Courtenay Strong

University of Utah

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ABSTRACT:

The results included are based on the HSPF model simulations developed for the Jordan River watersehds. The base model used to simulate was developed by a consultant (STANTEC in 2010/2011) and employed by the Salt Lake County, Utah. The model was calibrated and statistical results were checked only for the Big Cottonwood Canyons at the Canyons Mouth and other several location in the Jordan River. The model was applied to study climate and land use change in March 2017. The historical time periods considered are Jan1 1995 to Dec 31,2004. The calibration time period considered for the streamflow is Jan1,2005 to Dec 31,2006 (it varies for other water quality parameters considering the data availability). Future simulations include 2035 to 2044 and 2085 to 2094. The model results were simulated in an hourly time steps and this resource has the daily results. The results included are only for the climate change scenarios as the canyons have negligible effects of the land use and land cover changes. The three scenarios considered are based on the RCP6 climate scenario that was dynamically downscaled using the Weather Research & Forecasting (WRF) model and statistically downscaled two climate scenarios corresponding to the mean of the driest and wettest quartiles of the statistically downscaled CMIP5 database at https://gdo-dcp.ucllnl.org/downscaled_cmip_projections/dcpInterface.html .

For each streamflow tab in the file, the Observed column indicates results forcing the model with station observations. Future simulation columns labeled Min_* provide results for the mean of the driest quartile of CMIP5 simulations, RCP_* provide results for the WRF simulation of RCP 6.0, and Max_* provide results for the mean of the wettest quartile of CMIP5 simulations. The second tab in the file provides sample results from the HSPF standard calibration procedure.

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ABSTRACT:

This collection of MATLAB scripts runs the temperature component of the stochastic harmonic autoregressive parametric (SHArP) weather generator, which simulates trended, nonstationary precipitation occurrence, precipitation amount, and temperature values. The precipitation component is not included here because it is based off of previous weather generators and doesn't use any new methods. The temperature component follows a concept introduced in previous studies, but SHArP simulates temperature values directly as opposed to simulating the temperature residuals. SHArP weather generator validation and illustrations at a single site have been published in the Journal of Applied Meteorology and Climatology (Smith et al. 2017; "A new method for generating stochastic simulations of daily air temperature for use in weather generators"; https://doi.org/10.1175/JAMC-D-16-0122.1), and the multisite generalization of SHArP is in press in the same journal (Smith et al. 2018; "Multisite generalization of the SHArP weather generator"; https://doi.org/10.1175/JAMC-D-17-0236.1).

How to use:
- All input data need to be combined into a single .mat structure file and contain the number of locations to be simulated, the number of days to simulate, a time matrix (in format: year month day), the day of year corresponding to each day, the precipitation occurrence 1s and 0s in logical format corresponding to "wet" or "dry" days (determined from observations or downscaled climate model output), the maximum temperature training data for each site (i.e. observations or downscaled climate model output), and the minimum temperature training data for each site.
- This .mat structure file (called "sett") is the input to fit_SHArP_1, which fits the parameters needed to run the model. fit_SHArP_1 internally uses fit_SHArP_var_1a to determine the coefficients for each of the 26 variables per site in the model; this latter script is not run separately. The output from fit_SHArP_1 is called the driver_t structure and needs to contain maximum and minimum temperature data (from observations or downscaled climate model output), the precipitation occurrence 1s and 0s in logical format corresponding to "wet" or "dry" days (determined from observations or downscaled climate model output), a time matrix (in format: year month day), a variable structure that contains the 26 coefficients for each variable at each site, b_k, c_k, and the results from the EOF analysis on the precipitation occurrence patterns.
- The driver_t structure is the input to simulate_SHArP_2, which gives maximum temperature and minimum temperature as output. This script can also be modified to output the temperature mean and noise but note that this will increase computation time significantly.

