Erosion Prediction Models Research Articles

USING CLIGEN TO GENERATE RUSLE CLIMATE INPUTS

CLIGEN is a stochastic weather generator that produces continuous daily variables to drive processbased runoffand erosion prediction models such as WEPP. To test CLIGENs ability to generate precipitationrelated variables, whichare particularly important to runoff and erosion prediction, algorithms were developed to compute the Rfactor, its monthlydistribution, and 10year storm erosion index (EI) needed to apply the Revised Universal Soil Loss Equation (RUSLE).Measured Rfactor and 10year storm EI for 76 sites in the U.S. were used for calibration, and 89 additional sites were usedfor validation. It was found that the generated Rfactor using CLIGEN is highly correlated with the measured Rfactor forthe calibration sites (r2 = 0.96), although the generated Rfactor is systematically larger than the measured Rfactor. Thepredicted Rfactor for validation sites has a model efficiency (Ec) of 0.92 and a root mean squared error of around 600 MJmm ha1 h1 year1, or 24% of the average Rfactor for the 89 sites. In addition, CLIGENgenerated precipitation data canalso be used to predict 10year storm EI (Ec = 0.75) and monthly distribution of rainfall erosivity for a wide range of climateenvironments (average discrepancy = 2.6%). This represents considerable improvement over existing methods to estimateRfactor and 10year storm EI for locations with only monthly precipitation data, although the systematic overestimationof the Rfactor using CLIGENgenerated climate data suggests possible inadequacies in the assumed storm patterns in CLIGEN and WEPP.

Soil loss predictions with three erosion simulation models

Quantification of soil loss is one of the greatest challenges in natural resources and environmental planning. Computer simulation models are becoming increasingly popular in predicting soil loss for various land use and management practices. In this study, three soil erosion prediction models — the Water Erosion Prediction Project (WEPP), the Erosion Productivity Impact Calculator (EPIC), and the Areal Nonpoint Source Watershed Environment Response Simulation (ANSWERS) were used for simulating soil loss and testing the capability of the models in predicting soil losses for three different tillage systems (ridge-till, chisel-plow, and no-till). For each model, the most sensitive model parameters were calibrated using measured soil erosion data. After calibration, models were run and predicted soil loss values were compared with the measured soil loss values. The measured soil erosion data were collected from an erosion experiment field of Kansas State University at Ottawa (Kansas), USA. Field experiments were conducted from 1995 to 1997 on small plots to measure runoff and soil losses under all three tillage systems. All three models were evaluated on the basis of individual event, total yearly, and mean event-based soil loss predictions. Results showed that all the three models performed reasonably well and the predicted soil looses were within the range of measured values. For ridge-till and chisel-plow systems, WEPP and ANSWERS gave better predictions than those by EPIC model. For no-till system, WEPP and EPIC predictions were better than those by ANSWERS. The overall results indicate that WEPP predictions were better than those by the other two models in most of the cases, and it can be used with reasonable degree of confidence for soil loss quantification for all the three tillage systems.

Potential use of Rare Earth Oxides as Tracers for Soil Erosion and Aggregation Studies

Most existing soil loss data are spatially‐averaged, though various tracing techniques have been used for obtaining spatially‐distributed data. Spatially‐distributed soil erosion data are needed for validating physically‐based erosion prediction models and for better understanding soil erosion dynamics. The objectives of this study were to evaluate the feasibility of using rare earth element (REE) oxides directly as tracers for soil erosion studies by examining their binding ability with soil materials, and also to test a quick acid‐extraction procedure. Five REE oxide powders were directly mixed with a Miami silt loam soil (fine‐loamy, mixed, mesic Typic Hapludalfs) and then leached with deionized water in a soil box to evaluate the mobility of REEs. Following leaching, soil samples were sectioned in 25‐mm increments and analyzed for REEs. The REE‐tagged soil was wet sieved to obtain REE concentrations in each aggregate size group. A simple acid‐leaching method was used to extract REEs from all soil samples. The extracts were analyzed by Inductively Coupled Plasma‐Mass Spectrometry (ICP‐MS) techniques. The data indicated that the maximum coefficient of variation of the proposed procedure was <10% for all REEs. The REE oxides were uniformly incorporated into soil aggregates of different sizes (>53 μm) and were bound with silt‐size particles. This finding shows that the direct use of REE oxides is feasible, which should be superior to other REE‐tagged particulate tracers because it eliminates the need of tagging exotic particles with REEs. Also, direct mixing of a trace amount of REEs does not substantially alter physicochemical properties of soil particles and aggregates. This work has shown that REE oxides have a great potential for tracing soil erosion and aggregation.

