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Evaluation of Climate Model Performance for Water Supply Studies: Case Study for New York City
Florida A&M Univ, Coll Agr & Food Sci, Biol Syst Engn, Tallahassee, FL 32307 USA;Florida A&M Univ, Coll Agr & Food Sci, Ctr Water Resources, Tallahassee, FL 32307 USA.
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för ekologi och genetik, Limnologi.
CUNY, Hunter Coll, Dept Geog, New York, NY 10065 USA;CUNY, CUNY Inst Sustainable Cities, New York, NY 10065 USA.
2019 (engelsk)Inngår i: Journal of water resources planning and management, ISSN 0733-9496, E-ISSN 1943-5452, Vol. 145, nr 8, artikkel-id 06019006Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

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.

sted, utgiver, år, opplag, sider
ASCE-AMER SOC CIVIL ENGINEERS , 2019. Vol. 145, nr 8, artikkel-id 06019006
Emneord [en]
Evaluation GCM models, Global climate models (GCMs), Probability-based skill score, Fourth assessment report in Coupled Model Intercomparison Project (AR4, CMIP3), Adaptation, Water supply
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Identifikatorer
URN: urn:nbn:se:uu:diva-390080DOI: 10.1061/(ASCE)WR.1943-5452.0001054ISI: 000471689400003OAI: oai:DiVA.org:uu-390080DiVA, id: diva2:1340827
Tilgjengelig fra: 2019-08-06 Laget: 2019-08-06 Sist oppdatert: 2019-08-06bibliografisk kontrollert

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