Prepared in cooperation with the New York State Department of Transportation 

Development of Flood Regressions and Climate Change 

Scenarios To Explore Estimates of Future Peak Flows 

Open-File Report 2015–1235
 

U.S. Department of the Interior 

U.S. Geological Survey 
Cover. Background photography; April 2, 2005 flood on the Rondout Creek near Napanoch New York. Photograph by James Porter, New York City Dept. of Environmental 

Protection.
U.S. Department of the Interior 

U.S. Geological Survey 

Prepared in cooperation with the New York State Department of Transportation

Development of Flood Regressions and Climate Change 

Scenarios To Explore Estimates of Future Peak Flows

By Douglas A. Burns, Martyn J. Smith, and Douglas A. Freehafer 

Open-File Report 2015–1235
U.S. Department of the Interior 

SALLY JEWELL, Secretary 

U.S. Geological Survey 

Suzette M. Kimball, Director 

U.S. Geological Survey, Reston, Virginia: 2015 

For more information on the USGS—the Federal source for science about the Earth, 

its natural and living resources, natural hazards, and the environment—visit 

http://www.usgs.gov/ or call 1–888–ASK–USGS (1–888–275–8747). 

For an overview of USGS information products, including maps, imagery, and publications, 

visit http://www.usgs.gov/pubprod/.

Any use of trade, firm, or product names is for descriptive purposes only and does not imply 

endorsement by the U.S. Government. 

Although this information product, for the most part, is in the public domain, it also may 

contain copyrighted materials as noted in the text. Permission to reproduce copyrighted items 

must be secured from the copyright owner. 

Suggested citation: 

Burns, D.A., Smith, M.J., and Freehafer, D.A., 2015, Development of flood regressions and climate 

change scenarios to explore estimates of future peak flows: U.S. Geological Survey Open-File 

Report 2015–1235, 11 p., http://dx.doi.org/10.3133/ofr20151235. 

ISSN 2331-1258 (online) 
iii

Contents

Abstract ......................................................................................................................................................................... 4

Introduction .................................................................................................................................................................... 4

StreamStats Program .................................................................................................................................................... 5

Climate Change Application........................................................................................................................................... 5

Assumptions .................................................................................................................................................................. 6

Limitations and Uncertainty............................................................................................................................................ 8

Check Basin in Current StreamStats Before Using Climate Change Application ....................................................... 8

Sources of Uncertainty ............................................................................................................................................... 8

References Cited ........................................................................................................................................................... 9

Table 

1. Climate models, greenhouse-gas scenarios, and time periods in the Web-based tool that provides

estimates of future peak-flow magnitudes for streams in New York ................................................................. 6

Conversion Factors

International System of Units to Inch/Pound 

Multiply By To obtain

Length

kilometer (km) 0.6214 mile (mi)

Supplemental Information

An arc-second is a unit of angular measurement equal to 1/3,600 of 1 degree. The length of an arc-second on Earth depends on the 

latitude; at a mid-latitude location, such as New York State, 30 arc-seconds are approximately equal to 800 meters. 

Abbreviations

USGS U.S. Geological Survey 
4

Development of Flood Regressions and Climate Change 

Scenarios To Explore Estimates of Future Peak Flows

By Douglas A. Burns, Martyn J. Smith, and Douglas A. Freehafer 

Abstract

A new Web-based application, titled “Application of Flood Regressions and Climate Change 

Scenarios To Explore Estimates of Future Peak Flows,” has been developed by the U.S. Geological 

Survey, in cooperation with the New York State Department of Transportation, that allows a user to 

apply a set of regression equations to estimate the magnitude of future floods for any stream or river in 

New York State (exclusive of Long Island) and the Lake Champlain Basin in Vermont. The regression 

equations that are the basis of the current application were developed in previous investigations by the 

U.S. Geological Survey (USGS) and are described at the USGS StreamStats Web sites for New York 

(http://water.usgs.gov/osw/streamstats/new_york.html) and Vermont 

(http://water.usgs.gov/osw/streamstats/Vermont.html). These regression equations include several fixed 

landscape metrics that quantify aspects of watershed geomorphology, basin size, and land cover as well 

as a climate variable—either annual precipitation or annual runoff. 

