Browsing by Author "Kumar, Mukesh"

Human-induced changes in climate and landscape characteristics are driving the coupled climate-hydrological-ecological system (CHES) into unchartered territories, with major implications on natural resource availability and sustainability at both local and global scales. Given that soil-plant-atmosphere are part of a hydrologic continuum, the variability and changes in climate may impact hydrological states and fluxes, which in turn can increase vegetation stress potentially resulting in an abrupt regime shift in the ecohydrological system. Describing and predicting the non-linear dynamics of CHES is challenging in part due to uncertainties in the parameters that describe the system and insufficient understanding of the physical mechanisms that control these responses. This dissertation strives to bridge these gaps through synergistic use of data analytics and physically-based modeling so as to characterize a spectrum of dimensionality, nonlinearity, and stochasticity of CHES across a range of spatial-temporal scales. Three overarching questions frame the direction and scope of this dissertation: Q1 – how do meteorological conditions affect groundwater dynamics in forested wetlands? Q2 – how to evaluate forest mortality risk under long-term climate change, and predict near-term forest mortality? Q3 – how does plant hydraulics regulate plant water use under hydro-climatic stress across biomes? Addressing these questions will improve the understanding of CHES dynamics and representations of hydrologic and vegetation dynamics in Earth System Models. The findings and methodologies developed here can be leveraged for devising mitigation and adaptation strategies for water resource management and ecosystem conservation under current and future climate regimes.

Soil erosion by water is a major driven force causing land degradation. Laboratory experiments, on-site field study, and suspended sediments measurements were major fundamental approaches to study the mechanisms of soil water erosion and to quantify the erosive losses during rain events. The experimental research faces the challenge to extent the result to a wider spatial scale. Soil water erosion modeling provides possible solutions for scaling problems in erosion research, and is of principal importance to better understanding the governing processes of water erosion. However, soil water erosion models were considered to have limited value in practice. Uncertainties in hydrological simulations are among the reasons that hindering the development of water erosion model. Hydrological models gained substantial improvement recently and several water erosion models took advantages of the improvement of hydrological models. It is crucial to know the impact of changes in hydrological processes modeling on soil erosion simulation.

This dissertation work first created an erosion modeling tool (GEOtopSed) that takes advantage of the comprehensive hydrological model (GEOtop). The newly created tool was then tested and evaluated at an experimental watershed. The GEOtopSed model showed its ability to estimate multi-year soil erosion rate with varied hydrological conditions. To investigate the impact of different hydrological representations on soil erosion simulation, a 11-year simulation experiment was conducted for six models with varied configurations. The results were compared at varied temporal and spatial scales to highlight the roles of hydrological feedbacks on erosion. Models with simplified hydrological representations showed agreement with GEOtopSed model on long temporal scale (longer than annual). This result led to an investigation for erosion simulation at different rainfall regimes to check whether models with different hydrological representations have agreement on the soil water erosion responses to the changing climate. Multi-year ensemble simulations with different extreme precipitation scenarios were conducted at seven climate regions. The differences in erosion simulation results showed the influences of hydrological feedbacks which cannot be seen by purely rainfall erosivity method.

Hydrologic models may be used for both prediction and understanding of hydrologic responses. In this dissertation, I first evaluated the influence of representation of storage-discharge relation on streamflow prediction accuracy. Results suggest that derived storage-discharge relations vary significantly depending on the choice of recession analysis method, and this difference is large enough to appreciably affect the streamflow response obtained using it. Next, a process-explicit model was used to evaluate the relative role of different processes and states on hydrologic response. Specifically, I explored the causes of variability in flood response to large hurricane-season storms, and identified the dominant controls on this variability to be soil saturation near the ground surface and evapotranspirative losses. I also evaluated the role of the process controls on both intra- and inter-seasonal variations in daily peak time in a snow-dominated watershed, and identified subsurface flow as the most dominant control on daily peak time delays, with contribution from melt water translation through the snowpack comes a close second. Finally, I developed and implemented a physics-based 2D hydrologic model that accounts for interception, evapotranspiration, and subsurface flow processes to investigate how geomorphological modification of the landscape might have affected hydrologic responses of the piedmont hillslope. Results indicate that with increasing gully incision, groundwater flow out of the hillslope increases, while groundwater table, root zone moisture, transpiration and surface runoff reduces. The impact of gully incision can also propagate far and wide in the hillslope, with its extent mainly determined by hydraulic conductivity and hillslope steepness. Studies in this dissertation could help prioritize measurements during observation campaigns and could also aid in risk management under climate change and other disturbance conditions.