A field‐scale infiltration model accounting for spatial heterogeneity of rainfall and soil saturated hydraulic conductivity

This study first explores the role of spatial heterogeneity, in both the saturated hydraulic conductivity K_s and rainfall intensity r, on the integrated hydrological response of a natural slope. On this basis, a mathematical model for estimating the expected areal‐average infiltration is then formulated. Both K_s and r are considered as random variables with assessed probability density functions. The model relies upon a semi‐analytical component, which describes the directly infiltrated rainfall, and an empirical component, which accounts further for the infiltration of surface water running downslope into pervious soils (the run‐on effect). Monte Carlo simulations over a clay loam soil and a sandy loam soil were performed for constructing the ensemble averages of field‐scale infiltration used for model validation. The model produced very accurate estimates of the expected field‐scale infiltration rate, as well as of the outflow generated by significant rainfall events. Furthermore, the two model components were found to interact appropriately for different weights of the two infiltration mechanisms involved. Copyright © 2005 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.     

A new saturated/unsaturated model for stormwater infiltration systems

Infiltration systems are widely used as an effective urban stormwater control measure. Most design methods and models roughly approximate the complex physical flow processes in these systems using empirical equations and fixed infiltration rates to calculate emptying times from full. Sophisticated variably saturated flow models are available, but rarely applied owing to their complexity. This paper describes the development and testing of an integrated one‐dimensional model of flow through the porous storage of a typical infiltration system and surrounding soils. The model accounts for the depth in the storage, surrounding soil moisture conditions and the interaction between the storage and surrounding soil. It is a front‐tracking model that innovatively combines a soil‐moisture‐based solution of Richard's equation for unsaturated flow with piston flow through a saturated zone as well as a reservoir equation for flow through a porous storage. This allows the use of a simple non‐iterative numerical solution that can handle ponded infiltration into dry soils. The model is more rigorous than approximate stormwater infiltration system models and could therefore be valuable in everyday practice. A range of test cases commonly used to test soil water flow models for infiltration in unsaturated conditions, drainage from saturation and infiltration under ponded conditions were used to test the model along with an experiment with variable depth in a porous storage over saturated conditions. Results show that the model produces a good fit to the observed data, analytical solutions and Hydrus. Copyright © 2008 John Wiley & Sons, Ltd.     

Active microwave remote sensing for soil moisture measurement: a field evaluation using ERS‐2

Active microwave remote sensing observations of backscattering, such as C‐band vertically polarized synthetic aperture radar (SAR) observations from the second European remote sensing (ERS‐2) satellite, have the potential to measure moisture content in a near‐surface layer of soil. However, SAR backscattering observations are highly dependent on topography, soil texture, surface roughness and soil moisture, meaning that soil moisture inversion from single frequency and polarization SAR observations is difficult. In this paper, the potential for measuring near‐surface soil moisture with the ERS‐2 satellite is explored by comparing model estimates of backscattering with ERS‐2 SAR observations. This comparison was made for two ERS‐2 overpasses coincident with near‐surface soil moisture measurements in a 6 ha catchment using 15‐cm time domain reflectometry probes on a 20 m grid. In addition, 1‐cm soil moisture data were obtained from a calibrated soil moisture model. Using state‐of‐the‐art theoretical, semi‐empirical and empirical backscattering models, it was found that using measured soil moisture and roughness data there were root mean square (RMS) errors from 3·5 to 8·5 dB and r² values from 0·00 to 0·25, depending on the backscattering model and degree of filtering. Using model soil moisture in place of measured soil moisture reduced RMS errors slightly (0·5 to 2 dB) but did not improve r² values. Likewise, using the first day of ERS‐2 backscattering and soil moisture data to solve for RMS surface roughness reduced RMS errors in backscattering for the second day to between 0·9 and 2·8 dB, but did not improve r² values. Moreover, RMS differences were as large as 3·7 dB and r² values as low as 0·53 between the various backscattering models, even when using the same data as input. These results suggest that more research is required to improve the agreement between backscattering models, and that ERS‐2 SAR data may be useful for estimating fields‐scale average soil moisture but not variations at the hillslope scale. Copyright © 2004 John Wiley & Sons, Ltd.     

