Hydrological landscape classification: investigating the performance of HAND based landscape classifications in a central European meso-scale catchment

TLDR

Three landscape classes—wetland, hillslope, and plateau—correspond to saturation excess, storage excess, and deep percolation regimes, and are primarily controlled by topography, geology, land use, HAND, and slope. This paper presents a detailed performance and sensitivity analysis of a hydrological landscape classification method based on dominant runoff mechanisms, testing several indicators and their sensitivity to scale and resolution. The authors evaluate the method by applying HAND and slope indicators, assessing classification performance across different sample sizes and resolutions. The best results are achieved using HAND and slope alone, with performance comparable to the topographic wetness index, demonstrating that HAND-based classification efficiently reads the landscape for conceptual model development. Abstract.

Abstract

Abstract. This paper presents a detailed performance and sensitivity analysis of a recently developed hydrological landscape classification method based on dominant runoff mechanisms. Three landscape classes are distinguished: wetland, hillslope and plateau, corresponding to three dominant hydrological regimes: saturation excess overland flow, storage excess sub-surface flow, and deep percolation. Topography, geology and land use hold the key to identifying these landscapes. The height above the nearest drainage (HAND) and the surface slope, which can be easily obtained from a digital elevation model, appear to be the dominant topographical controls for hydrological classification. In this paper several indicators for classification are tested as well as their sensitivity to scale and resolution of observed points (sample size). The best results are obtained by the simple use of HAND and slope. The results obtained compared well with the topographical wetness index. The HAND based landscape classification appears to be an efficient method to ''read the landscape'' on the basis of which conceptual models can be developed.