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Climatic Change

, Volume 118, Issue 2, pp 307–320 | Cite as

Reservoir performance and dynamic management under plausible assumptions of future climate over seasons to decades

  • M. Neil WardEmail author
  • Casey M. Brown
  • Kye M. Baroang
  • Yasir H. Kaheil
Article

Abstract

An analysis procedure is developed to explore the robustness and overall productivity of reservoir management under plausible assumptions about climate fluctuation and change. Results are presented based on a stylized version of a multi-use reservoir management model adapted from Angat Dam, Philippines. Analysis focuses on October-March, during which climatological inflow declines as the dry season arrives, and reservoir management becomes critical and challenging. Inflow is assumed to be impacted by climate fluctuations representing interannual variation (white noise), decadal to multidecadal variability (MDV, here represented by a stochastic autoregressive process) and global change (GC), here represented by a systematic linear trend in seasonal inflow total over the simulation period of 2008–2047. Stochastic (Monte Carlo) simulations are undertaken to explore reservoir performance. In this way, reservoir reliability and risk of extreme persistent water deficit are assessed in the presence of different combinations and magnitudes of GC and MDV. The effectiveness of dynamic management is then explored as a possible climate change adaptation practice, focusing on reservoir performance in the presence of a 20 % downward inflow trend. In these dynamic management experiments, the October-March water allocation each year is adjusted based on seasonal forecasts and updated climate normals. The results illustrate how, in the near-term, MDV can be as significant as GC in impact for this kind of climate-related problem. The results also illustrate how dynamic management can mitigate the impacts. Overall, this type of analysis can deliver guidance on the expected benefits and risks of different management strategies and climate scenarios.

Keywords

Climate Scenario Adaptive Management Water Allocation Reservoir Model Seasonal Forecast 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Abbreviations

AR

Autoregressive

DA

Dynamic Allocation

ENSO

El Niño Southern Oscillation

GC

Global Change

JAS

July-September

JFM

January-March

MCD

Maximum Cumulative Deficit

MCM

Million Cubic Meters

MDV

Multidecadal Variability

OND

October-December

ONDJFM

October-March

SA

Static Allocation

Notes

Acknowledgments

This work has benefited from comments by Upmanu Lall, Shiv Someshwar, Andrew Robertson, Bradfield Lyon and David Watkins. All four authors are grateful for the experience of working at the International Research Institute for Climate and Society, Columbia University. We also acknowledge the valuable insights from water management and climate stakeholder discussions related to the Angat reservoir management in the Philippines, including with the National Water Resources Board, National Power Corporation, and PAGASA (the National Weather Service of the Philippines). Funding from the National Oceanic and Atmospheric Administration grant NA050AR4311004 and United States Agency for International Development grant DFD-A-00-03-00005-00 is gratefully acknowledged.

Supplementary material

10584_2012_616_MOESM1_ESM.pdf (441 kb)
ESM 1 (PDF 440 kb)
10584_2012_616_MOESM2_ESM.pdf (362 kb)
ESM 2 (PDF 362 kb)
10584_2012_616_MOESM3_ESM.pdf (306 kb)
ESM 3 (PDF 306 kb)
10584_2012_616_MOESM4_ESM.pdf (294 kb)
ESM 4 (PDF 294 kb)

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Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  • M. Neil Ward
    • 1
    Email author
  • Casey M. Brown
    • 2
  • Kye M. Baroang
    • 3
  • Yasir H. Kaheil
    • 4
  1. 1.Independent ScholarBasking RidgeUSA
  2. 2.Department of Civil and Environmental EngineeringUniversity of MassachusettsAmherstUSA
  3. 3.Earth Institute at Columbia UniversityNew YorkUSA
  4. 4.Risk Management SolutionsNewarkUSA

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