A particle batch smoother for soil moisture estimation using soil temperature observations

In this study, we present a particle batch smoother (PBS) to determine soil moisture profiles by assimilating soil temperatures at two depths (4 and 8 cm). The PBS can be considered as an extension of the standard particle filter (PF) in which soil moisture is updated within a window of fixed length using all observed soil temperatures in that window. This approach was developed with a view to assimilating temperature observations from distributed temperature sensing (DTS) observations, a technique which can provide temperature observations every meter or less along cables up to kilometers in length. Here, the PBS approach is tested using soil moisture and temperature, and meteorological data from an experimental site in Citra, Florida. Results demonstrate that the PBS provides a statistically significant improvement in estimated soil moisture compared to the PF, with only a marginal increase in computational expense ( < 3% of CPU time). This confirms that assimilating a sequence of temperature observations yields a better soil moisture estimate compared to sequential assimilation of individual temperature observations. The impact of observation interval was investigated for both PF and PBS, and the optimal window length was determined for the PBS. While increasing the observation interval is essential to maintain the spread of particle values in the PF, the PBS performance is best when all available observations are assimilated.     

Simultaneous estimation of surface soil moisture and soil properties with a dual ensemble Kalman smoother

In this paper, a new state-parameter estimation approach is presented based on the dual ensemble Kalman smoother(DEn KS) and simple biosphere model(Si B2) to sequentially estimate both the soil properties and soil moisture profile by assimilating surface soil moisture observations. The Arou observation station, located in the upper reaches of the Heihe River in northwestern China, was selected to test the proposed method. Three numeric experiments were designed and performed to analyze the influence of uncertainties in model parameters, atmospheric forcing, and the model's physical mechanics on soil moisture estimates. Several assimilation schemes based on the ensemble Kalman filter(En KF), ensemble Kalman smoother(En KS), and dual En KF(DEn KF) were also compared in this study. The results demonstrate that soil moisture and soil properties can be simultaneously estimated by state-parameter estimation methods, which can provide more accurate estimation of soil moisture than traditional filter methods such as En KF and En KS. The estimation accuracy of the model parameters decreased with increasing error sources. DEn KS outperformed DEn KF in estimating soil moisture in most cases, especially where few observations were available. This study demonstrates that the DEn KS approach is a useful and practical way to improve soil moisture estimation.     

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.     

Estimating hydraulic properties using a moving-model approach and multiple aquifer tests

A new method was developed for characterizing geohydrologic columns that extended >600 m deep at sites with as many as six discrete aquifers. This method was applied at 12 sites within the Southwest Florida Water Management District. Sites typically were equipped with multiple production wells, one for each aquifer and one or more observation wells per aquifer. The average hydraulic properties of the aquifers and confining units within radii of 30 to >300 m were characterized at each site. Aquifers were pumped individually and water levels were monitored in stressed and adjacent aquifers during each pumping event. Drawdowns at a site were interpreted using a radial numerical model that extended from land surface to the base of the geohydrologic column and simulated all pumping events. Conceptually, the radial model moves between stress periods and recenters on the production well during each test. Hydraulic conductivity was assumed homogeneous and isotropic within each aquifer and confining unit. Hydraulic property estimates for all of the aquifers and confining units were consistent and reasonable because results from multiple aquifers and pumping events were analyzed simultaneously.     

Estimating unsaturated soil hydraulic parameters using ant colony optimization

Although models are now routinely used for addressing environmental problems, both in research and management applications, the problem of obtaining the required parameters remains a major challenge. An attractive procedure for obtaining model parameters in recent years has been through inverse modeling. This approach involves obtaining easily measurable variables (model output), and using this information to estimate a set of unknown model parameters. Inverse procedures usually require optimization of an objective function. In this study we emulate the behavior of a colony of ants to achieve this optimization. The method uses the fact that ants are capable of finding the shortest path from a food source to their nest by depositing a trail of pheromone during their walk. Results obtained with the ant colony parameter optimization method are very promising; in eight different applications we were able to estimate the `true' parameters to within a few percent. One such study is reported in this paper plus an application to estimating hydraulic parameters in a lysimeter experiment. Despite the encouraging results obtained thus far, further improvements could still be made in the parameterization of the ant colony optimization for application to estimation of unsaturated flow and transport parameters.     