The application uses predictions of future annual precipitation from five climate models and two 

future greenhouse gas emissions scenarios and provides results that are averaged over three future 

periods—2025 to 2049, 2050 to 2074, and 2075 to 2099. Results are presented in ensemble form as the 

mean, median, maximum, and minimum values among the five climate models for each greenhouse gas 

emissions scenario and period. These predictions of future annual precipitation are substituted into 

either the precipitation variable or a water balance equation for runoff to calculate potential future peak 

flows. This application is intended to be used only as an exploratory tool because (1) the regression 

equations on which the application is based have not been adequately tested outside the range of the 

current climate and (2) forecasting future precipitation with climate models and downscaling these 

results to a fine spatial resolution have a high degree of uncertainty. This report includes a discussion of 

the assumptions, uncertainties, and appropriate use of this exploratory application. 

Introduction

The U.S. Geological Survey (USGS), in cooperation with the New York State Department of 

Transportation, developed a Web-based application to estimate the magnitude of future floods for any 

stream or river in New York State (excluding Long Island) and the Lake Champlain Basin in Vermont. 

This report describes how annual precipitation data from climate models are applied to the peak flow 

regression equations implemented in the StreamStats Web application 
5

(http://water.usgs.gov/osw/streamstats/) to provide new estimates of peak flow magnitudes for three 

periods in the 21st century: from 2025 to 2049, 2050 to 2074, and 2075 to 2099. 

The purpose of the new Web-based application, titled “Application of Flood Regressions and 

Climate Change Scenarios to Explore Estimates of Future Peak Flows,” is to allow the user to apply 

regression equations that provide predictions of current peak flows in ungaged basins and to provide 

estimates of the magnitudes of future peak flows for any stream or river in New York State (exclusive of 

Long Island) and the Lake Champlain Basin of Vermont (Burns and others, 2015). The assumptions that 

were used in this Web application are described, as well as the sources of uncertainty in the future peak 

flow estimates. Guidance about how to use the application, metadata, or to download the underlying 

geodatabase is provided from within the Web application.

StreamStats Program

The existing set of peak flow magnitude regression equations for current conditions were 

developed by the USGS (Lumia and others, 2006; Olson, 2014) and are implemented in the Web-based 

stream analysis application titled, “The StreamStats Program,” which provides information about 

streams and rivers in New York (http://water.usgs.gov/osw/streamstats/new_york.html) and Vermont 

(http://water.usgs.gov/osw/streamstats/Vermont.html), among other States. The StreamStats Program 

allows the user to delineate a watershed at any point on the stream channel within these two States and 

obtain information about the stream and watershed. The available peak flow information for New York 

includes the instantaneous magnitudes of flows with recurrence intervals of 1.25, 1.5, 2, 5, 10, 25, 50, 

100, 200, and 500 years (annual exceedance probabilities of 87.5, 75, 50, 20, 10, 4, 2, 1, 0.5, and 0.2 

percent, respectively). The available peak flow information for Vermont includes the instantaneous 

magnitudes of flows with recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years (annual 

exceedance probabilities of 50, 20, 10, 4, 2, 1, 0.5, and 0.2 percent, respectively). New York is divided 

into six regions for the purposes of this analysis, and separate regression equations were developed for 

each of these regions (Lumia and others, 2006). A single set of peak flow regression equations was 

developed for Vermont (Olson, 2014). 

