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.     

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.     

Modified models for better prediction of infiltration rates in trapezoidal permeable stormwater channels

ABSTRACT

In stormwater management, it is important to accurately quantify the infiltration rates to solve urban runoff-related problems. This study proposes a method to improve estimates of the infiltration rate in permeable stormwater channels. As part of the analysis, five infiltration models were evaluated: the Kostiakov, Horton, modified Kostiakov, Philip and SCS (Soil Conservation Service) models. Infiltration tests with various initial water levels were performed on channel models with differing base width and side slopes. The results show that the addition of three parameters that describe the trapezoidal cross-sectional area, i.e. the depth, side slope and base width, in the infiltration models yielded better estimates of the infiltration rate. A comparison of the infiltration capacity values obtained from the models after the three parameters were added with those that were experimentally obtained, shows that the improved modified Kostiakov model is the most suitable model to predict infiltration rates in trapezoidal permeable stormwater channels.     

Unified macroscopic model for unsaturated water flow in soils of bimodal porosity

Abstract

Natural soils very often contain micro- and macropores, having different hydraulic properties. At the macroscopic scale, the unsaturated flow in such soils can be described with various models, depending on the hydraulic diffusivity ratio of the components and the connectivity of the most conductive component. Three macroscopic models recently derived by the homogenization method are discussed. The limit passages between the models are studied. A unified model suitable for the entire range of the hydraulic diffusivity ratio is proposed. A numerical example shows the application of the model to macroscopically one-dimensional infiltration in a porous medium containing inclusions. A parametric study for varying conductivity (diffusivity) ratio is performed.     

Identifiability analysis: towards constrained equifinality and reduced uncertainty in a conceptual model

Abstract

Different sets of parameters and conceptualizations of a basin can give equally good results in terms of predefined objective functions. Therefore, a need exists to tackle equifinality and quantify the uncertainty bands of a model. In this paper we use the concepts of equifinality, identifiability and uncertainty to propose a simple method aimed at constraining the equifinal parameters and reducing the uncertainty bands of model outputs, and obtaining physically possible and reasonable models. Additionally, the uncertainty of equifinal solutions is quantified to estimate the amount by which output uncertainty can be reduced by knowing how to discard most of the equifinal solutions of a model. As a study case, a conceptual model of the Chillán basin in Chile is carried out. From the study it is concluded that using identifiability analysis makes it possible to constrain equifinal solutions with reduced uncertainty and realistic models, resulting in a framework that can be recommended to practitioners, especially due to the simplicity of the method.     

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.     

Parametric uncertainty and sensitivity analysis of hydrodynamic processes for a large shallow freshwater lake

Abstract

A parametric uncertainty and sensitivity analysis of hydrodynamic processes was conducted for a large shallow freshwater lake, Lake Taihu, China. Ten commonly used parameters in five groups were considered including: air–water interface factor, water–sediment interface factor, surrounding terrain factor, turbulent diffusion parameters and turbulent intensity parameters. Latin hypercube sampling (LHS) was used for sampling the parametric combinations, which gave predictive uncertainty results directly without using surrogate models, and the impacts of different parametric distribution functions on the results were investigated. The results showed that the different parametric distribution functions (e.g. uniform, normal, lognormal and triangular) for sampling had very little impact on the uncertainty and sensitivity analysis of the lake hydrodynamic model. The air–water interface factor (wind drag coefficient) and surrounding terrain factor (wind shelter coefficient) had the greatest influence on the spatial distribution of lake hydrodynamic processes, especially in semi-closed bays and lake regions with complex topography, accounting for about 60–70% and 20%, respectively, of the uncertainty on the results. Vertically, velocity in the surface layer was also largely influenced by the two factors, followed by velocity in the bottom layer; the middle velocity had minimal impact. Likewise, the water–sediment interface factor (i.e. bottom roughness height) ranked third, contributing about 10% to the uncertainty of the hydrodynamic processes of the lake. In contrast, turbulent diffusion parameters and turbulent intensity parameters in the lake hydrodynamic model had little effect on the uncertainty of simulated results (less than 1% contribution). The findings were sufficiently significant to reduce the parameter uncertainties and calibration workload of the hydrodynamic model in large shallow lakes.

Editor Z. W. Kundzewicz; Associate editor S. Grimaldi     

Climate models and hydrological parameter uncertainties in climate change impacts on monthly runoff and daily flow duration curve of a Mediterranean catchment

ABSTRACT

Climate models and hydrological parameter uncertainties were quantified and compared while assessing climate change impacts on monthly runoff and daily flow duration curve (FDC) in a Mediterranean catchment. Simulations of the Soil and Water Assessment Tool (SWAT) model using an ensemble of behavioural parameter sets derived from the Generalized Likelihood Uncertainty Estimation (GLUE) method were approximated by feed-forward artificial neural networks (FF-NN). Then, outputs of climate models were used as inputs to the FF-NN models. Subsequently, projected changes in runoff and FDC were calculated and their associated uncertainty was partitioned into climate model and hydrological parameter uncertainties. Runoff and daily discharge of the Chiba catchment were expected to decrease in response to drier and warmer climatic conditions in the 2050s. For both hydrological indicators, uncertainty magnitude increased when moving from dry to wet periods. The decomposition of uncertainty demonstrated that climate model uncertainty dominated hydrological parameter uncertainty in wet periods, whereas in dry periods hydrological parametric uncertainty became more important.

