Water Quality Modeling Group

New York City, NY, United States

Water Quality Modeling Group

New York City, NY, United States
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Anandhi A.,Kansas State University | Zion M.S.,Water Quality Modeling Group | Gowda P.H.,U.S. Department of Agriculture | Pierson D.C.,Water Quality Modeling Group | And 2 more authors.
Hydrological Processes | Year: 2013

Changes in frost indices in the New York's Catskill Mountains region, the location of water supply reservoirs for New York City, have potentially important implications. Frost day is defined as a day with Tmin<0 °C. The objective of this study was to investigate past and predicted changes in minimum temperature (Tmin) and six frost indices in the Catskill Mountains covering six reservoir watersheds. Studied frost indices included (1) number of frost days, (2) number of months with frost, (3) last spring freeze date (LSF), (4) first fall freeze date (FFF), (5) growing season length (GSL), and (6) frost season length. Past changes in the frost indices were studied using observed daily Tmin for each watershed for the periods 1960-2008. Future changes in frost indices for the periods (2045-2065 and 2080-2100) were studied for emission scenarios (A1B, A2, and B1) downscaled from global climate models (GCMs). Results indicated a general increase in average Tmin and GSL and a decrease in number of frost days, months with frost, frost season length, earlier LSF, and later FFF from the historical to the future periods, and the magnitude of change varied among the watersheds and GCMs. For the period 1960-2000, in all watersheds (except Cannonsville), LSF occurred earlier by 2.6-4.3days/decade, FFF occurred later by 2.7-3.2day/decade, and GSL was longer by 2.4-4day/decade. Among the scenarios and GCMs, LSF occurred earlier by 4-11 and 4.5-15days/decade for the periods 2045-2065 and 2081-2100, respectively; FFF occurred later by 1-10 and 4-13days/decade for the periods 2045-2065 and 2081-2100, respectively; and GSL was longer by 10-25 and 13-40days/decade for the periods 2045-2065 and 2081-2100, respectively. The increase in GSL is expected to affect hydrologic, ecosystem, and biogeochemical processes with increased net primary productivity and a resulting increase in total annual evapotranspiration. © 2013 John Wiley & Sons, Ltd.


Anandhi A.,City University of New York | Frei A.,City University of New York | Frei A.,City College of New York | Pradhanang S.M.,City University of New York | And 3 more authors.
Hydrological Processes | Year: 2011

In this study, we evaluate the ability of GCMs participating in the Intergovernmental Panel for Climate Change's (IPCC) Fourth Assessment Report (AR4) to simulate variability in the snow water equivalent (SWE) in New York City Water Supply watersheds located northwest of NYC in the Catskill Mountains. SWE is estimated using an empirical temperature-based degree day model. Inputs to this model are either measured with historical meteorological (1961-2000) data or a GCM model output for the same historical period. The evaluation of the GCMs is based on a skill score developed using probability distribution functions derived from the time series of simulated snowpack. From the skill scores (SS) calculated, the GCMs are ranked based on their ability to simulate the snowpack. These results have implications for selecting a subset of GCM simulations for climate change impact assessments in New York City's water supply. Results show that the GFDL 2·0 (gf001) model has the highest SS (0·93) and CCSM (ncc09) model has the lowest SS (0·26). On the basis of the SS, the GCM ensemble members are classified into three categories: high, medium and low performance. The probability density functions for the three performance classes show the largest between-model variability for models in low performance class. Differences between the means and medians of observation-based model simulation and GCM-based simulation were greatest in the low-performance class. © 2011 John Wiley & Sons, Ltd.


Anandhi A.,City University of New York | Frei A.,City University of New York | Frei A.,City College of New York | Pierson D.C.,Water Quality Modeling Group | And 4 more authors.
Water Resources Research | Year: 2011

A variety of methods are available to estimate values of meteorological variables at future times and at spatial scales that are appropriate for local climate change impact assessment. One commonly used method is Change Factor Methodology (CFM), sometimes referred to as delta change factor methodology. Although more sophisticated methods exist, CFM is still widely applicable and used in impact analysis studies. While there are a number of different ways by which change factors (CFs) can be calculated and used to estimate future climate scenarios, there are no clear guidelines available in the literature to decide which methodologies are most suitable for different applications. In this study several categories of CFM (additive versus multiplicative and single versus multiple) for a number of climate variables are compared and contrasted. The study employs several theoretical case studies, as well as a real example from Cannonsville watershed, which supplies water to New York City, USA. Results show that in cases when the frequency distribution of Global Climate Model (GCM) baseline climate is close to the frequency distribution of observed climate, or when the frequency distribution of GCM future climate is close to the frequency distribution of GCM baseline climate, additive and multiplicative single CFMs provide comparable results. Two options to guide the choice of CFM are suggested. The first option is a detailed methodological analysis for choosing the most appropriate CFM. The second option is a default method for use under circumstances in which a detailed methodological analysis is too cumbersome. Copyright 2011 by the American Geophysical Union.

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