Wavelet‐based regularization of the extracted topographic index from high‐resolution topography for hydro‐geomorphic applications

High‐resolution topography, e.g. 1‐m digital elevation model (DEM) from light detection and ranging (LiDAR), offers opportunity for accurate identification of topographic features of relevance for hydrologic and geomorphologic modelling. Yet, the computation of some derived topographic properties, such as the topographic index (TI), is characterized by daunting challenges that hamper the full exploration of topography‐based models. Particular problems, for example, arise when a distributed (or semi‐distributed) rainfall–runoff model is applied to high‐resolution DEMs. Indeed, the characteristic dependency between landscape resolution and the computed TI distribution results in the formation of un‐physical, unconnected saturated zones, which in turn cause unrealistic representations of rainfall–runoff dynamics. In this study, we present a methodology based on a multi‐resolution wavelet transformation that, by means of a soft‐thresholding scheme on the wavelet coefficients, filters the noise of high‐resolution topography to construct regularized sets of locally smoother topography on which the TI is computed. While the methodology needs a somewhat arbitrary definition of the wavelet coefficients threshold, our study shows that when the information content (entropy) of the TI distribution is used as a filtering efficiency metric, a critical threshold automatically emerges in the landscape reconstruction. The methodology is demonstrated using 1‐m LiDAR data for the Elder Creek River basin in California. While the proposed case study uses a TOPMODEL approach, the methodology can be extended to different topography‐based models and is not limited to hydrological applications. Copyright © 2012 John Wiley & Sons, Ltd.     

Prediction of daily suspended sediment load using wavelet and neurofuzzy combined model

This study investigated the prediction of suspended sediment load in a gauging station in the USA by neuro-fuzzy, conjunction of wavelet analysis and neuro-fuzzy as well as conventional sediment rating curve models. In the proposed wavelet analysis and neuro-fuzzy model, observed time series of river discharge and suspended sediment load were decomposed at different scales by wavelet analysis. Then, total effective time series of discharge and suspended sediment load were imposed as inputs to the neuro-fuzzy model for prediction of suspended sediment load in one day ahead. Results showed that the wavelet analysis and neuro-fuzzy model performance was better in prediction rather than the neuro-fuzzy and sediment rating curve models. The wavelet analysis and neuro-fuzzy model produced reasonable predictions for the extreme values. Furthermore, the cumulative suspended sediment load estimated by this technique was closer to the actual data than the others one. Also, the model could be employed to simulate hysteresis phenomenon, while sediment rating curve method is incapable in this event.