Optimal design of hydrometric station networks based on complex network analysis

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.

Inter-Comparison of Gauge-Based Gridded Data, Reanalysis and Satellite Precipitation Product with an Emphasis on Hydrological Modeling

“…However, rain gauges are often not enough to correctly (TMPA 3B42V7) showed a better understanding than the original 3b42v7 data. Similar inferences were reported by Bitew et al [29] and Tuo et al [2] in their respective analysis.…”

“…We selected the Nagavali River Basin (NRB) since the literature on semi-arid and semi-humid climatic regions is limited. NRB lies between the Godavari and Mahanadi river basins in the eastern part of India, with a catchment area of about 9510 km 2 (Figure 1). The river begins from the Eastern Ghats at an elevation of 1600 m. It traverses about 160 km through Odisha and enters Andhra Pradesh near Kuneru village of Vizianagaram district at an elevation of about 152 m [55].…”

“…In addition, errors and uncertainties associated with inputs (precipitation products) have a high probability of inducing it in hydrologic simulations [45]. A multitude of studies showed that the model recalibration using the precipitation product considerably increases the model performance [2,17,41,[46][47][48][49][50][51][52]. However, the model parameter ranges obtained through the recalibration with the precipitation data may be improbable, thereby questioning the model's applicability for real-world applications.…”

“…Therefore, in this study, we compared three different types of precipitation products that are commonly used, (i) gauge-based rainfall product (IMD), (ii) reanalysis data (NCEP-CFSR) and (iii) satellite precipitation (TRMM and TRMM corrected). This study aimed to (1) compare the statistical characteristics of four different precipitation data sets, (2) investigate the adequacy of these precipitation products in driving the semi-distributed SWAT hydrological model and (3) evaluate the parameter uncertainty involved. These precipitation inputs are used to develop a semi-distributed SWAT model for a semi-arid, forest dominated coastal basin, "Nagavali River Basin in India".…”

Synchronization of extreme rainfall during the Australian summer monsoon: Complex network perspectives

“…We lastly study communities that best partition our networks based on maximizing modularity of the network. 31,32 Communities in a network represent groups of nodes that are highly connected within each community but with weaker links among the communities. 33 These communities are representative of coherent rainfall zones over the study area, i.e., the rainfall over these zones is more homogeneous, synchronized, and have similar dynamical properties.…”