Evaluating Soil Quality–Soil Redistribution Relationship on Terraces and Steep Hillslope

Soil redistribution from tillage and water erosion have the potential to modify the spatial patterns of soil quality on terraced and steep cultivated hillslopes. However, few studies have investigated this relationship. Our objectives were to quantify soil quality parameters along terraced and steep hillslopes and determine the relationship between soil redistribution from tillage erosion and water erosion on soil quality parameters in the Chinese Loess Plateau. Soil quality indicators, i.e., soil organic matter (OM), available P, N, bulk density (Db), and clay and silt contents were measured at 5‐m intervals on a terraced field and at 10‐m intervals on a steep cultivated hillslope in a down slope transect. Soil redistribution rates from tillage and overland flow were obtained by 137Cs technique integrated with a tillage erosion prediction model (TEP). Water erosion was the primary cause for the overall decline in soil quality on the steep cultivated hillslope while tillage erosion had a comparable contribution to overall level in soil quality on the terraced hillslope. Soil movement by tillage controlled the spatial patterns in OM, N, and P on both terraced and steep cultivated hillslopes. Selective removal of finer particles by water erosion caused a linear decrease in clay content of 0.02% m−1 and corresponding increase in silt content of 0.04% m−1 downslope on the steep cultivated hillslope. The impact of tillage erosion on OM, N, and P on the steep cultivated hillslope can be assessed using the change in adjacent slope gradients (X) through a soil quality‐topography regression model, Y = aX + b

Evaluation of WEPP for runoff and soil loss prediction at Gunnedah, NSW, Australia

It is important to use historical data to test physically based runoff and soil erosion prediction models as well as the method to estimate model parameters. WEPP (Water Erosion Prediction Project) was validated for bare fallow and annual wheat treatments at Gunnedah, New South Wales, Australia. Wheat stubble was either burned or mulched. Climate, soil, management, and runoff and soil loss data were collected for the period 1980–87 for 3 bare fallow plots, and 1950–74 for 10 annual wheat plots. Three slope lengths from 21 to 62 m were established for the treatment with stubble burned. Slope steepness varied from 8% to 9% at the site. Effective saturated hydraulic conductivity and soil erodibility parameters were estimated from measured soil properties. No further calibration of these parameters was attempted in order to assess the true potential of the model for runoff and soil loss predictions. WEPP worked well for the bare fallow plots with prediction efficiency of 0.97 for event runoff and soil losses. WEPP generally over-predicted the runoff, and consequently, the soil loss for annual wheat treatments for the site. WEPP was able to predict the effect of slope length on sediment concentration and soil loss for the site. CLIGEN, which provides the continuous climate input to WEPP, was found to produce adequately the mean daily rainfall, but produced higher than expected peak rainfall intensity, resulting in higher runoff and soil loss for all treatments.

