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Data repository for: A multivariate approach to generate synthetic short-to-medium range hydro-meteorological forecasts across locations, variables, and lead times
The use of hydro-meteorological forecasts in water resources management holds great promise as a soft pathway to improve system performance. Methods for generating synthetic forecasts of hydro-meteorological variables are crucial for robust validation of forecast use, as numerical weather prediction hindcasts are only available for a relatively short period (10-40 years) that is insufficient for assessing risk related to forecast-informed decision-making during extreme events. We develop a generalized error model for synthetic forecast generation that is applicable to a range of forecasted variables used in water resources management. The approach samples from the distribution of forecast errors over the available hindcast period and adds them to long records of observed data to generate synthetic forecasts. The approach utilizes the Skew Generalized Error Distribution (SGED) to model marginal distributions of forecast errors that can exhibit heteroskedastic, auto-correlated, and non-Gaussian behavior. An empirical copula is used to capture covariance between variables, forecast lead times, and across space. We demonstrate the method for medium-range forecasts across Northern California in two case studies for 1) streamflow and 2) temperature and precipitation, which are based on hindcasts from the NOAA/NWS Hydrologic Ensemble Forecast System (HEFS) and the NCEP GEFS/R V2 climate model, respectively. The case studies highlight the flexibility of the model and its ability to emulate space-time structures in forecasts at scales critical for water resources management. The proposed method is generalizable to other locations and computationally efficient, enabling fast generation of long synthetic forecast ensembles that are appropriate for risk analysis.
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README file for data supporting: Brodeur, Z. & Steinschneider, S. (2021). A generalized, multivariate approach to generate synthetic short-to-medium range hydro-meteorological forecasts across locations, variables, and lead times. Submitted to Water Resources Research, April 2021.
Author: Zachary P. Brodeur
Data & metadata associated with R-scripts is stored at the following location due to Hydroshare storage constraints:
https://drive.google.com/drive/folders/16AqhZ6H0WhDmgStJ99t91YGZneqhDy5P
Project Files:
-'hefs_folsom': Contains scripts required to process HEFS streamflow forecast data and observed full natural flow data for Folsom reservoir, run the synthetic forecast model, and calculate verification/validation metrics
--'create-daily-hefs-inflow': Processes hourly HEFS data to daily inflow forecasts
--'fol_fit-model': Script to fit VAR, Empirical Copula, and GL/SGED model to HEFS data
--'fol_fit-model_var-length': Script to fit VAR, Empirical Copula, and GL/SGED model to HEFS data of varying fit lengths
--'fol_synthetic-gen': Script to generate synthetic forecasts from fitted model
--'fol_synthetic-gen_oos': Script to generate synthetic forecasts from fitted model in out-of-sample synthetic period (1948-1984)
--'fol_synthetic-gen_var-lengths': Script to generate synthetic forecasts from fitted model of varying fit lengths
--'folsom-fnf-4820': .csv file of full natural flow values for Folsom reservoir from 1948-2020
--'hefs_daily-inflow-forecast': HEFS daily inflow forecasts created by script above for 1985-2010
--'HEFS_flow_ens_mean_8510': Raw hourly data for HEFS ensemble mean forecasts for 1985-2010
-'hefs_lamc': Contains scripts required to process HEFS streamflow forecast data and observed full natural flow data for Russian River/Lake Mendocino (LAMC), run the synthetic forecast model, and calculate verification/validation metricss
--'lamc_fit-model': Script to fit VAR, Empirical Copula, and GL/SGED model to HEFS data
--'lamc_synthetic-gen': Script to generate synthetic forecasts from fitted model
--'lamc_synthetic-gen_oos': Script to generate synthetic forecasts from fitted model in out-of-sample synthetic period (1975-1984)
--'lamc-fnf-7519': .csv file of full natural flow values for LAMC from 1975-2019
--'lamc_hefs_daily-inflow-forecast': HEFS daily inflow forecasts for LAMC from 1985-2017
-'temp_precip': Contains scripts required to process NCEP GEFS V10 forecast data and NOAA-CIRES-DOE 20th Century Historical Reanalysis V3 observed data for variables of tmax, tmin, precip, run the synthetic forecast model, and calculate verification/validation metrics
--'temp_precip_data-process': Script to process forecast and observed meteorological data to prepare it for model fitting
--'temp_precip_model-fit': Script to fit VAR, Empirical Copula, and GL/SGED model to meteorological forecast data
--'temp_precip_synthetic': Script to generate synthetic meteorological forecasts from fitted model for fitted period (1984-2015)
--'temp_precip_synthetic_oos': Script to generate synthetic meteorological forecasts for 'synthetic period' (1948-1984)
--'temp_precip_oos-bounds': Script to calculate percentile bounds for synthetic forecasts for analysis
--'temp_precip_reliability': Script to generate data for reliability analyses
--'temp_precip_mae-skill': Script to calculate mean absolute error (MAE) skill metrics
--'temp_precip_fp-fn_rates': Script to calculate metrics related to false positives (FP) and false negatives (FN) in the precip forecasts
-'plot_figs': Plotting routines for figures in the manuscript
--'fol_plot-figs': Script to create primary figures for streamflow in manuscript
--'fol_plot-figs_var-length': Script to create SI figures for varying fit-length analyses
--'lamc_supp-figs': Script to create SI figures for Russian River/Lake Mendocino results
--'temp_precip_plotfigs': Script to create primary figures for meteorology in manuscript
--'supp-figs': Script to create supporting information (SI) figures for manuscript
-'GL_SGED': Primary equations for the Generalized Likelihood (GL) and Skew Generalized Error Distribution (SGED) model used by synthetic forecast scripts
-'GL_maineqs': Primary equations from body of Schoups & Vrugt (2010) manuscript or modifications thereof
-'GL_subeqs': Equations from appendix of Schoups & Vrugt (2010) required to execute primary equations
-'supplemental': Additional code for direct response to reviewer analyses
--'fol_lam-sted_model': Script for Lamontagne & Stedinger (2018) model application
--'fol_lam-sted_plot-figs': Script to plot Lamontagne & Stedinger (2018) model results
**END
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Google Drive Link for Data
This repository shares only code and smaller data files due to storage limitations. Data associated with this project are greater than the size constraints of the Hydroshare repository and are stored on the author's personal Google Drive through Cornell University. Access is available on request to the first author.
Related Resources
This resource is referenced by
Brodeur, Z., & Steinschneider, S. (2021). A multivariate approach to generate synthetic short-to-medium range hydro-meteorological forecasts across locations, variables, and lead times. Submitted Water Resources Research, December 2020.
The content of this resource is derived from
NOAA/NCEP, 2013: NCEP Global Ensemble Forecasting System (GEFS, version 10, updated daily). NOAA’s 2nd-generation global ensemble reforecast dataset. Subset used: December 1984 – December 2015, accessed 1 August 2020, https://www.esrl.noaa.gov/psd/forecasts/reforecast2/download.html.
Brodeur, Z. P. (2021). Data repository for: A multivariate approach to generate synthetic short-to-medium range hydro-meteorological forecasts across locations, variables, and lead times, HydroShare, https://doi.org/10.4211/hs.4382404b935f4fde99c7ff4ada264867
This resource is shared under the Creative Commons Attribution CC BY.
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