Simplified modelling of areal average infiltration at the hillslope scale

Two models for estimating expected areal‐average infiltration rate, ī, at the hillslope scale are presented. The first relies upon the condition of a negligible infiltration of surface water running downslope (run‐on process) into a previous heterogeneous soil. It is an adapted version of an earlier semi‐analytical model. The second incorporates the run‐on process and is based on a lumped approach that uses an effective saturated hydraulic conductivity. This latter was parameterized in terms of the main characteristics of rainfall and soil. Both the models were tested by comparison with the results carried out by Monte‐Carlo simulations over different soil types. It was found that the first model simulated ī with maximum errors in magnitude typically less than 10%. The second model provided similar errors in the total volume of overland flow, and the rising limb of the hydrograph experienced a distortion. Lastly, satisfactory results were obtained by comparing the model without run‐on with an empirical approach particularly accurate for fine‐textured soils. Copyright © 2002 John Wiley & Sons, Ltd.     

ESTIMATING INFILTRATION PARAMETERS FROM BASIC SOIL PROPERTIES

Infiltration data were collected on two rectangular grids with 25 sampling points each. Both experimental grids were located in tropical rain forest (Guyana), the first in an Arenosol area and the second in a Ferralsol field. Four different infiltration models were evaluated based on their performance in describing the infiltration data. The model parameters were estimated using non-linear optimization techniques. The infiltration behaviour in the Ferralsol was equally well described by the equations of Philip, Green–Ampt, Kostiakov and Horton. For the Arenosol, the equations of Philip, Green–Ampt and Horton were significantly better than the Kostiakov model. Basic soil properties such as textural composition (percentage sand, silt and clay), organic carbon content, dry bulk density, porosity, initial soil water content and root content were also determined for each sampling point of the two grids. The fitted infiltration parameters were then estimated based on other soil properties using multiple regression. Prior to the regression analysis, all predictor variables were transformed to normality. The regression analysis was performed using two information levels. The first information level contained only three texture fractions for the Ferralsol (sand, silt and clay) and four fractions for the Arenosol (coarse, medium and fine sand, and silt and clay). At the first information level the regression models explained up to 60% of the variability of some of the infiltration parameters for the Ferralsol field plot. At the second information level the complete textural analysis was used (nine fractions for the Ferralsol and six for the Arenosol). At the second information level a principal components analysis (PCA) was performed prior to the regression analysis to overcome the problem of multicollinearity among the predictor variables. Regression analysis was then carried out using the orthogonally transformed soil properties as the independent variables. Results for the Ferralsol data show that the parameters of the Green–Ampt and Kostiakov model were estimated relatively accurately (maximum R² = 0.76). For the Arenosol, use of the second information level together with PCA produced regression models with an R² value ranging from 0.38 to 0.68. For the Ferralsol, most of the variance was explained by the root content and organic matter content. In the Arenosol plot, the fractions medium and fine sand explained most of the observed variance.     

Runoff and suspended sediment yields from an unpaved road segment,St John,US Virgin Islands

Unpaved roads are believed to be the primary source of terrigenous sediments being delivered to marine ecosystems around the island of St John in the eastern Caribbean. The objectives of this study were to: (1) measure runoff and suspended sediment yields from a road segment; (2) develop and test two event‐based runoff and sediment prediction models; and (3) compare the predicted sediment yields against measured values from an empirical road erosion model and from a sediment trap. The runoff models use the Green–Ampt infiltration equation to predict excess precipitation and then use either an empirically derived unit hydrograph or a kinematic wave to generate runoff hydrographs. Precipitation, runoff, and suspended sediment data were collected from a 230 m long, mostly unpaved road segment over an 8‐month period. Only 3–5 mm of rainfall was sufficient to initiate runoff from the road surface. Both models simulated similar hydrographs. Model performance was poor for storms with less than 1 cm of rainfall, but improved for larger events. The largest source of error was the inability to predict initial infiltration rates. The two runoff models were coupled with empirical sediment rating curves, and the predicted sediment yields were approximately 0·11 kg per square meter of road surface per centimetre of precipitation. The sediment trap data indicated a road erosion rate of 0·27 kg m^?2 cm^?1. The difference in sediment production between these two methods can be attributed to the fact that the suspended sediment samples were predominantly sand and silt, whereas the sediment trap yielded mostly sand and gravel. The combination of these data sets yields a road surface erosion rate of 0·31 kg m^?2 cm^?1, or approximately 36 kg m^?2 year^?1. This is four orders of magnitude higher than the measured erosion rate from undisturbed hillslopes. The results confirm the importance of unpaved roads in altering runoff and erosion rates in a tropical setting, provide insights into the controlling processes, and provide guidance for predicting runoff and sediment yields at the road‐segment scale. Copyright © 2006 John Wiley & Sons, Ltd.     

