Abstract
Abstract. Hydrometric networks play a vital role in providing information for decision-making in water resource management. They should be set up optimally to provide as much information as possible that is as accurate as possible and, at the same time, be cost-effective. Although the design of hydrometric networks is a well-identified problem in hydrometeorology and has received considerable attention, there is still scope for further advancement. In this study, we use complex network analysis, defined as a collection of nodes interconnected by links, to propose a new measure that identifies critical nodes of station networks. The approach can support the design and redesign of hydrometric station networks. The science of complex networks is a relatively young field and has gained significant momentum over the last few years in different areas such as brain networks, social networks, technological networks, or climate networks. The identification of influential nodes in complex networks is an important field of research. We propose a new node-ranking measure – the weighted degree–betweenness (WDB) measure – to evaluate the importance of nodes in a network. It is compared to previously proposed measures used on synthetic sample networks and then applied to a real-world rain gauge network comprising 1229 stations across Germany to demonstrate its applicability. The proposed measure is evaluated using the decline rate of the network efficiency and the kriging error. The results suggest that WDB effectively quantifies the importance of rain gauges, although the benefits of the method need to be investigated in more detail.
Highlights
Hydrometric observation networks monitor a wide range of water quantity and water quality parameters such as precipitation, streamflow, groundwater, or surface water temperature (Keum et al, 2017)
Its comparison to existing measures demonstrates that weighted degree–betweenness (WDB) is more sensitive to the different roles of nodes, such as global connecting nodes or local centers, as it considers various aspects of the spatiotemporal relationships in observation network
We propose using WDB for ranking rain gauges in hydrometric networks
Summary
Hydrometric observation networks monitor a wide range of water quantity and water quality parameters such as precipitation, streamflow, groundwater, or surface water temperature (Keum et al, 2017). Even after the advent of remotesensing-based information, such as satellite precipitation estimates, in situ observations are considered to be an essential source of information in hydrometeorology (Rossi et al, 2017). The basic characteristics of hydrometric networks comprise the number of stations, their locations, observation periods, and sampling frequency (Keum et al, 2017). A higher station number elevates the cost of installation, operation, and maintenance, but it may provide redundant information and, not increase the information content obtained from the observation network.
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