Appendix D-1 Development of the Roughness Classification Table

Quarterly Journal of the Royal Meteorological Society, 1991

Several theories and a mesoscale model were applied in the calculation of the effective roughness length ZEff over heterogeneous flat terrain having varying proportions of land and water. Rules based on terrain classification tended to give large values of ZEff, while the simple logarithmic areal average of local roughness lengths gave small values compared with the model output. The most recent theories were quite close to the model results. These theories were then used to produce a map of ZEff for 150 X 150 km2 grid squares over Finland, based on a detailed knowledge of land use and water coverage.

2003

A parameterisation of the effective roughness length is presented for an arbitrary given roughness distribution z 0 (x, y) over flat terrain at neutral stratification. Beyond pure averaging, it takes into account the spatial structure of the distribution, especially the influence of length scales, and inflow direction. To allow for interactions between different rough areas, Boussinesqapproximated equations with a turbulence closure of first order are considered and solved using perturbation theory. As a result, the logarithm of the effective roughness length is represented as a sum over the product of the Fourier transformation of log z 0 and a so-called dynamic function, which describes the response of the flow field to a single wavelength of z 0 . Although the numerical expenditure is larger than for simple averaging formulae, this method could be used by large-scale models to calculate effective roughness lengths in every close-to-surface grid cell.

Journal of Geophysical Research: Planets, 2001

We examine the various methods and parameters in common use for quantifying and reporting surface topographic “roughness.” It is shown that scale‐dependent roughness parameters are almost always required, though not widely used. We suggest a method of standardizing the parameters that are computed and reported so that topographic data gathered by different workers using different field techniques can be directly and easily intercompared. We illustrate the proposed method by analyzing topographic data from 60 different surfaces gathered by five different groups and examine the information for common features. We briefly discuss the implications of our analysis for studies of planetary surface roughness, lander safety, and radar remote sensing modeling and analysis.

SOIL Discussions, 2014

Quantification of soil roughness, i.e. the irregularities of the soil surface due to soil texture, aggregates, rock fragments and land management, is important as it affects surface storage, infiltration, overland flow, and ultimately sediment detachment and erosion. Roughness has been measured in the field using both contact methods (such as roller chain and pinboard) and sensor methods (such as stereophotogrammetry and terrestrial laser scanning (TLS)). A novel depth-sensing technique, originating in the gaming industry, has recently become available for earth sciences: the Xtion Pro method. Roughness data obtained using various methods are assumed to be similar; this assumption is tested in this study by comparing five different methods to measure roughness in the field on 1 m 2 agricultural plots with different management (ploughing, harrowing, forest and direct seeding on stubble) in southern Norway. Subsequently, the values were used as input for the LISEM soil erosion model to test their effect on the simulated hydrograph at catchment scale. Results show that statistically significant differences between the methods were obtained only for the fields with direct seeding on stubble; for the other land management types the methods were in agreement. The spatial resolution of the contact methods was much lower than for the sensor methods (10 000 versus at least 57 000 points per square metre). In terms of costs and ease of use in the field, the Xtion Pro method is promising. Results from the LISEM model indicate that especially the roller chain overestimated the random roughness (RR) values and the model subsequently calculated less surface runoff than measured. In conclusion, the choice of measurement method for roughness data matters and depends on the required accuracy, resolution, mobility in the field and available budget. It is recommended to use only one method within one study.

Canadian Metallurgical Quarterly, 2002

The role that surface roughness characteristics play to anticorrosive protection and duration of coating is critical. For the characterization of surface roughness, a number of parameters are employed such as the amplitude parameters Ra and Rp. Using conventional statistical analysis, the latter cannot yield information about the spatial distribution of the irregularities and thus the homogeneity of the surface. In this paper, a new approach is developed using geostatistics to account for the location of each roughness measurement point and to produce a variogram function for each roughness parameter. This function, which is characteristic for a given surface, provides important information about the spatial distribution of the surface irregularities. Finally, an application example is presented to validate the above.

Transactions of the ASABE, 2013

Soil surface roughness is commonly identified as one of the dominant factors governing runoff and interrill erosion. The objective of this study was to compare several existing soil surface roughness indices and to test the use of the revised triangular prism surface area method (RTPM) to calculate the fractal dimension as a roughness index. A silty clay loam soil was sampled, sieved to four aggregate sizes, and each size was packed in soil trays in order to derive four different soil surface roughness classes. Rainfall simulations using an oscillating nozzle simulator were conducted for 90 min at 50.2 mm h -1 average intensity. The surface microtopography was digitized by an instantaneous profile laser scanner before and after the rainfall application. Calculated roughness indices included random roughness, variogram sill and range, fractal dimension and fractal length using a fractional Brownian motion (fBm) model, variance and correlation length according to a Markov-Gaussian (MG) model, and fractal dimension using the RTPM. Random roughness is shown to be the best estimator to significantly distinguish soil surface roughness classes. When taking spatial dependency into account, the variogram sill was the best alternative. The fractal dimension calculated from the fBm model did not yield good results, as only short-range variations were incorporated. The MG variance described the large-scale roughness better than the parameters of the fBm model did. The fractal dimension from the RTPM performed well, although it could not significantly discriminate between all roughness classes. Since it covered a greater range of scales, we believe that it is a good estimator of the overall roughness.

