ALBERT

Description: The quantification of spatial propagation of extreme precipitation events is vital in water resources planning and disaster mitigation. However, quantifying these extreme events has always been challenging as many traditional methods are insufficient to capture the nonlinear interrelationships between extreme event time series. Therefore, it is crucial to develop suitable methods for analyzing the dynamics of extreme events over a river basin with a diverse climate and complicated topography. Over the last decade, complex network analysis emerged as a powerful tool to study the intricate spatiotemporal relationship between many variables in a compact way. In this study, we employ two nonlinear concepts of event synchronization and edit distance to investigate the extreme precipitation pattern in the Ganga river basin. We use the network degree to understand the spatial synchronization pattern of extreme rainfall and identify essential sites in the river basin with respect to potential prediction skills. The study also attempts to quantify the influence of precipitation seasonality and topography on extreme events. The findings of the study reveal that (1) the network degree is decreased in the southwest to northwest direction, (2) the timing of 50th percentile precipitation within a year influences the spatial distribution of degree, (3) the timing is inversely related to elevation, and (4) the lower elevation greatly influences connectivity of the sites. The study highlights that edit distance could be a promising alternative to analyze event-like data by incorporating event time and amplitude and constructing complex networks of climate extremes. Extreme precipitation networks are constructed over a river basin using event synchronization (ES) and edit distance (ED). Edit distance is an alternative to event synchronization accounting for the sequences and the magnitude of events. Network-based measure degree is employed to understand the spatial synchronization pattern of the extreme precipitation in the Ganga river basin (GRB). The influence of the topography and rainfall characteristics on the extreme precipitation networks is also quantified. The proposed study can estimate the impact of artificial boundaries, thereby better understanding the extreme precipitation network's topology and spatial risk quantification of extreme events.

Description: This paper introduces the Graphics Processing Unit (GPU)-based tool Geo-Temporal eXplorer (GTX), integrating a set of highly interactive techniques for visual analytics of large geo-referenced complex networks from the climate research domain. The visual exploration of these networks faces a multitude of challenges related to the geo-reference and the size of these networks with up to several million edges and the manifold types of such networks. In this paper, solutions for the interactive visual analysis for several distinct types of large complex networks will be discussed, in particular, time-dependent, multi-scale, and multi-layered ensemble networks. Custom-tailored for climate researchers, the GTX tool supports heterogeneous tasks based on interactive, GPU-based solutions for on-the-fly large network data processing, analysis, and visualization. These solutions are illustrated for two use cases: multi-scale climatic process and climate infection risk networks. This tool helps one to reduce the complexity of the highly interrelated climate information and unveils hidden and temporal links in the climate system, not available using standard and linear tools (such as empirical orthogonal function analysis).

Description: Chapter 6 presents an introduction and sections on: earth system analysis from a nonlinear physics perspective; physics fields with relevance for energy technologies; towards green cities: the role of transport electrification; environmental safety; understanding and predicting space weather

Description: Hydrological model calibration is a quintessential step in model development and the time scale of calibration depends on the application. However, the implications of choice of time scale of calibration ACCEPTED MANUSCRIPT 2 have not been explored extensively. Here, we evaluate the effect of the timescale of calibration on model sensitivity, best parameter ranges, and predictive uncertainty for three river basins using the SWAT model. Multiple models were setup for three different catchments from southern India. Our results showed that the sensitivity of the parameters, best parameter ranges, and model performance is conditioned on the timescale of calibration. The models calibrated at coarser time scales marginally outperformed the models calibrated at fine time scale in terms of Nash-Sutcliffe Efficiency and percentage bias. Transfer of parameters across scales (both from coarse to fine and fine to coarse) have general tendency to worsen the model performance in all three catchments, leaving for few exceptions.

Description: Compound dry and hot extremes (CDHE) are periods of prolonged dry and hot weather. Their joint occurrence typically impacts society and nature stronger compared to the occurrence of the single hazards. Understanding the likelihood, variability and drivers of CDHE is challenging due to the complexity of the climate system involving interactions and feedbacks among atmosphere-land processes. In this study, we first investigate the role of the dependence between precipitation and temperature for the likelihood of CDHEs. We demonstrate that both the dependence strength and its type, i.e. the degree of tail dependence, substantially affect the CDHE likelihood. We then analyze the space-time variation of CDHE characteristics during the Indian Summer Monsoon across India for the period 1961–2014. We find strong negative association and substantial tail dependence between precipitation and temperature in some regions. Event coincidence analysis reveals that low soil moisture preconditioned by dry extremes is responsible for 55–65% of CDHE occurrence. Our analysis of the temporal evolution of CDHE characteristics finds an increasing negative association between precipitation and temperature leading to a 2 to 3-fold rise of CDHE frequency for some regions of India.

