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Comprehensive study on parameter sensitivity for flow and nutrient modeling in the Hydrological Simulation Program

Chuan Luo1, Zhaofu Li2, Min Wu1

  • 1College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing, 210095, China.

Environmental Science and Pollution Research International
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PubMed
Summary
This summary is machine-generated.

This study simplified Hydrological Simulation Program Fortran (HSPF) model calibration by identifying key flow and nutrient parameters using differential sensitivity analysis (DSA). Key parameters influencing flow and nutrients were pinpointed, improving model accuracy.

Keywords:
DSAHSPF modelHydrologyNutrientsParameter sensitivity analysis

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Area of Science:

  • Environmental Hydrology
  • Water Quality Modeling
  • Computational Science

Background:

  • The Hydrological Simulation Program Fortran (HSPF) model is complex, making calibration challenging.
  • Parameter sensitivity analysis is crucial for simplifying model calibration by identifying influential parameters.

Purpose of the Study:

  • To investigate the sensitivity of HSPF flow and nutrient parameters in the Xitiaoxi watershed, China.
  • To simplify HSPF model calibration through parameter sensitivity analysis.

Main Methods:

  • Employed differential sensitivity analysis (DSA) to assess parameter sensitivity.
  • Analyzed sensitivity of both flow and nutrient parameters within the HSPF model.

Main Results:

  • Flow is primarily influenced by groundwater and evapotranspiration parameters (e.g., DEEPFR, LZETP, AGWRC).
  • Nutrient components are sensitive to land process parameters (e.g., MON-SQOLIM, MON-ACCUM) and river system parameters (e.g., KATM20, MALGR).
  • Calibrating sensitive parameters improved model performance, with R² and Nash-Sutcliffe efficiency (ENS) > 0.75 for flow and > 0.5 for nutrients.

Conclusions:

  • Identified key parameters for HSPF flow and nutrient simulation.
  • The findings aid in simplifying HSPF model calibration and provide a reference for sensitivity analyses in similar models.