Year‐round estimation of soil moisture content using temporally variable soil hydraulic parameters

Soil moisture plays a key role in the hydrological cycle as it controls the flux of water between soil, vegetation, and atmosphere. This study is focused on a year‐round estimation of soil moisture in a forested mountain area using the bucket model approach. For this purpose, three different soil moisture models are utilised. The procedure is based on splitting the whole year into two complement periods (dormant and vegetation). Model parameters are allowed to vary between the two periods and also from year to year in the calibration procedure. Consequently, two sets of average model parameters corresponding to dormant and vegetation seasons are proposed. The process of splitting is strongly supported by the experimental data, and it enables us to variate saturated hydraulic conductivity and pore‐size characterisation. The use of the two different parameter sets significantly enhances the simulation of two (Teuling and Troch model and soil water balance model‐green–ampt [SWBM‐GA]) out of three models in the 6‐year period from 2009 to 2014. For these two models, the overall Nash‐Sutcliffe coefficient increased from 0.64 to 0.79 and from 0.55 to 0.80. The third model (the Laio approach) proved to be insensitive to parameter changes due to its insufficient drainage prediction. The variability of the warm and cold parameter sets between particular years is more pronounced in the warm periods. The cold periods exhibited approximately similar character during all 6 years.     

Pedotransfer functions estimating soil hydraulic properties using different soil parameters

Estimates of soil hydraulic properties using pedotransfer functions (PTF) are useful in many studies such as hydrochemical modelling and soil mapping. The objective of this study was to calibrate and test parametric PTFs that predict soil water retention and unsaturated hydraulic conductivity parameters. The PTFs are based on neural networks and the Bootstrap method using different sets of predictors and predict the van Genuchten/Mualem parameters. A Danish soil data set (152 horizons) dominated by sandy and sandy loamy soils was used in the development of PTFs to predict the Mualem hydraulic conductivity parameters. A larger data set (1618 horizons) with a broader textural range was used in the development of PTFs to predict the van Genuchten parameters. The PTFs using either three or seven textural classes combined with soil organic mater and bulk density gave the most reliable predictions of the hydraulic properties of the studied soils. We found that introducing measured water content as a predictor generally gave lower errors for water retention predictions and higher errors for conductivity predictions. The best of the developed PTFs for predicting hydraulic conductivity was tested against PTFs from the literature using a subdata set of the data used in the calibration. The test showed that the developed PTFs gave better predictions (lower errors) than the PTFs from the literature. This is not surprising since the developed PTFs are based mainly on hydraulic conductivity data near saturation and sandier soils than the PTFs from the literature. Copyright © 2007 John Wiley & Sons, Ltd.     

Simultaneous estimation of soil moisture and hydraulic parameters using residual resampling particle filter

Land data assimilation (DA) has gradually developed into an important earth science research method because of its ability to combine model simulations and observations. Integrating new observations into a land surface model by the DA method can correct the predicted trajectory of the model and thus, improve the accuracy of state variables. It can also reduce uncertainties in the model by estimating some model parameters simultaneously. Among the various DA methods, the particle filter is free from the constraints of linear models and Gaussian error distributions, and can be applicable to any nonlinear and non-Gaussian state-space model; therefore, its importance in land data assimilation research has increased. In this study, a DA scheme was developed based on the residual resampling particle filter. Microwave brightness temperatures were assimilated into the macro-scale semi-distributed variance infiltration capacity model to estimate the surface soil moisture and three hydraulic parameters simultaneously. Finally, to verify the scheme, a series of comparative experiments was performed with experimental data obtained during the Soil Moisture Experiment of 2004 in Arizona. The results show that the scheme can improve the accuracy of soil moisture estimations significantly. In addition, the three hydraulic parameters were also well estimated, demonstrating the effectiveness of the DA scheme.     