Climate Change Application

In the application described in this report, the original regression equations are applied with a 

new climate variable (either precipitation or runoff) substituted into a given equation. To evaluate how 

future climate might affect peak flow magnitudes, data are applied from five climate models that were 

part of the most recent global climate assessment, the 5th Phase of the Coupled Model Intercomparison 

Project (CMIP5; Taylor and others, 2012). These models were selected based on discussions with 

climate scientists as to which of the CMIP5 climate models best represent past trends in precipitation in 

the Lake Champlain basin based on an analysis described in Guilbert and others (2014). Precipitation 

data from these climate models were obtained from downscaled projections at a spatial resolution of 30 

arc-seconds that are available from the National Aeronautics and Space Administration, as described by 

Thrasher and others (2013). Precipitation data were evaluated for two future scenarios, termed 

“Representative Concentration Pathways” (RCP) in CMIP5, that provide estimates of the extent to 

which greenhouse-gas concentrations in the atmosphere are likely to change through the 21st-century. 

These scenarios, RCP 4.5 and RCP 8.5, were evaluated for each climate model in CMIP5 (Taylor and 

others, 2012). RCP refers to potential future emissions trajectories of greenhouse gases, such as carbon 
6

dioxide. RCP 4.5 is considered a midrange-emissions scenario, and RCP 8.5 is a high-emissions 

scenario. 

Results were averaged for three future periods, from 2025 to 2049, 2050 to 2074, and 2075 to 

2099, following the approach used in the USGS Climate Change Viewer 

(http://www.usgs.gov/climate_landuse/clu_rd/nccv.asp). The downscaled precipitation data for each 

model and RCP scenario averaged over these 25-year periods were obtained from the developers of the 

USGS Climate Change Viewer (Jay Alder, U.S. Geological Survey, written communication, 2015). 

The new Web-based application calculates an ensemble of results based on all five climate 

models for any of the two greenhouse-gas scenarios and three time periods, and reports the results as 

summary statistics. These available results are meant to reflect a range of variation predicted from 

among the five models and two greenhouse-gas scenarios. Information on the models, greenhouse gas 

scenarios, and time periods is listed in table 1. 

Table 1. Climate models, greenhouse-gas emissions scenarios, and time periods used for estimates of 

future peak-flow magnitudes for streams in New York. 

Name/description Abbreviation Reference

Climate models

Beijing Normal University Earth System Model BNU–ESM Ji and others (2014)

Community Earth System Model with Biogeochemical 

Cycling Model, Version 1.0

CESM1–BGC Lindsay and others (2014)

Centre National de Recherches Météorologique 

Climatological Model 5

CNRM–CM5 Voldoire and others (2012)

Institut Pierre Simon Laplace Climate Model 5A, LowResolutionIPSL–CM5A–LR Dufresne and others (2013)

Norwegian Earth System Model, Intermediate 

Resolution

NorESM1–M Bentsen and others (2013)

Greenhouse-gas emissions scenarios

Representative Concentration Pathway 4.5 RCP 4.5 Thomson and others (2011)

Representative Concentration Pathway 8.5 RCP 8.5 Riahi and others (2011)

Time periods

Average from 2025 to 2049 2025–2049 USGS Climate Change Viewer

(http://www.usgs.gov/climate_landuse

/clu_rd/nccv.asp)

Average from 2050 to 2074 2050–2074 USGS Climate Change Viewer

(http://www.usgs.gov/climate_landuse

/clu_rd/nccv.asp)

Average from 2075 to 2099 2075–2099 USGS Climate Change Viewer

(http://www.usgs.gov/climate_landuse

/clu_rd/nccv.asp)

Assumptions

The application of these peak flow regression equations involves a space-for-time substitution 

approach because the regression equations were originally derived to account for spatial variation in 

flood peaks across each hydrologic region of New York and across Vermont. Extending spatial analyses 

of hydrologic responses through space-for-time substitution holds promise as an alternative approach to 

rainfall-runoff modeling for improving understanding of how climate change is likely to affect future 

hydrology (Sivapalan and others, 2011; Liu and Schwartz, 2012; Gyawali and others, 2015). The logic 

of applying these regression equations to describe how streams and rivers may respond to future 
7

changes in the precipitation regime is grounded in how basin characteristics, such as drainage area and 

slope, affect the magnitudes of peak flows. Here, a physical basis is assumed for the manner in which 

the geomorphic and land-cover independent variables in the regression equations affect peak flows. The 

user is cautioned, however, that these are statistical models based on association and that the 

independent variables in the regression equations may represent a variety of physical factors that are 

actually driving the peak flow response of basins. For example, the precipitation or runoff variable may 

in part represent the effects of elevation, which varies with these two measures. 