Comparison of artificial neural network models for hydrologic predictions at multiple gauging stations in an agricultural watershed

This paper reports on an evaluation of the use of artificial neural network (ANN) models to forecast daily flows at multiple gauging stations in Eucha Watershed, an agricultural watershed located in north‐west Arkansas and north‐east Oklahoma. Two different neural network models, the multilayer perceptron (MLP) and the radial basis neural network (RBFNN), were developed and their abilities to predict stream flow at four gauging stations were compared. Different scenarios using various combinations of data sets such as rainfall and stream flow at various lags were developed and compared for their ability to make flow predictions at four gauging stations. The input vector selection for both models involved quantification of the statistical properties such as cross‐, auto‐ and partial autocorrelation of the data series that best represented the hydrologic response of the watershed. Measured data with 739 patterns of input–output vector were divided into two sets: 492 patterns for training, and the remaining 247 patterns for testing. The best performance based on the RMSE, R² and CE was achieved by the MLP model with current and antecedent precipitation and antecedent flow as model inputs. The MLP model testing resulted in R² values of 0·86, 0·86, 0·81, and 0·79 at the four gauging stations. Similarly, the testing R² values for the RBFNN model were 0·60, 0·57, 0·58, and 0·56 for the four gauging stations. Both models performed satisfactorily for flow predictions at multiple gauging stations, however, the MLP model outperformed the RBFNN model. The training time was in the range 1–2 min for MLP, and 5–10 s for RBFNN on a Pentium IV processor running at 2·8 GHz with 1 MB of RAM. The difference in model training time occurred because of the clustering methods used in the RBFNN model. The RBFNN uses a fuzzy min‐max network to perform the clustering to construct the neural network which takes considerably less time than the MLP model. Results show that ANN models are useful tools for forecasting the hydrologic response at multiple points of interest in agricultural watersheds. Copyright © 2008 John Wiley & Sons, Ltd.     

Development and application of a physically based landscape water balance in the SWAT model

Watershed scale hydrological and biogeochemical models rely on the correct spatial‐temporal prediction of processes governing water and contaminant movement. The Soil and Water Assessment Tool (SWAT) model, one of the most commonly used watershed scale models, uses the popular curve number (CN) method to determine the respective amounts of infiltration and surface runoff. Although appropriate for flood forecasting in temperate climates, the CN method has been shown to be less than ideal in many situations (e.g. monsoonal climates and areas dominated by variable source area hydrology). The CN model is based on the assumption that there is a unique relationship between the average moisture content and the CN for all hydrologic response units (HRUs), and that the moisture content distribution is similar for each runoff event, which is not the case in many regions. Presented here is a physically based water balance that was coded in the SWAT model to replace the CN method of runoff generation. To compare this new water balance SWAT (SWAT‐WB) to the original CN‐based SWAT (SWAT‐CN), two watersheds were initialized; one in the headwaters of the Blue Nile in Ethiopia and one in the Catskill Mountains of New York. In the Ethiopian watershed, streamflow predictions were better using SWAT‐WB than SWAT‐CN [Nash–Sutcliffe efficiencies (NSE) of 0·79 and 0·67, respectively]. In the temperate Catskills, SWAT‐WB and SWAT‐CN predictions were approximately equivalent (NSE > 0·70). The spatial distribution of runoff‐generating areas differed greatly between the two models, with SWAT‐WB reflecting the topographical controls imposed on the model. Results show that a water balance provides results equal to or better than the CN, but with a more physically based approach. Copyright © 2010 John Wiley & Sons, Ltd.     

