AbstractSeveral flood-generating mechanisms could produce high flows in catchments however, AMS/POT sampling is not considering these hydrological processes. Grouping the floods into homogenous samples (in terms of process) has many potential advantages, such as better estimation of return level. This study aims to develop methods to classify and group floods, based on the simple flood hydrograph characteristics, from the daily discharge data. This approach is based on the underlying hypothesis that similar hydrological and catchment conditions lead to similar hydrological responses. We used the Dresden gauge station on the Elbe river, Germany (1950-2019). Flood separation follows four steps: 1. Identification of peaks, i.e., points with a higher streamflow value than its prior and next values, 2. Pruning based on 90th percentile threshold value, 3. Application of independence criterion, 4. Identification of flood starting and ending position. From the separated flood events, six features are extracted for clustering, i.e., peak, volume, timescale, rise to duration ratio, occurrence season and the existence of multi peaks. Extracted flood features include both numerical and categorical variables thus, to deal with these mixed feature datasets, we employed the K-medoids technique for clustering. Further, various cluster validation indices robustly help to identify the optimal number of clusters. We also performed the feature relevancy analysis to understand the hydrograph features’ relative importance. Since hydrometeorological variables are not used for classification, we used the magnitude of the precipitation and snowmelt during the flood duration to characterize the various clusters. Clustering results show that the employed methods are effective in classifying the flood events driven by different flood drivers.Keywords: Flood classification, Flood separation, Flood frequency analysis

Exploration of SST-Streamflow connection unravels the large scale climate influences that have a potential role in modulating local hydrological components. Most studies exploring this relationship only focus on seasonal or annual scales however, various atmospheric and oceanic phenomena occur at different timescales, which need to be considered. This study investigates the influence of sea surface temperature (SST) on German streamflow, divided into Alpine, Atlantic and Continental streamflow regions, at timescales ranging from sub-seasonal to decadal by integrating wavelet transform and complex network techniques. Wavelet transform is used to decompose the time series into multiple frequency signals, and the spatial connections are identified based on these decomposed signals for the 99 percentile correlation coefficient value by applying network theory. The degree centrality metric is used to evaluate the characteristics of the spatially embedded networks. Our results re-establish known SST regions that have a potential connection with the various streamflow regions of Germany. Spatial patterns that resemble the North Atlantic SST tripole-like pattern is predominant for Alpine streamflow regions at lower timescale. Equatorial Atlantic Mode regions observed for Atlantic streamflow at inter-annual timescale and Vb weather system connected regions in the Mediterranean Sea have appeared for all the streamflow regions of Germany. Besides, continental streamflow regions exhibited combined characteristics of the Alpine and Atlantic streamflow spatial patterns. In addition to the above regions, we also identify the scale specific patterns in the Pacific, Indian and Southern Ocean regions at different timescales ranging from seasonal to decadal scale.

Drought is a natural disaster that mainly affects water resources, agriculture, and a country’s social and economic development due to its long-term and frequent occurrence. Therefore, it is important to characterize and monitor drought and its propagation to minimize the impact. Precipitation and Terrestrial water storage are popular hydrological components used frequently to understand the drought process. However, spatiotemporal assessment of drought propagation over India based on terrestrial water storage is unexplored. In this study, the terrestrial water storage anomalies (TWSA) obtained from a Gravity Recovery and Climate Experiment (GRACE) and Combined Climatological Deviation Index (CCDI) are used to characterize the drought at different spatial scales. The changing pattern of TWSA and CCDI is investigated using the non-parametric Mann-Kendall test and its slope is estimated using the Theil-Sen slope estimator. TWSA trends unravel that the Ganga and Indus River Basin have significant negative trends throughout the year. However, the trends are oscillating for Peninsular basins. The major river basins Ganga, Indus, Brahmaputra shows decreasing trends for all seasons; however, Cauvery, Mahanadi, Krishna, and the Godavari show decreasing trends for winter and Post-monsoon season and increasing trends for pre-monsoon and monsoon season. It shows that Ganga, Indus, and Brahmaputra river basins are more prone to drought in the near future.