Independent Scientific Review of the State Water Project Delivery Capability Report


The State Water Project (SWP) is a multi-purpose water storage and delivery system that delivers water across California for residential, agriculture, municipal, and industrial uses. The system’s water supply depends on a host of variables such as rainfall, snowpack, runoff, reservoir levels, pumping capability, and regulatory mandates. To provide essential information about the current and projected future water supply reliability, the Department of Water Resources (DWR) issues a Delivery Capability Report (DCR) every two years. The DCR is used extensively by SWP contractors and others to plan their water uses, including Integrated Regional Water Management Plans, Urban Water Management Plans, Agricultural Water Management Plans, and Integrated Resource Plans.

The next DCR is due in December 2023 and will include two major changes to the data and methods used to model conditions that have changed and will continue to evolve as a result of climate change. DWR requested a review of the climate adjusted hydrology datasets and CalSim3 model used for the DCR. However, many of the other models DWR uses rely on similar datasets; thus, the work being reviewed may apply to other modeling tools and processes.

The Delta Science Program coordinates reviews in accordance with its mission to provide the best possible unbiased scientific information to inform water and environmental decision-making. Scientific review policies and procedures are outlined in the Delta Science Plan (Appendix H).

  • View the letter requesting the review from Ted Craddock, deputy director of the State Water Project, to Dr. Laurel Larsen, Delta lead scientist.
  • View Dr. Larsen’s response.
  • View the charge that the Delta Science Program provided to the independent review panel with the direction, context, and timeline for the review. The Charge included orientation and focus for the review, background materials, and specific questions for the panel to address.

The review will occur in two parts. For each part, a panel of three subject matter experts will review the data, models, and methods used to prepare the 2023 Delivery Capability Report. Each reviewer will prepare an individual review letter for each part of the review. Review work will occur during summer 2023. There will be no public meetings. Review letters are expected to be available in fall 2023 and will be posted on this web page.

Part 1: Climate Adjusted Historical Hydrology

The DCR is developed with the CalSim3 model, which uses a historical trace from 1922-2021 of natural hydrology as the key input forcing dataset. The DCR assumes that these hydrologic data reflect a stationary climate. However, given the scientific consensus on climate change, questions are now arising as to whether the historical trace of natural hydrology is itself stationary and whether it is adequate for reliable modeling of what we consider to be current conditions. If hydrologic data are non-stationary, DWR needs a reliable and robust process for evaluating the significance of hydrologic changes and developing supplemental or replacement data that deal with the loss of stationarity.

This part of the review will examine 1) DWR’s findings on hydrologic changes in key watersheds throughout California’s Sierra Nevada and Central Valley, focusing on key parameters for water supply analysis (e.g., average annual flow, standard deviation of annual flow, timing of runoff, runoff efficiency); and 2) the process and dataset developed by DWR to translate identified statistical changes in historical hydrology to a useable adjusted hydrologic and meteorological timeseries that can be input to existing planning and operational models, including CalSim3.

Read DWR's report: Evaluation and Adjustment of Historical Hydroclimate Data.

Read the review letters:

Part 2: Risk Informed Future Scenarios

To model future conditions, past versions of the DCR have generally included a single scenario of future delivery capability 20 years from the year of the report. To improve transparency and water management planning at the state and local levels, DWR has developed new risk-informed climate change scenarios for the 2023 SWP DCR. These new scenarios were developed by drawing on both bottom-up climate vulnerability analysis and top-down scenario analysis work done by DWR, partner agencies, and the academic community. The scenarios explore a range of climate and sea level rise conditions for the year 2043 that pose increasing levels of concern to system performance.

This part of the review will examine the process developed by DWR to construct risk-informed climate change scenarios for the 2023 DCR.

Read DWR’s report: Risk Informed Future Climate Scenario Development for the State Water Project Delivery Capability Report.

Read the review letters:

Review Panel Members

Daniel Feldman, Ph.D.

Earth Staff Scientist, Lawrence Berkeley National Laboratory

Daniel Feldman is an expert in climate modeling and remote sensing. He develops algorithms that allow for the direct comparison of climate models with satellite observations and uses this information to ask questions about detecting changes in the climate system and differentiating among climate model results. He analyzes existing decadal-length, high-quality satellite observations and observations from the Atmospheric Radiation Measurement Climate Research Facility to reduce uncertainty in climate projections. He is the Principal Investigator of the Surface Atmosphere Integrated Field Laboratory Campaign and, in collaboration with hydrologists (, leads efforts to develop atmosphere-through-bedrock observations and models to support improved prediction of mountainous hydrology. He also leads the development of statistical and dynamical downscaling from climate models over the United States, an effort that resulted in the production of one of the key datasets that supports the 5th National Climate Assessment. He has a Ph.D. and M.S. in Environmental Science and Engineering from Caltech and a B.S. in Environmental Engineering from MIT.

Jon Herman, Ph.D.

Associate Professor of Civil and Environmental Engineering, UC Davis

Jon Herman is an associate professor in the Department of Civil and Environmental Engineering at UC Davis. His research group focuses on computational methods for simulation and optimization of water resources systems, including climate adaptation planning under uncertainty, multi-objective reservoir control, and parameter sensitivity in flood and drought prediction. His current research focuses on systems where water supply is strongly linked to agricultural production, energy generation, and land use, such as California and the Western U.S. Jon is an associate editor for the ASCE Journal of Water Resources Planning and Management and a regular contributor to open source software for scientific computing. He has a Ph.D. in Civil and Environmental Engineering from Cornell University, an M.S. in Civil and Environmental Engineering from Penn State University, and a B.E. in Engineering Sciences from Dartmouth College.

Lai-Yung (Ruby) Leung, Ph.D.

Batelle Fellow in Earth Systems Analysis and Modeling, Pacific Northwest National Laboratory

L. Ruby Leung’s research broadly cuts across multiple areas in modeling and analysis of climate and the hydrological cycle including land-atmosphere interactions, orographic processes, monsoon climate, extreme events, land surface processes, and aerosol-cloud interactions. She is the Chief Scientist of Energy Exascale Earth System Model (E3SM) supported by the U.S. Department of Energy, a major effort to develop state-of-the-art capabilities for modeling human-Earth system processes on DOE’s next generation high performance computers.

Dr. Leung has published over 450 papers in peer-reviewed journals, and her research on climate change has been featured in Science, Popular Science, Wall Street Journal, National Public Radio, and many major newspapers. She has a Ph.D. and M.S. in atmospheric sciences from Texas A&M University and a B.S. in physics and statistics from the Chinese University of Hong Kong.

Further Reading