According to this approach, the key variable that will govern the change in peak flows is the 

exponent of either precipitation or runoff in the regression equations. If this exponent is greater than 

one, then peak flows will increase by a relative amount that is greater than the relative increase in 

precipitation or runoff. The opposite will occur if the exponent is less than one. For example, if 

precipitation in region 1 of New York increases by 10 percent in a future climate scenario, then the 50-

year recurrence-interval peak flow will increase by 11.38 percent because the exponent of the 

precipitation variable for this region and flow is 1.131 (table 1; Lumia and others, 2006).

Several additional simplifying assumptions were made in the development of this Web-based 

tool. A broad assumption is that the relation between annual precipitation or runoff and the magnitudes 

of peak flows will be the same in the future as these values were over the time periods for which the 

regression equations were developed. These relations were developed based on an analysis of all 

pertinent and available streamgage discharge data in New York through 1999 (Lumia and others, 2006) 

and in Vermont through 2011 (Olson, 2014). Discharge data from surrounding States and Canadian 

Provinces were also used in developing these relations. Several analyses of historical climate data and 

projections of future climate based on model output have indicated that the magnitude and frequency of 

large precipitation events is increasing (Groisman and others, 2005; Hodgkins and Dudley, 2011) and is 

likely to further increase in the future (Toreti and others, 2013; Jannssen and others, 2014). These and 

other analyses suggest that the relation between annual precipitation and runoff and the size and 

intensity of large precipitation events may change in the future (Silliman and others, 2013). The 

development of this Web-based application necessitated the use of the available regression equations, 

which consider only annual values of precipitation or runoff. 

Another important assumption made in the development of this Web-based application is 

applicable to the peak flow regression equations developed for hydrologic regions 2, 3, 4, and 6 in New 

York, which use annual runoff as the climatological variable. These annual runoff values are based on 

the hydrologic analysis of Randall (1996) from 1951 to 1980. Annual runoff is the difference between 

annual precipitation and annual evapotranspiration (ET) in the absence of changes in water storage or 

major human alteration of the water cycle in a basin. Recent reports have indicated that ET is increasing 

in the Northern Hemisphere and that continued increases are likely during the 21st-century (Miralles 

and others, 2014); however, some conflicting evidence has shown that changes in ET are complex, of 

high spatial variability, and likely to be influenced by multi-decadal climate oscillations (Jung and 

others, 2010). In this Web-based application, the ET-to-precipitation ratio is held constant and future 

changes in annual runoff are governed by changes in precipitation and resulting changes in ET. The 

effects of future changes in ET on the magnitude of peak flows are not well known at present but are 

likely to be substantial based on analyses of the role of low soil moisture in moderating the hydrologic 

impact of past large precipitation events (Ivancic and Shaw, 2015). 

A final assumption is pertinent only to the regression equations for region 3 in New York. In this 

region, the median maximum seasonal snow depth is one of the predictive variables in the regression 

(Lumia and others, 2006). Future snowfall and snowpack depth for region 3, which includes the Catskill 

Mountains, is expected to decrease during the 21st-century as the climate warms (Matonse and others, 
8

2013). The effects of a decreasing snowpack on floods in this region are not well known and were not 

considered in the development of this Web-based application. 

Limitations and Uncertainty

This application has several sources of uncertainty, which in total are difficult to quantify and 

are discussed below. The recommendation is to use this tool in an exploratory manner and to consider 

the results along with other sources of information to decide how future climate change may affect peak 

flow magnitudes. This field of investigation is evolving rapidly, and new and better applications and 

approaches are likely to emerge in the future. 

Check Basin in Current StreamStats Before Using Climate Change Application 

The peak flow regression equations implemented in StreamStats are not readily applicable to 

two types of watersheds: (1) those that are greatly affected by stream regulation such as reservoirs and 

(or) by withdrawals or additions for water supply or irrigation and (2) those where urban land use 

exceeds 15 percent of basin area (Lumia and others, 2006). None of the basins in Vermont are 

considered urbanized (Olson, 2014). 

