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      <namePart>Rubinstein, Benjamin J.</namePart>
   </name>
   <titleInfo>
      <title>The Effect of Time Step on HSPF Model Performance</title>
   </titleInfo>
   <originInfo>
      <dateCreated keyDate="yes">2023</dateCreated>
   </originInfo>
   <note displayLabel="Degree Awarded">Spring 2023</note>
   <typeOfResource authority="aat" valueURI="http://vocab.getty.edu/page/aat/300028029">Thesis</typeOfResource>
   <name type="corporate">
      <affiliation>Illinois Institute of Technology</affiliation>
   </name>
   <name type="corporate">
      <namePart>CAEE / Civil, Architectural, and Environmental Engineering</namePart>
   </name>
   <name>
      <role>
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      <namePart>Lampert, David J.</namePart>
   </name>
   <subject>
      <topic>Environmental engineering</topic>
   </subject>
   <subject>
      <topic>HSPF</topic>
   </subject>
   <subject>
      <topic>hydrological modeling</topic>
   </subject>
   <subject>
      <topic>hydrology</topic>
   </subject>
   <subject>
      <topic>Oklahoma Mesonet</topic>
   </subject>
   <subject>
      <topic>Python</topic>
   </subject>
   <subject>
      <topic>time step</topic>
   </subject>
   <language>
      <languageTerm type="code" authority="rfc3066">en</languageTerm>
   </language>
   <abstract>Hydrological modeling is a mature and well researched field; however, because most climate data are collected on hour or greater time intervals there is very little research on the effect of using high resolution data as inputs for the models. A Python tool for downloading high resolution five minute interval data from the Oklahoma Mesonet was created and the PyHSPF Python package was used to generate, calibrate, and validate HSPF models using five minute, one hour, and daily time steps. Flow errors, R², and Nash-Sutcliffe efficiency for simulated outflows, and resource usage were compared for each model. The hourly and five minute models performed similarly well, and the daily model performed significantly worse. The results of this work could prove useful for policy makers and researchers looking to update or create new climate data collection protocols, and the tools used can be applied to many different kinds of future research.</abstract>
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<identifier type="hdl">http://hdl.handle.net/10560/islandora:1024342</identifier></mods>