The role of overland flow and subsurface flow on the spatial distribution of soil moisture in the topsoil

Many investigations show relationships between topographical factors and the spatial distribution of soil moisture in catchments. However, few quantitative analyses have been carried out to elucidate the role of different hydrological processes in the spatial distribution of topsoil moisture in catchments. A spatially distributed rainfall—runoff model was used to investigate contributions of subsurface matric flow, macropore flow and surface runoff to the spatial distribution of soil moisture in a cultivated catchment. The model results show that lateral subsurface flow in the soil matrix or in macropores has a minor effect on the spatial distribution of soil moisture. Only when a perched groundwater table is maintained long enough, which is only possible if the subsurface is completely impermeable, may a spatial distribution in moisture content occur along the slope. Surface runoff, producing accumulations of soil moisture in flat flow paths of agricultural origin (field boundaries), was demonstrated to cause significant spatial variations in soil moisture within a short period after rainfall (<2 days). When significant amounts of surface runoff are produced, wetter moisture conditions will be generated at locations with larger upstream contributing areas. Copyright © 2001 John Wiley & Sons, Ltd.     

Functional behaviour of pedotransfer functions in soil water flow simulation

Soil hydraulic parameter values for large‐scale modelling cannot be obtained by direct methods. Pedotransfer functions (PTFs) that relate soil hydraulic properties (SHPs) to generally available soil texture data may provide an alternative. A considerable number of PTF models has been developed, the application of three recent PTFs is evaluated. As a first step sets of SHPs derived from the PTFs are compared with measured sets of SHPs for three sites. No good agreement was found statistically between measured and PTF results or between PTF results. As a second step and from a practical point of view results for three hydrologically functional variables were compared and evaluated. The three selected functional variables are saturated hydraulic conductivity, k₀, in relation to infiltration excess runoff, available soil water amounts for evapotranspiration and water table depth for a specified upward flux or capillary rise. Derived k₀ distributions from PTFs show substantially less variance than from the measured data at all three sites. This can have a considerable impact on infiltration excess runoff, depending on the actual rainfall regime. Simulated available soil water amounts for evapotranspiration for some combinations of PTFs and sites are close to those obtained for measured SHPs, however, no consistency in results can be detected. Water table depths for specified upward flux densities using PTF derived SHPs are generally deeper than those based on measured SHPs and means a potentially higher water availability. Overall, differences in capillary rise among the selected PTFs and between measured and PTF based results are again inconsistent and show no clear relationship with soil texture. Finally, as a third step, effective SHPs were calculated by using spatially averaged texture as PTF input representing areal average behaviour. For these effective SHPs the calculated effective values for the three selected functional variables appear to be close to the areally averaged values obtained with step 2. The selected functional variables thus appear to depend linearly on the PTFs over the range for which the data are representative. This suggests that for our specific PTFs areal mean or effective values for the three functional variables can be obtained fairly accurately from a single measurement of a bulk collection of soil samples as input. Copyright © 2003 John Wiley & Sons, Ltd.     

Development of pedotransfer functions for soil hydraulic properties in the critical zone on the Loess Plateau,China

Soil hydraulic properties (SHPs) including the soil water retention curve and saturated soil hydraulic conductivity (Ks) are crucial input data for simulations of soil water and solute transport in the Earth's critical zone. However, obtaining direct measurements of SHPs at a wide range of scales is time consuming and expensive. Pedotransfer functions (PTFs) are employed as an alternative method for indirectly estimating these parameters based on readily measured soil properties. However, PTFs for SHPs for the deep soil layer in the Earth's critical zone are lacking. In this study, we developed new PTFs in the deep soil profile for Ks and soil water retention curve on the Loess Plateau, China, which were fitted with the van Genuchten equation. In total, 206 data sets comprising the hydraulic and basic soil properties were obtained from three typical sites. Samples were collected from the top of the soil profile to the bedrock by soil core drilling. PTFs were developed between the SHPs and basic soil properties using stepwise multiple linear regression. The PTFs obtained the best predictions for Ks (R_adj² = 0.561) and the worst for van Genuchten α (R_adj² = 0.474). The bulk density and sand content were important input variables for predicting Ks, α, and θs, and bulk density, clay content, and soil organic carbon were important for n. The PTFs developed in this study performed better than existing PTFs. This study contains the first set of PTFs of SHPs to be developed for the deep profile on the Loess Plateau, and they may be applicable to other regions.     