CROPPING AND TILLAGE EFFECTS ON SOIL ROUGHNESS INDEXES

Several soil roughness indexes have been proposed or are being used in wind erosion prediction models, yet littleis known about the effects of tillage, soils and cropping systems on the indexes. In this study, we test the hypotheses thatroughness indexes related to the wind erosion process vary with type of tillage, can be functionally related to precipitation,and can be estimated by a random roughness (RR) index. We measured roughness using a pintype microrelief meter for threeseasons on fallow, cotton (Gossypium hirsutum L.) and sorghum [Sorghum bicolor (L.) Moench] fields on three soils (twoPaleustalfs and a Paleustoll) of west Texas. Roughness was characterized by RR expressed as the standard deviation ofadjusted surface elevations, a ridge height index, and the fraction of the surface susceptible to particle abrasion measuredparallel and perpendicular to the tillage direction. These indexes varied considerably in their ability to resolve differencesamong soils, tillage tools and crops. In nearly every case, each index was significantly affected (P < 0.05) by tillage type andsoil. Random roughness, however, did not vary among soils. No significant differences were found among crops whencomparing only roughness measured immediately after tillage. In general, the roughness indexes fell into three broad tillagegroups. The ridging tools (rolling cultivator and lister) produced the greatest roughness. The chisel, knife sweep and cultivatorproduced the least roughness and the remaining tillage tools comprised an intermediate group. We were successful indeveloping models to estimate the four roughness indexes using initial roughness value and the cumulative amount ofprecipitation as independent variables.

Can [formula omitted] replace [formula omitted] as an erosion tracer in agricultural landscapes contaminated with Chernobyl fallout?

Erosion studies often use 137 Cs from the global fallout (main period: 1953–1964) as a tracer in the soil. In many European countries, where 137 Cs was deposited in considerable amounts also by the Chernobyl fallout in 1986, the global fallout fraction (GF-Cs) has to be separated from the Chernobyl fraction by means of the isotope 134 Cs . In a few years, this will no longer be possible due to the short half-life of 134 Cs (2 yr). Because GF-Cs in the soil can then no longer be determined, the potential of using 239+240 Pu as a tracer is evaluated. This radionuclide originates in most European countries essentially only from the global fallout. The activities and spatial distributions of Pu and GF-Cs were compared in the soil of a steep field (inclination about 20%, area ca. 3 ha, main soil type Dystric Eutrochrept), sampled at 48 nodes of a 25×25 m 2 grid. The reference values were determined at 12 points adjacent to the field. Their validity was assured by an inventory study of radiocaesium in a 70 ha area surrounding the field sampling 275 nodes of a 50×50 m 2 grid. In the field studied, the activity concentrations of GF-Cs and Pu in the Ap horizon were not correlated (Spearman correlation coefficient R=0.20, p>0.05), and the activity balance of Pu differed from that of GF-Cs. Whereas no net loss of GF-Cs from the field was observed as compared to the reference site, Pu was more mobile with an average loss of ca. 11% per unit area. In addition, the spatial pattern of GF-Cs and Pu in the field differed significantly. The reason may be that due to their different associations with soil constituents, Pu and Cs represent different fractions of the soil, exhibiting different properties with respect to erosion/deposition processes. This indicates that both radionuclides or one of them may not be appropriate to quantify past erosion. When tracer losses are used to calibrate or verify erosion prediction models, systematic deviations may not only stem from model shortcomings but also from tracer technique.

391 C-factor Research on Horticultural Crops for Erosion Prediction: Philosophy and Methodology of Data Collection

Even though research and education systems have transformed agriculture from a traditional to a high-technology sector, soil erosion still remains as a major universal problem to agricultural productivity. The Universal Soil Loss Equation (USLE) and its replacement, the Revised Universal Soil Loss Equation (RUSLE) are the most widely used of all soil erosion prediction models. Of the five factors in RUSLE, the cover and management (C) factor is the most important one from the standpoint of conservation planning because land use changes meant to reduce erosion are represented here. Even though the RUSLE is based on the USLE, this modern erosion prediction model is highly improved and updated. Alcorn State Univ. entered into a cooperative agreement with the NRCS of the USDA in 1988 to conduct C-factor research on vegetable and fruit crops. The main objective of this research is to collect plant growth and residue data that are used to populated databases needed to develop C-factors in RUSLE, and used in databases for other erosion prediction and natural resource models. The enormous data collected on leaf area index (LAI), canopy cover, lower and upper biomass, rate of residue decomposition, C:N ratio of samples of residues and destructive harvest and other gorwth parameters of canopy and rhizosphere made the project the largest data bank on horticultural crops. The philosophy and methodology of data collection will be presented.

