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Las Vegas valley has undergone significant development, thus increasing urban flooding. This study analyzes the impacts of urban development on urban flooding in the Flamingo watershed by using a watershed model. The input data includes precipitation, soil characteristics, elevation, and land cover. Urban development is incorporated through increasing percent impervious. Sub-watersheds and streamlines were delineated in ArcGIS using digital elevation model (DEM) dataset. Natural Resources Conservation Service (NRCS) curve-number method was used for the calculation of runoff. The Hydrologic Engineering Center-Hydrologic Management System (HEC-HMS) was used to estimate the discharge hydrograph. The model was calibrated through changing the curve number of the sub-basins. Two urbanization scenarios created with a 5% and 10% increase in impervious surfaces were generated. The results showed that peak discharge occurred earlier due to increase in impervious surfaces. Moreover, the total discharge volume and peak discharge for a given storm event were increasing due to increased imperviousness from urbanization. This study provides useful insight into a hydrological response to urban development that can be helpful in flood remediation. 

Abstract. This paper studies how to improve the accuracy of hydrologic models using machine-learning models as post-processors and presents possibilities to reduce the workload to create an accurate hydrologic model by removing the calibration step. It is often challenging to develop an accurate hydrologic model due to the time-consuming model calibration procedure and the nonstationarity of hydrologic data. Our findings show that the errors of hydrologic models are correlated with model inputs. Thus motivated, we propose a modeling-error-learning-based post-processor framework by leveraging this correlation to improve the accuracy of a hydrologic model. The key idea is to predict the differences (errors) between the observed values and the hydrologic model predictions by using machine-learning techniques. To tackle the nonstationarity issue of hydrologic data, a moving-window-based machine-learning approach is proposed to enhance the machine-learning error predictions by identifying the local stationarity of the data using a stationarity measure developed based on the Hilbert–Huang transform. Two hydrologic models, the Precipitation–Runoff Modeling System (PRMS) and the Hydrologic Modeling System (HEC-HMS), are used to evaluate the proposed framework. Two case studies are provided to exhibit the improved performance over the original model using multiple statistical metrics.