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Rain gauge network is important for collecting rainfall information effectively and efficiently. Rain gauge networks have been studied for several decades from a range of hydrological perspectives, where rain gauges with unique or non-repeating information are considered as important. However, the problem of quantification of node importance and subsequent identification of the most important nodes in rain gauge networks have not yet been extensively addressed in the literature. In this study, we use the concept of the complex networks to evaluate the Indian Meteorological Department (IMD) monitored 692 rain gauge in the Ganga River Basin. We consider the complex network theory-based Degree Centrality (DC), Clustering Coefficient (CC) and Mutual Information (MI) as the parameters to quantify the rainfall variability associated with all the rain gauges in the network. Multiple rain gauge network scenario with varying rain gauge density (i.e. Network Size (NS) = 173, 344, 519, and 692) and Temporal Resolution (i.e. TR = 3 hours, 1 day, and 1 month) are introduced to study the effect of rain gauge density, gauge location and temporal resolution on the node importance quantification. Proxy validation of the methodology was done using a hydrological model. Our results indicate that the network density and temporal resolution strongly influence a node’s importance in rain gauge network. In addition, we concluded that the degree centrality along with clustering coefficient is the preferred parameter than the mutual information for the node importance quantification. Furthermore, we observed that the network properties (spatial distribution, DC, Collapse Correlation Threshold (CCT), CC Range distributions) associated with TR = 3 hours and 1 day are comparable whereas TR = 1 month exhibit completely different trends. We also found that the rain gauges situated at high elevated areas are extremely important irrespective of the NS and TR. The encouraging results for the quantification of nodes importance in this study seem to indicate that the approach has the potential to be used in extreme rainfall forecasting, in studying changing rainfall patterns and in filling gaps in spatial data. The technique can be further helpful in the ground-based observation network design of a wide range of meteorological parameters with spatial correlation.

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<p>The river discharge data is one of the most important pieces of information to regulate various water resources, including flood frequency analysis, drought and flood prediction, etc. The missing observer discharge data, even a short gap, influences the whole analysis and gives a totally different result. Filling data gaps in streamflow data is thus a critical step in any hydrological study. Interpolation, regression-based analysis, artificial neural networks, and modeling are all methods for generating missing data. While using the hydrological model to generate the data, we first need to calibrate the hydrological model. The single-site calibration of the hydrological model has its own limitations, due to which it does not correctly predict the streamflow at intermediate gauge locations. This is because, while calibrating the model for the final outlet, we tune the parameters that affect the results for the final outlet only and neglect the intermediate sites' output. In this study, we demonstrate the importance of multi-site calibration and use the calibrated hydrological model to generate the missing data at intermediate sites.</p> <p>For this study, we selected the Godavari River basin and calibrated it at the final outlet (single-site calibration) and at 18 + 1 outlets (multi-site calibration). The whole basin is divided into 103 subbasins, and the Soil and Water Assessment Tool (SWAT) hydrological model is used for this study. After the successful multi-site calibration, we generated the missing data at 25 different gauging locations. The initial results from single-site calibration (NSE (0.57) and R2 (0.61)) show good agreement between observed and simulated discharge for the final outlet. The multi-site calibration analysis is in progress, and full results will be presented at the conference.</p>