Applications of fuzzy logic to the prediction of soil erosion in a large watershed

There is a need in many emerging nations to develop simple methods for predicting areas of extensive soil erosion using imprecise, but real-world, input data at low cost with considerable accuracy. The objectives of this study are to (1) develop fuzzy logic models that predict soil erosion in a relatively large watershed using a limited number of input variables, (2) determine the effects of scale and grid-size variations in input data on fuzzy logic model output, and (3) compare the predictions of soil erosion using fuzzy logic methodologies with those of the Universal Soil Loss Equation (USLE). Two fuzzy-logic rule bases were constructed: (1) a two-variable-based model which required inputs of slope angle and landuse ratio or landuse, and (2) a three-variable-based model which required inputs of slope length (FLS), soil erodibility (FK) and vegetative cover (FC). Reducing the grid size of input data resulted in a decrease in the areal extent of predicted high soil erosion. The more dispersed pattern of soil erosion, observed with a 4-mile grid at a scale of 1:250,000, became more clustered in the 2-mile grid and even more clustered in a 2-mile grid at a scale of 1:24,000. The trend of clustering towards smaller areas of high erosion potential as map scale increased was also found at the grid sizes 1 and 0.4 miles. Soil erosion, predicted with the two-variable fuzzy logic model using 30 m resolution GIS-based data sets of slope and landuse, was similarly distributed among the low, moderately low and moderate soil erosion categories. This was attributed to the lack of inherent fuzziness in the input data obtained from raster format. The areal extent and locations of soil erosion predicted by the USLE model and by the two-variable-fuzzy logic model were similar. Differences between the predictions of the USLE model and the three-variable fuzzy logic model were primarily in the moderately low and moderate soil erosion categories and were attributed to the effects of the two non-fuzzy variables used in the USLE model, annual rainfall erosivity and cropping practices, which were not used in the fuzzy logic model. Compared with the USLE model predictions, the fuzzy logic-soil erosion prediction models were successful at locating and differentiating areas of soil erosion with minimum input data.

Influence of Abiotic and Biotic Factors in Measuring and Modeling Soil Erosion on Rangelands: State of Knowledge

The first standardized soil erosion prediction equation used on rangelands was the Universal Soil Loss Eguation (USLE). The Revised Universal Soil Loss Equation (RUSLE) was developed to address deficiencies in the USLE by accounting for temporal changes in soil erodibility and plant factors which were not originally considered. Improvements were also made to the rainfall, length, slope, and management practice factors of the original USLE model. The Water Erosion Prediction Project (WEPP) model was developed to estimate soil erosion from single events, long-term soil loss from hillslopes, and sediment yield from small watersheds. Temporal changes in biomass, soil erodibility, and land management practices, and to a limited extent, spatial distribution of soil, vegetation, and land use are addressed in the WEPP model. To apply new process-based erosion prediction technology, basic research must be conducted to better model the interactions and feedback mechanisms of plant communities and landscape ecology. Thresholds at which accelerated soil erosion results in unstable plant communities must be identified. Research is needed to determine the confidence limits for erosion predictions generated by simulation models so that the probability of meeting specified soil loss values (kg ha-1 yr-1) for given management systems can be calculated at specific significance levels. As the technology for modeling soil erosion on rangelands has improved, limitations with the techniques of parameter estimation have been encountered. Improvements in model parameterization techniques and national databases that incorporate vegetation and soil variability are required before existing erosion prediction models can be implemented.