Using a pore‐scale model to quantify the effect of particle re‐arrangement on pore structure and hydraulic properties

A pore‐scale model based on measured particle size distributions has been used to quantify the changes in pore space geometry of packed soil columns resulting from a dilution in electrolyte concentration from 500 to 1 mmol l^?1 NaCl during leaching. This was applied to examine the effects of particle release and re‐deposition on pore structure and hydraulic properties. Two different soils, an agricultural soil and a mining residue, were investigated with respect to the change in hydraulic properties. The mining residue was much more affected by this process with the water saturated hydraulic conductivity decreasing to 0·4% of the initial value and the air‐entry value changing from 20 to 50 cm. For agricultural soil, there was little detectable shift in the water retention curve but the saturated hydraulic conductivity decreased to 8·5% of the initial value. This was attributed to localized pore clogging (similar to a surface seal) affecting hydraulic conductivity, but not the microscopically measured pore‐size distribution or water retention. We modelled the soil structure at the pore scale to explain the different responses of the two soils to the experimental conditions. The size of the pores was determined as a function of deposited clay particles. The modal pore size of the agricultural soil as indicated by the constant water retention curve was 45 µm and was not affected by the leaching process. In the case of the mining residue, the mode changed from 75 to 45 µm. This reduction of pore size corresponds to an increase of capillary forces that is related to the measured shift of the water retention curve. Copyright © 2007 John Wiley & Sons, Ltd.     

Dual state-parameter estimation of root zone soil moisture by optimal parameter estimation and extended Kalman filter data assimilation

With well-determined hydraulic parameters in a hydrologic model, a traditional data assimilation method (such as the Kalman filter and its extensions) can be used to retrieve root zone soil moisture under uncertain initial state variables (e.g., initial soil moisture content) and good simulated results can be achieved. However, when the key soil hydraulic parameters are incorrect, the error is non-Gaussian, as the Kalman filter will produce a persistent bias in its predictions. In this paper, we propose a method coupling optimal parameters and extended Kalman filter data assimilation (OP-EKF) by combining optimal parameter estimation, the extended Kalman filter (EKF) assimilation method, a particle swarm optimization (PSO) algorithm, and Richards’ equation. We examine the accuracy of estimating root zone soil moisture through the optimal parameters and extended Kalman filter data assimilation method by using observed in situ data at the Meiling experimental station, China. Results indicate that merely using EKF for assimilating surface soil moisture content to obtain soil moisture content in the root zone will produce a persistent bias between simulated and observed values. Using the OP-EKF assimilation method, estimates were clearly improved. If the soil profile is heterogeneous, soil moisture retrieval is accurate in the 0-50 cm soil profile and is inaccurate at 100 cm depth. Results indicate that the method is useful for retrieving root zone soil moisture over large areas and long timescales even when available soil moisture data are limited to the surface layer, and soil moisture content are uncertain and soil hydraulic parameters are incorrect.     

