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Sensitivity of Simulating Hydrologic Processes to Gauge and Radar Rainfall Data in Subtropical Coastal Catchments

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  • Jairo Diaz-Ramirez
  • William McAnally
  • James Martin

Abstract

This research evaluated the impact of three rainfall datasets on hydrologic process simulations in two coastal catchments located in Alabama. In this study, rain gauge time series recorded by the U.S. National Oceanic and Atmospheric Administration (NOAA) and Geological Survey (USGS) along with radar precipitation data derived from NOAA National Weather Service (NWS) were input into the Hydrological Simulation Program—FORTRAN (HSPF). Automatic parameter calibration was performed using daily streamflow data recorded at USGS Fish and Magnolia River gauge stations from 07/01/2002 to 12/31/2008. HSPF parameters were optimized using the Model-Independent Parameter Estimation (PEST) program. Model parameter ranges were refined by incorporating physical characteristic of the study areas and after analyzing observed streamflow time series. This approach, in turn, helped PEST optimization tool to find the most physically-related set of parameters that can be transferred to any watershed with similar characteristics and minimum parameter calibration. On average, annual USGS and radar rainfall values were around 480 mm and 250 mm, respectively lower than NOAA precipitation records. Overall, it is found that the NOAA precipitation input data resulted in better daily flow simulations than results from radar and USGS rainfall time series. Streamflows derived from USGS rainfall time series showed the worst model performance at both catchment outlets because of missing data, low amounts, and temporal delay of peaks. This study found that annual actual evapotranspiration values were closed among rainfall time series and varied from 900 to 958 mm. Deep percolation values for Magnolia and Fish River, regardless of rainfall source, ranged from 66 to 192 mm/year. Major discrepancies were found at storm runoff values. Gauge rainfall time series yielded the closest streamflow values compared to observed flow time series at both watershed outlets. Rainfall derived from radar yielded consistent and acceptable runoff results in Fish and Magnolia River models. In both case studies, the high spatial variability of rainfall storm events was not adequately captured by any of the rainfall datasets and yielded high uncertainty in model results. Copyright Springer Science+Business Media B.V. 2012

Suggested Citation

  • Jairo Diaz-Ramirez & William McAnally & James Martin, 2012. "Sensitivity of Simulating Hydrologic Processes to Gauge and Radar Rainfall Data in Subtropical Coastal Catchments," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(12), pages 3515-3538, September.
    • Handle: RePEc:spr:waterr:v:26:y:2012:i:12:p:3515-3538
    DOI: 10.1007/s11269-012-0088-z
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    References listed on IDEAS

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    1. Meiyan Yu & Xi Chen & Lanhai Li & Anming Bao & Mupenzi Paix, 2011. "Streamflow Simulation by SWAT Using Different Precipitation Sources in Large Arid Basins with Scarce Raingauges," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(11), pages 2669-2681, September.
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    Cited by:

    1. Xi Chen & Tao Yang & Xiaoyan Wang & Chong-Yu Xu & Zhongbo Yu, 2013. "Uncertainty Intercomparison of Different Hydrological Models in Simulating Extreme Flows," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(5), pages 1393-1409, March.
    2. Chuan Luo & Zhaofu Li & Hengpeng Li & Xiaomin Chen, 2015. "Evaluation of the AnnAGNPS Model for Predicting Runoff and Nutrient Export in a Typical Small Watershed in the Hilly Region of Taihu Lake," IJERPH, MDPI, vol. 12(9), pages 1-19, September.
    3. Chih-Chiang Wei, 2020. "Real-time Extreme Rainfall Evaluation System for the Construction Industry Using Deep Convolutional Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(9), pages 2787-2805, July.
    4. Simon Deslauriers & Tew-Fik Mahdi, 2018. "Flood modelling improvement using automatic calibration of two dimensional river software SRH-2D," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 91(2), pages 697-715, March.
    5. Zhaofu Li & Chuan Luo & Kaixia Jiang & Rongrong Wan & Hengpeng Li, 2017. "Comprehensive Performance Evaluation for Hydrological and Nutrients Simulation Using the Hydrological Simulation Program–Fortran in a Mesoscale Monsoon Watershed, China," IJERPH, MDPI, vol. 14(12), pages 1-18, December.
    6. Hai-Long Liu & An-Ming Bao & Xiang-Liang Pan & Xi Chen, 2013. "Effect of Land-Use Change and Artificial Recharge on the Groundwater in an Arid Inland River Basin," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(10), pages 3775-3790, August.
    7. Andrzej WALEGA & Leszek KSIAZEK, 2016. "Influence of rainfall data on the uncertainty of flood simulation," Soil and Water Research, Czech Academy of Agricultural Sciences, vol. 11(4), pages 277-284.

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