CPIDS: a plant parameter selection program for erosion prediction modeling

Newly developed erosion prediction models require detailed input parameters including those describing cropping systems. Each model exhibits a different level of complexity for crop growth modeling, and many plant parameters for these models are difficult to obtain. Furthermore, numerous parameters require interpretation from other measurable plant characteristics. We developed a database containing measurable plant characteristics to supply users of these new erosion prediction technologies with the necessary parameters. The new database supports the Revised Universal Soil Loss Equation (RUSLE) and the Water Erosion Prediction Project (WEPP) erosion prediction models. The Crop Parameter Intelligent Database System (CPIDS) was developed to assist crop database builders and users of RUSLE and WEPP in interpretation of plant characteristics and selection of plant parameters. CPIDS is a Microsoft® Windows™-based program, currently running on a personal computer (PC) under Windows 3.1™, Windows 95™ and Windows NT 4.0™.

Temporal variation in properties of an uncropped, ploughed Miamian soil in relation to seasonal erodibility

Knowledge of seasonal variation in soil structural and related properties is important for the determination of critical periods during which soil is susceptible to accelerated erosion and other degradative processes. The purpose of this research was to evaluate the magnitude of seasonal variations in selected soil and deposited sediment properties in relation to soil erodibility for a Miamian silt-loam soil (Typic Hapludalf) in central Ohio. Erosion plots (USLE-type) were established on a 4·5% slope and maintained under bare, ploughed conditions from 1988 to 1991. Particle size distribution, bulk density(ρb), percentage water stable aggregates (WSA), soil organic carbon (SOC), and total soil nitrogen (TSN) of both soil and sediment samples were monitored between Autumn 1989 and Spring 1991. The soil and sediment particle size distributions followed no clear seasonal trends. Soil ρb increased following tillage (1·20 Mg m−3) and was highest (1·45 Mg m−3) during the autumn owing to soil slumping and consolidation upon drying. Low winter and spring values of ρb and %WSA (20–50% lower than in autumn) were attributed to excessive wetness and freeze–thaw effects. Both SOC and soil TSN contents progressively declined (from 2·18 to 1·79% and 1·97 to 1·75 g kg−1, respectively) after ploughing owing to maintenance of plots under bare, fallow conditions. Spring highs and autumn lows of sediment SOC (3·12 vs. 2·44%) and TSN (2·70 vs. 1·96 g kg−1) contents were a result of the combined effects of soil microbial activity and rainfall erosivity. Soil properties such as bulk density, SOC and WSA, which vary seasonally, can potentially serve as predictors of seasonal soil erodibility, which, in turn, could improve the predictive capacity of soil erosion prediction models. © 1998 John Wiley & Sons, Ltd.

Runoff and erosion control with conservation tillage and reduced-input practices on cropped watersheds

The North Appalachian Experimental Watershed near Coshocton, OH was established in 1935 to develop, evaluate, and refine conservation practices that reduce runoff and erosion under the hilly, humid conditions of the northeastern United States. Small (0.5 to 1 ha), single-practice, gaged watersheds comprised of sandstone- and shale-derived residual soils are used to evaluate the interaction of management, climate, and soils. In a 28-year, nine-watershed study, 92% of the erosion occurred during the corn ( Zea mays L.) years of a 4-year corn/wheat ( Triticum aestivum L.)/meadow/meadow rotation. These watersheds were moldboard plowed prior to planting corn and cultivation was used for weed control. By tilling and planting on the contour and increasing fertility levels, soil loss was reduced more than 3-fold, but still averaged 4.7 Mg ha −1 during corn years. Thus, annual production of row crops on a sustainable basis was not without risk. A 6-year, six-watershed study indicated that by using reduced tillage (no-till, chisel, or paraplow) and herbicides, corn and soybean [ Glycine max (L.) Merr.] can be grown in rotation with an average soil loss of 0.5 Mg ha −1 yr −1 , well below the stipulated soil loss tolerance of 7.8 Mg ha −1 yr −1 , if a winter cover crop of rye ( Secale cereale L.) followed soybean. Under these conditions, however, concentrations of surface-applied herbicides and nitrate in runoff frequently exceeded drinking water standards, particularly in the first few runoff events after application, and may be a concern. A reduced-input management practice for corn and soybean production with light disking and cultivation for weed control and manure and a legume (red clover, Trifolium pratense L.) to supply some of the nitrogen was implemented to determine if a balance between losses of soil and purchased chemical inputs could be obtained. In a 6-year comparison, soil losses were similar to those under conservation tillage, but the risk of yield loss increased due to inability to cultivate in a timely manner due to weather conditions. Regardless of tillage practice, infrequent, severe storms during years when row crops were grown caused most of the soil loss from the watersheds. Erosion prediction models must account for the contribution of such events and management practices must limit erosion caused by these storms if long-term sustainability is to be maintained.

