Feike J. Leij

Bio: Feike J. Leij is an academic researcher from California State University, Long Beach. The author has contributed to research in topics: Hydraulic conductivity & Water flow. The author has an hindex of 40, co-authored 91 publications receiving 9146 citations. Previous affiliations of Feike J. Leij include University of California, Riverside & California State University.

The RETC code for quantifying the hydraulic functions of unsaturated soils

Abstract: This report describes the RETC computer code for analyzing the soil water retention and hydraulic conductivity functions of unsaturated soils. These hydraulic properties are key parameters in any quantitative description of water flow into and through the unsaturated zone of soils. The program uses the parametric models of Brooks-Corey and van Genuchten to represent the soil water retention curve, and the theoretical pore-size distribution models of Mualem and Burdine to predict the unsaturated hydraulic conductivity function from observed soil water retention data. The report gives a detailed discussion of the different analytical expressions used for quantifying the soil water retention and hydraulic conductivity functions. A brief review is also given of the nonlinear least-squares parameter optimization method used for estimating the unknown coefficients in the hydraulic models. Several examples are presented to illustrate a variety of program options. The program may be used to predict the hydraulic conductivity from observed soil water retention data assuming that one observed conductivity value (not necessarily at saturation) is available. The program also permits one to fit analytical functions simultaneously to observed water retention and hydraulic conductivity data. The report serves as both a user manual and reference document. Detailed information is given on the computer program along with instructions for data input preparation and sample input and output files. A listing of the source code is also provided.

Neural network analysis for hierarchical prediction of soil hydraulic properties

Abstract: The solution of many field-scale flow and transport problems requires estimates of unsaturated soil hydraulic properties. The objective of this study was to calibrate neural network models for prediction of water retention parameters and saturated hydraulic conductivity, K s , from basic soil properties. Twelve neural network models were developed to predict water retention parameters using a data set of 1209 samples containing sand, silt, and clay contents, bulk density, porosity, gravel content, and soil horizon as well as water retention data. A subset of 620 samples was used to develop 19 neural network models to predict K s . Prediction of water retention parameters and K s generally improved if more input data were used. In a more detailed investigation, four models with the following levels of input data were selected: (i) soil textural class, (ii) sand, silt, and clay contents, (iii) sand, silt, and clay contents and bulk density, and (iv) the previous variables and water content at a pressure head of 33 kPa. For water retention, the root mean square residuals decreased from 0.107 for the first to 0.060 m 3 m -3 for the fourth model while the root mean square residual K s decreased from 0.627 to 0.451 log(cm d -1 ). The neural network models performed better on our data set than four published pedotransfer functions for water retention (by 0.01-0.05 m 3 m -3 ) and better than six published functions for K s (by 0.1-0.9 order of magnitude). Use of the developed hierarchical neural network models is attractive because of improved accuracy and because it permits a considerable degree of flexibility toward available input data.

Database-related accuracy and uncertainty of pedotransfer functions

Abstract: Pedotransfer functions (PTFs) are becoming a more common way to predict soil hydraulic properties from soil texture, bulk density, and organic matter content. Thus far, the calibration and validation of PTFs has been hampered by a lack of suitable databases. In this paper we employed three databases

Improved Prediction of Unsaturated Hydraulic Conductivity with the Mualem-van Genuchten Model

Abstract: In many vadose zone hydrological studies, it is imperative that the soil's unsaturated hydraulic conductivity is known. Frequently, the Mualem-van Genuchten model (MVG) is used for this purpose because it allows prediction of unsaturated hydraulic conductivity from water retention parameters. For this and similar equations, it is often assumed that a measured saturated hydraulic conductivity (K s ) can be used as a matching point (K o ) while a factor S L e is used to account for pore connectivity and tortuosity (where S e is the relative saturation and L = 0.5). We used a data set of 235 soil samples with retention and unsaturated hydraulic conductivity data to test and improve predictions with the MVG equation. The standard practice of using K o = K, and L = 0.5 resulted in a root mean square error for log(K) (RMSE K ) of 1.31. Optimization of the matching point (K o ) and L to the hydraulic conductivity data yielded a RMSE K of 0.41. The fitted K 0 were, on average, about one order of magnitude smaller than measured K s . Furthermore, L was predominantly negative, casting doubt that the MVG can be interpreted in a physical way, Spearman rank correlations showed that both K 0 and L were related to van Genuchten water retention parameters and neural network analyses confirmed that K 0 and L could indeed be predicted in this way. The corresponding RMSE K was 0.84, which was half an order of magnitude better than the traditional MVG model. Bulk density and textural parameters were poor predictors while addition of K s improved the RMSE K only marginally. Bootstrap analysis showed that the uncertainty in predicted unsaturated hydraulic conductivity was about one order of magnitude near saturation and larger at lower water contents.

Soil Water Characteristic Estimates by Texture and Organic Matter for Hydrologic Solutions

Abstract: Hydrologic analyses often involve the evaluation of soil water infiltration, conductivity, storage, and plant-water relationships. To define the hydrologic soil water effects requires estimating soil water characteristics for water potential and hydraulic conductivity using soil variables such as texture, organic matter (OM), and structure. Field or laboratory measurements are difficult, costly, and often impractical for many hydrologic analyses. Statistical correlations between soil texture, soil water potential, and hydraulic conductivity can provide estimates sufficiently accurate for many analyses and decisions. This study developed new soil water characteristic equations from the currently available USDA soil database using only the readily available variables of soil texture and OM. These equations are similar to those previously reported by Saxton et al. but include more variables and application range. They were combined with previously reported relationships for tensions and conductivities and the effects of density, gravel, and salinity to form a comprehensive predictive system of soil water characteristics for agricultural water management and hydrologic analyses. Verification was performed using independent data sets for a wide range of soil textures. The predictive system was programmed for a graphical computerized model to provide easy application and rapid solutions and is available at http://hydrolab.arsusda. gov/soilwater/Index.htm.

