical failure states shows that the 0.2 m – below – surface zone remains unsaturated with S_r 40–60%, regardless of their distances from the toe and the rainfall intensity. Non-uniform distributions of ω (or S_r) along the soil–bedrock interface at critical failure states were always associated with near-saturation states (S_r 80–100%) around the point of bedrock slope change or around the transient ‘toe’ upstream of the slumped mass induced by the retrogressive failure of the slope. These observations suggest the important role of the interflow along the soil–bedrock interface and the high soil water content (or high porewater pressure) around the point of bedrock slope deflection in the rainfall-induced failure of sandy slopes consisting of shallow impermeable bedrocks. The present study proposes an ‘internal response time’ criterion to substantiate the prediction of rainfall-induced shallow slope failures. It is believed that the ‘internal response time’ reflects the overall characteristics of a slope under rainfall infiltration and can be as useful as the conventional meteorology-based threshold times. The ‘internal response time’ theory can be generalized via numerical modeling of slope hydrology, slope geology and slope stability in the future.     

Multivariate analysis of soil moisture history for a hillslope

In this study, the spatial distribution of measured soil moisture was analyzed on the platform of multivariate modeling. Soil moisture time series for two seasons were selected and used for analysis to reveal similarities and differences in soil moisture responses for a few rainfall events. The development of a soil moisture transport process that considers the representative element volume and uncertainty of soil media provides the hydrological basis for time series modeling. The systematic procedure of Box–Jenkins with noise modeling was used to delineate the final models for all monitoring points. The physical basis of mass balance and the continuity in inflow contribution, as well as statistical criteria, were used in the model selection procedure. Heuristic approaches provide the spatial distribution of selected models along the transect of a hillside. Comparative analysis for two different depths and seasons provide an understanding of the variation in soil moisture transfer processes at the hillslope scale. Differences in soil moisture models for both depths and seasons are associated with eco-hydrological processes. The relationships between distributed topographic features and modeling results were explored to configure dominant hydrological processes for each season.     

Simulating sensitivity of soil moisture and evapotranspiration under heterogeneous soils and land uses

Soil moisture (SM) plays an important role in land surface and atmospheric interactions. It modifies energy balance at the surface and the rate of water cycling between the land and atmosphere. In this paper we provide a sensitivity assessment of SM and ET for heterogeneous soil physical properties and for three land uses including irrigated maize, rainfed maize, and grass at a climatological time-scale by using a water balance model. Not surprisingly, the study finds increased soil water content in the root zone throughout the year under irrigated farming. Soil water depletes to its lowest level under rainfed maize cultivation. We find a ‘land use’ effect as high as 36 percent of annual total evapotranspiration, under irrigated maize compared to rainfed maize and grass, respectively. Sensitivity analyses consisting of comparative simulations using the model show that soil characteristics, like water holding capacity, influence SM in the root zone and affect seasonal total ET estimates at the climatological time-scale. This ‘soils’ effect is smaller than the ‘land use’ effect associated with irrigation but, it is a source of consistent bias for both SM and ET estimates. The ‘climate’ effect basically masks the ‘soils’ effect under wet conditions. These results lead us to conclude that appropriate representation of land use, soils, and climate are necessary to accurately represent the water and energy balance in real landscapes.     

Real-time forecasting of water table depth and soil moisture profiles

We present a method for real-time forecasting of water table depth and soil moisture profiles. The method combines a simple form of data-assimilation with a moving window calibration of a deterministic model describing flow in the unsaturated zone and local as well as regional drainage. The local drainage level is calibrated on-line using a moving window calibration. Assigning more weight to the last available measurements then yields a form of model adaptation that is in between on-line calibration and data-assimilation (i.e. a simplified form of Newtonian nudging). Five-day hydrological forecasts are performed based on 5-day weather forecasts, while on-line observations of phreatic level and soil moisture content are assimilated on a daily basis. Advantages of the proposed method are that it improves the real-time forecasts compared to off-line calibration and ordinary moving window calibration and that it yields physically consistent soil moisture profiles.     