Description: Studies around the world have shown that increases in extreme precipitation are not directly resulting in reported instances of flooding. Despite the evidence that antecedent soil moisture conditions play a significant role in regulating the runoff response during extreme storm events, fewer works have tried to understand the spatial and temporal dynamics of such coupled interactions. In the present work, we aim to improve our understanding of the dominant spatiotemporal patterns of soil moisture (SM) – precipitation (P) dependence over Europe. Using an event-based, non-parametric Event Coincidence Analysis (ECA) approach, we first quantified the probability of SM preceding P by the precursor coincidence rate. We also examined the statistical precursor relationship between SM-P joint episodes and reported annual flood events using the conditional precursor coincidence rate. The results show that seasonality is a major driving force in the spatiotemporal dynamics of SM-P coupling over Europe. The conditional precursor coincidence rates are in line with previous flood hazard studies over Europe and show the importance of considering such diverse interdependencies in flood risk mitigation. Our work has implications for understanding the occurrence of preconditioned compound events worldwide and provides a vital starting point for characterising the major processes that need to be accounted for in comprehensive state of the art flood risk assessment studies.

Description: Integrated water vapor transport (IVT) is evaluated to assess anomalous high moisture transports (AHMT) over the Indian Subcontinent, the Arabian Sea, and the Bay of Bengal during the 2013 Uttarakhand and 2015 Tamil Nadu flood events. Using a high-resolution daily gridded rainfall data set, an attempt has been made to evaluate the spatiotemporal characteristics of anomalous high moisture transports (AHMT) responsible for the occurrence of heavy precipitation events during flood events. The spatiotemporal characteristics of flood-causing rainfall events associated with the occurrence of AHMT show the existence of a strong relationship between the presence of AHMT and extreme precipitation events for the northwestern region where AHMT penetrates inland (over Uttarakhand in 2013) and for the east coast region where AHMT make landfalls (over Tamil Nadu in 2015). Further analysis suggests that extreme precipitation events are predominantly influenced by the strong moisture convergence associated with the low-level pressure systems, wind speed, and direction developed in the vicinity of affected regions.

Description: Precipitation is a fundamental input for many hydrological and water management studies. With the advancement in science, a variety of satellite precipitation products are available. In this study, the ability of three satellite precipitation products (TRMM-3B42v7, PERSIANN-CDR and GPM-IMERGv6) to capture rainfall were evaluated with ground-based Indian Meteorological Department (IMD) gridded data and also by driving the Soil Water Assessment Tool (SWAT) hydrological model for a cyclone prone coastal river basin in the southeast of India. Results indicate that among the three, GPM-IMERG v6 outperformed in all the statistical metrics, followed by TRMM-3B42 v7 at different temporal scales. GPM-IMERG v6 exhibited the highest correlation coefficient (0.48) and lowest root mean square error (9.52 mm/day), followed by TRMM-3B42 v7 (CC = 0.44, RMSE = 9.58 mm/day) at daily scale. In contrast to other studies in similar regions, IMERGV6 showed better performance than the PERSIANN CDR in detection skills of low, medium and high intensity rainfall events as well as False Alarm Ratio. Hydrological evaluation of the three products using the SWAT model over the study area showed satisfactory results at daily and monthly scale during the calibration and validation period. IMERGV6 is found to have better performance in hydrological evaluation as well.

Description: Assessment of water level fluctuations helps to understand the hydrological processes, its driving factors, and seasonal variation, which plays a vital role in water resources management. The satellite observations using advanced sensors facilitate to monitor large continental water bodies at fixed spatial-temporal resolution, especially in poorly gauged basins. In the present work, satellite radar and laser altimeters (Jason-2/3, SARAL/AltiKa, Sentinel-3A/3B, ICESat-1, ICESat-2) are utilized to examine the variation of water level of Ganga River. The accuracy assessment of altimetry-based water levels is carried out using in-situ observations at virtual stations. It is found that, the quality of the altimetry observations has been improved over a period of time with no marginal impact of temporal resolution on their accuracy. Lidar-based altimeters having small footprint were able to examine the water level at relatively narrow river reaches. Further, we analyzed the seasonal variation of water level (pre-monsoon, monsoon and post-monsoon) using generated long-term water level time series. This study provided a comprehensive understanding of measurement accuracies of seven spaceborne altimetry dataset, their comparative advantages and limitations, and its implications for water level assessment.