No abstract

Drought is a natural disaster affects water resources, agriculture, and social and economic development due to its long-term and frequent occurrence. It is crucial to characterize and monitor drought and its propagation to minimize the impact. However, spatiotemporal assessment of drought characteristics over India at the sub-basin scale based on terrestrial water storage is unexplored. In this study, the Terrestrial water storage anomalies (TWSA) obtained from a Gravity Recovery and Climate Experiment and precipitation data are used to characterize the propagation of drought. Combined Climatological Deviation Index (CCDI) and GRACE-Drought Severity Index (GRACE-DSI) were computed as CCDI utilizes both precipitation and TWSA data while GRACE-DSI uses only TWSA data. Our results showed that GRACE-DSI exhibits significant negative trends over most of the Indian sub-basins compared to CCDI, indicating that most of the drought events are due to depletion of TWS. While other sub-basins show changing trends for GRACE-DSI and CCDI. The number of sub-basins showing significant negative trends for GRACE-DSI is more than that for CCDI. Hence TWS is depleting for most of the subbasins in India. Our results show that Indo-Gangetic plains face many drought events during 2002–2004, 2009–2014 & 2015–2017. Maximum drought duration and drought severity obtained for the area of North Ladakh (not draining into Indus basins) by GRACE-DSI are 26 months (2002–2004) and − 44.2835, respectively. The maximum drought duration and drought severity obtained for the Shyok sub-basin by CCDI is 17 months (2013–2015) and − 13.4392, respectively. Monthly trend analysis revealed that 39 & 23 no. of sub-basins show significant negative GRACE-DSI trends for October and CCDI for November, respectively. At the same time, the seasonal trend shows that total 34 and 14 sub-basins exhibited a significant negative trend at post-monsoon Kharif season for both the GRACE-DSI & CCDI, respectively.

This study evaluates the relationship between flow variability of unregulated and regulated streamflow stations and global climate indicators. Mann–Kendall and change-point analysis is applied to investigate the gradual and abrupt changes in streamflow data, followed by the investigation of multi-scale fluctuations in streamflow data using Continuous Wavelet Analysis. Linkages between streamflow and global climate indicators are examined using Cross-Wavelet and Wavelet Coherence Analysis. Results showed contrasting trend values for unregulated and regulated streamflow stations. Surprisingly, all unregulated stations experienced a significant abrupt shift in change point contrary to the regulated streamflow. Further, for unregulated stations, streamflow variability and hydroclimatic teleconnections were observed at a lower scale, indicating that variations in streamflow are more frequent and generally occur on an intra-annual to inter-annual scale. Contrary, regulated stations observed the streamflow variability and hydroclimatic teleconnections at a larger scale (8–10 years), indicating that all the fluctuations are smoothened out. Thus, unregulated stations cannot be used as a proxy for regulated stations in any given basin. Indeed, for better water resource planning and management, both regulated and unregulated streamflow should be investigated.

Investigation of SST-streamflow connectivity unravels the large-scale climate influences that may have a potential role in modulating local hydrological components. Most studies exploring this relationship only focus on a single timescale; however, various atmospheric and oceanic phenomena occur at different temporal scales, which must be considered. This study examines the association of sea surface temperature (SST) and streamflow in Germany, divided into three regions, viz. Alpine, Atlantic and Continental, at timescales ranging from seasonal to interannual by integrating wavelet transform and complex network techniques. Wavelet transform is used to decompose the time series into multiple frequency signals. The network theory identifies the spatial connections for the 99 percentile correlation coefficient value based on these decomposed signals. 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 finer timescale. Equatorial Atlantic Mode regions observed for Atlantic streamflow at interannual 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.

<p>Different flood-generating mechanisms are responsible for high flows in different catchments. This mixture of generating mechanisms could violate the homogeneity assumption of the extreme value distribution used often in flood frequency analysis. Thus, this study aims to classify flood samples into homogenous process-based groups and estimate the flood quantiles for different return periods. Furthermore, this study also deals with the sample inadequacy in the flood classification by pooling ensemble reforecast datasets based on the UNprecedented Simulated Extreme ENsemble (UNSEEN) approach. The Dresden gauge in the Elbe River is selected as the study site. Daily discharge data are extracted from the GRDC, and flood events are separated based on our proposed ‘Peak-identification flood separation algorithm’, which 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 90<sup>th</sup> percentile threshold value, 3. Application of independence criterion, 4. Identification of flood starting and ending position. After flood separation, hydrograph features-based flood grouping and ensemble data pooling are performed. We observe the difference in the distribution characteristics of the observed in comparison to the pooled datasets. A relative difference of 0.25 (cumecs/cumecs) is noticed for the 100-year return level between observed and pooled data. As our key contribution, we address the sample mixing problem using the flood classification technique and establish the importance of data pooling.</p>