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.     

Estimating soil moisture using remote sensing data: A machine learning approach

Soil moisture is an integral quantity in hydrology that represents the average conditions in a finite volume of soil. In this paper, a novel regression technique called Support Vector Machine (SVM) is presented and applied to soil moisture estimation using remote sensing data. SVM is based on statistical learning theory that uses a hypothesis space of linear functions based on Kernel approach. SVM has been used to predict a quantity forward in time based on training from past data. The strength of SVM lies in minimizing the empirical classification error and maximizing the geometric margin by solving inverse problem. SVM model is applied to 10 sites for soil moisture estimation in the Lower Colorado River Basin (LCRB) in the western United States. The sites comprise low to dense vegetation. Remote sensing data that includes backscatter and incidence angle from Tropical Rainfall Measuring Mission (TRMM), and Normalized Difference Vegetation Index (NDVI) from Advanced Very High Resolution Radiometer (AVHRR) are used to estimate soil water content (SM). Simulated SM (%) time series for the study sites are available from the Variable Infiltration Capacity Three Layer (VIC) model for top 10 cm layer of soil for the years 1998–2005. SVM model is trained on 5 years of data, i.e. 1998–2002 and tested on 3 years of data, i.e. 2003–2005. Two models are developed to evaluate the strength of SVM modeling in estimating soil moisture. In model I, training and testing are done on six sites, this results in six separate SVM models – one for each site. Model II comprises of two subparts: (a) data from all six sites used in model I is combined and a single SVM model is developed and tested on same sites and (b) a single model is developed using data from six sites (same as model II-A) but this model is tested on four separate sites not used to train the model. Model I shows satisfactory results, and the SM estimates are in good agreement with the estimates from VIC model. The SM estimate correlation coefficients range from 0.34 to 0.77 with RMSE less than 2% at all the selected sites. A probabilistic absolute error between the VIC SM and modeled SM is computed for all models. For model I, the results indicate that 80% of the SM estimates have an absolute error of less than 5%, whereas for model II-A and II-B, 80% and 60% of the SM estimates have an error less than 10% and 15%, respectively. SVM model is also trained and tested for measured soil moisture in the LCRB. Results with RMSE, MAE and R of 2.01, 1.97, and 0.57, respectively show that the SVM model is able to capture the variability in measured soil moisture. Results from the SVM modeling are compared with the estimates obtained from feed forward-back propagation Artificial Neural Network model (ANN) and Multivariate Linear Regression model (MLR); and show that SVM model performs better for soil moisture estimation than ANN and MLR models.     

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.     

Estimating field‐saturated soil hydraulic conductivity by a simplified Beerkan infiltration experiment

Field‐saturated soil hydraulic conductivity, K_fs, is highly variable. Therefore, interpreting and simulating hydrological processes, such as rainfall excess generation, need a large number of K_fs data even at the plot scale. Simple and reasonably rapid experiments should be carried out in the field. In this investigation, a simple infiltration experiment with a ring inserted shortly into the soil and the estimation of the so‐called α* parameter allowed to obtain an approximate measurement of K_fs. The theoretical approach was tested with reference to 149 sampling points established on Burundian soils. The estimated K_fs with the value of first approximation of α* for most agricultural field soils (α* = 0.012 mm^?1) differed by a practically negligible maximum factor of two from the saturated conductivity obtained by the complete Beerkan Estimation of Soil Transfer parameters (BEST) procedure for soil hydraulic characterization. The measured infiltration curve contained the necessary information to obtain a site‐specific prediction of α*. The empirically derived α* relationship gave similar results for K_fs (mean = 0.085 mm s^?1; coefficient of variation (CV) = 71%) to those obtained with BEST (mean = 0.086 mm s^?1; CV = 67%), and it was also successfully tested with reference to a few Sicilian sampling points, since it yielded a mean and a CV of K_fs (0.0094 mm s^?1 and 102%, respectively) close to the values obtained with BEST (mean = 0.0092 mm s^?1; CV = 113%). The developed method appears attractive due to the extreme simplicity of the experiment. Copyright © 2012 John Wiley & Sons, Ltd.     

