High spatial resolution remote sensing for improved hydrologic modeling

Precise and accurate coverage and location of impervious surface area (ISA) are key parameters in environmental studies associated with urban and suburban landscape. This study developed an algorithm of multiple agent segmentation and classification (MASC) to extract high spatial resolution ISA information. The MASC algorithm achieves more precise and accurate information on ISA distribution compared with conventional classification techniques. Based on the MASC algorithm, this study established the 1-meter high spatial resolution ISA dataset for the state of Rhode Island and revealed its spatial patterns. It is important to develop scientific basis for observing and modeling watershed hydrologic dynamic with emphasis on how the hydrologic processes are affected by the spatial heterogeneity of ISA. This study developed a distributed object-oriented rainfall-runoff simulation (DORS) model to enhance the capability and performance of hydrologic simulation with incorporation of high spatial resolution ISA information. This study used USGS stream gaging data to validate the temporal variation of simulated discharge in two watersheds. The results indicate that the DORS model performs well for the purpose of modeling the runoff and base flow in complex watersheds and the performance of hydrologic simulation is improved with the incorporation of high spatial resolution ISA. Change of watershed hydrology is one of the most important and direct impacts from increasing ISA. This study performed hydrologic simulation using DORS model in twenty watersheds at HUC 12 level with various degrees of urbanization in the state of Rhode Island. This study built indicators of hydrology pattern including ratio of runoff to base flow and discharge per area from hydrologic simulations, and watersheds characteristics including percentage of ISA, distance from ISA to streams, and stream density. This study employed spatial error model and spatial lag model, as well as regular regression model, to analyze the relationship between watersheds characteristics and hydrology pattern. The results demonstrate that ISA plays most important role in watershed hydrology compared with other indicators. The results from this study, such as the developed MASC algorithm and DORS model, statewide ISA dataset, spatial pattern of ISA, and relationship between watershed characteristics and hydrology pattern, provide useful information for decision-making activities related to watershed management