The user of this Web-based application is encouraged to first obtain a table of basin 

characteristics from StreamStats before considering how climate change may affect flood magnitudes. 

Note that the current application provides a percent urban land-area value for any delineated basin 

reported as “Percentage of land-use from NLCD 2011 classes 21-24.” Regression-based estimates of 

current peak flow magnitudes for any basin with substantial regulation or diversion or for urban land 

that covers more than 15 percent of basin area are considered to have unacceptably high uncertainty, 

indicating that estimates of future peak flow magnitudes in such basins will also have unacceptably high 

uncertainty. Methods for estimating peak flows in ungaged urban watersheds are described by Sauer and 

others (1983). 

Another reason to explore a delineated basin in StreamStats before applying this new Web-based 

application is that some basins have geomorphic or land-cover characteristics, including basin drainage 

area, that are outside the linear range used to develop the peak flow regression equations in each region 

of New York or in Vermont. StreamStats provides a warning when an out-of-range basin has been 

delineated, and estimated flood magnitudes from such basins are considered to be poorly defined and 

should be used with extreme caution (Lumia and others, 2006). Lastly, the current climate explorer Web 

application requires that delineation points lie within hydrologic regions defined by Lumia and others 

(2006). For this reason, the stream delineation point (also termed “pourpoint”) cannot be in 

Pennsylvania, Canada, or offshore in the Great Lakes. 

Sources of Uncertainty 

There are several sources of uncertainty in the use of a regression-based approach to estimate 

peak flow magnitudes, especially when the role of future climate is being considered. The uncertainty of 

current [in this case, 1999 for New York and 2011 for Vermont] peak flow magnitude estimates can be 

obtained by using the standard error of prediction of the regression equations for each region of New 

York as described by Lumia and others (2006) and for Vermont as described by Olson (2014). Other 
9

sources of uncertainty in current peak flow estimates arise from inaccuracies in the basin delineation 

and in the predictive variables. 

The application of regression equations derived from spatial analysis to climate change involves 

a space-for-time substitution. First, the regression equations indicate associations only and not physical 

processes, so changes in future hydrologic processes, such as runoff, may not be adequately represented 

in the set of predictive variables in each regression equation as used in this original version of the Webbased application. Second, the regression equations were developed for a 20th-century climate regime 

that may not adequately reflect the changes in temperature, precipitation, and snowfall patterns that are 

forecast for the 21st-century. These challenges to this statistical approach indicate a need to devise 

strategies for testing these regression equations in a climate-change context. At the time of development 

of the Web-based application, this approach had not yet been adequately tested or validated. 

Considerable uncertainty also results from the assumptions and calculations embedded in each 

climate model and greenhouse-gas scenario. This source of uncertainty is difficult to evaluate; however, 

one approach has been to examine the ensemble of results available from various climate models and 

greenhouse-gas scenarios. This application has been designed to provide ensemble peak flow estimates 

reported as the mean, median, maximum, and minimum values for each selected combination of 

greenhouse-gas scenario and future 21st-century period for a given delineated basin. A final major 

source of uncertainty derives from the approach used for downscaling results from global climate 

models that have a spatial resolution of about 50 to 500 kilometers (Taylor and others, 2012) to the scale 

of 30 arc-seconds for the data used in this application. This source of uncertainty is potentially large but 

difficult to quantify (Mearns and others, 2014). The approach used to derive the NEX–DCP30 

downscaled dataset can be broadly described as a statistical approach, as described by Thrasher and 

others (2013) and the National Aeronautics and Space Administration (2013). 

References Cited

Bentsen, M., Bethke, I., Debernard, J.B., Iversen, T., Kirkevag, Seland, O., Drange, H., Roelandt, C., 

Seirstad, I.A., Hoose, C., and Kristjansson, J.E., 2013, The Norwegian earth system model, 

NorESM1–M—Part 1—Description and basic evaluation of the physical climate: Geoscientific Model 

Development, v. 6, p. 687–720.