Finding the most suitable slope stability model for the assessment of the impact of climate change on a ­landslide in southeast France

The response of a landslide near Barcelonnette (southeast France) to climatic factors was simulated with three slope stability models: a fully empirical gross precipitation threshold, a semi‐empirical threshold model for net precipitation, and a fully conceptual slope stability model. The three models performed with similar levels in reproducing the present‐day temporal pattern of landslide reactivation, using dendrogeomorphological information as test data. The semi‐empirical and conceptual models were found to be overparameterized, because more than one parameter setting matching the test data was identified. In the case of the conceptual model, this resulted in strongly divergent scenarios of future landslide activity, using downscaled climate scenarios as inputs to the model. The uncertainty of the landslide scenarios obtained with the semi‐empirical model was much lower. In addition, the simulation of strongly different scenarios by the fully empirical threshold was attributed to its incomplete representation of the site‐specific landslide reactivation mechanism. It is concluded that the semi‐empirical model constitutes the best compromise between conceptual representation and model robustness. Copyright © 2000 John Wiley & Sons, Ltd.     

Recalibrating aeolian sand transport models

A quality‐controlled data set comprising measurements of aeolian sand transport rates obtained at three disparate field sites is used to evaluate six commonly employed transport rate models (those of Bagnold, Kawamura, Zingg, Owen, Hsu, and Lettau and Lettau) and to recalibrate the empirical constants in those models. Shear velocity estimates were obtained using the von Kármán constant and an apparent von Kármán parameter. Models were recalibrated using non‐linear regression and non‐linear regression with least‐squares lines forced through axes origins. Recalibration using the apparent von Kármán parameter and forced regression reduced the empirical constants for all models. The disparity between the predictions from the different models is reduced from about an order of magnitude to about a quarter of an order of magnitude. The recalibrated Lettau and Lettau model provided the greatest statistical agreement between observed and predicted transport rates, with a coefficient of determination of 0·77. Evaluation of the results suggests that our estimations of threshold shear velocity may be too slow, causing errors in predicted transport rates. Copyright © 2012 John Wiley & Sons, Ltd.     

A transport‐distance approach to scaling erosion rates: 3. Evaluating scaling characteristics of Mahleran

In the two previous papers of this series, we demonstrated how a novel approach to erosion modelling (Mahleran – Model for Assessing Hillslope‐Landscape Erosion, Runoff And Nutrients) provided distinct advantages in terms of process representation and explicit scaling characteristics when compared with existing models. A first evaluation furthermore demonstrated the ability of the model to reproduce spatial and temporal patterns of erosion and their particle‐size characteristics on a large rainfall‐simulation plot. In this paper, we carry out a more detailed evaluation of the model using monitored erosion events on plots of different size. The evaluation uses four plots of 21·01, 115·94, 56·84 and 302·19 m², with lengths of 4·12, 14·48, 18·95 and 27·78 m, respectively, on similar soils to the rainfall‐simulation plot, for which runoff and erosion were monitored under natural rainfall. Although the model produces the correct ranking of the magnitude of erosion events, it performs less well in reproducing the absolute values and particle‐size distributions of the eroded sediment. The implications of these results are evaluated in terms of requirements for process understanding and data for parameterization of improved soil‐erosion models. We suggest that there are major weaknesses in the current understanding and data underpinning existing models. Consequently, a more holistic re‐evaluation is required that produces functional relationships for different processes that are mutually consistent, and that have appropriate parameterization data to support their use in a wide range of environmental conditions. Copyright © 2008 John Wiley & Sons, Ltd.     

