Abstract
With the increase in global urbanization, satellite imagery and other types of geospatial data have been extensively used in urban landscape change research, which includes environmental modeling in order to assess the change impact on urban watersheds. For urban hydrological modeling, as a focus of this study, several related research questions are raised: (1) How sensitive are runoff simulation to land use and land cover change patterning? (2) How will input data quality impact the simulation outcome? (3) How effective is integrating and synthesizing various forms of geospatial data for runoff modeling? These issues were not fully or adequately addressed in previous related studies. With the aim of answering these questions as research objectives, we conducted a spatial land use and land cover (LULC) change analysis and an urban runoff simulation in the Blue River watershed in the Kansas City metropolitan area between 2003 and 2017. In this study, approaches were developed to incorporate the Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS) model with remote sensing, geographic information systems (GIS), and radar rainfall data. The impact of data quality on the model simulation outcome was also analyzed. The results indicate that there are no significant differences between simulated runoff responses in the two study years (2003 and 2017) due to spatial and temporal heterogeneity of urbanization processes in the region. While the metropolitan area has been experiencing remarkable urban development in the past few decades, the gain in built-up land in the study watershed during the study period is insignificant. On the other hand, the gain in vegetated land caused by forestation activities is offset by a decrease in farmland and grassland. The results show that increasing spatial data resolution does not necessarily or noticeably improve the HEC-HMS model performance or outcomes. Under these conditions, using Next Generation Weather Radar (NEXRAD) rainfall data in the simulation provides a satisfactory fit in hydrographs’ shapes, peak discharge amounts and time after calibration efforts, while they may overestimate the amount of rainfall as compared with gauge data. This study shows that the developed approach of synthesizing satellite, GIS, and radar rainfall data in hydrological modeling is effective and useful for incorporating urban landscape and precipitation change data in dynamic flood risk assessment at a watershed level.
Highlights
Flooding is one of the most severe and damaging natural hazards
This study shows that the developed approach of synthesizing satellite, geographic information systems (GIS), and radar rainfall data in hydrological modeling is effective and useful for incorporating urban landscape and precipitation change data in dynamic flood risk assessment at a watershed level
Urban runoff was simulated in an urban watershed that has diverse land use and land cover (LULC) and development activities, and its streams tend to be frequently flooded
Summary
Flooding is one of the most severe and damaging natural hazards. As revealed by the EmergencyEvents Database (EM-DAT) Center for Research on the Epidemiology of Disasters (CRED) [1], from 2005 to 2014, floods accounted for 46% of all natural disasters and affected about 85 million people.During the 20th century, floods were the number-one natural disaster in the United States in terms ofWater 2020, 12, 2715; doi:10.3390/w12102715 www.mdpi.com/journal/waterWater 2020, 12, 2715 the number of lives lost and property damage [2]. Agency (FEMA), from 1978 to 2016, the total amount of paid losses for significant flood events was about USD 46.6 billion. This may indicate that climate change is responsible for the increase in flood occurrence and damages, this process is primarily intensified by urbanization [3]. The scenario of urbanization is that impervious surfaces, including roads, sidewalks, parking lots, airports, buildings, etc., are replacing the natural soil layer and, as a result, reducing infiltration, which leads to a decrease in travel time and the generation of rapid overland flow.
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