Abstract
Flooding induced by extreme rainfall events causes tremendous loss of life and property and infrastructure failure. Accurate representation of precipitation, which has high variation in space and time, is critical to hydrologic model simulations and flood analyses. In this study, we examined responses of differently sized United States Geological Survey (USGS) hydrologic units to heavy precipitation using three different data sets. The first consists of rainfall observed at individual meteorological gauges. The second uses the National Centers for Environmental Prediction (NCEP) Environmental Modeling Center (EMC) 4 km gridded radar-estimated precipitation (GRIB) Stage IV data. The third one derives from the method we developed that blends gauge data with the spatial coverage of the Parameter-elevation Relationships on Independent Slopes Model (PRISM) data. We examined how two watersheds in South Carolina respond to the three different representations of heavy rainfall, using the Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) developed by the U.S. Army Corps of Engineers. We found that the latter two precipitation inputs that consider spatial representation of rainfall yielded similar performance and improved simulated streamflow as compared to simulation using rainfall observed at individual meteorological gauges. The method we developed overcomes the spatial sparsity of rain gauges required for interpolation and extends availability of precipitation surfaces. Our study advances the understanding of advantages and limitations of different precipitation products for flood simulation.
Highlights
Flooding is one of the most frequently occurring and costly natural hazards in the world, causing death and injury, displacement of communities, and loss of private and public properties [1]
Land use and land cover information was obtained from the National Land Cover Dataset 2011 created by United States Geological Survey (USGS). e gridded curve number (CN) technique suggested by the Soil Conservation Service (SCS), which enables spatially distributed infiltration calculations, was used to simulate infiltration loss. e CN technique quantifies the infiltration capacity area based on land use, soil and land cover type, and hydrologic soil group [37]
We investigated the effects of three precipitation sources on flood simulation including (1) station observations, (2) radar data, and (3) station observations adjusted by the Parameter-elevation Relationships on Independent Slopes Model (PRISM) data which provide daily total precipitation in the continental United States, from 1981 to present [36]
Summary
Flooding is one of the most frequently occurring and costly natural hazards in the world, causing death and injury, displacement of communities, and loss of private and public properties [1]. E most widely used source of rainfall data is observations at rain gauges, which provide direct measurement of precipitation intensity and duration at individual points. Rain gauge observations inadequately capture the spatial and temporal variability of short-duration storm events, especially in small catchments, limiting the accuracy of streamflow simulation [18]. E comparison of hydrologic simulations using radar and gauge precipitation data involves the spatial and temporal resolution and coverage of the rainfall data, model mechanisms (e.g., simplicity or complexity and parameterization), and watershed characteristics (e.g., size and climatologic and physiographic settings) [15, 32]. We assess the suitability of different precipitation data sources that are constrained by spatial and temporal coverage and resolutions of these data in the flood simulation using HEC-HMS for South Carolina. We propose a new method that integrates merits of precipitation gauge data and the widely used gridded daily Parameter-elevation Relationships on Independent Slopes Model (PRISM) [36] data to overcome the sparsity of rain gauge data and test the effectiveness of the new method
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