Spatial and temporal variability of annual and seasonal precipitation over the desert region of China during 1951–2005

The spatial and temporal variations of precipitation in the desert region of China (DRC) from 1951 to 2005 were investigated using a rotated empirical orthogonal function (REOF), the precipitation concentration index (PCI) and the Mann–Kendall trend test method (M‐K method). In addition, the association between variation patterns of precipitation and large‐scale circulation were also explored using the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis data. The results indicated that the spatial pattern of precipitation was primarily the local climate effect significant type, with the first three EOFs explaining a total of 55·3% of the variance, and the large‐scale climate system effect type, which explained 9·8% of the variance. Prior to the 1970s, the East Asian summer monsoon was stronger, which resulted in abundant precipitation in the Inner Mongolia region. Conversely, the climate of the Xinjiang region was controlled by westerly circulation and had lower precipitation. However, this situation has been reversed since the 1980s. It is predicted that precipitation will decrease by 15–40 and 0–10 mm/year in the Inner Mongolia plateau and southern Xinjiang, respectively, whereas it will likely increase by 10–40 mm/year in northern Xinjiang. Additionally, 58–62% of the annual rainfall occurred during summer in the DRC, with precipitation increasing during spring and summer and decreasing in winter. The intra‐annual precipitation is becoming uniform, but the inter‐annual variability in precipitation has been increasing in the western portions of the DRC. The probability of precipitation during the study period increased by 30% and 22·2% in the extreme‐arid zones and arid zones, respectively. Conversely, the probability of precipitation during the study period decreased by 18·5% and 37·5% in the semi‐arid zones and semi‐wet zones, respectively. It is predicted that the northwest portion of the DRC will become warmer and wetter, while the central portion will become warmer and drier and the northeast portion will be subjected to drought. Copyright © 2010 John Wiley & Sons, Ltd.     

Synergistic effects of water repellency and macropore flow on the hydraulic conductivity of a burned forest soil,south‐east Australia

Research shows that water repellency is a key hydraulic property that results in reduced infiltration rates in burned soils. However, more work is required in order to link the hydrological behaviour of water repellent soils to observed runoff responses at the plot and hillslope scale. This study used 5 M ethanol and water in disc infiltrometers to quantify the role of macropore flow and water repellency on spatial and temporal infiltration patterns in a burned soil at plot (<10 m²) scale in a wet eucalypt forest in south‐east Australia. In the first summer and winter after wildfire, an average of 70% and 60%, respectively, of the plot area was water repellent and did not contribute to infiltration. Macropores (r > 0·5 mm), comprising just 5·5% of the soil volume, contributed to 70% and 95%, respectively, of the field‐saturated and ponded hydraulic conductivity (K_p). Because flow occurred almost entirely via macropores in non‐repellent areas, this meant that less than 2·5% of the soil surface effectively contributed to infiltration. The hydraulic conductivity increased by a factor of up to 2·5 as the hydraulic head increased from 0 to 5 mm. Due to the synergistic effect of macropore flow and water repellency, the coefficient of variation (CV) in K_p was three times higher in the water‐repellent soil (CV = 175%) than under the simulated non‐repellent conditions (CV = 66%). The high spatial variability in K_p would act to reduce the effective infiltration rate during runoff generation at plot scale. Ponding, which tend to increase with increasing scale, activates flow through macropores and would raise the effective infiltration rates at larger scales. Field experiments designed to provide representative measurements of infiltration after fire in these systems must therefore consider both the inherent variability in hydraulic conductivity and the variability in infiltration caused by interactions between surface runoff and hydraulic conductivity. Copyright © 2010 John Wiley & Sons, Ltd.     

Evaluating best evaporation estimate model for water surface evaporation in semi‐arid region,India

