Department of Biological and Agricultural Engineering

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Browsing Department of Biological and Agricultural Engineering by Author "Singh, V. P."

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    Granulometric characterization of sediments transported by surface runoff generated by moving storms
    (Copernicus Publications on behalf of the European Geosciences Union and the American Geophysical Union, 2008-12-16) de Lima, J. L. M. P.; Souza, C. C. S.; Singh, V. P.
    Due to the combined effect of wind and rain, the importance of storm movement to surface flow has long been recognized, at scales ranging from headwater scales to large basins. This study presents the results of laboratory experiments designed to investigate the influence of moving rainfall storms on the dynamics of sediment transport by surface runoff. Experiments were carried out, using a rain simulator and a soil flume. The movement of rainfall was generated by moving the rain simulator at a constant speed in the upstream and downstream directions along the flume. The main objective of the study was to characterize, in laboratory conditions, the distribution of sediment grain-size transported by rainfall-induced overland flow and its temporal evolution. Grain-size distribution of the eroded material is governed by the capacity of flow that transports sediments. Granulometric curves were constructed using conventional hand sieving and a laser diffraction particle size analyser (material below 0.250 mm) for overland flow and sediment deliveries collected at the flume outlet. Surface slope was set at 2%, 7% and 14%. Rainstorms were moved with a constant speed, upslope and downslope, along the flume or were kept static. The results of laboratory experiments show that storm movement, affecting the spatial and temporal distribution of rainfall, has a marked influence on the grain-size characteristics of sediments transported by overland flow. The downstream-moving rainfall storms have higher stream power than do other storm types.
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    Quantifying the effect of land use and land cover changes on green water and blue water in northern part of China
    (Copernicus Publications on behalf of the European Geosciences Union, 2009-06-12) Liu, X.; Ren, L.; Yuan, F.; Singh, V. P.; Fang, X.; Yu, Z.; Zhang, W.
    Changes in land use and land cover (LULC) have been occurring at an accelerated pace in northern parts of China. These changes are significantly impacting the hydrology of these parts, such as Laohahe Catchment. The hydrological effects of these changes occurring in this catchment were investigated using a semi-distributed hydrological model. The semi-distributed hydrological model was coupled with a two-source potential evaportranspiration (PET) model for simulating daily runoff. Model parameters were calibrated using hydrometeorological and LULC data for the same period. The LULC data were available for 1980, 1989, 1996 and 1999. Daily streamflow measurements were available from 1964 to 2005 and were divided into 4 periods: 1964–1979, 1980–1989, 1990–1999 and 2000–2005. These periods represented four different LULC scenarios. Streamflow simulation was conducted for each period under these four LULC scenarios. The results showed that the change in LULC influenced evapotranspiration (ET) and runoff. The LULC data showed that from 1980 to 1996 grass land and water body had decreased and forest land and crop land had increased. This change caused the evaporation from vegetation interception and vegetation transpiration to increase, whereas the soil evaporation tended to decrease. Thus during the period of 1964–1979 the green water or ET increased by 0.95%, but the blue water or runoff decreased by 8.71% in the Laohahe Catchment.
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    A stochastic model for sediment yield using the Principle of Maximum Entropy
    (American Geophysical Union, 1987-05) Singh, V. P.; Krstanovic, P. F.
    The principle of maximum entropy was applied to derive a stochastic model for sediment yield from upland watersheds. By maximizing the conditional entropy subject to certain constraints, a probability distribution of sediment yield conditioned on the probability distribution of direct runoff volume was obtained. This distribution resulted in minimally prejudiced assignment of probabilities on the basis of given information. The parameters of this distribution were determined from such prior information about the direct runoff volume and sediment yield as their means and covariance. The stochastic model was verified by using three sets of field data and was compared with a bivariate normal distribution. The model yielded sediment yield reasonably accurately.