Over the past few decades, increasing fire frequency and severity in southern California - and across the western United States - has posed a concern to the safety and well-being of communities and ecosystems. Increased aridity coupled with water stressed vegetation from prolonged droughts are leading to a higher propensity for larger, more intense fires that directly impact ecohydrological processes such as streamflow and evapotranspiration (ET). Accurate characterization of these processes are required to improve rapid response efforts and resource management to promote resilient communities along the wildland-urban interface. This thesis presents methods to improve emergency rapid predictions of post-fire streamflow and characterization of ecohydrological recovery after fire. A random forest machine learning algorithm with 45 catchment parameters was created to predict post-fire peak streamflow during the period 1920 to 2019. By incorporating additional characteristics about meteorological and catchment properties, the random forest, flood forecasting technique provided more realistic predictions of peak streamflow in relation to Rowe et al. (1949), a commonly used flood frequency method. The time elapsed after fire, peak hourly rainfall intensity, and drainage area were important factors that increased accuracy of the random forest predictions. To improve vegetation assessments and resource management, two satellite-based ET products, ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) Priestley-Taylor (PT-JPL) algorithm and Operational Simplified Surface Energy Balance Model (SSEBop), were used to evaluate conditions in relation to the 2018 Holy Fire. There was high uncertainty in post-fire ET between ECOSTRESS PT-JPL and SSEBop daily scaled ET due to the coarse spatial resolution of SSEBop and high spatial heterogeneity of the burn severity. To link recovery and hydrology, hydrologic signatures were quantified at the annual timescale for burned and unburned catchments. Post-fire water balance calculation for WY 2020 showed high uncertainty between ECOSTRESS PT-JPL and SSEBop, where differences in storage between catchments varied by over 500-mm depending on the model. Finally, ECOSTRESS PT-JPL was used to differentiate the landscape recovery by soil burn severity, vegetation species, slope aspect, and riparian area. The findings of this thesis improve upon our current methods in hydrologic modeling associated with fire in southern California.