Nonparametric catchment clustering using the data depth function

Abstract

The clustering of catchments has been important for prediction in ungauged basins, model parameterization and watershed development and management. The aim of this study is to explore a new measure of similarity among catchments, using a data depth function and comparing it with catchment clustering indices based on flow and physical characteristics. A cluster analysis was performed for each similarity measure using the affinity propagation clustering algorithm. We evaluated the similarity measure based on depth-depth plots (DDplots) as a basis for transferring parameter sets of a hydrological model between catchments.A case study was developed with 21 catchments in a diverse New Zealand region. Results show that clustering based on the depth-depth measure is dissimilar to clustering on catchment characteristics, flow, or flow indices. A hydrological model was calibrated for 21 catchments and the transferability of model parameters among similar catchments was tested within and between clusters defined by each clustering method. The mean model performance for parameters transferred within group always outperformed those from outside the group. The DD-plot based method was found to produce the best in-group performance and second-highest difference between in-group and out-group performance.

Surface water and groundwater: Unifying conceptualization and quantification of the two water worlds

“…A statement like: "This is because the beta parameter is 0.9" is not very insightful, as conceptual model parameters cannot, despite numerous regionalization efforts (He et al, 2011a), be related to measurable catchment characteristics in a unique and generalizable manner. Of course, we acknowledge that regionalization functions maybe derived successfully, which, for example, relates the beta of the HBV soil moisture accounting scheme to soil type and land use (as shown by, e.g., Hundecha and Bardossy, 2004;Samaniego and Bardossy, 2006;He et al, 2011b;Singh et al, 2016), but such relations remain specific to the landscape of interest. Savenije (2010) and Fencia et al (2011) partially overcame this limitation in their flexible model framework, by subdividing the landscape into different functional units (plateaus, hillslopes, wetlands, rivers), representing each of them by a specific combination of conceptual model components to mimic their dominant runoff generation.…”

Understanding hydrologic variability across Europe through catchment classification

“…Approaches include the use of physical and climatic characteristics (e.g., Winter, 2001;Brown et al, 2013;Buttle, 2006;Leibowitz et al, 2016), the use of hydrologic signatures (e.g., Ley et al, 2011;Olden et al, 2012;Sawicz et al, 2011;Singh et al, 2016), or the inclusion of water quality (Arheimer et al, 1996;Arheimer and Lidén, 2000). The advantage of the first approach is that physical characteristics such as topography and land cover are now available for any location on earth (though with varying quality of the data available), while the second approach groups catchments directly by the characteristic we mainly care about, i.e., their hydrologic behavior (see the discussion in Wagener et al, 2007).…”

Surface water and groundwater: unifying conceptualization and quantification of the two “water worlds”

“…In the following, we elaborate briefly on the specific model paradigms in catchment and groundwater hydrology with an emphasis on preferential pathways for fluid flow and chemical transport, and on the resulting ubiquitous, anomalous early and late arrivals of chemicals to measurement outlets. Catchment hydrology developed largely as an engineering discipline around traditional tasks of designing and operating reservoirs, flood risk assessment, and water resources management (Sivapalan, 2018). Although the catchment concept is elementary to these tasks, we think it worthwhile to reflect briefly on it here.…”

“…First, hydrological models can be benchmarked against integral water balance observations. We posit that this unique property of catchments is the reason why integral conceptual hydrological models, which largely ignore the momentum balance, allow successful predictions of streamflow to the catchment outlet (Sivapalan, 2018). As conceptual models directly address processes at the system level without accounting for sub-scale mechanistic reasons, their application is often referred to as "top-down" modelling.…”