Many transport processes on or across the soil surface boundary are controlled by surface microtopography, or roughness. How roughness affects the transport process depends on the length scale of the process. The most commonly used method of expressing soil surface roughness, the roughness length or random roughness, is contrained by the measurement technique and does not embody the concept of scale. The structural function, or variogram, plotted on a log‐log scale was used in this study to express the surface roughness at different scales. With the aid of a laser scanner, surface topography was measured down to 0.5‐mm grid spacing. Data collected from a variety of surface conditions showed that soil roughness can be quantified by a combination of fractal and Markov‐Gaussian processes at different scales. Potential applications of the roughness quantification were also discussed.

No abstract

duction into sediment production, that is, the reduced erosion is caused by a reduced water runoff. Hence it Most of prior research showed increasing soil roughness delayed does not differentiate between the roughness effect on runoff and reduced total runoff and sediment yields but failed to water runoff and the roughness effect on sediment prodifferentiate roughness effects on water runoff and on sediment production. duction. This study was conducted to assess separately the effects of soil surface depressions on runoff initiation and water and particle Even in a process-based model, such as the Water fluxes. A 5-m long soil box, filled with a silt loam, was split into 0.6-m Erosion Prediction Project (WEPP), where the process wide paired smooth vs. rough plots with manually formed depressions, of runoff production supposedly has been isolated from and subjected to a sequence of 24 mm h Ϫ1 simulated rainstorms at 5% the sediment production, an increased surface roughslope. Eight experiments were conducted under different upstream ness also results in an overall reduction in sediment inflows and subsurface regimes (drainage or seepage). Collected data delivery. In WEPP, an increased surface roughness include time to runoff initiation and fluxes of water and particles after causes a decrease in interrill sediment delivery and an an apparent steady state was reached. Depressions delayed the runoff increase in critical shear resistance in the rills (Flanagan initiation by storing water into puddles and enhancing infiltration. and Nearing, 1995). Once runoff reached an apparent steady state, surfaces with initial Despite the dominance of research results and predepressions produced 10% greater water flux than the initially smooth surfaces, regardless, the subsurface moisture regime. Roughness had dictive models showing that an increased roughness deno significant effect on steady-state particle flux and concentration. creases erosion, there is evidence pointing the other Our results indicate that the only assured soil and water conservation direction. Burwell et al. (1968) and Burwell and Larson benefit from surface depressions is due to the delay in runoff initiation (1969) showed that after runoff had initiated, a rougher at the beginning of the rain event before the entire surface is contributsurface might not have the distinctly higher infiltration ing to runoff. as a smooth surface as shown before runoff. The laboratory study of Helming et al. (1998) showed that while runoff was marginally affected, rough surfaces did show

Processes during rain events, such as infiltration, runoff, soil erosion, and crust formation, are influenced in part by depressional storage and surface roughness. If the surface topography is known, its potential depressional storage can be calculated. The objective of this paper was to relate the statistical parameters for quantifying surface roughness to depressional storage. Analysis of topographic data sets digitized at millimeter grids by a laser scanner showed that soil roughness can be quantified by a Markov‐Gaussian (M‐G) type random process. A Monte Carlo simulation procedure was used to find the mean ponding characteristics from simulated M‐G surfaces. Depressional storages were found to be functions of two M‐G parameters, two sample length scales, and the slope steepness. The Markov parameters are the global variance (σ2) and the correlation length scale (L), and the sample length scales are grid spacing (Δx) and side length (Ls). After proper scaling, all storage functions collapsed into two nondimensional relationships: (1) storage at zero slope as a function of relative sample length scale, and (2) storage as a function of scaled slope. When L = 0, simulated surfaces followed the random Gaussian model and the nondimensional storage was only a function of scaled slope. Storages calculated from digitized elevation data sets with M‐G type statistics agreed well with results obtained from simulated surfaces.

No abstract

No abstract

Agricultural nutrient losses contribute to hypoxia in the Gulf of Mexico and eutrophication in the Great Lakes. Our objective was to assess effects of topography, geomorphology, climate, cropping systems and land use and conservation practices on hydrology and nutrient fate and transport in the St. Joseph River watershed. We monitored five sites (298 to 4,300 ha [736 to 10,600 ac]) on two drainage ditches within the St. Joseph River watershed in northeastern Indiana. Row crop agriculture, primarily corn (Zea mays L.) and soybean (Glycine max [L.] Merr.), is the dominant land use (~60%) in this pothole or closed depression landscape. The hydrology is dominated by subsurface tile drainage supplemented with surface drainage of remote potholes. Vegetative buffer strips have been implemented along >60% of the agricultural drainage ditches. The vegetative buffer strips play an invaluable role protecting water quality though by acting as natural setbacks during fertilizer and pesticide applications. Multiple regressions indicated land cropped to corn and areas with direct drainage or potholes are highly sensitive to nutrient losses. Future conservation assessment efforts in this and similar watersheds should focus on management of potholes in cropped fields and the subsequent effect of those practices on tile drainage water.

Adding anionic polyacrylamide (PAM) to soils stabilizes existing aggregates and improves bonding between and aggregation of soil particles. However, the dependence of PAM efficacy as an aggregate stabilizing agent with soils having different clay mineralogy has not been studied. Sixteen soil samples (loam or clay) with predominantly smectitic, illitic, or kaolinitic clay mineralogy were studied. We measured aggregate sensitivity to slaking in soils that were untreated or treated with an anionic high‐molecular‐weight PAM using the high energy moisture characteristic (HEMC) method and deionized water. The index for aggregate susceptibility to slaking, termed stability ratio (SR), was obtained from quantifying differences in the water retention curves at a matric potential range of 0 to −5.0 J kg−1 for the treatments studied. For the untreated soils, the SR ranged widely from 0.24 to 0.80 and generally SR of kaolinitic > illitic > smectitic soils. The SR of PAM‐treated aggregates exhibited a narrow range from 0.70 to 0.94. The efficiency of PAM in improving aggregate and structural stability relative to untreated soils ranged from 1.01 to 3.90 and the relative SR of kaolinitic < illitic < smectitic samples. These results suggest that the less stable the aggregates the greater the effectiveness of PAM in increasing aggregates stability (i.e., smectitic vs. kaolinitic samples). The effectiveness of PAM in improving structure and aggregate stability was directly related to clay activity and to soil conditions affecting PAM adsorption (e.g., electrolyte resources, pH, and exchangeable cations) to the soil particles and inversely to the inherent aggregate stability.