Application of NEXRAD Radar-Based Quantitative Precipitation Estimations for Hydrologic Simulation Using ArcPy and HEC Software

Younghyun Cho

doi:10.3390/w12010273

Abstract

Recent availability of various spatial data, especially for gridded rainfall amounts, provide a great opportunity in hydrological modeling of spatially distributed rainfall–runoff analysis. In order to support this advantage using gridded precipitation in hydrological application, (1) two main Python script programs for the following three steps of radar-based rainfall data processing were developed for Next Generation Weather Radar (NEXRAD) Stage III products: conversion of the XMRG format (binary to ASCII) files, geo-referencing (re-projection) with ASCII file in ArcGIS, and DSS file generation using HEC-GridUtil (existing program); (2) eight Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) models of ModClark and SCS Unit Hydrograph transform methods for rainfall–runoff flow simulations using both spatially distributed radar-based and basin-averaged lumped gauged rainfall were respectively developed; and (3) three storm event simulations including a model performance test, calibration, and validation were conducted. For the results, both models have relatively high statistical evaluation values (Nash–Sutcliffe efficiency—ENS 0.55–0.98 for ModClark and 0.65–0.93 for SCS UH), but it was found that the spatially distributed rainfall data-based model (ModClark) gives a better fit regarding observed streamflow for the two study basins (Cedar Creek and South Fork) in the USA, showing less requirements to calibrate the model with initial parameter values. Thus, the programs and methods developed in this research possibly reduce the difficulties of radar-based rainfall data processing (not only NEXRAD but also other gridded precipitation datasets—i.e., satellite-based data, etc.) and provide efficiency for HEC-HMS hydrologic process application in spatially distributed rainfall–runoff simulations.

Highlights

In watershed-scale hydrologic modeling, the required inputs of watershed characteristics and precipitation data are readily available on various public websites, including the National Oceanic and Atmospheric Administration (NOAA) National Weather Service (NWS) Advanced Hydrologic Predictions Service (AHPS) in the U.S, which provides remotely sensed rainfall such as the Weather Surveillance Radar–1988 Doppler (WSR-88D) used Generation
Hydrologic Rainfall Analysis Project (HRAP) grid into an standard hydrologic grid (SHG) grid the file name of which is “shg_MMDDYYYYHHz.asc”
Python script programs were developed for radar rainfall data type conversion and data map projections

Summary

Introduction

In watershed-scale hydrologic modeling, the required inputs of watershed characteristics (i.e., elevation, land use, soil, etc.) and precipitation data are readily available on various public websites, including the National Oceanic and Atmospheric Administration (NOAA) National Weather Service (NWS) Advanced Hydrologic Predictions Service (AHPS) in the U.S, which provides remotely sensed rainfall such as the Weather Surveillance Radar–1988 Doppler (WSR-88D) used GenerationWeather Radar (NEXRAD) radar-based quantitative precipitation estimations (QPEs) for weather and flash flood forecasts, etc. [1,2]. It provides great opportunities to simulate hydrologic model processes enabling the use of grid-based spatially distributed precipitation instead of point-based rain gauge observations for non-uniform landscapes and storm events as they exist in nature [3,4]. The areal rainfall data from gauges which are estimated by an averaged method (e.g., Thiessen polygon, etc.) have been adopted for simulating the rainfall–runoff process in a watershed as adequate input with its relatively simple application procedure rather than radar-based data, which requires rather complicated data processing. A hydrologic model which enables the use of radar-based high spatiotemporal resolution precipitation and the implementation of spatially distributed rainfall–runoff simulations is needed to be developed in order to gain the advantage of flow computations with adequate temporal and fine spatial resolution data

Methods

Results

Conclusion