Environmental change resulting from intensified human interventions and climate change has impacted the hydrological function of many large river systems, largely altering the production and transport of run‐off and sediment. It is thus vital to quantitatively evaluate the influence of climate change and human activities on streamflow and sediment discharge. Water balance equations, hydrological models, and comparative analyses are commonly used to fulfil this need. Double mass curves (DMCs), being one useful method for comparative analyses, are characterized by low data requirements and high transferability, and thus more practical than water balance equations and hydrological models for hydrologic benefit evaluations. However, the detailed derivation procedure of the DMC has, to date, yet been described in literature. Moreover, in previous studies, changing points of the DMC were determined either rather empirically or as the changing point of streamflow or sediment discharge (i.e., precipitation was not considered). Hence, the changing point detected may be subject to inaccuracies. This paper, for the first time, comprehensively detailed the derivation procedure of the DMC; a new way was proposed to quantitatively examine the changing point of the DMC; an example was also given to demonstrate the use of the DMC in the hydrologic benefit evaluation. It is hopeful that the method given in our paper will be widely adopted by future studies as a standard procedure to derive and use the DMC.

This paper presents a robust method designed to detect and track a road lane from images provided by an on-board monocular monochromatic camera. The proposed lane detection approach makes use of a deformable template model to the expected lane boundaries in the image, a maximum a posteriori formulation of the lane detection problem, and a Tabu search algorithm to maximize the posterior density. The model parameters completely determine the position of the host vehicle within the lane, its heading direction and the local structure of the lane ahead. Based on the lane detection result in the first frame of the image sequence, a particle filter, having multiple hypotheses capability and performing nonlinear filtering, is used to recursively estimate the lane shape and the vehicle position in the sequence of consecutive images. Experimental results reveal that the proposed lane detection and tracking method is robust against broken lane markings, curved lanes, shadows, strong distracting edges, and occlusions in the captured road images.