Editor M.C. Acreman; Associate editor S. Kanae     

Modification and discussion of the Green-Ampt model for an evolving wetting profile

ABSTRACT

The Green-Ampt (GA) model has been widely used to evaluate soil water infiltration. While a simple piston profile is commonly used, the wetting profile of a soil changes during infiltration and a quarter-ellipse has been found to better describe its evolution. This study aims to improve the GA model and discuss the model parameters when the quarter-ellipse profile is utilized. The soil column is divided into three zones: saturated, transient and dry. The variable γ is introduced to express the ratio of the saturated zone depth to the wetting front depth. A modified GA model is derived via mathematical methods, but an exact solution is difficult to obtain. Therefore, a simplified (SGA) model is developed via a segmented method. Compared with the measured results, the SGA model is more accurate than the traditional model. Finally, the model parameters are discussed and a value of γ = 0.5 is recommended.     

Comparison of non-parametric and parametric water temperature models on the Nivelle River,France

Abstract

Water temperature is an important abiotic variable in aquatic habitat studies and may be one of the factors limiting the potential fish habitat (e.g. salmonids) in a stream. Stream water temperatures are modelled using statistical approaches with air temperature and streamflow as exogenous variables in the Nivelle River, southern France. Two different models are used to model mean weekly maximum temperature data: a non-parametric approach, the k-nearest neighbours method (k-NN) and a parametric approach, the periodic autoregressive model with exogenous variables (PARX). The k-NN is a data-driven method, which consists of finding, at each point of interest, a small number of neighbours nearest to this value, and the prediction is estimated based on these neighbours. The PARX model is an extension of commonly-used autoregressive models in which parameters are estimated for each period within the years. Different variants of air temperature and flow are used in the model development. In order to test the performance of these models, a jack-knife technique is used, whereby model goodness of fit is assessed separately for each year. The results indicate that both models give good performances, but the PARX model should be preferred, because of its good estimation of the individual weekly temperatures and its ability to explicitly predict water temperature using exogenous variables.     

Determining short-term changes in the hydraulic properties of a sandy-loam soil by a three-run infiltration experiment

ABSTRACT

Soil structure-dependent parameters can vary rapidly as a consequence of perturbing events such as intense rainfall. Investigating their short-term changes is therefore essential to understand the general behaviour of a porous medium. The aim of this study is to gain insight into the effects of wetting, perturbation and recovery processes through different sequences of Beerkan infiltration experiments performed on a sandy-loam soil. Two different three-run infiltration experiments (LHL and LLL) were carried out by pouring water at low (L, non-perturbing) and high (H, perturbing) heights above the soil surface and at short time intervals (hours, days). The results demonstrate that the proposed method allows one to capture short-term variations in soil structure-dependent parameters. The developed methodology is expected to simplify the parameterization of hydrological models with temporally variable soil hydraulic properties.     

Modelling of infiltration using artificial intelligence techniques in semi-arid Iran

ABSTRACT

Infiltration plays a fundamental role in streamflow, groundwater recharge, subsurface flow, and surface and subsurface water quality and quantity. In this study, adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and random forest (RF) models were used to determine cumulative infiltration and infiltration rate in arid areas in Iran. The input data were sand, clay, silt, density of soil and soil moisture, while the output data were cumulative infiltration and infiltration rate, the latter measured using a double-ring infiltrometer at 16 locations. The results show that SVM with radial basis kernel function better estimated cumulative infiltration (RMSE = 0.2791 cm) compared to the other models. Also, SVM with M4 radial basis kernel function better estimated the infiltration rate (RMSE = 0.0633 cm/h) than the ANFIS and RF models. Thus, SVM was found to be the most suitable model for modelling infiltration in the study area.     

An approach to measure parameter sensitivity in watershed hydrological modelling

Hydrological responses vary spatially and temporally according to watershed characteristics. In this study, the hydrological models that we developed earlier for the Little Miami River (LMR) and Las Vegas Wash (LVW) watersheds in the USA were used for detailed sensitivity analyses. To compare the relative sensitivities of the hydrological parameters of these two models, we used normalized root mean square error (NRMSE). By combining the NRMSE index with the flow duration curve analysis, we derived an approach to measure parameter sensitivities under different flow regimes. Results show that the parameters related to groundwater are highly sensitive in the LMR watershed, whereas the LVW watershed is primarily sensitive to near-surface and impervious parameters. The high and medium flows are more impacted by most of the parameters. The low flow regime was highly sensitive to groundwater-related parameters. Moreover, our approach is found to be useful in facilitating model development and calibration.

