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Manning’s roughness coefficient is one of the input parameters in many surface irrigation simulation models. It affects the velocity of flow and thereby its variation with time and distance along the field length influence water application. In this study, variation of Manning’s roughness coefficient was studied for a furrow plot consisting of three 40 m long free drained furrows of parabolic shape and having a top width of 0.30 m, a depth of 0.15 m and a slope of 0.5%. The irrigation experiments were carried out with the inflow rates of 0.2, 0.3, 0.4, and 0.5 L s1; and 0.3, 0.4, 0.5, 0.6, and 0.7 L s-1 under bare; and cropped field conditions, respectively. Furrow cross-section data were collected before each irrigation event at 0.5, 13, 26 and 39.5 m from the head end along the center furrow using a profilometer. During the irrigation event, water depth and velocity of flow were measured at these locations at an interval of 15 min using point gauge and color dye, respectively. The furrow cross-section data were fitted into a second-degree polynomial equation to determine the furrow shape parameters that were used along with the flow depth data for determining the wetted area and wetted perimeter. The wetted area, wetted perimeter, and the velocity data were used to estimate Manning’s roughness coefficient spatially and temporally. It is found that for both bare and cropped field conditions, Manning’s roughness coefficient was more at second and last quarter of the furrow due to soil erosion at these locations. Manning’s roughness coefficient at these locations varied from 0.019 to 0.022 and 0.015 to 0.018 for bare field whereas from 0.02 to 0.024, and 0.019 to 0.022 for cropped field, respectively. The temporal variation of Manning’s roughness coefficient for both bare and cropped furrow conditions decreased with the elapsed time. However, these decreasing trends were observed more for lower inflow rates. Further, the average Manning’s roughness coefficient for the subsequent irrigations was varied from 0.018 to 0.02 and from 0.019 to 0.0245 for bare and cropped conditions, respectively. Thus, the values of Manning’s roughness coefficients were more for cropped furrow conditions than for bare furrow. [ABSTRACT FROM AUTHOR]

Inverse methods are often used for estimating soil hydraulic parameters from experiments on flow of water through soil. We propose here an alternative method using neural networks. We teach a problem-adapted network of radial basis functions(RBF) the relationship between soil parameters and transient flow patterns using a numerical flow model. The trained RBF network accurately identifies soil parameters from flow patterns not contained in the training scenarios. A comparison with the inverse method(Annealing-Simplex) reveals a similarly good prediction by both approaches for randomly perturbed data and data from the real world. Nonetheless, the inverse method showed dependency on initial parameter estimates not required by the RBF network. Training demands moderately more computation and manpower than the inverse technique, but the absolutely stable and simple network application requires negligible resources. Thus, for individual applications, the network approach is slightly surpassed by the Annealing-Simplex method. However, the RBF network has to be trained only once and, subsequently, it can be applied easily and without effort upon any number of laboratory experiments with standardized experimental setups. [ABSTRACT FROM AUTHOR]