Renee Murch’s professional experience has focused on water resources, hydrology, and civil infrastructure. Her areas of expertise include the development and application of hydraulic, hydrologic, and statistical models to support minimum flow and level development (MFL), restoration of surface water resources, evaluation of saltwater and freshwater interaction, simulation of regional- and local-scale hydrologic conditions as part of water resource planning efforts, and assessment of scouring and erosion processes associated with the construction of bridges and other civil infrastructure. Renee has specialized expertise in the development and application of statistical models including multiple linear regression, artificial neural networks, and Markov Chain Monte Carlo probabilistic simulations. Her work has focused on surface water, groundwater, and integrated modeling applications using modeling applications such as HEC-RAS, MODFLOW, HSPF, ELM, and the Integrated Hydrologic Model. She has evaluated radiological and hydrologic data using methods such as principal component analysis, agglomerative hierarchical cluster analysis, analysis of variance, bivariate correlation, multivariate regression, artificial neural networks, and hypothesis testing. Renee’s experience also includes the application of geographic information system tools for data analysis and model input development and hydrologic data collection. She has a variety of field experience related to instrumentation and data collection on both water resources- and geotechnical-related projects, including the installation and maintenance of well transducers, weather stations, evaporation pans, and stream stage, runoff test bed, soil moisture, and tide gauges. Her current work is focused on the development, calibration, and application of surface water, groundwater, and statistical models using applications such as MODFLOW, HEC-RAS, CE-QualW2, ELM, HSPF, IHM, SPLUS, and R to support water supply planning and MFL development.