Innovations in Detecting and Modeling Dryland Hydrologic Changes

The hydrologic cycle in drylands is complex with large spatiotemporal variationsacross scales and is particularly vulnerable to changes in climate and land cover. To address the challenges posed by hydrologic changes, a synergistic approach that combines numerical models, ground and remotely sensed observations, and data analysis is crucial. This dissertation uses innovative detection and modeling techniques to assess key hydrologic variables in drylands, including irrigated water use, streamflow, and snowpack conditions, answering following research questions that also have broad societal implications: (1) What are the individual and combined effects of future climate and land use change on irrigation water use (IWU) in the Phoenix Metropolitan Area (PMA)?; (2) How can temporal changes in streamflow and the impacts of flash flooding be detected in dryland rivers?; and (3) What are the impacts of rainfall-snow partitioning on future snowpack and streamflow in the Colorado River Basin (CRB)? Firstly, I conducted a scenario modeling using the Variable Infiltration Capacity (VIC) model under future climate and land use change scenarios. Results showed that future IWU will change from -0.5% to +6.8% in the far future (2071-2100) relative to the historical period (1981-2010). Secondly, I employed CubeSat imagery to map streamflow presence in the Hassayampa River of Arizona, finding that the imaging capacity of CubeSats enabled the detection of ephemeral flow events using the surface reflectance of the near-infrared (NIR) band. Results showed that 12% of reaches were classified as intermittent, with the remaining as ephemeral. Finally, I implemented a physically-based rainfall-snow partitioning scheme in the VIC model that estimates snowfall fraction from the wet-bulb temperature using a sigmoid function. The new scheme predicts more significant declines in snowfall (-8 to -11%) and streamflow (-14 to -27%) by the end of the 21st century over the CRB, relative to historical conditions. Overall, this dissertation demonstrates how innovative technologies can enhance the understanding of dryland hydrologic changes and inform decision-making of water resources management. The findings offer important insights for policymakers, water managers, and researchers who seek to ensure water resources sustainability under the effects of climate and land use change.