Global Hydropower Simulation - Forecast_2022


Authors:
Owners: Jia Yi Ng
Type: Resource
Storage: The size of this resource is 285.9 MB
Created: Dec 22, 2020 at 2:03 a.m.
Last updated: May 31, 2022 at 1:27 a.m. (Metadata update)
Published date: Apr 08, 2022 at 3:31 p.m.
DOI: 10.4211/hs.ca365ffb1a1f49df8b77e393be965fd8
Citation: See how to cite this resource
Sharing Status: Published
Views: 1603
Downloads: 30
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Abstract

This resource contains the hydropower time series for 735 headwater hydropower dams operating under 3 different schemes – control rules, forecast-informed operations with perfect forecast, and forecast informed operations with deterministic forecast. The deterministic streamflow forecasts depend on seven drivers, that is, four large scale climate drivers— El Niño Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), and Atlantic Multidecadal Oscillation (AMO)—and three variables accounting for local processes—lagged inflow, snowfall, and soil moisture.

Start exploring the data by downloading the Rdata together with the open_file.R script. You will be able to find monthly-resolution time series outputs of our simulation, including the hydropower production, storage level, and water releases for each dam.

Coverage

Spatial

Coordinate System/Geographic Projection:
WGS 84 EPSG:4326
Coordinate Units:
Decimal degrees
North Latitude
68.8154°
East Longitude
176.7896°
South Latitude
-45.8796°
West Longitude
-125.5871°

Temporal

Start Date: 10/01/1958
End Date: 06/30/2000
Leaflet Map data © OpenStreetMap contributors

Content

    No files to display.

Related Resources

This resource is referenced by Lee, D., Ng, J. Y., Galelli, S., & Block, P. (2022). Unfolding the relationship between seasonal forecast skill and value in hydropower production: a global analysis. Hydrology and Earth System Sciences, 26(9), 2431-2448.
This resource belongs to the following collections:
Title Owners Sharing Status My Permission
Global Hydropower Simulation Collection Jia Yi Ng  Discoverable &  Shareable Open Access

How to Cite

Lee, D., J. Y. Ng, S. Galelli, P. Block (2022). Global Hydropower Simulation - Forecast_2022, HydroShare, https://doi.org/10.4211/hs.ca365ffb1a1f49df8b77e393be965fd8

This resource is shared under the Creative Commons Attribution CC BY.

http://creativecommons.org/licenses/by/4.0/
CC-BY

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