Physically Based Mountain Hydrological Modeling Using ... · Physically Based Mountain Hydrological Modeling Using Reanalysis Data in Patagonia SEBASTIAN A. KROGH Department of Civil

Physically Based Mountain Hydrological Modeling Using ReanalysisData in Patagonia

SEBASTIAN A. KROGH

Department of Civil Engineering, Universidad de Chile, Santiago, Chile

JOHN W. POMEROY

Centre for Hydrology, University of Saskatchewan, Saskatoon, Saskatchewan, Canada

JAMES MCPHEE

Department of Civil Engineering, and Advanced Mining Technology Center, Universidad

de Chile, Santiago, Chile

(Manuscript received 30 October 2013, in final form 15 August 2014)

ABSTRACT

A physically based hydrological model for the upper Baker River basin (UBRB) in Patagonia was de-

veloped using the modular Cold Regions Hydrological Model (CRHM) in order to better understand the

processes that drive the hydrological response of one of the largest rivers in this region. The model includes

a full suite of blowing snow, intercepted snow, and energy balance snowmelt modules that can be used to

describe the hydrology of this cold region. Within this watershed, snowfall, wind speed, and radiation are not

measured; there are no high-elevation weather stations; and existing weather stations are sparsely distributed.

The impact of atmospheric data fromECMWF interim reanalysis (ERA-Interim) andClimate Forecast System

Reanalysis (CFSR) on improving model performance by enhancing the representation of forcing variables was

evaluated. CRHM parameters were assigned for local physiographic and vegetation characteristics based on

satellite land cover classification, a digital elevation model, and parameter transfer from cold region environ-

ments in western Canada. It was found that observed precipitation has almost no predictive power [Nash–

Sutcliffe coefficient (NS), 0.3]when used to force the hydrologicmodel, whereasmodel performanceusing any

of the reanalysis products—after bias correction—was acceptable with very little calibration (NS . 0.7). The

modeled water balance shows that snowfall amounts to about 28% of the total precipitation and that 26% of

total river flow stems from snowmelt. Evapotranspiration losses account for 7.2%of total precipitation, whereas

sublimation and canopy interception losses represent about 1%. The soil component is the dominantmodulator

of runoff, with infiltration contributing as much as 73.7% to total basin outflow.

1. Introduction

Chilean Patagonia contains some of the largest water

reserves in the southern cone of South America. Tied

with economic growth, increasing energy demands have

turned public attention to the region’s rivers (the largest

in Chile) and their yet untapped hydropower potential.

Although the statistical properties of flow for the major

rivers in the area are known because of a reasonably

comprehensive hydrometric network, the processes re-

sponsible for the hydrology of Patagonia are poorly

understood, and so the reliability of historical information

for streamflow prediction cannot be evaluated. In light of

ongoing environmental change, hydrological investiga-

tions are crucial to achieve a better understanding of en-

vironmental systems dynamics and their possible response

to human interference across spatial and temporal scales.

A particular concern is climate change, which has recently

resulted in substantial glacier retreat in the region (Rivera

et al. 2007) with unknown impacts on river flow.

In spite of these concerns, very few studies of the hy-

drology and climatology of the region have been con-

ducted. Aravena (2007) developed a 400-yr precipitation

Corresponding author address: James McPhee, Department of

Civil Engineering, Universidad de Chile, Av. Blanco Encalada

2002, Santiago 8370449, Chile.

E-mail: [email protected]

172 JOURNAL OF HYDROMETEOROLOGY VOLUME 16

DOI: 10.1175/JHM-D-13-0178.1

� 2015 American Meteorological Society

mailto:[email protected]

reconstruction using tree ring data and glacier fluctua-

tions in the Austral Chilean Andes, finding important

decadal variations for the northwest and central Patago-

nia and also a strong biannual oscillation for the south-

ernmost region. Garreaud et al. (2009) described the

mean annual and decadal patterns of precipitation in

South America and how they are influenced both by cli-

matic indexes (Pacific decadal oscillation, Antarctic

Oscillation, and El Niño–Southern Oscillation) and oro-

graphic effects using a large-scale paleoclimatic approach.