Evaluating performances of four commonly used evaporation estimate methods, namely; Bowen ratio energy balance (BREB), mass transfer (MT), Priestley–Taylor (PT) and pan evaporation (PE), based on 4 years experimental data, the most effective and the reliable evaporation estimates model for the semi‐arid region of India has been derived. The various goodness‐of‐fit measures, such as; coefficient of determination (R²), index of agreement (D), root mean square error (RMSE), and relative bias (RB) have been chosen for the performance evaluation. Of these models, the PT model has been found most promising when the Bowen ratio, β is known a priori, and based on its limited data requirement. The responses of the BREB, the PT, and the PE models were found comparable to each other, while the response of the MT model differed to match with the responses of the other three models. The coefficients, β of the BREB, µ of the MT, α of the PT and K_P of the PE model were estimated as 0·07, 2·35, 1·31 and 0·65, respectively. The PT model can successfully be extended for free water surface evaporation estimates in semi‐arid India. A linear regression model depicting relationship between daily air and water temperature has been developed using the observed water temperatures and the corresponding air temperatures. The model helped to generate unrecorded water temperatures for the corresponding ambient air temperatures. Copyright © 2007 John Wiley & Sons, Ltd.     

Modelling surface runoff to evaluate the effects of wildfires in multiple semi‐arid,shrubland‐dominated catchments

Wildfires change the infiltration properties of soil, reduce the amount of interception and result in increased runoff. A wildfire at Northeast Attica, Central Greece, in August 2009, destroyed approximately one third of a study area consisting of a mixture of shrublands, pastures and pines. The present study simultaneously models multiple semi‐arid, shrubland‐dominated Mediterranean catchments and assesses the hydrological response (mean annual and monthly runoff and runoff coefficients) during the first few years following wildfires. A physically based, hydrological model (MIKE SHE) was chosen. Calibration and validation results of mean monthly discharge presented very good agreement with the observed data for the pre‐wildfire and post‐wildfire period for two subcatchments (Nash–Sutcliffe Efficiency coefficient of 79.7%). The model was then used to assess the pre‐wildfire and post‐wildfire runoff responses for each of seven catchments in the study area. Mean annual surface runoff increased for the first year and after the second year following the wildfires increased by 112% and 166%, respectively. These values are within the range observed in similar cases of monitored sites. This modelling approach may provide a way of prioritizing catchment selection with respect to post‐fire remediation activities. Additionally, this modelling assessment methodology would be valuable to other semi‐arid areas because it provides an important means for comprehensively assessing post‐wildfire response over large regions and therefore attempts to address some of the scaled issues in the specific literature field of research. Copyright © 2015 John Wiley & Sons, Ltd.     

Surfactant and irrigation effects on wettable soils: runoff,erosion, and water retention responses

Surfactants are chemical compounds that can change the contact angle of a water drop on solid surfaces and are commonly used to increase infiltration into water repellent soil. Since production fields with water repellent soil often contain areas of wettable soil, surfactants applied to such fields worldwide will likely be applied to wettable soil, with unknown consequences for irrigation‐induced erosion, runoff, or soil water relations. We evaluated surfactant and simulated sprinkler irrigation effects on these responses for three wettable, Pacific Northwest soils, Latahco and Rad silt loams, and Quincy sand. Along with an untreated control, we studied three surfactants: an alkyl polyglycoside (APG) in solution at a concentration of 18 g active ingredient (AI) kg^?1, a block copolymer at 26 g kg^?1, and a blend of the two at 43 g kg^?1. From 2005 to 2009 in the laboratory, each surfactant was sprayed at a rate of 46·8 l ha^?1 onto each soil packed by tamping into 1·2‐ by 1·5‐m steel boxes. Thereafter, each treated soil was irrigated twice at 88 mm h^?1 with surfactant‐free well water. After each irrigation, runoff and sediment loss were measured and soil samples were collected. While measured properties differed among soils and irrigations, surfactants had no effect on runoff, sediment loss, splash loss, or tension infiltration, compared to the control. Across all soils, however, the APG increased volumetric water contents by about 3% (significant at p≤0·08) at matric potentials from 0 to ? 20 kPa compared to the control. With a decrease in the liquid–solid contact angle on treated soil surfaces, surfactant‐free water appeared able to enter, and be retained in pores with diameters ≥ 15 µm. All told, surfactants applied at economic rates to these wettable Pacific Northwest soils posed little risk of increasing either runoff or erosion or harming soil water relations. Moreover, by increasing water retention at high potentials, surfactants applied to wettable soils may allow water containing pesticides or other agricultural chemicals to better penetrate soil pores, thereby increasing the efficacy of the co‐applied materials. Copyright © 2010 John Wiley & Sons, Ltd.     