Identification of optimal soil hydraulic functions and parameters for predicting soil moisture

Abstract

The accuracy of six combined methods formed by three commonly-used soil hydraulic functions and two methods to determine soil hydraulic parameters based on a soil hydraulic parameter look-up table and soil pedotransfer functions was examined for simulating soil moisture. A novel data analysis and modelling approach was used that eliminated the effects of evapotranspiration so that specific sources of error among the six combined methods could be identified and quantified. By comparing simulated and observed soil moisture at six sites of the USDA Soil Climate Analysis Network, we identified the optimal soil hydraulic functions and parameters for predicting soil moisture. Through sensitivity tests, we also showed that adjusting only the soil saturated hydraulic conductivity, K_s , is insufficient for representing important effects of macropores on soil hydraulic conductivity. Our analysis illustrates that, in general, soil hydraulic conductivity is less sensitive to K_s than to the soil pore-size distribution parameter.

Editor D. Koutsoyiannis; Associate editor D. Hughes

Citation Pan, F., McKane, R.B. and Stieglitz, M., 2012. Identification of optimal soil hydraulic functions and parameters for predicting soil moisture. Hydrological Sciences Journal, 57 (4), 723–737.     

Research on the method for retrieving soil moisture using thermal inertia model

1 Introduction Thermal inertia is a bulk property that shows the re- sistance of a material to an input or output of heat. This plays a very important role in certain geological and hydrological studies, and climate modeling. In the 1970s, a simple thermal inertia model was proposed by Watson et al.[1―3]. Pratt (1979)[4] improved the thermal inertia model based on application tests where more factors were considered such as solar ra- diance, thermal conductivity effect, average humidity of g…     

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.     

Generation of continuous surface soil moisture dataset using combined optical and thermal infrared images

Surface soil moisture (SSM) is a critical variable for understanding water and energy flux between the atmosphere and the Earth's surface. An easy to apply algorithm for deriving SSM time series that primarily uses temporal parameters derived from simulated and in situ datasets has recently been reported. This algorithm must be assessed for different biophysical and atmospheric conditions by using actual geostationary satellite images. In this study, two currently available coarse‐scale SSM datasets (microwave and reanalysis product) and aggregated in situ SSM measurements were implemented to calibrate the time‐invariable coefficients of the SSM retrieval algorithm for conditions in which conventional observations are rare. These coefficients were subsequently used to obtain SSM time series directly from Meteosat Second Generation (MSG) images over the study area of a well‐organized soil moisture network named REMEDHUS in Spain. The results show a high degree of consistency between the estimated and actual SSM time series values when using the three SSM dataset‐calibrated time‐invariable coefficients to retrieve SSM, with coefficients of determination (R²) varying from 0.304 to 0.534 and root mean square errors ranging from 0.020 m³/m³ to 0.029 m³/m³. Further evaluation with different land use types results in acceptable debiased root mean square errors between 0.021 m³/m³ and 0.048 m³/m³ when comparing the estimated MSG pixel‐scale SSM with in situ measurements. These results indicate that the investigated method is practical for deriving time‐invariable coefficients when using publicly accessed coarse‐scale SSM datasets, which is beneficial for generating continuous SSM dataset at the MSG pixel scale.     

Soil hydraulic properties for moisture redistribution in a large-scale heterogeneous landscape

Abstract

Guidelines of effective soil hydraulic parameters were developed to be applicable in simulating average infiltration and subsequent moisture redistribution over a large-scale heterogeneous field. Average large-scale infiltration and redistribution in heterogeneous soils were quantified through multiple simulations of local-scale processes. The effective hydraulic parameters were derived to simulate the average amount of infiltrating water, and to capture the subsequent surface soil moisture redistribution averaged over the large heterogeneous landscape. The results demonstrated that the effective hydraulic parameters typically exhibited a step change from infiltration to redistribution, with the size of the step change being related to the degree of hydraulic parameter heterogeneity and the correlations among the hydraulic parameters. However, the effective hydraulic parameters did not change significantly over time for the moisture redistribution. It was further demonstrated that the size of the step change was smallest for effective saturated hydraulic conductivity.