Bragantia, 2010

The objective of this work was to investigate the decay of initial surface roughness induced by simulated rainfall under different soil residue cover and to compare classical statistical indices with geostatistical parameters. A conventionally tilled loamy soil with low structure stability, thus prone to crusting was placed at 1 m² microplots. Each microplot received three successive rainfall events which bring about cumulative 25 mm, 50 mm and 75 mm at 65 mm h-1 intensity. Five treatments without replication were tested with different corn straw quantities (0, 1, 2, 3 and 4 Mg ha"1). Soil surface microrelief was measured at the initial stage and after each simulated rainfall event. Five treatments and four surface stages were monitored, resulting in 20 data sets. Point elevation data were taken at 0.03 m intervals using a pinmeter. Digital elevation models were generated and analysed using semivariograms. All data sets showed spatial dependence and spherical models were fitted...

Journal of Applied Meteorology, 1998

Quality evaluation of a material's surface is performed through roughness analysis of surface samples. Several techniques have been presented to achieve this goal, including geometrical analysis and surface roughness analysis. Geometric analysis allows a visual and subjective evaluation of roughness (a qualitative assessment), whereas computation of the roughness parameters is a quantitative assessment and allows a standardized analysis of the surfaces. In civil engineering, the process is performed with mechanical profilometer equipment (2D) without adequate accuracy and laser profilometer (3D) with no consensus on how to interpret the result quantitatively. This work proposes a new method to evaluate surface roughness, starting from the generation of a visual surface roughness signature, which is calculated through the roughness parameters computed in hierarchically organized regions. The evaluation tools presented in this new method provide a local and more accurate evaluation of the computed coefficients. In the tests performed it was possible to quantitatively analyze roughness differences between ceramic blocks and to find that a quantitative microscale analysis allows to identify the largest variation of roughness parameters R a avg, R a sdv, R a min and R a max between samples, which benefit the evaluation and comparison of the sampled surfaces.

Iraqi Bulletin of Geology and Mining, 2023

Surface roughness (SR) is a significant geomorphic factor that has been effectively applied to determine material characteristics, present and past processes, and the period passed by since development in the environmental and earth sciences. Thus, this type of study is essential for analyzing SR and its importance. The research efforts to display a coherent evaluation of techniques and remote sensing data. The digital elevation models (DEMs) data were taken into account for quantifying the SR of landscape in different scales in selected areas that belong to the Kurdistan region, north of Iraq. The various types of DEMs {ALOS PALSAR (12.5 m) and SRTM in (30 m) and (90 m) resolution} as remotely sensed data integrated with GIS techniques were used to calculate SR regarding different neighborhood sizes {51 × 51 and 100 × 100 cells moving window size (MWZ)} in the both selected areas. The first study area is a mountainous terrain characterized by a number of anticlines and synclines that are located in High Folded Zones (HFZ), while the second study area is virtually a plain that is located in Low Folded Zones (LFZ). It found that SR calculation in wide and mountainous areas can be efficiently analyzed in more detail by using coarseresolution DEMs from large MWZ, such as 100 × 100 cells, whereas high-resolution DEMs can be considered for studying SR in small, urban, and plain areas to reach detail information regarding smaller MWZ. The results show that the E and W segments of Safeen and the western segment of Shakrok anticlines have the highest SR values, which indicate high incision at elevated and extremely eroded parts of area one, although Bani Bawi, Pirmam, and Khatibian anticlines have moderate to low SR values due poorly incised. On the other hand, the areas within LFZ are mainly flat and have the lowest values of SR, which indicate a very poorly dissected landscape except for the northwest part of the area two relatively has a higher SR value.

Transportation Research Record, 1984

Since the AASHO Road Test there has been great interest in the measurement of road roughness for evaluation of serviceability as defined by Carey and Irick, and, perhaps more broadly and importantly, for evaluation of road roughness as it affects vehicle operating costs and road maintenance, particularly in developing countries. In this paper work done in the United States, Brazil, Canada, Bolivia, Nigeria, Panama, and elsewhere with respect to the selection of a uniform method for calibrating road roughness devices is reviewed. Because most roughness measurements are made with response-type roughness measuring instruments, there needs to be a calibration technique for such instruments that can be easily used by any country. It is essential that the method be based on characteristics of the road surface and not on characteristics of any individual vehicle or measuring velocity of the response-type roughness meter. A specific calculation algorithm is also needed. A calibration techni...