Validation of a vegetated filter strip model (VFSMOD)

Vegetated filter strips (VFS) are designed to reduce sediment load and other pollutants into water bodies. However, adaptation of VFS in the field has been limited owing to lack of data about their efficiency and performance under natural field conditions. A number of models are available that simulate sediment transport and trapping in VFS, but there is a general lack of confidence in VFS models owing to limited validation studies and model limitations that prevent correct application of these models under field conditions. The objective of this study is to test and validate a process‐based model (VFSMOD) that simulates sediment trapping in VFS. This model links three submodels: modified Green–Ampt's infiltration, Quadratic overland flow submodel based on kinematic wave approximation and University of Kentucky sediment filtration model. A total of 20 VFS, 2, 5, 10 and 15 m long and with various vegetation covers, were tested under simulated sediment and runoff conditions. The results of these field experiments were used to validate the VFS model. The model requires 25 input parameters distributed over five input files. All input parameters were either measured or calculated using experimental data. The observed sediment trapping efficiencies varied from 65% in the 2‐m long VFS to 92% in the 10‐m long filters. No increase in sediment removal efficiency was observed at higher VFS length. Application of the VFS model to experimental data was satisfactory under the condition that actual flow widths are used in the model instead of the total filter width. Predicted and observed sediment trapping efficiencies and infiltration volume fitted very well, with a coefficient of determination (R²) of 0·9 and 0·95, respectively. Regression analyses revealed that the slope and intercept of the regression lines between predicted versus observed infiltration volume and trapping efficiency were not significantly different than the line of perfect agreement with a slope of 1·0 and intercept of 0·0. Copyright © 2001 John Wiley & Sons, Ltd.     

Parameterizing canopy resistance using mechanistic and semi‐empirical estimates of hourly evapotranspiration: critical evaluation for irrigated crops in the Mediterranean

In this paper two models are presented for calculating the hourly evapotranspiration λE (W m^?2) using the Penman–Monteith equation. These models were tested on four irrigated crops (grass, soya bean, sweet sorghum and vineyard), with heights between 0·1 and 2·2 m at the adult growth stage. In the first model (Katerji N, Perrier A. 1983. Modélisation de l'évapotranspiration réelle ETR d'une parcelle de luzerne : rôle d'un coefficient cultural. Agronomie 3(6): 513–521, KP model), the canopy resistance r_c is parameterized by a semi‐empirical approach. In the second model (Todorovic M. 1999. Single‐layer evapotranspiration model with variable canopy resistance. Journal of Irrigation and Drainage Engineering—ASCE 125: 235–245, TD model), the resistance r_c is parameterized by a mechanistic model. These two approaches are critically analysed with respect to the underlying hypotheses and the limitations of their practical application. In the case of the KP model, the mean slope between measured and calculated values of λE was 1·01 ± 0·6 and the relative correlation coefficients r² ranged between 0·8 and 0·93. The observed differences in slopes, between 0·96 and 1·07, were not associated with the crop height. This model seemed to be applicable to all the crops examined. In the case of the TD model, the observed slope between measured and calculated values of λE for the grass canopy was 0·79. For the other crops, it varied between 1·24 and 1·34. In all the situations examined, the values of r² ranged between 0·73 and 0·92. The TD model underestimated λE in the case of grass and overestimated it in the cases of the other three crops. The under‐ or overestimation of λE in the TD model were due: (i) to some inaccuracies in the theory of this model, (ii) to not taking into account the effect of aerodynamic resistance r_a in the canopy resistance modelling. Therefore, the values of r_c were under‐ or overestimated in consequence of mismatching the crop height. The high value of air vapour pressure deficit also contributed to the overestimation of λE, mainly for the tallest crop. The results clarify aspects of the scientific controversy in the literature about the mechanistic and semi‐empirical approaches for estimating λE. From the practical point of view the results also present ways for identifying the most appropriate approach for the experimental situations encountered. Copyright © 2010 John Wiley & Sons, Ltd.     

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

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