Dissertations and Theses

Date of Award


Document Type



Civil Engineering

First Advisor

Naresh Devineni


Hydrology, Dam impact, Flow regulation, Catchment water balance, Hydrometeorological extremes, Water resources management


The contemporary hydrosystems of the United States involve a complex combination of natural and modified basins in the presence of changing climate and anthropogenic impacts. An enhanced understanding of the interdependence between climate forcings, human-induced interventions, and water balance in both natural and modified basins are essential for developing reliable and resilient hydrosystems and for better water resources management. In response, this dissertation focuses on investigating the hydroclimatology of natural and modified basins across the contiguous United States. It has three research objectives: (1) to explain flow alterations due to anthropogenic activities, especially dam operations, in modified basins and understand how dam attributes contribute to these alterations, (2) to enhance our understandings of the interactions between catchment attributes, climate forcings, and water balance in natural basins across the contiguous United States, including mountainous and snow-dominated regions, (3) to better understand and predict the spatial manifestation of precipitation extremes by identifying their concurrent nature across the contiguous United States and inferring the significant drivers that govern their spatiotemporal variability. For the first objective, an extensive investigation of anthropogenic alterations in streamflow regimes is performed. The influence of a network of dams on the frequency of streamflow and the propagational effect of its variability are explored across dendritic streamflow networks. The second objective is achieved by developing and testing a physics-based conceptual water balance model that includes snow melting process for natural basins at the intra-annual timescales. The model is used as a basis to better quantify the time-varying catchment response to climate forcings. For the third objective, a systematic framework based on modern machine learning techniques is developed to identify the spatial manifestation of precipitation extremes (wet and dry) and explain their climate teleconnections. The findings from this dissertation have the potential to provide mitigation plans for future extremes by optimizing water allocations and catchment land use and land covers.



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