Information and complexity measures applied to observed and simulated soil moisture time series

Abstract

Time series of soil moisture-related parameters provide important insights into the functioning of soil water systems. Analysis of patterns within such time series has been used in several studies. The objective of this work was to compare patterns in observed and simulated soil moisture contents to understand whether modelling leads to a substantial loss of information or complexity. The time series were observed at four plots in sandy soils within the USDA-ARS OPE3 experimental watershed, for a year; precipitation and evapotranspiration (ET) were measured and estimated, respectively, and used for soil water flow simulation with the HYDRUS-1D software. The information content measures are the metric entropy and the mean information gain, and complexity measures are the fluctuation complexity and the effective measure complexity. These measures were computed based on the binary encoding of soil moisture time series, and used probabilities of patterns, i.e. probabilities of joint or sequential appearance of symbol sequences. The information content of daily soil moisture time series was much smaller than that of rainfall data, and had higher complexity, indicating that soil worked essentially as an information filter. Information content and complexity decreased and increased with depth, respectively, demonstrating the increase in the information filtering action of soil. The information measures of simulated soil moisture content were close to those of the measurements, indicating the successful simulation of patterns in the data. The spatial variability of the information measures for simulated soil moisture content at all depths was less pronounced than the one of measured time series. Compared with precipitation and estimated ET, soil moisture time series had more structure and less randomness in this work. The information measures can provide useful complementary knowledge about model performance and patterns in observation and modelling results.

Citation Pan, F., Pachepsky, Y. A., Guber, A. K., & Hill, R. L. (2011) Information and complexity measures applied to observed and simulated soil moisture time series. Hydrol. Sci. J. 56(6), 1027–1039.     

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.     

Time stability and variability of Electronically Scanned Thinned Array Radiometer soil moisture during Southern Great Plains hydrology experiments

Variability and time‐stability analysis for field‐scale (800 m) Electronically Scanned Thinned Array Radiometer soil moisture within a satellite scale footprint (∼ 50 km) were quantified using observations from the Southern Great Plains Hydrology Experiment 1997 and 1999 (SGP97 and SGP99). The pixels' time‐stability properties were examined with respect to soil, vegetation and topographic parameters in order to determine which physical parameters can be used to identify good candidate observation locations for validating soil moisture from satellite observations and global‐scale model output. The results show that the time‐stability concept remains valid at the satellite scale. The root mean square error values were 1·47, 1·51, 1·93 and 2·32% for the 1st, 2nd, 50th and 100th most stable fields, respectively. The most stable locations had sand and clay percentages consistent with sandy loam soils and moderate to high normalized difference vegetation index values. Neither land cover nor topography properties could be used to identify potentially stable fields in the study region. Copyright © 2010 John Wiley & Sons, Ltd.     

Scaling characteristics of spatial patterns of soil moisture from distributed modelling

Characterizing the spatial dynamics of soil moisture fields is a key issue in hydrology, offering an avenue to improve our understanding of complex land surface–atmosphere interactions. In this paper, the statistical structure of soil moisture patterns is examined using modelled soil moisture obtained from the North American Land Data Assimilation System (NLDAS) at 0.125° resolution. The study focuses on the vertically averaged soil moisture in the top 10 cm and 100 cm layers. The two variables display a weak dependence for lower values of surface soil moisture, with the strength of the relationship increasing with the water content of the top layer. In both cases, the variance of the soil moisture follows a power law decay as a function of the averaging area. The superficial layer shows a lower degree of spatial organization and higher temporal variability, which is reflected in rapid changes in time of the slope of the scaling functions of the soil moisture variance. Conversely, the soil moisture in the top 100 cm has lower variability in time and larger spatial correlation. The scaling of these patterns was found to be controlled by the changes in the soil water content. Results have implications for the downscaling of soil moisture to prevent model bias.     

Spatial patterns from EOF analysis of soil moisture at a large scale and their dependence on soil,land-use,and topographic properties

We hypothesize that the spatial and temporal variation in large-scale soil moisture patterns can be described by a small number of spatial structures that are related to soil texture, land use, and topography. To test this hypothesis, an empirical orthogonal function (EOF) analysis is conducted using data from the 1997 Southern Great Plains field campaign. When considering the spatial soil moisture anomalies, one spatial structure (EOF) is identified that explains 61% of the variance, and three such structures explain 87% of the variance. The primary EOF is most highly correlated with the percent sand in the soil among the regional characteristics considered, but the correlation with percent clay is largest if only dry days are analyzed. When considering the temporal anomalies, one EOF explains 50% of the variance. This EOF is still most closely related to the percent sand, but the percent clay is unimportant. Characteristics related to land use and topography are less correlated with the spatial and temporal variation of soil moisture in the range of scales considered.     

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.