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Script Resource Script Resource
Stochastic Harmonic Autoregressive Parametric (SHArP) Weather Generator
Created: June 25, 2018, 8:21 p.m.
Authors: Kimberly Smith · Courtenay Strong · Firas Rassoul-Agha

ABSTRACT:

This collection of MATLAB scripts runs the temperature component of the stochastic harmonic autoregressive parametric (SHArP) weather generator, which simulates trended, nonstationary precipitation occurrence, precipitation amount, and temperature values. The precipitation component is not included here because it is based off of previous weather generators and doesn't use any new methods. The temperature component follows a concept introduced in previous studies, but SHArP simulates temperature values directly as opposed to simulating the temperature residuals. SHArP weather generator validation and illustrations at a single site have been published in the Journal of Applied Meteorology and Climatology (Smith et al. 2017; "A new method for generating stochastic simulations of daily air temperature for use in weather generators"; https://doi.org/10.1175/JAMC-D-16-0122.1), and the multisite generalization of SHArP is in press in the same journal (Smith et al. 2018; "Multisite generalization of the SHArP weather generator"; https://doi.org/10.1175/JAMC-D-17-0236.1).

How to use:
- All input data need to be combined into a single .mat structure file and contain the number of locations to be simulated, the number of days to simulate, a time matrix (in format: year month day), the day of year corresponding to each day, the precipitation occurrence 1s and 0s in logical format corresponding to "wet" or "dry" days (determined from observations or downscaled climate model output), the maximum temperature training data for each site (i.e. observations or downscaled climate model output), and the minimum temperature training data for each site.
- This .mat structure file (called "sett") is the input to fit_SHArP_1, which fits the parameters needed to run the model. fit_SHArP_1 internally uses fit_SHArP_var_1a to determine the coefficients for each of the 26 variables per site in the model; this latter script is not run separately. The output from fit_SHArP_1 is called the driver_t structure and needs to contain maximum and minimum temperature data (from observations or downscaled climate model output), the precipitation occurrence 1s and 0s in logical format corresponding to "wet" or "dry" days (determined from observations or downscaled climate model output), a time matrix (in format: year month day), a variable structure that contains the 26 coefficients for each variable at each site, b_k, c_k, and the results from the EOF analysis on the precipitation occurrence patterns.
- The driver_t structure is the input to simulate_SHArP_2, which gives maximum temperature and minimum temperature as output. This script can also be modified to output the temperature mean and noise but note that this will increase computation time significantly.

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Model Instance Resource Model Instance Resource

ABSTRACT:

The results included are based on the HSPF model simulations developed for the Jordan River watersehds. The base model used to simulate was developed by a consultant (STANTEC in 2010/2011) and employed by the Salt Lake County, Utah. The model was calibrated and statistical results were checked only for the Big Cottonwood Canyons at the Canyons Mouth and other several location in the Jordan River. The model was applied to study climate and land use change in March 2017. The historical time periods considered are Jan1 1995 to Dec 31,2004. The calibration time period considered for the streamflow is Jan1,2005 to Dec 31,2006 (it varies for other water quality parameters considering the data availability). Future simulations include 2035 to 2044 and 2085 to 2094. The model results were simulated in an hourly time steps and this resource has the daily results. The results included are only for the climate change scenarios as the canyons have negligible effects of the land use and land cover changes. The three scenarios considered are based on the RCP6 climate scenario that was dynamically downscaled using the Weather Research & Forecasting (WRF) model and statistically downscaled two climate scenarios corresponding to the mean of the driest and wettest quartiles of the statistically downscaled CMIP5 database at https://gdo-dcp.ucllnl.org/downscaled_cmip_projections/dcpInterface.html .

For each streamflow tab in the file, the Observed column indicates results forcing the model with station observations. Future simulation columns labeled Min_* provide results for the mean of the driest quartile of CMIP5 simulations, RCP_* provide results for the WRF simulation of RCP 6.0, and Max_* provide results for the mean of the wettest quartile of CMIP5 simulations. The second tab in the file provides sample results from the HSPF standard calibration procedure.

Show More