Climate change will profoundly affect hydrological processes at various temporal and spatial scales. This study is focused on assessing the alteration of water resources availability and low flows frequencies driven by changing climates in different time periods of the 21 st century. This study evaluates the adaptability of prevailing Global Circulation Models (GCMs) on a particular watershed through streamflow regimes. This analysis was conducted in the Great Miami River Watershed, Ohio by analyzing historical and future simulated streamflow using 10 climate model outputs and the Soil and Water Assessment Tool (SWAT). The climate change scenarios, consisting of ten downscaled Coupled Model Intercomparision Project Phase 5 (CMIP5) climate models in combination with two Representative Concentration Pathways (RCP 4.5 and RCP 8.5) were selected based on the correlation between observed records and model outputs. Streamflow for three future periods, 2016-2043, 2044-2071 and 2072-2099, were independently analyzed and compared with the baseline period . Results from the average of ten models projected that 7-day low flows in the watershed would increase by 19% in the 21 st century under both RCPs. This trend was also consistent for both hydrological (7Q10, 1Q10) and biological low flow statistics (4B3, 1B3). Similarly, average annual flow and monthly flows would also increase in future periods, especially in the summer. The flows simulated by SWAT in response to the majority of climate model projections showed a consistent increase in low flow patterns. However, the flow estimates using the Max-Planck-Institute Earth System Model (MPI-ESM-LR) climate output resulted in the biological based low flows (4B3, 1B3) decreasing by 22.5% and 33.4% under RCP 4.5 and 56.9% and 63.7% under RCP 8.5, respectively, in the future when compared to the baseline period. Regardless, the low flow ensemble from the 10 climate models for the 21 st century seemed to be slightly higher than that of historical low flows.

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Flooding is one of the most frequent natural disasters across the world, which damages properties and may take the lives of people. Flood warning systems can play a significant role in minimizing those effects by helping to evacuate people from the probable affected areas during peak flash flood times. Therefore, a conceptual approach of an automated flood warning system is presented in this research to protect several houses, roads, and infrastructures along the Grand River, which are vulnerable to flooding during a 500 year return period flash flood. The Grand River is a tributary of Lake Erie, which lies in the Grand River watershed in the northeastern region of the United States and has a humid continental climate and receives lake-effect precipitation. The flood warning system for the Grand River was developed specifically during high flow conditions by calculating flood travel time and generating the inundation mapping for 12 different selected flood stages, which were approximately 2 to 500 years in recurrence interval, ranging from 10 ft. to 21 ft. at gage station 04212100, near the City of Painesville, OH. A Hydraulic Engineering Center-River Analysis System (HEC-RAS) was utilized for hydraulic modeling. Geospatial data required for HEC-RAS was obtained using a Digital Elevation Model (DEM) derived from Light Detection and Ranging (LiDAR) datasets, which were pre-processed and post-processed in HEC-GeoRAS to produce flood inundation maps. The flood travel time and flood inundation maps were generated by integrating LiDAR data with field verified survey results in order to provide the evacuation lead time needed for the people of probable affected areas, which is different from earlier studies. The generated inundation maps estimate the aerial extent of flooding along the Grand River corresponding to the various flood stages at the gage station near the City of Painesville and Harpersfield. The inundation maps were overlaid on digital orthographic maps to visualize its aerial extents, which can be uploaded online to provide a real-time inundation warning to the public when the flood occurs in the river.

The main objective of this study was to quantify the error associated with input data, including various resolutions of elevation datasets and Manning's roughness for travel time computation and floodplain mapping. This was accomplished on the test bed, the Grand River (Ohio, USA) using the HEC-RAS model. LiDAR data integrated with survey data provided conservative predictions, whereas coarser elevation datasets provided a positive difference in the travel time (11.03-15.01%) and inundation area (32.56-44.52%). The minimum differences in travel time and inundation area were 0.50-4.33% and 3.55-7.16%, respectively, when the result from LiDAR integrated with survey data was compared with a 10-m DEM integrated with survey data. The results suggest that a 10-m DEM in the channel and LiDAR data in the floodplain combined with survey data would be appropriate for a flood warning system. Additionally, Manning's roughness of the channel section was found to be more sensitive than that of the floodplain. The decrease in inundation area was highest (8.97%) for the lower value of Manning's roughness.