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Introduction

Special Section on Climate Change andWater Resources: Climate Nonstationarity and

Water Resources Management

Jose D. Salas, M.ASCEEmeritus Professor, Dept. of Civil and Environmental Engineering,

Colorado State Univ., Fort Collins, CO 80523.

Balaji Rajagopalan, M.ASCE

Professor and Associate Chair, Dept. of Civil, Environmental and Archi-

tectural Engineering, and Fellow, Cooperative Institute for Research in

Environmental Sciences (CIRES), Univ. of Colorado, UCB 428, Room

ECOT 549, Boulder, CO 80309.

Laurel Saito, M.ASCE

Associate Professor, Dept. of Natural Resources and Environmental

Science, and Director, Graduate Program of Hydrologic Sciences, Univ.

of Nevada Reno, Mail Stop 186, Reno, NV 89557 (corresponding author).

E-mail: [email protected]

Casey Brown, M.ASCE

Assistant Professor, Dept. of Civil and Environmental Engineering, Univ.

of Massachusetts, 12B Marston Hall, Amherst, MA 01003-9293.

DOI: 10.1061/(ASCE)WR.1943-5452.0000279

Over the past three decades, hydrologists and water resources

specialists have been concerned with the issue of nonstationarity

arising from several factors. First is the effect of human intervention

on the landscape that may cause changes in the precipitationrunoff

relationships at various temporal and spatial scales. Second is theoccurrence of natural events such as volcanic explosions or forest

fires that may cause changes in the composition of the air, the soil

surface, and geomorphology. Third is the low-frequency compo-

nent of oceanicatmospheric phenomena that may have significant

effects on the variability of hydrological processes such as annual

runoff, peak flows, and droughts. Fourth is global warming, which

may cause changes to oceanic and atmospheric processes, thereby

affecting the hydrological cycle at various temporal and spatial

scales. There has been a significant amount of literature on the

subject and thousands of research and project articles and books

published in recent decades.

Examples of human intrusion on the landscape are the changes

in land use resulting from agricultural developments in semiarid

and arid lands (e.g., Pielke et al. 2007, 2011), changes caused

by large-scale deforestation (e.g., Gash and Nobre 1997), changes

resulting from open-pit mining operations (e.g., Salas et al. 2008),

and changes from increasing urbanization in watersheds (e.g.,

Konrad and Booth 2002, Villarini et al. 2009). These intrusions

change hydrologic response characteristics such as the magnitude

and timing of floods. In many situations, current systems and man-

agement practices will be ill equipped to cope with such changes

unless adjustments are made. Large-scale landscape changes such

as deforestation in the tropical regions can potentially alter atmos-

pheric circulation patterns, and consequently affect global weather

and climate (e.g., Lee et al. 2008, 2009).

Major natural events, such as the volcanic explosion of MountSt. Helens in 1980 or the El Chichon volcanic explosion of 1982

induce a shock to the climate system in the form of global cooling

that continues for several years. These events can also affect

global circulation. Low-frequency climate drivers of the oceanic

atmospheric system such as the El Nio/Southern Oscillation

(ENSO), Pacific Decadal Oscillation (PDO), Atlantic Multidecadal

Oscillation (AMO), and Arctic Oscillation (AO) modulate global

climate at interannual and multidecadal time scales. These drivers

are the main sources of nonstationarity in global climate and hy-

drology. Large numbers of papers documenting the effect of these

drivers on global hydroclimatology continue to emerge (e.g., Dilley

and Heyman 1995; Mantua et al. 1997; Enfield et al. 2001; Akintug

and Rasmussen 2005; Hamlet et al. 2005).