The RETC code for quantifying the hydraulic functions of unsaturated soils

Abstract: This report describes the RETC computer code for analyzing the soil water retention and hydraulic conductivity functions of unsaturated soils. These hydraulic properties are key parameters in any quantitative description of water flow into and through the unsaturated zone of soils. The program uses the parametric models of Brooks-Corey and van Genuchten to represent the soil water retention curve, and the theoretical pore-size distribution models of Mualem and Burdine to predict the unsaturated hydraulic conductivity function from observed soil water retention data. The report gives a detailed discussion of the different analytical expressions used for quantifying the soil water retention and hydraulic conductivity functions. A brief review is also given of the nonlinear least-squares parameter optimization method used for estimating the unknown coefficients in the hydraulic models. Several examples are presented to illustrate a variety of program options. The program may be used to predict the hydraulic conductivity from observed soil water retention data assuming that one observed conductivity value (not necessarily at saturation) is available. The program also permits one to fit analytical functions simultaneously to observed water retention and hydraulic conductivity data. The report serves as both a user manual and reference document. Detailed information is given on the computer program along with instructions for data input preparation and sample input and output files. A listing of the source code is also provided.

Soil compaction in cropping systems: A review of the nature, causes and possible solutions

Abstract: Soil compaction is one of the major problems facing modern agriculture. Overuse of machinery, intensive cropping, short crop rotations, intensive grazing and inappropriate soil management leads to compaction. Soil compaction occurs in a wide range of soils and climates. It is exacerbated by low soil organic matter content and use of tillage or grazing at high soil moisture content. Soil compaction increases soil strength and decreases soil physical fertility through decreasing storage and supply of water and nutrients, which leads to additional fertiliser requirement and increasing production cost. A detrimental sequence then occurs of reduced plant growth leading to lower inputs of fresh organic matter to the soil, reduced nutrient recycling and mineralisation, reduced activities of micro-organisms, and increased wear and tear on cultivation machinery. This paper reviews the work related to soil compaction, concentrating on research that has been published in the last 15 years. We discuss the nature and causes of soil compaction and the possible solutions suggested in the literature. Several approaches have been suggested to address the soil compaction problem, which should be applied according to the soil, environment and farming system. The following practical techniques have emerged on how to avoid, delay or prevent soil compaction: (a) reducing pressure on soil either by decreasing axle load and/or increasing the contact area of wheels with the soil; (b) working soil and allowing grazing at optimal soil moisture; (c) reducing the number of passes by farm machinery and the intensity and frequency of grazing; (d) confining traffic to certain areas of the field (controlled traffic); (e) increasing soil organic matter through retention of crop and pasture residues; (f) removing soil compaction by deep ripping in the presence of an aggregating agent; (g) crop rotations that include plants with deep, strong taproots; (h) maintenance of an appropriate base saturation ratio and complete nutrition to meet crop requirements to help the soil/crop system to resist harmful external stresses.

Hydrologic and Water Quality Models: Performance Measures and Evaluation Criteria

Abstract: Performance measures (PMs) and corresponding performance evaluation criteria (PEC) are important aspects of calibrating and validating hydrologic and water quality models and should be updated with advances in modeling science. We synthesized PMs and PEC from a previous special collection, performed a meta-analysis of performance data reported in recent peer-reviewed literature for three widely published watershed-scale models (SWAT, HSPF, WARMF), and one field-scale model (ADAPT), and provided guidelines for model performance evaluation. Based on the synthesis, meta-analysis, and personal modeling experiences, we recommend coefficient of determination (R2; in conjunction with gradient and intercept of the corresponding regression line), Nash Sutcliffe efficiency (NSE), index of agreement (d), root mean square error (RMSE; alongside the ratio of RMSE and standard deviation of measured data, RSR), percent bias (PBIAS), and several graphical PMs to evaluate model performance. We recommend that model performance can be judged satisfactory for flow simulations if monthly R2 0.70 and d 0.75 for field-scale models, and daily, monthly, or annual R2 0.60, NSE 0.50, and PBIAS ≤ ±15% for watershed-scale models. Model performance at the watershed scale can be evaluated as satisfactory if monthly R2 0.40 and NSE 0.45 and daily, monthly, or annual PBIAS ≤ ±20% for sediment; monthly R20.40 and NSE 0.35 and daily, monthly, or annual PBIAS ≤ ±30% for phosphorus (P); and monthly R2 0.30 and NSE 0.35 and daily, monthly, or annual PBIAS ≤ ±30% for nitrogen (N). For RSR, we recommend that previously published PEC be used as detailed in this article. We also recommend that these PEC be used primarily for the four models for which there were adequate data, and used only with caution for other models. These PEC can be adjusted within acceptable bounds based on additional considerations, such as quality and quantity of available measured data, spatial and temporal scales, and project scope and magnitude, and updated based on the framework presented herein. This initial meta-analysis sets the stage for more comprehensive meta-analysis to revise PEC as new PMs and more data become available.