Harper Wavra
Harper Wavra is a Computer Scientist with Web Informatics and Mapping (WIM) as part of the Upper Midwest Water Science Center.
Professional Experience
2022-present, Computer Scientist, USGS Upper Midwest Water Science Center
2020-2022, Hydrologic Technician, USGS Dakota Water Science Center.
2015-2020, Student (admin), USGS Dakota Water Science Center.
Education and Certifications
Bachelor of Science - Computer Science, 2020, University of North Dakota, Grand Forks, North Dakota
Science and Products
Peak Streamflow Data, Climate Data, and Results from Investigating Hydroclimatic Trends and Climate Change Effects on Peak Streamflow in the Central United States, 1921–2020
Peak-flow frequency analysis is crucial in various water-resources management applications, including floodplain management and critical structure design. Federal guidelines for peak-flow frequency analyses, provided in Bulletin 17C, assume that the statistical properties of the hydrologic processes driving variability in peak flows do not change over time and so the frequency distribution of annu
Introduction and methods of analysis for peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin
Flood-frequency analysis, also called peak-flow frequency or flood-flow frequency analysis, is essential to water resources management applications including critical structure design and floodplain mapping. Federal guidelines for doing flood-frequency analyses are presented in a U.S. Geological Survey Techniques and Methods Report known as Bulletin 17C. A basic assumption within Bulletin 17C is t
Authors
Karen R. Ryberg, Thomas M. Over, Sara B. Levin, David C. Heimann, Nancy A. Barth, Mackenzie K. Marti, Padraic S. O'Shea, Christopher A. Sanocki, Tara J. Williams-Sether, Harper N. Wavra, T. Roy Sando, Steven K. Sando, Milan S. Liu
Estimated daily mean streamflow in Iowa using the Flow-Duration Curve Transfer Method StreamStats application
The U.S. Geological Survey (USGS) operates many streamgages throughout the country that provide historical and real-time daily streamflow information. Accurate estimates of daily streamflow and the percentage of time that a certain volume of streamflow occurs or is exceeded in a stream is crucial information for structure design and other activities conducted by federal, state, and local officials
Authors
Mackenzie K. Marti, Harper N. Wavra, Andrea Medenblik
GageStats Services
The Gage Statistic (GageStats) Services were developed to provide gage characteristics and streamflow statistics to support the StreamStats application via RESTful principles. The StreamStats application uses GageStats Services to display the gages and related gage pages. These services provide U.S. Geological Survey developed and published gage characteristics such as drainage area or mean basin
StreamStats Channel Width Weighting Services
This software release contains Python functions for weighting multiple non-independent estimates of a variable. Weighting is determined by the standard error of prediction of each estimate and the correlation between the estimation methods. The methodology for weighting estimates from either two or three different methods is described in Chase and others (2020). A function is also included to han
A National Tool for Graphing and Synthesizing Continuous and Discrete Water-Quality Data
Provide synthesis of water quality data to better understand the Nation’s water resources
Science and Products
Peak Streamflow Data, Climate Data, and Results from Investigating Hydroclimatic Trends and Climate Change Effects on Peak Streamflow in the Central United States, 1921–2020
Peak-flow frequency analysis is crucial in various water-resources management applications, including floodplain management and critical structure design. Federal guidelines for peak-flow frequency analyses, provided in Bulletin 17C, assume that the statistical properties of the hydrologic processes driving variability in peak flows do not change over time and so the frequency distribution of annu
Introduction and methods of analysis for peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin
Flood-frequency analysis, also called peak-flow frequency or flood-flow frequency analysis, is essential to water resources management applications including critical structure design and floodplain mapping. Federal guidelines for doing flood-frequency analyses are presented in a U.S. Geological Survey Techniques and Methods Report known as Bulletin 17C. A basic assumption within Bulletin 17C is t
Authors
Karen R. Ryberg, Thomas M. Over, Sara B. Levin, David C. Heimann, Nancy A. Barth, Mackenzie K. Marti, Padraic S. O'Shea, Christopher A. Sanocki, Tara J. Williams-Sether, Harper N. Wavra, T. Roy Sando, Steven K. Sando, Milan S. Liu
Estimated daily mean streamflow in Iowa using the Flow-Duration Curve Transfer Method StreamStats application
The U.S. Geological Survey (USGS) operates many streamgages throughout the country that provide historical and real-time daily streamflow information. Accurate estimates of daily streamflow and the percentage of time that a certain volume of streamflow occurs or is exceeded in a stream is crucial information for structure design and other activities conducted by federal, state, and local officials
Authors
Mackenzie K. Marti, Harper N. Wavra, Andrea Medenblik
GageStats Services
The Gage Statistic (GageStats) Services were developed to provide gage characteristics and streamflow statistics to support the StreamStats application via RESTful principles. The StreamStats application uses GageStats Services to display the gages and related gage pages. These services provide U.S. Geological Survey developed and published gage characteristics such as drainage area or mean basin
StreamStats Channel Width Weighting Services
This software release contains Python functions for weighting multiple non-independent estimates of a variable. Weighting is determined by the standard error of prediction of each estimate and the correlation between the estimation methods. The methodology for weighting estimates from either two or three different methods is described in Chase and others (2020). A function is also included to han
A National Tool for Graphing and Synthesizing Continuous and Discrete Water-Quality Data
Provide synthesis of water quality data to better understand the Nation’s water resources