In addition to climate variability and change due to the previ-ously mentioned factors, anthropogenic warming of the oceans and

atmosphere because of increased greenhouse gas concentrations

and the ensuing changes to the hydrologic cycle are topics of seri-

ous pursuit. The international scientific community is making

strides in understanding the potential warming and its effects on

all aspects of climate variability [Intergovernmental Panel on

Climate Change (IPCC) 2007], but the impacts on the hydrologic

cycle remain debatable and inconclusive (e.g., Cohn and Lins 2005;

Legates et al. 2005; Hirsch and Ryberg 2011). Based on analyses of

the global mean CO2 (GMCO2) and annual flood records in the

United States, no strong statistical evidence for flood magnitudes

increasing with GMCO2 increases were found (Hirsch and Ryberg

2011). Although general circulation models have had success in the

attribution of warming global temperatures to anthropogeniccauses, their credibility and utility in reproducing variables that

are relevant to hydrology and water resources applications is less

clear. For example, the IPCC Report for Latin America acknowl-

edges that the current GCMs do not produce projections of

changes in the hydrological cycle at regional scales with confi-

dence. In particular the uncertainty of projections of precipitation

remain high : : : That is a great limiting factor to the practical use of

such projections for guiding active adaptation or mitigation poli-

cies (Magrin et al. 2007; Boulanger et al. 2007).

A variety of methods exist that address the concern of nonsta-

tionarity in hydrological processes and the topic remains an active

research area. For example, in watersheds in which increasing

urbanization has been documented causing significant effects in

the flood response and magnitude, watershed modeling has beenutilized to estimate the possible changes in the flood frequency

and magnitude. Frequency analysis methods also have been applied

when the parameters (or the moments such as the mean and vari-

ance) of a given model (e.g., the Gumbel model) may vary with

time (e.g., Strupczewski et al. 2001; Clarke 2002). In addition,

the role that low-frequency components of the oceanic

atmospheric system (represented, for example, by large-scale

oscillations such as ENSO, PDO, and AMO) have on extreme

events such as floods has been recognized. These large-scale forc-

ing factors have been shown to exert in-phase and out-of-phase

oscillations in the magnitude of floods, mean flows, and droughts

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(e.g., Jain and Lall 2000; Franks and Kuczera2002; Sveinsson et al.

2003; Mote et al. 2003). Several approaches have been proposed in

the literature to address nonstationarity, including flood frequency

distributions with mixed components (e.g., Waylen and Caviedes

1986; Rossi et al. 1984; Salas et al. 1990), flood frequency models

embedded with trend components (e.g., Strupczewski et al. 2001;

Clarke 2002; El Adlouni et al. 2007), flood frequency modeling

considering shifting patterns (e.g., Kiem et al. 2003 and Sveinsson

et al. 2005), and flood frequency modeling considering covariates

(e.g., Katz et al. 2002; Clarke 2002; Griffis and Stedinger 2007).

Also, multicentury variability of climatic information has beenincorporated into paleoflood frequency analysis techniques

(e.g., Stedinger and Cohn 1986 and Frances et al. 1994).

In addition, stochastic approaches have been developed to deal

with nonstationarities to simulate, for example, monthly and yearly

hydrologic processes such as streamflows (e.g., for drought studies

and designing reservoirs) using both short memory models such

as shifting mean models and regime-switching models that have

features of nonstationarity (e.g., Boes and Salas 1978; Thyer

and Kuczera 2000; Sveinsson et al. 2003; Akintug and

Rasmussen 2005) and long memory models such as fractionally

differenced autoregressive integrated moving average (e.g.,

Montanari et al. 1997) and fractional Gaussian noise (Mandelbrot

1971; Koutsoyiannis 2002). The effects of long-term climatic fluc-

tuations on the dynamics of wet and dry periods also have beenincorporated through the development of paleo reconstructions of

streamflows (e.g., Cook et al. 1999 Biondi et al. 2008). The field of

stochastic hydrology has expanded in past decades to accommodate

both stationary and nonstationary features of hydrologic regimes.

These stochastic models and approaches are capable of represent-

ing a wide range of hydroclimatic variability including year-to-

year and multidecadal variability, and have been quite useful in

practice for generating alternative hydrologic scenarios that

may occur in the future (e.g., Sveinsson et al. 2003 and

Koutsoyiannis 2011).

