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Stochastic Decadal Projections of Colorado River Streamflow and Reservoir Pool Elevations Conditioned on Temperature Projections
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| Storage: | The size of this resource is 1.2 GB | |
| Created: | Nov 19, 2021 at 10:24 a.m. | |
| Last updated: | Nov 19, 2021 at 10:24 a.m. | |
| Citation: | See how to cite this resource | |
| Content types: | Geographic Feature Content |
| Sharing Status: | Public |
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| Views: | 459 |
| Downloads: | 11 |
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Abstract
Decadal (~10-years) scale flow projections in the Colorado River Basin (CRB) are increasingly important for water resources management and planning of its reservoir system. Physical models – Ensemble Streamflow Prediction (ESP) – do not have skill beyond interannual time scales. However, Global Climate Models have good skill in projecting decadal temperatures. This, combined with the sensitivity of CRB flows to temperature from recent studies, motivate the research question - can skill in decadal temperature projections be translated to operationally skillful flow projections and consequently, water resources management? To explore this, we used temperature projections from the Community Earth System Model – Decadal Prediction Large Ensemble (CESM-DPLE) along with past basin runoff efficiency as covariates in a Random Forest (RF) method to project ensembles of multi-year mean flow at the key aggregate gauge of Lees Ferry, Arizona. RF streamflow projections outperformed both ESP and climatology in a 1982-2017 hindcast, as measured by ranked probability skill score. The projections were disaggregated to monthly and sub-basin scales to drive the Colorado River Mid-term Modeling System to generate ensembles of water management variables. The projections of pool elevations in Lakes Powell and Mead – the two largest U.S. reservoirs that are critical for water resources management in the basin – were found to reduce the hindcast median root mean square error by up to -20 and -30% at lead times of 48- and 60-months, respectively, relative to projections generated from ESP. This suggests opportunities for enhancing water resources management in the CRB and potentially elsewhere.
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Temporal
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Content
ReadMe.txt
Read Me For the decadal flow projections and MTOM simulations, see below steps for running code. 1. For decadal forecasts, first run the .Rmd file "midTermForecast-v2.07-DPLE---MultiYears---Blind---RFonly.Rmd" in the /Code/Multi-year Projections/ folder. a) Ensure that the associated library "midTermForecast_Library_v1.4.R" is loaded b) The required input data for this program is in the /Data/Input Data/ folder (including subdirectories) c) You should be able to run this program as an R notebook all at once and it will output the decadal forecasts and skill scores. d) The output should match all the contents of /Data/Output Data and Analysis Results/Multi-year Projections/ e) Aggregated forecasts and skill scores for all mean lengths are located in "RF_forecasts_skillscores_v2.07_blindKFold.Rds" and "RF_forecasts_v2.07_blindKFold.Rds" 2. For MTOM simulations from the decadal forecasts, follow the below steps: a) In the /Code and Data/Code/MTOM simulations/ directory, first run the "WaterYear_Flow_Disaggregation - v1.8.R" file. i)This dissaggregates the decadal forecasts to a monthly, sub-basin resolution. ii) You will need to change the directories of the input files (i.e., point to where the decadal forecasts from 1d and 1e are stored) b) Open the macros.xlsm file and go to the Developer Tab then open Macros. i) Edit the 'ReplaceDate_RF' macro so that it point to where the disaggregated forecasts created in 2a are stored. ii) This macro changes the date format of the forecasts so it can be read by MTOM c) The forecasts are now ready for MTOM input. d) After running MTOM via RiverSMART, run the 'mtom_plotting_loop - v1.9.R' code. Make sure the correct path is set for reading in the MTOM simulations and the 'mtom_postProcessing_library - v1.4.R' function library i) This code will calculate various performance metrics on the MTOM simulations and output related graphics.
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Credits
Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
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| Bureau of Reclamation |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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