Surface runoff can be estimated directly from conceptual models such as the curve number (CN) method or indirectly from physically-based infiltration models such as the Green-Ampt (GA) method. Both methods are widely accepted models for predicting surface runoff in both agricultural and urbanized watersheds due to their simplicities and the limited number of parameters required for runoff prediction. In addition, they have been integrated into many hydrologic, storm water management, and water-quality models (e.g., SWAT, SWMM, CREAMS, EPIC). The key parameters involved in the CN and the GA methods are the runoff curve number (CN) and the saturated hydraulic conductivity (Ksat), respectively, which can be obtained from tables as functions in soil texture, management practice, and land use. The use of singular tabulated CN and Ksat values without verification can result in large errors in predicting surface runoff and closing the water budget of a watershed. The frequent flooding in the Midwest over the past 2 decades (e.g., 1993 and 2008 floods) due to the global climate changes raised the need for revised CN and Ksat values to estimate accurately surface runoff and close the water budget of different watersheds. In this study, the authors reevaluated the range of the existing CN and Ksat values for the different hydrologic soil groups in the State of Iowa. Representative counties from Iowa with different soils were chosen to estimate the CN and Ksat. The study provided detailed methodological steps to estimate in-situ runoff CN and Ksat from rainfall simulators. The rainfall simulators proved to be useful instruments for estimating in-situ CN and Ksat because they eliminate the need of natural storm events and rainfall intensity can be adjusted during an experimental run to mimic natural rain. The study showed that the range of the estimated CN and Ksat values in summer agreed well with the reported values (deviation less than 6%). However, the range of the estimated CN and Ksat values in fall has higher deviation than the reported values (deviation of about 40%).