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- Anandhi, Aavudai, et al.
(författare)
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Evaluation of Climate Model Performance for Water Supply Studies : Case Study for New York City
- 2019
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Ingår i: Journal of water resources planning and management. - : ASCE-AMER SOC CIVIL ENGINEERS. - 0733-9496 .- 1943-5452. ; 145:8
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Tidskriftsartikel (refereegranskat)abstract
- Evaluating the suitability of data from global climate models (GCMs) for use as input in water supply models is an important step in the larger task of evaluating the effects of climate change on water resources management such as that of water supply operations. The purpose of this paper is to present the process by which GCMs were evaluated and incorporated into the New York City (NYC) water supply's planning activities and to provide conclusions regarding the overall effectiveness of the ranking procedure used in the evaluation. A suite of GCMs participating in Phase 3 of the Coupled Model Intercomparison Project (CMIP3) were evaluated for use in climate change projections in the watersheds of the NYC water supply that provide 90% of the water consumed by NYC. GCM data were aggregated using the seven land-grid points surrounding NYC watersheds, and these data with a daily timestep were evaluated seasonally using probability-based skill scores for various combinations of five meteorological variables (precipitation, average, maximum and minimum temperatures, and wind speed). These are the key variables for the NYC water supply because they affect the timing and magnitude of water, energy, sediment, and nutrient fluxes into the reservoirs as well as in simulating watershed hydrology and reservoir hydrodynamics. We attempted to choose a subset of GCMs based on the average of several skill metrics that compared baseline (20C3M) GCM results to observations. Skill metrics for the study indicate that the skill in simulating the frequency distributions of measured data is highest for temperature and lowest for wind. However, our attempts to identify the best model or subgroup of models were not successful because we found that no single model performs best when considering all of the variables and seasons.
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