In addition to these advances in dealing with nonstationarity in

hydrologic processes, climate research has identified and devel-

oped a number of tools and products for dealing with a changing

climate that may be useful for addressing various societal needsincluding water resources. In particular, advances in projections

of future climate from global circulation models (GCMs) have been

an active area of research. However, such projection approaches

have several constraints that must be taken into account. Chiefly,

they provide information at a coarse spatial and temporal scale, and

they are better at capturing global circulation features rather than

regional or precipitation and temperature at a location. This has

necessitated downscaling tools to translate coarse-scale GCM

outputs to the local scale that is needed for water resources decision

making. Clearly, this adds another layer of uncertainty to the infor-

mation in addition to all the attendant uncertainties in the GCM

projections. This puts decision makers in a conundrum; on the

one hand, they wish to make planning decisions on the basis of

the GCM projections, but on the other hand, the significant uncer-

tainties in the information is problematic for confident decision

making. This dilemma is well articulated in an aptly titled recent

paper by Kundzewicz and Stakhiv (2010)Are climate models

ready for prime time water resources management applications,

or is more research needed? Kundzewicz and Stakhiv (2010) sug-

gest that more research is needed in reducing climate uncertainties

before GCM outputs can be used effectively for adaptation plan-

ning and design. However, Dessai et al. (2009) argue that the ac-

curacy of climate predictions is limited by fundamental irreducible

uncertainties arising from limitations in knowledge of the underly-

ing physical processes, the chaotic nature of the climate system,

and from human actions. Furthermore, Trenberth (2010) predicts

that the uncertainty of the climate predictions and projections in

the next IPCC assessment report (due in 2013) will be much greater

than in previous reports.

Thus, the water resources community is confronted with a new

paradigm in which a suite of valuable information has become avail-

able (i.e., observed data, reconstructed paleodata, GCM projections,

downscaled results, and hydrologic model outputs), but they all in-

volve various degrees of uncertainties. Water resources planners,

managers, and consultants must deal with the difficult task of in-

stituting robust management policies with uncertain information.Literature abounds with papers that discuss the outputs from global

climate models, the approaches to downscale this climate informa-

tion to regional scale hydrology, and the applications of various

physically based and statistical models for estimating the hydro-

logic quantities needed for decision making. However, there is less

literature on risk-based decision making and adaptation of vulner-

able water systems considering the effect of uncertain information,

although in the last few years a number of suggestions have been

made for developing and implementing robust designs and policies

that accommodate uncertain information (e.g., Dessai and van der

Sluijs 2007; Brown et al. 2009; Stakhiv 2011).

It is clear that the world is warming, and despite the shortcom-

ings for estimating its effects, water planners and managers need to

consider what is known and uncertain and make decisions (e.g.,

Wiley and Palmer2008; McKinney et al. 2011). Rogers (2008) cau-

tions against choosing mitigation over adaptation in managing for

climate change, stressing the need to keep the focus on what is

known and the scientific basis of that knowledge when making de-

cisions to adapt to climate change. Techniques need to be developed

concurrently on the use of uncertain information in water resources

management. This is the motivating view for this special section. To

this end, this special section of the Journal of Water Resources

Planning and Management focuses on providing tools and

example implementations that can be used by water resources plan-

ners and decision makers to adapt to climate variability and change.

Thus, papers in this section demonstrate to water resources manag-

ers and practitioners the utility of tools that can take in uncertainclimate information for robust decision making in all aspects of

water resources management (e.g., quantity, quality, and demand).

Nine papers have emerged from the review process to form this

special section. The papers in the section cover the following range

of topics: (1) How to make water resources systems operations and

management decisions in a changing climate (Miller et al. and Ray

et al.); (2) understanding the effect of climate change on population

and consequently on flood management (Kollat et al.); (3) how to

view uncertainty in climate science and water utility planning and

the tools that can combine the two (Barsugli et al.); (4) managing

stream water quality for healthy aquatic life in a warmer world

(Thompson et al.); (5) managing irrigated agriculture amid climate

uncertainty (Meza et al. and Vicua et al.); and (6) incorporating

climate change information in water quality management (Towleret al. and Johnson et al.). These topics cover a broad range of water

resources management issues, including both quantity and quality,

and will therefore be of interest to a wide audience of researchers

and practitioners.

Acknowledgments

Partial funding was provided to the first and third authors

from P2C2: Multi-Century Streamflow Records Derived from

Watershed Modeling and Tree Ring Data, National Science

386 / JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT ASCE / SEPTEMBER/OCTOBER 2012



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Foundation ATM-0823480. We also would like to thank Professor

R. Palmer for his important suggestions and editing of this article.

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