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

The spatiotemporal patterns of precipitation are critical for understanding the underlying mechanism of many hydrological and climate phenomena. Over the last decade, applications of the complex network theory as a data-driven technique has contributed significantly to study the intricate relationship between many variable in a compact way. In our work, we conduct a study to compare an extreme precipitation pattern in Ganga River Basin, by constructing the networks using two nonlinear methods - event synchronization (ES) and edit distance (ED). Event synchronization has been frequently used to measure the synchronicity between the climate extremes like extreme precipitation by calculating the number of synchronized events between two events like time series. Edit distance measures the similarity/dissimilarity between the events by reducing the number of operations required to convert one segment to another, that consider the events’ occurrence and amplitude. Here, we compare the extreme precipitation patterns obtained from both network construction methods based on different network’s characteristics. We used degree to understand network topology and identify important nodes in the networks. We also attempted to quantify the impact of precipitation seasonality and topography on extreme events. The study outcomes suggested that the degree is decreased in the southwest to the northwest direction and the timing of peak precipitation influences it. We also found an inverse relationship between elevation and timing of peak precipitation exists and the lower elevation greatly influences the connectivity of the stations. The study highlights that Edit distance better captures the network’s topology without getting affected by artificial boundaries.

Concurrent temperature and precipitation extremes during Indian summer monsoon generally have signicant effects on agriculture, society and ecosystems. Due to climate change, frequency and spatial extent of concurrent extremes have changed, and there is a need to advance our understanding in this domain. Quantication of individual extremes (temperature and precipitation) during the summer monsoon season and its teleconnections to climate indices have been studied comprehensively. But, less attention is devoted to the quantication of concurrent extremes and its teleconnections to climate indices. In this study, concurrent extremes (dry/hot and wet/cold) based on mean monthly temperature and total monthly precipitation during the Indian summer season from 1951 to 2019 over the Indian mainland are investigated. Next, the study uses wavelet coherence analysis to unravel the teleconnections of the spatial extent of concurrent extremes to climate indices (Nino 3.4, WEIO SST and SEEIO SST). Results show that the frequency of wet/hot concurrent extremes has increased signicantly, while the frequency of wet/cold concurrent has decreased for the time window 1985 to 2019 relative to 1951-1984. Also, a statistically signicant increase (decrease) in the spatial extent exists in concurrent dry/hot (wet/cold) extremes during the July, August and September months. The ndings of this study could advance our understanding of changes in concurrent extremes during the Indian summer monsoon due to climate change.

Compound extremes exhibit greater adverse impacts than their univariate counterparts. Studies have reported changes in frequency and the spatial extent of extremes in India; however, investigation of compound extremes is in the infancy state. This study investigates the historical variation of compound dry and hot extremes (CDHE) and compound wet and cold extremes (CWCE) during the Indian summer monsoon period from 1951-2019 using monthly data. Results are analyzed for 10 identified homogeneous regions for India. Our results unraveled that CDHE (CWCE) frequency has increased (decreased) by 1-3 events per decade for the recent period (1977-2019) relative to the base period (1951-1976). Overall, there is increasing (decreasing) pattern of CDHE (CWCE) is high across North-central India, Western India, North-eastern India and South-eastern coastlines. Our findings help in identification of the parts of the country were affected by frequent and widespread CDHE during the recent period, which is alarming. More detailed assessments are required to disentangle the complex physical process of compound extremes to improve risk management options.

Compound dry and hot extremes (CDHE) during the Indian summer monsoon significantly affect agriculture. Due to climate change, the frequency, spatial extent and severity of CDHE have changed over several parts of the world. Understanding the variability of CDHE is critical for designing adaptation strategies to reduce the adverse impacts on agricultural systems. In particular, traditional assessments have focused on the variability of frequency and spatial extent using the quantile-based approach. However, counting the number of events excess over the threshold helps to understand the variability in frequency and spatial extent but fails to detect the changes in the severity. Further, limited studies have investigated the changes in CDHE severity over India. Hence, in the present study, the variability of CDHE severity is assessed during the summer monsoon from 1951 to 2020 over homogenous regions of India using a copula-based Standardized Compound Event Indicator. A significant increase in the severity of CDHE during the summer season was found in eight homogenous regions out of ten. The most vulnerable regions are northeast India and peninsular India, and interestingly, a significant decrease in the severity is observed for the north rain-belt Western Himalayan region. In addition, a significant increase in the spatial extent of the CDHE severe category is also found in all the homogenous regions over the past three decades. This study highlights that severe CDHE is associated with a high risk of severe agricultural drought for a large part of the country. Uni-variate assessments based on precipitation or temperature can underestimate the risks associated with CDHE if there is a strong dependence among the drivers.

Due to the advancement in satellite and remote sensing technologies, a number of satellite precipitation products (SPPs) are easily accessible online at free of cost. These precipitation products have a huge potential for hydro-meteorological applications in data-scare catchments. However, the use of such products is still limited owing to their lack of accuracy in capturing the ground truth. To improve the accuracy of these products, we have developed a quantile based Bayesian model averaging (QBMA) approach to merge the satellite precipitation products. QBMA is a probabilistic approach to assign optimal weights to the SPPs depending on their relative performances. The QBMA approach is compared with simple model averaging and one outlier removed. TRMM, PERSIANN-CDR, CMORPH products were experimented for QBMA merging during the monsoon season over India’s coastal Vamsadhara river basin. QBMA optimal weights were trained using 2001 to 2013 daily monsoon rainfall data and validated for 2014 to 2018. Results indicated that QBMA approach with bias corrected precipitation inputs outperformed the other merging methods. On monthly evaluation, it is observed that all the products perform better during July and September than that in June and August. The QBMA approaches do not have any significant improvement over the SMA approach in terms of POD. However, the bias-corrected QBMA products have lower FAR. The developed QBMA approach with bias-corrected inputs outperforms the IMERG product in terms of RMSE.