Runoff and erosion control with conservation tillage and reduced-input practices on cropped watersheds

The North Appalachian Experimental Watershed near Coshocton, OH was established in 1935 to develop, evaluate, and refine conservation practices that reduce runoff and erosion under the hilly, humid conditions of the northeastern United States. Small (0.5 to 1 ha), single-practice, gaged watersheds comprised of sandstone- and shale-derived residual soils are used to evaluate the interaction of management, climate, and soils. In a 28-year, nine-watershed study, 92% of the erosion occurred during the corn ( Zea mays L.) years of a 4-year corn/wheat ( Triticum aestivum L.)/meadow/meadow rotation. These watersheds were moldboard plowed prior to planting corn and cultivation was used for weed control. By tilling and planting on the contour and increasing fertility levels, soil loss was reduced more than 3-fold, but still averaged 4.7 Mg ha −1 during corn years. Thus, annual production of row crops on a sustainable basis was not without risk. A 6-year, six-watershed study indicated that by using reduced tillage (no-till, chisel, or paraplow) and herbicides, corn and soybean [ Glycine max (L.) Merr.] can be grown in rotation with an average soil loss of 0.5 Mg ha −1 yr −1, well below the stipulated soil loss tolerance of 7.8 Mg ha −1 yr −1, if a winter cover crop of rye ( Secale cereale L.) followed soybean. Under these conditions, however, concentrations of surface-applied herbicides and nitrate in runoff frequently exceeded drinking water standards, particularly in the first few runoff events after application, and may be a concern. A reduced-input management practice for corn and soybean production with light disking and cultivation for weed control and manure and a legume (red clover, Trifolium pratense L.) to supply some of the nitrogen was implemented to determine if a balance between losses of soil and purchased chemical inputs could be obtained. In a 6-year comparison, soil losses were similar to those under conservation tillage, but the risk of yield loss increased due to inability to cultivate in a timely manner due to weather conditions. Regardless of tillage practice, infrequent, severe storms during years when row crops were grown caused most of the soil loss from the watersheds. Erosion prediction models must account for the contribution of such events and management practices must limit erosion caused by these storms if long-term sustainability is to be maintained.

Sediment Regimes under Different Slope and Surface Hydrologic Conditions

AbstractDuring rainfall events, many physical processes occur simultaneously. Conceptual frameworks for infiltration, runoff, surface sealing, sediment detachment, transport, and deposition processes have long been recognized. Nevertheless, interactions between hydrologic and sediment regimes and how they are affected by changes in slope steepness, soil erodibility, and rainfall erosivity have not been evaluated. A laboratory study on the Glynwood clay loam (fine, illitic, mesic Aquic Hapludalf) was designed to demonstrate interactions between hydrologic and sediment regimes. Four rainfall intensities, from 30 to 120 mm h−1, and two inflows, equivalent to 30 and 60 mm h−1 rainfall, were applied to create different combinations of rainfall detachment and shallow flow transport. Sediment delivery was quantified from two rainfall events: first on freshly packed soil under free drainage and later on the eroded surface under seepage. During the first rainfall event, average sediment deliveries from 30 to 100 mm h−1 runoff were 1.8, 3.4, and 3.3 kg m−2 h−1 for slopes of 5, 10, and 15%, respectively. The lack of slope effects from 10 to 15% slope indicates the shift from a transport‐limiting sediment regime at low slopes to a detachment‐limiting sediment regime at high slopes. Under the potentially more erodible seepage condition, the second rainfall event produced only 1.3, 1.8, and 1.5 kg m−2 h−1 sediment from the same range of slopes and runoff rates. Reduction in sediment delivery during the second run was caused by removal of the easily erodible fraction during the first run. Increased profile drainage at the 15% slope further limited sediment detachment. These results show interactions between surface hydrologic conditions and erosion processes and the importance of understanding dominant sediment regimes in developing erosion prediction models.