The effect of conservation practices in sloped croplands on soil hydraulic properties and root‐zone moisture dynamics

Rain‐induced erosion and short‐term drought are the two factors that limit the productivity of croplands in the red soil region of subtropical China. The objective of this study was to estimate the effects of conservation practices on hydraulic properties and root‐zone water dynamics of the soil. A 3‐year experiment was performed on a slope at Xianning. Four treatments were evaluated for their ability to reduce soil erosion and improve soil water conditions. Compared with no practices (CK) and living grass strips (GS), the application of polyacrylamide (PAM) significantly reduced soil crust formation during intense rainfall, whereas rice straw mulching (SM) completely abolished soil crust formation. The SM and PAM treatments improved soil water‐stable aggregates, with a redistribution of micro‐aggregates into macro‐aggregates. PAM and SM significantly increased the soil water‐holding capacity. These practices mitigated the degradation of the soil saturated hydraulic conductivity (Ks) during intense rainfalls. These methods increased soil water storage but with limited effects during heavy rainfalls in the wet period. In contrast, during the dry period, SM had the highest soil water storage, followed by PAM and CK. Grass strips had the lowest soil water storage because of the water uptake during the vigorous grass growth. A slight decline in the soil moisture resulted in a significant decrease in the unsaturated hydraulic conductivity (Ku) of the topsoil. Therefore, the hydraulic conductivity in the field is governed by soil moisture, and the remaining soil moisture is more important than improving soil properties to resist short‐term droughts. As a result, SM is the most effective management practice when compared with PAM and GS, although they all protect the soil hydraulic properties during wet periods. These results suggest that mulching is the best strategy for water management in erosion‐threatened and drought‐threatened red soils. Copyright © 2014 John Wiley & Sons, Ltd.     

Determining hydraulic properties of a loam soil by alternative infiltrometer techniques

Testing infiltrometer techniques to determine soil hydraulic properties is necessary for specific soils. For a loam soil, the water retention and hydraulic conductivity predicted by the BEST (Beerkan Estimation of Soil Transfer parameters) procedure of soil hydraulic characterization was compared with data collected by more standard laboratory and field techniques. Six infiltrometer techniques were also compared in terms of saturated soil hydraulic conductivity, K_s. BEST yielded water retention values statistically similar to those obtained in the laboratory and K_s values practically coinciding with those determined in the field with the pressure infiltrometer (PI). The unsaturated soil hydraulic conductivity measured with the tension infiltrometer (TI) was reproduced satisfactorily by BEST only close to saturation. BEST, the PI, one‐potential experiments with both the TI and the mini disk infiltrometer (MDI), the simplified falling head (SFH) technique and the bottomless bucket (BB) method yielded statistically similar estimates of K_s, differing at the most by a factor of three. Smaller values were obtained with longer and more soil‐disturbing infiltration runs. Any of the tested infiltration techniques appears usable to obtain the order of magnitude of K_s at the field site, but the BEST, BB and PI data appear more appropriate to characterize the soil at some stage during a rainfall event. Additional investigations on both similar and different soils would allow development of more general procedures to apply infiltrometer techniques for soil hydraulic characterization. Copyright © 2015 John Wiley & Sons, Ltd.     

Spatial and temporal dynamics of soil moisture after rainfall events along a slope in Regosols of southwest China

High frequency seasonal drought in purple soils (Regosols in FAO taxonomy) of the hilly upland areas of Sichuan basin, China, is one of the key restrictive factors for crop production. In order to manage irrigation and fertilizer application in these soils effectively, the soil water content in a sloped plot with 60 cm soil depth was measured by neutron probe devices to investigate the soil moisture regime during the 1998 rainy season after various amounts of rainfall events. The results showed that variation of soil moisture along the slope positions was highest in the top soil layer during the period of sporadic rainfall that did not induce any runoff. The coefficients of variation of soil moisture at various slope positions (upper, middle, and lower) are 17·36%, 8·95%, 10·25%, 8·58%, 8·05% and 9·21% at the 10 cm, 20 cm, 30 cm, 40 cm, 50 cm and 60 cm soil depths respectively. When surface runoff occurred, the soil moisture dynamics at various positions on the plot were then very different. Soil water content decreased more rapidly on the upper slope than on the middle and lower slope positions. When both surface runoff and throughflow occurred, the soil moisture dynamics in the various layers showed a stable period (soil water content is near constant as time elapses) that lasted about 1 week. Also, the pattern of moisture dynamics is ‘decreasing–stabilization–decreasing’. Thus, irrigation and fertilization management according to the spatial and temporal features of soil moisture dynamics on sloped land can increase the water and fertilizer utilization efficacy by reducing their losses during the stable period. Copyright © 2006 John Wiley & Sons, Ltd.     

