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Hydropower Energy Storage Capacity Dataset, Version 2

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Dataset Overview

The Hydropower Energy Storage Capacity (HESC) dataset catalogs characteristics that are relevant to evaluating reservoir storage and estimates of energy storage capacity based on varying levels of detail. Hydropower dams and reservoirs were included based on information from the National Inventory of Dams (NID; USACE, 2021) and Global Reservoir and Dam (GRanD v1.3), and Existing Hydropower Assets datasets. These data provide a foundation for understanding available resources at existing hydropower facilities and their potential to provide storage of energy and more flexible generation. Estimates of energy storage capacity include:

  • Level 1 – nominal energy storage capacity based on maximum storage capacities and hydraulic head
  • Level 2 – nominal energy storage capacity based on historical models or observations of reservoir volume and hydraulic head. These estimates are provided based on capacity from the entire historical period as well as monthly values.
  • Level 3 – modeled energy generation based on volume-elevation relationships, historical storage and observed/modeled inflows, and hydraulic capacity of turbines and calculated both as overall and on a monthly basis.
  • Level 4 – modeled energy generation incorporating information from Level 3 and operational constraints.

For facilities where installed capacity is known, there are also estimates for discharge duration (the length of time when a facility could provide generation at a given capacity).

Methodology used to collect and analyze the data

Data were acquired from the NID (USACE, 2021), GRanDv1.3 (Lehner et al., 2011, ), HydroLAKES (Messager et al., 2016), Existing Hydropower Assets (Johnson et al., 2022), USACE via the Duke Nicholas Institue Reservoir Data efforts (Patterson et al., 2018), USBR RISE, the “Hydropower reservoir data in the CONUS” dataset (Gao and Huilin, 2020) and ResOpsUS (Steyaert et al., 2022) datasets. The HILARRIv2 database of hydropower-hydrographic links was used to cross-walk between NID, GRanD, and HydroLAKES (obtaining inventoried volumes, dam height, and surface area characteristics). Statistics were derived for historical observed and modeled volumes/head records. The EHA (Johnson et al, 2022) dataset was used to obtain installed capacity, hydraulic capacity, and basic operational mode information.

Nominal energy storage was calculated using the basic equation:

E= (ρ×g×V×H)/(3.6×10^9)

where E is the energy storage in MWh, ρ is the density of water in kg/m3, V is the storage volume in m3, H is the hydraulic head in m, and 3.6×109 is a unit conversion factor to go from Joules to MWh.

  • For Level 1 estimates:
    • Inventory-reported volume and head were used. If the user would like to consider partial volume or efficiency, these can be incorporated by multiplying the estimate by a factor. For example, assuming 50% volume and 90% efficiency, following the method used in the IEA Special Hydropower Market Report (IEA, 2022), E is multiplied by 0.45. applying a scaling factor to the estimates).
    • These estimates are available for 2,116 dams in the US (including AK, HI, and PR). An additional 50 dams are included in the inventory but have no nominal energy storage estimates because some key information is missing.
  • For Level 2 estimates:
    • Historical volume and head were used.
    • These estimates are available for 233 dams, mostly larger, federally-owned facilities.

Modeled energy storage was calculated at a daily resolution by applying the nominal energy equation and including daily inflow from Dayflow (Ghimire et al, 2022) dataset and discharges to reflect changes in volume with inflow and discharge. A power-law relationship between elevation and volume was used to reflect how the hydraulic head decreases as volume is discharged.

  • For Level 3 estimates:
    • Estimates are available for 176 dams with sufficient historical records (including volume and elevation), and inflow data. Energy storage is limited to 1 year for ENRGY03A-F and 1 month for ENRGY03JAN-DEC, and by a lower bound of volume = 50% of the initial volume.
  • For Level 4 estimates:

Estimates are available for 142 dams with additional data describing the upper and lower bounds of normal operations, retrieved from the ISTARF dataset (Turner et al., 2021). The same limitations as level 3 are applied, with the additional constraint of the upper and lower operating levels.

Change Log:

Version 1 contained only estimates of nominal energy storage capacity
- Level 1 - based on inventoried data and
- Level 2 - based on historical records of volume

Version 2:
- include estimates of nominal energy storage capacity with additional QA performed to exclude anomalous values of volume/dam height from the calculations of Level 1 and 2 estimates.
- have added additional QA and informational flags that help further describe the facilities.
- provide all reported values of volume and dam height from the various input sources rather than the maximum and minimum values.
- provide surface area because of interest in related characteristics at these storage facilities.
- provide equations used to approximate volume-elevation relationships from historical records of storage and volume
- include modeled generation:
- Level 3 – incorporates inflow and physical volume-elevation relationships and reflects storage overall and on a monthly resolution
- Level 4 - incorporates information from Level 3 as well as operational limits


  • Gao, Huilin, 2020, "Hydropower reservoir data in the CONUS, V1", Texas Data Repository
  • Ghimire, G. R., Hansen, C., Gangrade, S., Kao, S.-C., Thornton, P. E., and Singh, D. (2023). Insights from Dayflow: A historical streamflow reanalysis dataset for the conterminous United States. Water Resources Research, 59, e2022WR032312.
  • IEA 2022. Hydropower Special Market Report: Analysis and forecast to 2030.
  • Johnson, M, Kao, S-C, and R Uria-Martinez. 2022. Existing Hydropower Assets, 2022. HydroSource. Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA.
  • Lehner, B., C. Reidy Liermann, C. Revenga, C. Vörösmarty, B. Fekete, P. Crouzet, P. Döll, M. Endejan, K. Frenken, J. Magome, C. Nilsson, J.C. Robertson, R. Rodel, N. Sindorf, and D. Wisser. 2011. High-Resolution Mapping of the World's Reservoirs and Dams for Sustainable River-Flow Management. Frontiers in Ecology and the Environment 9 (9): 494-502.
  • Messager, M.L., Lehner, B., Grill, G., Nedeva, I., Schmitt, O. 2016. Estimating the volume and age of water stored in global lakes using a geo-statistical approach. Nature Communications, 7: 13603.
  • Patterson, L, Doyle, M, and S Kuzma. 2018. Creating Data as a Service for U.S. Army Corps of Engineers Reservoirs. Nicholas Institute for Environmental Policy Solutions, Duke University
  • Steyaert, J.C., Condon, L.E., Turner, S. and N Voisin. 2022. ResOpsUS, a dataset of historical reservoir operations in the contiguous United States. Sci Data 9, 34
  • Turner, S, Voisin, N, Steyaert, J, and L Condon. 2021. Inferred Storage Targets and Release Functions for CONUS large reservoirs.
  • USACE 2021. National Inventory of Dams. Retrieved February 21, 2022. Available at
  • USBR Reclamation Information Sharing Environment (RISE). Retrieved June 1, 2021.