Why soil erosion models over-predict small soil losses and under-predict large soil losses

Evaluation of various soil erosion models with large data sets have consistently shown that these models tend to over-predict soil erosion for small measured values, and under-predict soil erosion for larger measured values. This trend appears to be consistent regardless of whether the soil erosion value of interest is for individual storms, annual totals, or average annual soil losses, and regardless of whether the model is empirical or physically based. The hypothesis presented herein is that this phenomenon is not necessarily associated with bias in model predictions as a function of treatment, but rather with limitations in representing the random component of the measured data within treatments (i.e., between replicates) with a deterministic model. A simple example is presented, showing how even a `perfect' deterministic soil erosion model exhibits bias relative to small and large measured erosion rates. The concept is further tested and verified on a set of 3007 measured soil erosion data pairs from storms on natural rainfall and run-off plots using the best possible, unbiased, real-world model, i.e., the physical model represented by replicated plots. The results of this study indicate that the commonly observed bias, in erosion prediction models relative to over-prediction of small and under-prediction of large measured erosion rates on individual data points, is normal and expected if the model is accurately predicting erosion rates as a function of environmental conditions, i.e., treatments.

Landscape position, surface hydraulic gradients and erosion processes

Different hydraulic gradients, especially due to seepage or drainage, at different locations on a hillslope profile may have a profound effect on the dominant erosion processes. A laboratory study was designed to simulate hillslope processes and quantify effects of surface hydraulic gradients on erosion for a Glynwood clay loam soil (fine, illitic, mesic Aquic Hapludalf). A 5 m long, 1·2 m wide soil pan was used at 5 and 10 per cent slopes with an external watering tube to vary the soil bed's hydrological conditions. Different combinations of slope steepness with seepage or drainage gradients were used to simulate the hydrologic conditions on a 5 m segment of a hillslope profile. Runoff samples were taken during rainfall-only and rainfall with added inflow. Results showed that, under drainage conditions, interrill processes dominated and rilling was limited. The surface contained scattered crescent-shaped pits after the run. Under seepage conditions, rilling processes dominated and the inflow introduced at the top of the soil pan further accelerated the headward erosion of the rills. Erosion rates increased by as much as 60 times under seepage conditions representative of the lower backslope when compared to drainage conditions that generally occur at the upper backslope. This indicated that rills and gullies on backslopes and footslopes may be catalysed or enhanced by seepage conditions rather than form from flow hydraulic shear stress alone. An understanding of spatial and temporal changes that affect both hillslope hydrology and erosional processes is needed to develop accurate process-based erosion prediction models. This knowledge may lead to different management practices on landscape positions where seepage occurs. © 1998 John Wiley & Sons, Ltd.

