Efficient Method of Discharge Measurements in Rivers and Streams

[9415862] Chiu河流和小溪的流量是地球上水文循环的一个主要组成部分。为了研究和预测河流和溪流的流量和各种过程，如泥沙和污染物的运输，必须测量或估计一个重要的水文变量是流量。为了确定放电，传统的方法需要进行大量的速度测量，因此，对于许多至关重要的应用场合来说，过于耗时、低效和昂贵。拟议的项目将发展一种有效的流量测量方法，该方法将具有可靠的理论基础，并且只需要进行少量的速度测量。该方法还可以提高现代速度剖面测量设备的性能，例如，由于技术原因，声学多普勒电流剖面仪（ADCP）无法获得床层和水面附近的速度数据。待开发的方法可以填补缺失的数据，并快速将设备测量的速度剖面转换为横截面平均速度，从而转换为放电。因此，它甚至应该适用于随时间迅速变化的河流和溪流，例如在冰、风和潮汐作用下的流量。它也适用于连两湖相连的河流，其中连流向都有周期性的变化。该方法的另一个重要应用是确定（卡尔曼）滤波方案的流量，该方案可以设计成利用监测的流量和水位顺序更新流量阻力，以减少流量预测中的不确定性。该方法将基于一个速度分布模型，该模型能够表示从河床到水面的各种速度分布模式，而不管最大速度是在水面上还是在水面以下。该模型基于概率和熵的概念，并结合了使用曲线坐标系的几何技术。

9415862 Chiu The flow in rivers and streams is a major component of the hydrologic cycle on earth. To study and forecast the flow and various processes such as the transport of sediment and pollutants in rivers and streams, an essential hydrologic variable that must be measured or estimated is the discharge. To determine the discharge, conventional methods require a great amount of velocity measurement and are, therefore, too time-consuming, inefficient and costly for applications in many situations of critical importance. The proposed project will develop an efficient method of discharge measurements, which will have a sound, theoretical basis, and will require only a small amount of velocity measurement. The method can also enchance a modern, velocity-profile measuring device, such as an Acoustic Doppler Current Profiler (ADCP) which,for technical reasons, misses velocity data near the bed and water surface. The method to be developed can fill the missed data and quickly translate a velocity profile measured by the device into the cross-sectional mean velocity and, hence, the discharge. Therefore, it should be applicable even to flows in rivers and streams that change rapidly with time, such as flows under the effects of ice, wind, and tides. It is also applicable to rivers connecting two lakes, in which even the flow direction changes periodically. Another important application of the method is to determine the discharge for (Kalman) filtering schemes, that can be designed to sequentially update the flow resistance by using the monitored discharge and water level, to reduce the uncertainty in flow forecasting. The method will be based on a velocity distribution model capable of representing various velocity distribution patterns, from the channel bed to the water surface, regardless of whether the maximum velocity occurs on or below the water surface. The model is based on the probability and entropy concepts, coupled with a geometrical technique using a curvilinear coordinate s ystem.