Burns, D.A., Smith, M.J., and Freehafer, D.A., 2015, Application of flood regressions and climate 

change scenarios to explore estimates of future peak flows: U.S. Geological Survey data release, 

http://dx.doi.org/10.5066/F7WS8R9S. 

Dufresne, J.-L., Foujols, M.-A., Denvil, S., Caubel, A., Marti, O., Aumont, O., Balkanski, Y., Bekki, S., 

Bellenger, H., Benshila, R., Bony, S., Bopp, L., Braconnot, P. ,Brockmann, P., Cadule, P., Cheruy, F., 

Codron, F., Cozic, A., Cugnet, D., de Noblet, N., Duvel, J.-P., Ethé., C., Fairhead, L., Fichefet, T., 

Flavoni, S., Friedlingstein, P., Grandpeix, J-Y., Guez, L., Guilyardi, L., Hauglustaine, D., Hourdin, F., 

Idelkadi, A., Ghattas, J., Joussaume, S., Kageyama, M., Krinner, G., Labetoulle, S., Lahellec, A., 

Lefevbre, M.-P., Lefevre, F., Levy, C., Li, Z.X., Lloyd, J., Lott, F., Madec, G., Mancip, M., 

Marchand, M., Masson, S., Meurdesoif, Y., Mignot, J., Musat, I., Parouty, S., Polcher, J., Rio, C., 

Schulz, M., Swingedouw, D., Szopa, S., Talandier, C., Terray, P., Viovy, N., and Vuichard, N., 2013, 

Climate change projections using the IPSL–CM5 earth system model—From CMIP3 to CMIP5: 

Climate Dynamics, v. 40, no. 9, p. 2123–2165.

Groisman, P.Y., Knight, R.V., Easterling, D.R., Karl, T.R., Hegerl, G.C., and Razuvaev, V.N., 2005, 

Trends in intense precipitation in the climate record: Journal of Climate, v. 18, p. 1326–1350.
10

Guilbert, Justin, Beckage, Brian, Winter, J.M., Horton, R.M., Perkins, Timothy, and Bomblies, Arne, 

2014, Impacts of projected climate change over the Lake Champlain basin in Vermont: Journal of 

Applied Meteorology and Climatology, v. 53, p. 1861–1875.

Gyawali, Rabi, Griffis, V.W., Watkins, D.W., and Fennessey, N.M., 2015, Regional regression models 

for hydro-climate change impact assessment: Hydrological Processes, v. 29, p. 1972–1985.

Hodgkins, G.A., and Dudley, R.A., 2011, Historical summer base flow and stormflow trends for New 

England rivers: Water Resources Research, v. 47, no. 7, paper W07528, 16 p., accessed November 15, 

2015, at http://dx.doi.org/10.1029/2010WR009109.

Ivancic, T.J., and Shaw, S.B., 2015, Examining why trends in very heavy precipitation should not be 

mistaken for trends in very high river discharge: Climatic Change, v. 133, no. 4, p. 681–693.

Jannssen, Emily, Wuebbles, D.J., Kunkel, K.E., Olsen, S.C., and Goodman, Alex, 2014, Observationaland model-based trends and projections of extreme precipitation over the contiguous United States: 

Earth’s Future, v. 2, no. 2, p. 99–113.

Ji, D., Wang, L., Feng, J., Wu, Q., Cheng, H., Zhang, Q., Yang, J., Dong, W., Dai, Y., Gong, D., 

Zhang, R.-H., Wang, X., Liu, J., Moore, J. C., Chen, D., and Zhou, M., 2014, Description and basic 

evaluation of Beijing Normal University earth system model (BNU–ESM) version 1: Geoscientific 

Model Development, v. 7, p. 2039–2064.