Changes over time in near-saturated hydraulic conductivity of peat soil following reclamation for agriculture

Reclamation of peat bogs for agriculture changes the physical and chemical characteristics of the peat matrix, for example, drainage and tillage accelerate decomposition, altering peat porosity, pore size distribution, and hydraulic properties. This study investigated changes in near-saturated hydraulic conductivity over time after drainage of peat soil for agricultural use by conducting tension infiltrometer measurements in a mire that has been gradually drained and reclaimed for agriculture during the past 80 years (with fields drained 2, 12, 40, and 80 years before the measurements). At pore water pressure closest to saturation (pressure head −1 cm), hydraulic conductivity in the newest field was approximately nine times larger than that in the oldest field, and a decreasing trend with field age was observed. A similar (but weaker) trend was observed with −3 cm pressure head (approximately four times larger in the newest field in comparison to the oldest), but at −6 cm head, there were no significant differences. These results indicate that peat degradation reduces the amount of millimetre-sized pores in particular. They also indicate that changes in peat macroporosity continue for several decades before a new steady state is reached.     

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.     

Estimating growing-season root zone soil moisture from vegetation index-based evapotranspiration fraction and soil properties in the Northwest Mountain region,USA

A soil moisture retrieval method is proposed, in the absence of ground-based auxiliary measurements, by deriving the soil moisture content relationship from the satellite vegetation index-based evapotranspiration fraction and soil moisture physical properties of a soil type. A temperature–vegetation dryness index threshold value is also proposed to identify water bodies and underlying saturated areas. Verification of the retrieved growing season soil moisture was performed by comparative analysis of soil moisture obtained by observed conventional in situ point measurements at the 239-km² Reynolds Creek Experimental Watershed, Idaho, USA (2006–2009), and at the US Climate Reference Network (USCRN) soil moisture measurement sites in Sundance, Wyoming (2012–2015), and Lewistown, Montana (2014–2015). The proposed method best represented the effective root zone soil moisture condition, at a depth between 50 and 100 cm, with an overall average R² value of 0.72 and average root mean square error (RMSE) of 0.042.     

Error distribution modelling of satellite soil moisture measurements for hydrological applications

Satellite‐based soil moisture data accuracies are of important concerns by hydrologists because they could significantly influence hydrological modelling uncertainty. Without proper quantification of their uncertainties, it is difficult to optimize the hydrological modelling system and make robust decisions. Currently, the satellite soil moisture data uncertainty has been limited to summary statistics with the validations mainly from the in situ measurements. This study attempts to build the first error distribution model with additional higher‐order uncertainty modelling for satellite soil moisture observations. The methodology is demonstrated by a case study using the Soil Moisture and Ocean Salinity satellite soil moisture observations. The validation is based on soil moisture estimates from hydrological modelling, which is more relevant to the intended data use than the in situ measurements. Four probability distributions have been explored to find suitable error distribution curves using the statistical tests and bootstrapping resampling technique. General extreme value is identified as the most suitable one among all the curves. The error distribution model is still in its infant stage, which ignores spatial and temporal correlations, and nonstationarity. Further improvements should be carried out by the hydrological community by expanding the methodology to a wide range of satellite soil moisture data using different hydrological models. Copyright © 2016 John Wiley & Sons, Ltd.     

