Climate vulnerability assessments rely on water infrastructure system models that imperfectly predict performance metrics under ensembles of future scenarios. There is a benefit to reduced complexity system representations to support these assessments, especially when large ensembles are used to better characterize future uncertainties. An important question is whether the total uncertainty in the output metrics is primarily attributable to the climate ensemble or to the systems model itself. Here we develop a method to address this question by combining time series error models of performance metrics with time‐varying Sobol sensitivity analysis. The method is applied to a reduced complexity multi‐reservoir systems model of the Sacramento‐San Joaquin River Basin in California to demonstrate the decomposition of flood risk and water supply uncertainties under an ensemble of climate change scenarios. The results show that the contribution of systems model error to total uncertainty is small (∼5%–15%) relative to climate based uncertainties. This indicates that the reduced complexity systems model is sufficiently accurate for use in the context of the vulnerability assessment. We also observe that climate uncertainty is dominated by the choice of general circulation model and its interactive effects with the representative concentration pathway (RCP), rather than the RCP alone. This observation has implications for how climate vulnerabilities should be interpreted.
Adaptation of Multiobjective Reservoir Operations to Snowpack Decline in the Western United States
Long-term snowpack decline is among the best-understood impacts of climate change on water resources systems. This trend has
been observed for decades and is projected to continue even in climate projections in which total runoff volumes do not change significantly.
For basins in which snowpack has historically provided intra-annual water storage, snowpack decline creates several issues that may require
adaptation to infrastructure, operations, or both. This study develops an approach to analyze vulnerabilities and adaptations specifically
focused on the challenge of snowpack decline, using the northern California reservoir system as a case study. We first introduce an
open-source daily time-step simulation model of this system, which is validated against historical observations of operations. Multiobjective
vulnerabilities to snowpack decline are then examined using a set of downscaled climate scenarios to capture the physically based effects of
rising temperatures. A statistical analysis shows that the primary impacts include water supply shortage and lower reservoir storage resulting
from the seasonal shift in runoff timing. These challenges identified from the vulnerability assessment inform proposed adaptations to operations
to maintain multiobjective performance across the ensemble of plausible future scenarios, which include other uncertain hydrologic
changes. To adapt seasonal reservoir management without the cost of additional infrastructure, we specifically propose and test adaptations
that parameterize the structure of existing operating policies: a dynamic flood control rule curve and revised snowpack-to-streamflow forecasting
methods to improve seasonal runoff predictability given declining snowpack. These adaptations are shown to mitigate the majority of
vulnerabilities caused by snowpack decline across the scenario ensemble, with remaining opportunities for improvement using formal policy
search and dynamic adaptation techniques. The coupled approach to vulnerability assessment and adaptation is generalizable to other
snowmelt-dominated water resources systems facing the loss of seasonal storage due to rising temperatures.
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- Award ID(s):
- 1639268
- NSF-PAR ID:
- 10314238
- Date Published:
- Journal Name:
- Journal of water resources planning and management
- Volume:
- 146
- Issue:
- 12
- ISSN:
- 1943-5452
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract On the Arctic Coastal Plain (ACP) in northern Alaska (USA), permafrost and abundant surface‐water storage define watershed hydrological processes. In the last decades, the ACP landscape experienced extreme climate events and increased lake water withdrawal (LWW) for infrastructure construction, primarily ice roads and industrial operations. However, their potential (combined) effects on streamflow are relatively underexplored. Here, we applied the process‐based, spatially distributed hydrological and thermal Water Balance Simulation Model (10 m spatial resolution) to the 30 km2Crea Creek watershed located on the ACP. The impacts of documented seasonal climate extremes and LWW were evaluated on seasonal runoff (May–August), including minimum 7‐day mean flow (MQ7), the recovery time of MQ7 to pre‐perturbation conditions, and the duration of streamflow conditions that prevents fish passage. Low‐rainfall scenarios (21% of normal, one to three summers in a row) caused a larger reduction in MQ7 (−56% to −69%) than LWW alone (−44% to −58%). Decadal‐long consecutive LWW under average climate conditions resulted in a new equilibrium in low flow and seasonal runoff after 3 years that included a disconnected stream network, a reduced watershed contributing area (54% of total watershed area), and limited fish passage of 20 days (vs. 6 days under control conditions) throughout summer. Our results highlight that, even under current average climatic conditions, LWW is not offset by same‐year snowmelt as currently assumed in land management regulations. Effective land management would therefore benefit from considering the combined impact of climate change and industrial LWWs.
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1061/(ASCE) WR.1943-5452.0001300