Editor Z.W. Kundzewicz; Associate editor Y. Guttman

Citation Zhu, J.T. and Sun, D.M., 2012. Soil hydraulic properties for moisture redistribution in a large-scale heterogeneous landscape. Hydrological Sciences Journal, 57 (6), 1196–1206.     

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.     

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.     

Temporal stability of soil water content as affected by climate and soil hydraulic properties: a simulation study

Temporal stability of soil water content (TS SWC) is an often‐observed phenomenon, which characterization finds multiple applications. Climate and variability in soil properties are usually mentioned as factors of TS SWC, but their effects are far from clear. The objective of this work was to use SWC modeling to evaluate the effects of climate and soil hydraulic properties on the TS of soil water at different measurement schedules. We selected four representative climates found in USA and simulated the multiyear SWC dynamics for sandy loam, loam, and silty clay loam soils, all having the lognormal spatial distribution of the saturated hydraulic conductivity. The CLIMGEN and the HYDRUS6 codes were used to generate weather patterns and to simulate SWC, respectively. Four different methods were applied to select the representative location (RL). The low probability of having the same variability of mean relative differences of soil water under different climates was found in most of the cases. The probability that the variance of mean relative differences depended on sampling frequency was generally higher than 91% for the three soils. The interannual difference in mean relative differences variation from short and intensive summer campaigns was highly probable for all climates and soils. The RLs changed as climate and measurement scheduling changed, and they were less pronounced for coarse‐textured soils. The RL selection methods based solely on bias provided more consistency as compared with other methods. The TS appears to be the result of the interplay between climate, soil properties, and survey protocols. One implication of this factor interaction effect on TS SWC is that a simulation study can be useful to decide on the feasibility of including a search for TS‐based RLs for a specific site. Copyright © 2013 John Wiley & Sons, Ltd.     

Estimating water balance components in irrigated agriculture using a combined approach of soil moisture and energy balance monitoring,and numerical modelling

Information on water balance components such as evapotranspiration and groundwater recharge are crucial for water management. Due to differences in physical conditions, but also due to limited budgets, there is not one universal best practice, but a wide range of different methods with specific advantages and disadvantages. In this study, we propose an approach to quantify actual evapotranspiration, groundwater recharge and water inflow, i.e. precipitation and irrigation, that considers the specific conditions of irrigated agriculture in warm, arid environments. This approach does not require direct measurements of precipitation or irrigation quantities and is therefore suitable for sites with an uncertain data basis. For this purpose, we combine soil moisture and energy balance monitoring, remote sensing data analysis and numerical modelling using Hydrus. Energy balance data and routine weather data serve to estimate ET₀. Surface reflectance data from satellite images (Sentinel-2) are used to derive leaf area indices, which help to partition ET₀ into energy limited evaporation and transpiration. Subsequently, first approximations of water inflow are derived based on observed soil moisture changes. These inflow estimates are used in a series of forward simulations that produce initial estimates of drainage and ET_act, which in turn help improve the estimate of water inflow. Finally, the improved inflow estimates are incorporated into the model and then a parameter optimization is performed using the observed soil moisture as the reference figure. Forward simulations with calibrated soil parameters result in final estimates for ET_act and groundwater recharge. The presented method is applied to an agricultural test site with a crop rotation of cotton and wheat in Punjab, Pakistan. The final model results, with an RMSE of 2.2% in volumetric water content, suggest a cumulative ET_act and groundwater recharge of 769 and 297 mm over a period of 281 days, respectively. The total estimated water inflow accounts for 946 mm, of which 77% originates from irrigation.     

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.