The value of remotely sensed surface soil moisture for model calibration using SWAT

Remotely sensed (RS) data can add value to a hydrological model calibration. Among this, RS soil moisture (SM) data have mostly been assimilated into conceptual hydrological models using various transformed variable or indices. In this study, raw RS surface SM is used as a calibration variable in the Soil and Water Assessment Tool model. This means the SM values were not transformed into another variable (e.g., soil water index and root zone SM index). Using a nested catchment, calibration based only on RS SM and optimizing model parameters sensitive to SM using particle swarm optimization improved variations in streamflow predictions at some of the gauging stations compared to the uncalibrated model. This highlighted part of the catchments where the SM signal directly influenced the flow distribution. Additionally, highlighted high and low flow signals were mostly influenced. The seasonal breakdown indicates that the SM signal is more useful for calibrating in wetter seasons and in areas with higher variations in elevation. The results identified that calibration only on RS SM improved the general rainfall–runoff response simulation by introducing delays but cannot correct the overall routing effect. Furthermore, catchment characteristics (e.g., land use, elevation, soil types, and precipitation) regulating SM variation in different seasons highlighted by the model calibration are identified. This provides further opportunities to improve model parameterization.     

Effects of initial soil water content and saturated hydraulic conductivity variability on small watershed runoff simulation using LISEM

Abstract

Soil water content (θ) and saturated hydraulic conductivity (K_s) vary in space. The objective of this study was to examine the effects of initial soil water content (θ_i) and K_s variability on runoff simulations using the LImburg Soil Erosion Model (LISEM) in a small watershed in the Chinese Loess Plateau, based on model parameters derived from intensive measurements. The results showed that the total discharge (TD) and peak discharge (PD) were underestimated when the variability of θ_i and K_s was partially considered or completely ignored compared with those when the variability was fully considered. Time to peak (TP) was less affected by the spatial variability compared to TD and PD. Except for TP in some cases, significant differences were found in all hydrological variables (TD, PD and TP) between the cases in which spatial variability of θ_i or K_s was fully considered and those in which spatial variability was partially considered or completely ignored. Furthermore, runoff simulations were affected more strongly by K_s variability than by θ_i variability. The degree of spatial variability influences on runoff simulations was related to the rainfall pattern and θ_i. Greater rainfall depth and instantaneous rainfall intensity corresponded to a smaller influence of the spatial variability. Stronger effects of the θ_i variability on runoff simulation were found in wetter soils, while stronger effects of the K_s variability were found in drier soils. For accurate runoff simulation, the θ_i variability can be completely ignored in cases of a 1-h duration storm with a return period greater than 10 years, while K_s variability should be fully considered even in the case of a 1-h duration storm with a return period of 20 years.

Editor D. Koutsoyiannis; Associate editor A. Fiori     

Regional soil moisture simulation for Shaanxi Province using SWAT model validation and trend analysis

The soil moisture in Shaanxi Province,a region with complex topography,is simulated using the distributed hydrological model Soil Water Assessment Tool(SWAT).Comparison and contrast of modeled and observed soil moisture show that the SWAT model can reasonably simulate the long-term trend in soil moisture and the spatiotemporal variability of soil moisture in the region.Comparisons to NCEP/NCAR and ERA40 reanalysis of soil moisture show that the trend of variability in soil moisture simulated by SWAT is more...     

Estimation of soil water content in watershed using artificial neural networks

Soil water content (SWC) is an important factor in transfer processes between soil and air, contributing to water and energy balances, and quantifying it remains a challenge. This study uses artificial neural networks (ANNs) to analyse spatial and temporal variation of SWC in a Brazilian watershed, based on climate information, soil physical properties and topographic variables. Thirty eight input variables were tested in 200 models. The outputs were compared with 650 gravimetric moisture measurements collected at 26 points (25 field studies). The results show that it is possible to estimate SWC efficiently (Nash-Sutcliffe statistic, NS = 0.77) using topographic data, soil physical properties and rainfall. If only climate information is considered, modelling is less efficient (NS = 0.28). Using many variables does not necessarily improve performance. Alternatively, SWC can be estimated by simplified models using rainfall and topographic maps information, although the performance is less good (NS = 0.65).