The rainfall network is an infrastructure of discrete rain gauges that generate reliable and approximate rain field over a continuous spatial area. This rain field (rainfall estimation) is vital for most hydrological studies. For instance, rainfall observations are required for the design of hydraulic structures, flood estimation and forecasting, assessment of water availability, and climate impact studies.
However, in most situations, rain gauges are scarce because (i) the entire rain gauge setup and post-installation maintenance cost a huge amount of taxpayer money, (ii) and inaccessible locations require knowledge of rainfall characteristics at related stations nearby.
A common and simple way to overcome these challenges is to assume that rainfall characteristics at nearby points are more closely related than those at distant points, as described by (Tobler, 1970) in his “First Law of Geography.” This assumption is also the foundation of geostatistics, which in turn is fundamental to many classical approaches to spatial data analysis, interpolation throughout hydrology, and other geoscientific disciplines.
While this assumption is often reasonable, it may not hold true in every situation, especially in regions with complex topography. In such areas, statistics of rainfall recorded at neighboring stations can significantly vary due to the high topographic gradients and, hence, changes in rainfall pattern between them. In addition, in the scenario of global warming, this assumption doesn’t hold true as it leads to intense dynamic coupling in remote regions. As a consequence, there is an open challenge: how to effectively and efficiently use the available information from existing rain gauge networks for better planning and water management purposes.
To address this problem, we followed a fast-emerging complex network approach. As the name indicates, the complex network consists of several entities (nodes), and the link between each pair of an entity (node) is set up based on how they interact with each other. For instance, in a family network, each person is considered as a node, and the relationship between them is a link; in a computer network, each computer is a node, and links are the connections between different computers; in brain networks, neurons are nodes, and links represent the paired neurons’ interaction. In general, complex networks consist of small, densely interconnected groups called communities — or a homogeneous region in the rain gauge network.
In this study, we used the complex network-based approach on the rain gauge network to quantitatively and qualitatively assess the universal role of each rain gauge station in India. Our analysis reveals that in a community (homogeneous region), each rain gauge station plays a different role. These roles can be of ultra-peripheral or peripheral nodes (most connections inside homogeneous regions), satellite connectors (most connections to other community), kinless nodes (wrongly assigned nodes), local center (hub of a community), hybrid nodes (connecting two different communities), and global connector (equally connected to all different community). This information is vital for many crucial decisions in hydrological engineering.
Key results indicate that (i) rainfall network consists of disparate communities (homogeneous regions) and the way communities are connected within themselves and with others are not trivial; (ii) complex network yields zoomed-in details of individual rainfall station within each community; (iii) investigating and quantifying the role of each station would certainly help in reducing uncertainty in various hydrological applications; and (iv) the results of the study have significant implication in identifying key node locations in climate systems which play a major role in affecting the climate in the given community
These findings are described in the article entitled Quantifying the roles of single stations within homogeneous regions using complex network analysis, recently published in the Journal of Hydrology. This work was conducted by Ankit Agarwal from the University of Potsdam, under the close supervision of Norbert Marwan, Rathinasamy Maheswaran, Prof. Bruno Merz, and Prof. Jürgen Kurths.
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