Sensitivity and Performance Analyses of the Distributed Hydrology–Soil–Vegetation Model Using Geomorphons for Landform Mapping

Abstract

Landform classification is important for representing soil physical properties varying continuously across the landscape and for understanding many hydrological processes in watersheds. Considering it, this study aims to use a geomorphology map (Geomorphons) as an input to a physically based hydrological model (Distributed Hydrology Soil Vegetation Model (DHSVM)) in a mountainous headwater watershed. A sensitivity analysis of five soil parameters was evaluated for streamflow simulation in each Geomorphons feature. As infiltration and saturation excess overland flow are important mechanisms for streamflow generation in complex terrain watersheds, the model’s input soil parameters were most sensitive in the “slope”, “hollow”, and “valley” features. Thus, the simulated streamflow was compared with observed data for calibration and validation. The model performance was satisfactory and equivalent to previous simulations in the same watershed using pedological survey and moisture zone maps. Therefore, the results from this study indicate that a geomorphologically based map is applicable and representative for spatially distributing hydrological parameters in the DHSVM.

Beaver-Driven Peatland Ecotone Dynamics: Impoundment Detection Using Lidar and Geomorphon Analysis

“…Geomorphon analysis [32], a technique that we employ in this research, is a machine vision terrain classifier of rasterized terrain models. Geomorphons have been often used to represent and even predict geomorphological and hydrological changes in river valleys [33][34][35][36]. In the form of input to a physically based hydrological model, geomorphons were successfully represented in the spatially distributed hydrological parameters of the upper reaches of a montane watershed in Brazil [33].…”

“…Geomorphons have been often used to represent and even predict geomorphological and hydrological changes in river valleys [33][34][35][36]. In the form of input to a physically based hydrological model, geomorphons were successfully represented in the spatially distributed hydrological parameters of the upper reaches of a montane watershed in Brazil [33]. Yan et al (2020) found geomorphons to be optimal in the characterization of subaqueous riverbed features on the Yangtze River, China [35]; while the work of Gioia et al (2021) showed geomorphon-based classification to be a robust tool for the identification of geomorphological landscape elements at a large scale in southern Italy [37].…”