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• An integrated groundwater – surface water model is developed for a 2056 km2 watershed. • Sub-mesh scale topographic detail utilizes a spatially distributed parameterization. • Topographic depression storage, soil permeability, and land roughness are evaluated. • Depression storage had the strongest influence over flood peak flow reduction. • Landscape factors that promote flood mitigation also enhance groundwater recharge. Optimizing hydrologic resiliency in agricultural landscapes is critical for the sustainability of food production and ecosystem services, and optimization strategies must include a holistic understanding of the hydrologic cycle, including groundwater-surface water interactions. Here, a large-scale (2056 km2), high resolution (50–150 m), structurally complex, fully-integrated groundwater-surface water model was constructed for an agriculturally-dominated watershed in the Northern Great Plains. The sensitivity of surface water flow and groundwater conditions were examined in the context of wetland/depression water storage capacity, soil hydraulic conductivity (K s), field surface roughness (Manning's n), as well as variability in precipitation and potential evapotranspiration (PET). The model evaluation interval extended from 2010 to 2015, which included both flood and drought conditions. Results showed that stream flow rates in the watershed exhibit a larger response to 10 % changes in either precipitation or PET relative to the response associated with a 50 % change in depression storage, a 4x change in K s , or a 50 % change in n , thus highlighting the region's susceptibility to weather variability. Of the landscape parameters examined, increasing depression storage provided a greater reduction in peak flood flows than an increase in K s or n ; however, increases in all three landscape parameters contributed to flood peak mitigation as well as groundwater recharge. When model configuration approximated predevelopment (primarily native prairie) landscape conditions, simulated flood peaks were reduced by 22 to 25 %. This study presents a fully-integrated groundwater-surface water modeling framework to determine the hydrologic influences and risk mitigation value of landscape management practices. [ABSTRACT FROM AUTHOR]