Understanding spatiotemporal patterns and trends of Indian Summer Monsoon extremes have always been an important task mostly because the impacts of extreme events have an enormous effect on agriculture, economy, life and eco-system. In general, findings from the exploration of extreme events with limited data will have high uncertainty, and it is important to investigate the trends with a high long-term dataset for improved understanding of extreme events. Further, the patterns and interactions become further unusual, unexpected and unpredictable, coupled with the existing challenges of global warming-induced climate change. Hence, the study was primarily prompted by these realizations and an implied aspiration to quantify spatiotemporal patterns and trends of Indian Summer Monsoon extremes. In the present study, Extreme rainfall is defined in three categories, namely severe (99th percentile), very high (95th percentile) and high (90th percentile) during the monsoon season in each year. The temporal changes in extreme rainfall have been detected over the period 1901–2019 using non-parametric Mann-Kendall and Sen’s slope estimator tests, respectively. The analysis revealed significantly intensifying rainfall magnitudes in Jammu and Kashmir, parts of Gujarat, Rajasthan and Peninsular India. The outcomes indicate that the magnitudes of extreme rainfall are likely to increase in future in these regions. On the contrary, Central and some regions of North-eastern India shows deceasing trends in the extreme rainfall significantly. Consistency of the trends, both temporally and spatially has been explored considering three time windows with an overlap of 10 years. The findings of the temporal evolution of extreme rainfall reveal spatiotemporal pattern is not consistent in different periods of the study. The results of the present study will provide an improved understanding of spatiotemporal patterns of the daily rainfall extremes during the Indian summer monsoon.

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.

One of the unresolved and debated topics in climatic and atmospheric sciences is whether soil moisture (SM) anomalies can trigger extreme precipitation (P) events? Researchers have used coupled atmospheric model experiments, models based on simple water budget equations, and water vapour tracing studies; however, a consensus is lacking. Some studies reported that the excess SM anomalies trigger subsequent P; a few also postulate about a negative feedback loop. In the present study, we used a novel Event Coincidence Analysis to investigate this trigger relationship between SM and P. Using SM and P data from 2004-2020, we identified hotspots of SM-P coupling over India. A statistical significance test (α = 0.10) was carried out to ensure that the observed coincidences are not by chance. On increasing, the temporal window from one day to three-day the extent and severity of the hotspots increased significantly. The highest values of trigger coincidence rates are observed over central India (>70%). Our observed results agree with the widely regarded hypothesis of stronger SM-P coupling in transitional regions between wet and dry climates. The results obtained in our work has vast potential for atmospheric forecast purposes, including flood early warning systems for India.

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.

Compound event research has gained significant momentum over the past few years. Traditionally risk assessment studies considered either one climatic driver or process at a time. However, it is now being recognised that it is the combination of multiple drivers and their statistical dependencies that lead to aggravated, non-linear impacts. We aim to identify hotspots for SM-P coupling over India from 2004 to 2020 using Event Coincidence Analysis (ECA) and an extremal tail dependence measure. We further characterise how these complex interconnected interactions can lead to more significant flash floods and landslide risk. The analysis is done at different temporal scales to pinpoint a location prone to floods during the year. High precursor coincidence rates (>60%) were obtained for traditional flash flood-prone areas over India, indicating the robustness of the approach. ECA results were compared with the probabilistic extreme value approach, and a similar pattern was observed in both. The increase in hotspots from 2004 to 2020 matches the observed increase in flood-prone districts reported by earlier studies. We also used the trigger coincidence rate to identify areas where soil moisture anomalies can trigger extreme precipitation. The seasonal variations in precursor coincidence rates are observed to be the same as those usually expected due to changing atmospheric circulation patterns. Our results will complement the traditional flood risk assessment studies and have implications for better understanding the dynamic, ever-evolving nature of compound preconditioned flooding events worldwide.

Quantifying the spatiotemporal variability of precipitation is the principal component for the assessment of the impact of climate change on the hydrological cycle. A better understanding of the quantification of variability and its trend is vital for water resources planning and management. Therefore, a multitude of studies has been dedicated to quantify the precipitation variability over the years. Despite their importance for modeling precipitation variability, the studies mainly focused on the amount of precipitation and its spatial patterns. The studies investigating the spatial and temporal variability of precipitation across the Indian subcontinent, in general, and at multiscale, in particular, are limited. In this study, we introduce a novel measure, Standardized Variability Index (SVI), based on information entropy to investigate the spatiotemporal variability of precipitation. The proposed measure is independent of the temporal scale, the length of the data and can compare the precipitation variability at multiple timescales. Distinct spatial patterns were observed for information entropies at the monthly and seasonal scale. Stations with statistically significant trends were observed and vary from monthly to seasonal scale. There is an increase in the variability of precipitation amount across Central India. Trend analysis revealed there is changing behaviour in the precipitation amount as well as rainy days, showing an increase in the probability of occurrence of extreme events in the near future. In addition, coupling the mean annual rainfall with SVI enables a relative assessment of the water resources availability.