2019 Volume 35 Issue 4 Pages 69-80
In recent years, the increased occurrence of sudden heavy rains in Japan has increased the occurrences of surface runoff from sloping farmlands. These runoff events negatively affect the ecosystems owing to the leaching of fertilizer components and/or heavy metals into local watersheds. Parshall flumes have been conventionally used for measuring the extent of surface runoff combined with water-level gauges installed at the bottom edge of sloping farmlands. However, data obtained using this equipment are unreliable because of the direct contact of sensors with various objects, such as pile-ups of crop residues and/or soil particles. To avoid this problem, we propose a novel method herein. Firstly, we devised a time-lapse camera which shot the inside-view images of the Parshall flume and used automatically captured time-lapse images of water flow running through the Parshall flume to estimate the precise runoff extent by detecting the instantaneous water depth using image processing techniques. Second, we devised an automated video-capturing system and positioned it 1.8 m above the ground in an outdoor artificial sloping field in Kagoshima, Japan. Vertically captured video-images of the surface runoff were obtained during a severe runoff event. We analyzed the captured video-images using particle tracking velocimetry to determine the velocity of the moving surface water. The widths of surface water flow were also measured on the video-images. We estimated the runoff amounts using the velocity and the flow width, in addition to the given slope gradient and a literature value of the equivalent roughness coefficient for the soil. The results of two techniques are comparable, especially at severe rainfall and runoff occasions, in which one was measured using the Parshall flume with time-lapse images and the other was measured using the vertically captured video-images. The proposed method may provide estimates of the amount of the surface runoff during intense rainfall events solely using video-images.