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Scott Ensign

Stroud Water Research Center | Assistant Director, Research Scientist

Subject Areas: river ecosystem ecology

 Recent Activity

ABSTRACT:

This resource demonstrates the workflow developed to prepare downscaled GCM data for input to Model My Watershed. GCM data for the Delaware River Basin was assembled from 19 GCMs including each model's RCP4.5 and RCP8.5; this was performed by Dr. Tim Hawkins, Shippensburg University. Downscaled precipitation data from global climate models (GCM) does not accurately retain the magnitude and frequency of individual storm events for a given location. This lack of predictive resolution of event magnitude and frequency limits realism of rainfall-runoff models used to for predicting watershed hydrology under future climate scenarios. To address this problem, Maimone et al (2019) developed a method for summarizing the statistical distribution of precipitation event magnitude and frequency that could be applied to downscaled GCM precipitation predictions. Application of the methods here to down-scaled GCM scenarios requires that the those predictions do not include an increase in the number of days of precipitation per year. Maimone et al (2019) state this requirement: "Because GCM projections for the Philadelphia region do not indicate an increase in the number of wet days per year, future increases in precipitation are the result of the existing number and distribution of wet days becoming more intense."

There are three sections of the R Markdown document that contains the workflow and examples. The first section seeks to replicate the synthetic weather generator developed by Maimone et al (2019) using an example dataset. The second section applies those methods to the downscaled GCM ensamble average conditions for the Delaware River Basin provided by Dr. Hawkins. The third section develops depth-duration-frequency statistics for the 24 hour storm event relevant to the 2080-2100 predictions. To open the R Markdown document and execute the workflow yourself, find the Open With dropdown list in the upper right hand corner of the screen and select CUAHSI JupyterHub.

The first section uses an example precipitation dataset from the Philadelphia Airport for the period 01 January 1995 through 31 December 2013. The data were downloaded from NOAA's Climate Data Online Search portal: https://www.ncdc.noaa.gov/cdo-web/search.

The downloaded data and metadata for this NOAA Climate Data are available on Hydroshare here: http://www.hydroshare.org/resource/60058ceda8334e68be141516c5b8de3f.
Additional data on precipitation frequency at the Philadelphia Airport was downloaded from the NOAA Hydrometeorological Design Studies Center: https://hdsc.nws.noaa.gov/hdsc/pfds/index.html.

An example of working with this type of NOAA Climate Data is provided on the NEON website here:
https://www.neonscience.org/da-viz-coop-precip-data-R.

References:
Maimone, M., S. Malter, J. Rockwell, and V. Raj. 2019. Transforming Global Climate Model Precipitation Output for Use in Urban Stormwater Applications. Journal of Water Resources Planning and Management 145:04019021.

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WCC SMC Nutrient Management
Created: May 20, 2019, 6:52 p.m.
Authors: Scott Ensign

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asd

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example project
Created: June 11, 2019, 4:40 p.m.
Authors: Scott Ensign

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example

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Composite Resource Composite Resource
Modeling Future Climate for Model My Watershed
Created: Jan. 21, 2020, 9:27 p.m.
Authors: Ensign, Scott

ABSTRACT:

This resource demonstrates the workflow developed to prepare downscaled GCM data for input to Model My Watershed. GCM data for the Delaware River Basin was assembled from 19 GCMs including each model's RCP4.5 and RCP8.5; this was performed by Dr. Tim Hawkins, Shippensburg University. Downscaled precipitation data from global climate models (GCM) does not accurately retain the magnitude and frequency of individual storm events for a given location. This lack of predictive resolution of event magnitude and frequency limits realism of rainfall-runoff models used to for predicting watershed hydrology under future climate scenarios. To address this problem, Maimone et al (2019) developed a method for summarizing the statistical distribution of precipitation event magnitude and frequency that could be applied to downscaled GCM precipitation predictions. Application of the methods here to down-scaled GCM scenarios requires that the those predictions do not include an increase in the number of days of precipitation per year. Maimone et al (2019) state this requirement: "Because GCM projections for the Philadelphia region do not indicate an increase in the number of wet days per year, future increases in precipitation are the result of the existing number and distribution of wet days becoming more intense."

There are three sections of the R Markdown document that contains the workflow and examples. The first section seeks to replicate the synthetic weather generator developed by Maimone et al (2019) using an example dataset. The second section applies those methods to the downscaled GCM ensamble average conditions for the Delaware River Basin provided by Dr. Hawkins. The third section develops depth-duration-frequency statistics for the 24 hour storm event relevant to the 2080-2100 predictions. To open the R Markdown document and execute the workflow yourself, find the Open With dropdown list in the upper right hand corner of the screen and select CUAHSI JupyterHub.

The first section uses an example precipitation dataset from the Philadelphia Airport for the period 01 January 1995 through 31 December 2013. The data were downloaded from NOAA's Climate Data Online Search portal: https://www.ncdc.noaa.gov/cdo-web/search.

The downloaded data and metadata for this NOAA Climate Data are available on Hydroshare here: http://www.hydroshare.org/resource/60058ceda8334e68be141516c5b8de3f.
Additional data on precipitation frequency at the Philadelphia Airport was downloaded from the NOAA Hydrometeorological Design Studies Center: https://hdsc.nws.noaa.gov/hdsc/pfds/index.html.

An example of working with this type of NOAA Climate Data is provided on the NEON website here:
https://www.neonscience.org/da-viz-coop-precip-data-R.

References:
Maimone, M., S. Malter, J. Rockwell, and V. Raj. 2019. Transforming Global Climate Model Precipitation Output for Use in Urban Stormwater Applications. Journal of Water Resources Planning and Management 145:04019021.

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