Jung, Martin, Reichstein, Markus, Ciais, PhilippeSeneviratne, S.I., Sheffield, Justin, Goulden, M.L., 

Bonan, Gordon, Cescatti, Alessandro, Chen, Jiquan, de Jeu, Richard, Dolman, A.J., Eugster, Werner, 

Gerten, Dieter, Gianelle, Damiano, Gobron, Nadine, Heinke, Jens, Kimball, John, Law, B.E., 

Montagnani, Leonardo, Qiaozhen Mu, Mueller, Brigitte, Oleson, Keith, Papale, Dario, Richardson, 

A.D., Roupsard, Olivier, Running, Steve, Tomelleri, Enrico, Viovy, Nicolas, Weber, Ulrich, 

Williams, Christopher, Wood, Eric, Zaehle, Sönke, and Ke Zhang, 2010, Recent decline in the global 

land evapotranspiration trend due to limited moisture supply: Nature, v. 467, no. 7318, p. 951–954.

Lindsay, Keith, Bonan, G.B., Doney, S.C., Hoffman, F.M., Lawrence, D.M., Long, M.C., Mahowald, 

N.M., Moore, J.K., Randerson, J.T., and Thornton, P.E., 2014, Preindustrial control and 20th century 

carbon cycle experiments with the earth system model CESM1 (BGC): Journal of Climate, v. 27, no. 

24, p. 8981–9005. 

Liu, Ganming, and Schwartz, F.W., 2012, Climate-driven variability in lake and wetland distribution 

across the Prairie Pothole region—From modern observations to long-term reconstructions with 

space-for-time substitution: Water Resources Research, v. 48, no. 8, paper W08526, 11 p., accessed 

October 16, 2015, at http://dx.doi.org/10.1029/2011WR011539. 

Lumia, Richard, Freehafer, D.A., and Smith, M.J., 2006, Magnitude and frequency of floods in New 

York: U.S. Geological Survey Scientific Investigations Report 2006–5112, 152 p. 

Matonse, A.H., Pierson, D.C., Frei, Allan, Zion, M.A., Anandhi, Aavudai, Schneiderman, Elliot, and 

Wright, Ben, 2013, Investigating the impact of climate change on New York City’s primary water 

supply: Climatic Change, v. 116, nos. 3–4, p. 437–456. 

Mearns, L.O., Bukovsky, M.S., Pryor, S.C., and Magaña, Victor, 2014, Downscaling of climate 

information, chap. 5 of Ohring, George, ed., Climate change in North America—Regional climate 

studies: New York, Springer, p. 201–250. 

Miralles, D.G., van den Berg, M.J., Gash, J.H., Parinussa, R.M., de Jeu, R.A.M., Beck, H.E., Holmes, 

T.R.H., Jiménez, Carlos, Verhoest, N.E.C., Dorigo, W.A., Teuling, A.J., and Dolman, A.J., 2014, El 

Niño-La Niña cycle and recent trends in continental evaporation: Nature Climate Change, v. 4, 

p. 122–126.

National Aeronautics and Space Administration, 2013, NEX–DCP30—Downscaled 30 arc-second 

CMIP5 climate projections for studies of climate change impacts in the United States: National 
11

Aeronautics and Space Administration tech note, April 26, 10 p., accessed November 19, 2015, at 

https://cds.nccs.nasa.gov/wp-content/uploads/2014/04/NEX-DCP30_Tech_Note_v0.pdf. 

Olson, S.A., 2014, Estimation of flood discharges at selected annual exceedance probabilities for 

unregulated, rural streams in Vermont, with a section on Vermont regional skew regression by 

Veilleux, A.G.: U.S. Geological Survey Scientific Investigations Report 2014–5078, 27 p., 

appendixes, accessed October 16, 2015, at http://dx.doi.org/10.3133/sir20145078.

Randall, A.D., 1996, Mean annual runoff, precipitation, and evapotranspiration in the glaciated 

northeastern United States, 1951–80: U.S. Geological Survey Open-File Report 96–395, 2 sheets. 

[Also available at http://pubs.er.usgs.gov/publication/ofr96395.] 