EDITOR D. Koutsoyiannis

ASSOCIATE EDITOR S. Huang     

Neuro-fuzzy models employing wavelet analysis for suspended sediment concentration prediction in rivers

Abstract

The study of sediment load is important for its implications to the environment and water resources engineering. Four models were considered in the study of suspended sediment concentration prediction: artificial neural networks (ANNs), neuro-fuzzy model (NF), conjunction of wavelet analysis and neuro-fuzzy (WNF) model, and the conventional sediment rating curve (SRC) method. Using data from a US Geological Survey gauging station, the suspended sediment concentration predicted by the WNF model was in satisfactory agreement with the measured data. Also the proposed WNF model generated reasonable predictions for the extreme values. The cumulative suspended sediment load estimated by this model was much higher than that predicted by the other models, and is close to the observed data. However, in the current modelling, the ANN, NF and SRC models underestimated sediment load. The WNF model was successful in reproducing the hysteresis phenomenon, but the SRC method was not able to model this behaviour. In general, the results showed that the NF model performed better than the ANN and SRC models.

Citation Mirbagheri, S. A., Nourani, V., Rajaee, T. & Alikhani, A. (2010) Neuro-fuzzy models employing wavelet analysis for suspended sediment concentration prediction in rivers. Hydrol. Sci. J. 55(7), 1175–1189.     

The influence of parametric uncertainty on the relationships between HBV model parameters and climatic characteristics

Abstract

An HBV rainfall–runoff model was applied to test the influence of climatic characteristics on model parameter values. The methodology consisted of the calibration and cross-validation of the HBV model on a series of 5-year periods for four selected catchments (Axe, Kamp, Wieprz and Wimmera). The model parameters were optimized using the SCEM-UA method which allowed for their uncertainty also to be assessed. Nine climatic indices were selected for the analysis of their influence on model parameters, and divided into water-related and temperature-related indices. This allowed the dependence of HBV model parameters on climate characteristics to be explored following their response to climate change conditioned on the catchment’s physical characteristics. The Pearson correlation coefficient and weighted Pearson correlation coefficient were used to test the dependence. Most parameters showed a statistically significant dependence on several climatic indices in all catchments. The study shows that the results of the correlation analysis with and without parametric uncertainty taken into account differ significantly.     

Investigating the impact of initial soil moisture conditions on total infiltration by using an adapted double-ring infiltrometer

ABSTRACT

This study presents an adaptation of the double-ring infiltrometer (DRI) device, which allows several infiltration experiments to be conducted at the same location. Hence, it becomes possible to use the DRI method to investigate infiltration behaviour under different initial soil moisture conditions. The main feature is the splitting of the inner ring into two parts. While the lower part remains in the soil throughout the investigation period, the upper part is attached to the lower one just before the infiltration experiment. This method was applied to eight test sites in an Alpine catchment, covering different land-use/cover types. The results demonstrated the applicability of the adapted system and showed correlations between total water infiltration and initial soil moisture conditions on pastures, independent of the underlying soil type. In contrast, no correlation was found at forest sites or wetlands. Thus, the study emphasizes the importance of paying special attention to the impact of initial soil moisture conditions on the infiltration—and consequently the runoff behaviour—at managed areas. Given the differences in the total infiltrated water of between 30 and 1306 mm, consideration of the interplay between initial soil moisture conditions, land-use/cover type, and soil properties in rainfall–runoff models is a prerequisite to predict runoff production accurately.

EDITOR Z.W. Kundzewicz; ASSOCIATE EDITOR not assigned     

Simulation of flood hydrographs based on lumped and semi-distributed models for two tropical catchments in Kenya

Abstract

The design and operation of flood management systems require computation of flood hydrographs for both design floods and flood forecasting purposes, since observed data are usually inadequate for these tasks. This is particularly relevant for most developing countries, i.e. mainly for tropical catchments. One possible way of obtaining information about flood hydrographs is through the use of rainfall—runoff models. Two such models, namely the Bochum model and the Nash Cascade—Diskin Infiltration model, which are semi-distributed and lumped models, respectively, were used in the present study. These models were applied to two catchments in Kenya with drainage areas of 6.71 km² and 26.03 km². A set of 13 selected rainfall—runoff events was used to calibrate and validate the models. The physical parameters required by the models were derived from catchment characteristics using GIS and remote sensing data while the conceptual parameters were obtained by optimization. The flood hydrographs simulated using the parameters so derived indicated that it is possible to use the two models in this tropical environment.     

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.     

Simulation of infiltration from porous clay pipe in subsurface irrigation

Abstract

Advances in the traditional method of subsurface porous clay pipe irrigation rely on knowledge of the distribution of water in the soil. Knowing the relationships among the hydraulic and physical parameters in the system is important for both the design and management of the system. To simulate the infiltration from the porous clay pipe and predict the wetted zone geometry in the soil, a computer model is developed herein. Laboratory experiments were conducted on soil samples representing two different soil textures in a specially designed bin to understand the flow phenomenon and to validate the developed model. In a given soil texture, the installation depth of the pipe and the volume of water applied in the soil are the major factors affecting the wetted zone. The relationships among various parameters, namely lateral spacing, installation depth, irrigation run time, hydraulic conductivity of the body of the pipe, and hydraulic head in the system, were established using the developed model.