California’s recent drought has brought an acceleration of groundwater overdraft, depletion of surface water supplies, and reduction of hydropower production. Procuring accurate and detailed drought forecasts can create potential solutions to these problems. This research provides models and explanations on the complexities of balancing energy production, economic benefits, and maximum water storage.
California’s recent drought has exacerbated the depletion of surface water supplies, accelerating groundwater overdraft, and reducing hydropower production from the surface water reservoirs. California’s inter-tied water and energy systems always seek enhanced drought forecasts and robust adaptation strategies to address these challenges. The concept of flood flows for Managed Aquifer Recovery (Flood-MAR) is a promising option to refill depleted underground water storage during drought periods. This study will exploit the previously developed simulation and optimization models and devise a new, improved decision tree-based calendar (DtC). Within the mission set by the Sustainable Groundwater Management Act (SGMA), this project protocol will integrate the climate extremes with hydropower, land use, and water management strategies. A hybrid optimization model is proposed to set explicit decision-making rules for better economic benefits in need of the tradeoff between energy production and maximum water storage in the system. In this regard, the objectives of this research include:
(a) Enhancing the streamflow and drought forecast system over a range of lead times based on knowledge of atmospheric forecasts, initial hydrological conditions (observations), and regional land-surface hydrologic states;
(b) Translating the system performance into tangible economic measures and analyzing tradeoffs of water and energy sectors;
Within the California NIDIS Drought Early Warning System (DEWS), this research will demonstrate the Flood-MAR benefits for the American River basin. The framework and tools as an outcome of this project will be transferable to other NIDIS-DEWS regions where drought prediction plays a vital role in the decision-making process.
This research explores climate, economics, and hydrology and issues appropriate decisions to optimally operate the surface water reservoir under the following research tasks from different institutions:
(c) Improving decision-making in response to drought via an enhanced decision calendar.
(d) Engaging decision-makers, including the California Department of Water Resources (CA-DWR) as well as state’s investor-owned utilities (IOUs) and publicly-owned utilities (POUs).
- Task I: Develop hydro-climate forecast system and climate change scenarios
- Task II: Economic analysis of drought implications for water and energy sectors
- Task III: Update reservoir simulation-optimization model based on Tasks I & II
- Task IV: Develop a decision tree-based calendar for reservoir operation
- Task V: Evaluate of decision calendar robustness to drought events and climate change
Figure 1: American River and Folsom Lake showing the location o artificial recharge and portion of inter-tied California’s water network. The solid line represents the proposed conveyance facility.
Figure 2: Modified California Water Network in the vicinity of AmericanRiver and the Folsom Lake with proposed recharge facility (RECH8) and diversion point (DP9).
This task concentrates on economic analysis of drought implications employing the existing statewide hydro-economic model, CALVIN (California Value Network). This research modifies the inter-tied California Water Network by adding one recharge facility and one diversion point near the American River below the Nimbus Dam. The primary objective of this facility is to get the benefit from artificial recharge by the community below the downstream of the American River (Figure 1). A portion of the statewide water network entailing the proposed recharge facility, which directly connects to the groundwater storage facility, is shown in Figure 2.
This study relies on existing hydrology from 1921-2003 and 2050 land use data and urban population to minimize water allocation costs. In addition to one diversion node and recharge facility (Figure 2), we penalized both the surface water and groundwater ending storage by $900 per acre-feet and $100 per acre-feet to remove the ending storage constrained at storage facilities so that there is an increase in the groundwater storage as a part of the Flood Managed Aquifer Recharge program without impacting hydropower generation and Delta outflow.
This study makes usage of different CALVIN runs Recharge-LF and Recharge-PF ( after adding recharge facility and diversion point, Figure 2) together with Base Case (before recharge facility). While LF refers to limited foresight, PF refers to perfect foresight run sticking to the base case with perfect foresight runs. Preliminary results, till now, show:
- Increased surface water storage (Figure 3)
- Increased hydropower and hydropower revenue from October to March (Figure 4)
- Increased groundwater storage (Figure 5, top)
- Increased groundwater pumping in dry years (Figure 5, bottom)
Figure 3: Surface water storage behind the Folsom Lake before (base case) and after recharge g=facility (Recharge-LF and PF) under different water year types. LF refers to limited foresight run and PF to perfect foresight run.
Figure 4: Average hydropower generation, MW/year (top) and hydropower revenue, M$/year under different water year types for different scenarios, including base case.
Figure 5: Top: Groundwater storage and bottom: groundwater pumping in different water year types for different scenarios.
The future analysis includes a complete and detailed analysis of the system, and they are agricultural water delivery, Delta Outflow, system operating cost, and scarcity cost. In addition, this study will be expanded towards sensitivity analysis with different configurations, climate change scenarios, and land-use decisions.
Dr. Josue Medellin-Azuara – UC Merced
Dr. Erfan Goharian – University of South Carolina
Dr. Dennis P. Lettenmaier – UCLA
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