Riahi, Keywan, Rao, Shilpa, Krey, Voker, Cho, Cheolhung, Chirkov, Vadim, Fischer, Guenther,

Kindermann, Georg, Nakicenovic, Nebojsa, and Rfaj, Peter, 2011, RCP 8.5—A scenario of 

comparatively high greenhouse gas emissions: Climatic Change, v. 109, no. 1, p. 33–57.

Sauer, V.B., Thomas, W.O., Jr., Stricker, V.A., and Wilson, K.V., 1983, Flood characteristics of urban 

watersheds in the United States: U.S. Geological Survey Water-Supply Paper 2207, 63 p. [Also 

available at http://pubs.er.usgs.gov/publication/wsp2207.] 

Silliman, J., Kharin, V.V., Zwiers, F.W., Zhang, X., and Bronaugh, D., 2013, Climate extremes indices 

in the CMIP5 multimodel ensemble—Part 2—Future climate projections: Journal of Geophysical 

Research Atmospheres, v. 118, no. 6, p. 2473–2493.

Sivapalan, Murugesu, Yaeger, M.A., Harman, C.J., Xu, Xiangyu, and Troch, P.A., 2011, Functional 

model of water balance variability at the catchment scale—1. Evidence of hydrologic similarity and 

space-time symmetry: Water Resources Research, v. 47, no. 2, paper W02522, 18 p., accessed 

October 16, 2015, at http://dx.doi.org/10.1029/2010WR009568. 

Taylor, K.E., Stouffer R.J., and Meehl, G.A., 2012, An overview of CMIP5 and the experiment design: 

Bulletin of the American Meteorological Society, v. 93, p. 485–498.

Thomson, A.M., Calvin, K.V., Smith, S.J., Kyle, G.P., Volke, April, Patel, Pralit, Delgado-Arias, 

Sabrina, Bond-Lamberty, Ben, Wise, M.A., Clarke, L.E., and Edmonds, J.A., 2011, RCP4.5—A 

pathway for stabilization of radiative forcing by 2100: Climatic Change, v. 109, no. 4, p. 77–94.

Thrasher, Bridget, Xiong, Jun, Wang, Weile, Melton, Forrest, Michaelis, Andrew, and Nemani, 

Ramakrishna, 2013, Downscaled climate projections suitable for resource management: Eos, v. 94, 

no. 37, p. 321–323.

Toreti, Andrea, Naveau, Philippe, Zampieri, Matteo, Schindler, Anne, Scoccimarro, Enrico, Xoplaki, 

Elena, Dijkstra, H.A., Gualdi, Silvio, and Luterbacher, Jürg, 2013, Projections of global changes in 

precipitation extremes from Coupled Model Intercomparison Project phase 5 models: Geophysical 

Research Letters, v. 40, no. 18, p. 4887–4892.

Voldoire, A., Sanchez-Gomez, E., Salas y Mélia, D., Decharme, B., Cassou, C., Sénési, S., Valcke, S., 

Beau, I., Alias, A., Chevallier, M., Déqué, M., Deshayes, J., Douville, H., Fernandez, E., Madec, G., 

Maisonnave, E., Moine, M.-P., Planton, S., Saint-Martin, D., Szopa, S., Tyteca, S., Alkama, R., 

Belamari, S., Braun, A., Coquart, L., and Chauvin, F., 2012, The CNRM–CM5.1 global climate 

model—Description and basic evaluation: Climate Dynamics, v. 40, no. 9, p. 2091–2121.
ISSN2331-1258 (online) 

http://dx.doi.org/10.3133/ofr20151235
For additional information write to: 

Director, New York Water Science Center 

U.S. Geological Survey 

425 Jordan Road 

Troy, NY 12180-8349 

Email: dc_ny@usgs.gov 

Information requests: 

(518) 285-5602
 

or visit our Web site at:
 

http://ny.water.usgs.gov
 

Publishing support by: 


The Pembroke Publishing Service Center.
 
Burns and others—Development of Flood Regressions and Climate Change Scenarios To Explore Estimates of Future Peak Flows—Open-File Report 2015-1235

ISSN 2331-1258 (online) 

http://dx.doi.org/10.3133/ofr20151235