IMPINGING JET CALIBRATION OF EXCESS SHEAR SEDIMENT DETACHMENT PARAMETERS

An excess shear sediment detachment relation is commonly used in erosion prediction models. Traditionalfield methods for calibration of soil-dependent empirical constants are both expensive and conceptually suspect. The subprocessesof sediment detachment and transport cannot be physically separated, therefore they cannot be calibratedindependently. Impingement of a clear-water jet on a soil bed produces detachment without transport at the point ofmaximum scour. In this article, a mathematical description of the impinging jet scour process is used to calibrate anexcess shear detachment relation. Four calibration models are developed based on variants of the jet-scour rateequations (differential and integral form, each evaluated using arithmetic and logarithmic values for dependentvariables). All models assume detachment is linearly related to excess shear.<br><br>The validity of this calibration technique has been tested in the laboratory by measuring scour depth versus time forsix different soils, all evaluated in a disturbed, saturated condition. A total of 14 replicates on the soils were conductedwhere hydraulic inputs were varied with replicate. Statistical analyses were used to place confidence limits about theoptimal sediment detachment values for each of the four models. Only the arithmetic differential model produced resultsconsiderably different than the other three. The integrated log model produced the tightest confidence intervals and this issuggested for further use. The size of the confidence intervals demonstrates the replicability of the technique. Thetechnique produced detachment rate constants that are similar to field calibration studies for soils of similar properties.Critical shear values are an approximate order of magnitude lower, but all soils were evaluated only in the highly erosive,disturbed, unconsolidated, and saturated condition. The technique can be applied to soils in other conditions and it ispossible that this discrepancy is due to different soil conditions.

Laboratory rainfall simulator studies of selected open-cut coal mine overburden spoils from Central Queensland

Data on the erodibility of overburden spoils resulting from open-cut coal mining are limited. These data are required to derive parameters for erosion prediction models which can be used in the design of re-formed landscapes after mining. In this study, laboratory rainfall simulation data were used to derive CREAMS (Chemicals, Runoff and Erosion from Agricultural Management Systems) interrill erodibility parameters K and nbov for Central Queensland coal mine overburden spoils. For the spoils studied, K values ranged from 0·0111 to 0·1398 Mg·ha·h/(ha·MJ·mm) and nbov values ranged from 0·0004 to 0·0081. Sediment yield was transport controlled. Results for 1 spoil, South Blackwater Terang, showed that total soil loss increased linearly with slope, and both K and nbov varied non-linearly with slope. If a constant set of K and nbov values is used in modelling, CREAMS over-predicts spoil loss for slopes greater than that for which the parameter values were determined, and under-predicts spoil loss for slopes less than that for which the parameter values were determined. The results show evidence of differences between mine spoils and agricultural soils, including modality of size distribution of surface sediment available for erosion, a higher percentage of dispersed clays in eroded sediment from mine spoils than from agricultural soils, and the observed amount of surface sealing and crust formation. These differences indicate that erosion{erodibility parameters for mine spoils should be measured and that agricultural soil parameters should not be a basis for estimates of mine spoil parameters.

Influence of rainfall energy flux density and antecedent soil moisture content on splash transport and aggregate enrichment ratios for a Hawaiian Oxisol

Erosion process information from aggregated tropical soils is under-represented in the literature. Thus, an Oxisol was used in laboratory rainfall simulation experiments to examine the importance of antecedent moisture content (AMC) and rainfall energy flux density (EFD) on splash. Ten splash experiments were conducted at EFD-values ranging from 0.064 to 0.53 W m −2 for 1 h dry and 1 h wet runs. Results indicated that AMC had no significant influence on total splash flux or splash flux per aggregate size fraction. However, splash increased linearly with EFD. The Oxisol exhibited a very high resistance to splash detachment (whole-soil basis), and the 2000–4000 μm aggregate size fraction was the most resistant to splash detachment. There was no significant difference for total splash flux between dry AMC-low EFD and wet AMC-low EFD events; or between dry AMC-high EFD and wet AMC-high EFD events. However, time trends differed significantly, and these reflected the influence of variable water content on near-surface soil strength, panicle shielding and availability of material. Splash enrichment ratios (ER) for all AMC and EFD data indicated that the 425–2000 μm aggregates were preferentially transported (i.e., ER significantly > 1.0). Aggregates of 250–500 μm were splashed in similar proportion to their content in the original soil, and aggregates < 250 μm and > 2000 μm were significantly depleted in splashed material (i.e., ER < 1.0). The reasons for depletion include either increased cohesion with fine grained aggregates or the inability of simulated rainfall to transport the most massive aggregates. Our data show that splash is a time-dependent process influenced primarily by rainfall energy flux and to a lesser extent by interactions with antecedent soil moisture status. Data of this type are essential for incorporation into state-of-the-art erosion prediction models if they are to be truly universal.