Lopez et al. (2008) studied variability in snow-covered

areas (SCAs) of the Northern Patagonian Ice Field

(NPIF) during 2000–06 and correlated SCA to precip-

itation and air temperature, obtaining the highest corre-

lations for temperature (r2 5 0.75). Rivera et al. (2007)

quantified decreases in the NPIF of up to 4.0 60.97myr21 for ice thickness and up to 3.2% 6 1.5% or

140 6 61km2 for area over 1975–2001, based on remote

sensing and in situ data.

Although the above studies represent improvements

on their respective fields, there remains a major gap in

published comprehensive hydrological investigations in

the region. Dussaillant et al. (2012) present the first

description of the main hydrological patterns (pre-

cipitation, temperature, and streamflow) of the Baker

River basin using observed data. They also discuss the

difficulties associated with undertaking this task given

the sparse distribution of gauging stations, together with

the significant topographical and climatological gradi-

ents existing in the region. Barría (2010) developed

a statistical approach for obtaining monthly streamflow

forecasts for the Baker and Pascua river basins. How-

ever, because this research was entirely data driven, it

does not increase knowledge of the physical processes

governing water movements in the basin. Given the

combination of climate change and increased pressure

to use the water resources in the region, it becomes

paramount for the scientific and decision-making

community to increase their knowledge on the hy-

drological functioning of this relatively pristine envi-

ronmental system.

Physically based models offer the opportunity of

comprehending physical interactions between processes

and variables within the hydrological cycle, an advan-

tage that cannot be achieved with other types of models

(empirical, conceptual, or statistical, for example). The

Cold Regions Hydrological Model (CRHM; Pomeroy

et al. 2007) is a physically based model developed at the

Centre for Hydrology, University of Saskatchewan, with

the aim of improving the understanding of hydrological

processes in cold environments, which are particular in

the sense that a host of specific phenomena such as snow

and ice accumulation, interception, transport and melt,

infiltration through frozen soils, and cold water bodies

control the hydrograph timing. CRHM has a limited

need for calibration (Pomeroy et al. 2007), and most

(but not all) of its parameters can be inferred from in-

tensive field or modeling studies. This, together with its

modular nature and open structure, makes it particularly

suitable for testing hydrological hypothesis in poorly

gauged or ungauged basins. Gonthier (2011) developed

the first hydrological study in Chile using CRHM. He

analyzed three high mountain basins in the Chilean

semiaridAndes (328S), calibrating parameters regarding

soil moisture and routing processes against streamflow

records. All results showed Nash–Sutcliffe coefficient

(NS) values below 0.6 and overestimation of snow ac-

cumulation up to 400%with respect to local snow pillow

data. Poor modeling results were attributed in part to

the very low density of meteorological measurements

within the basin and thus great uncertainty in meteo-

rological driving variables. Fang and Pomeroy (2007)

developed a CRHM with the aim of understanding the

dynamical processes that govern drought phenomena in

the Canadian prairies. A sensitivity analysis to meteo-

rological input data was carried out, showing that even

under moderate drought scenarios of 15% reduction in

winter precipitation and 2.58C increase in winter mean

air temperature, spring runoff may disappear com-

pletely. Ellis et al. (2010) developed a CRHM to assess

the differences in snowmelt and snow accumulation in

forest and clearing sites, achieving an NS model effi-

ciency value of 0.51 for snow water equivalent (SWE),

with slightly better representation on clearing sites;

these results show the CRHM predictive potential when

no calibration is undertaken. Another study was de-

veloped by Fang and Pomeroy (2009), who character-

ized blowing snow redistribution in prairie wetlands,

obtaining good results either with an aggregated or

a fully distributed spatial representation. Pomeroy et al.

(2012) developed a CRHM in a forested mountainous

basin with minimal calibration in order to simulate the

impacts of forest disturbance in the basin hydrology.

Results show different streamflow volume responses

for each scenario, ranging from 2% for small forest

reduction impacts to 8% for a complete forest-burning

event. A recent study developed a CRHM for an alpine

to subalpine Canadian Rockies catchment and evalu-

ated uncalibrated model performance against snow

accumulation, soil moisture, groundwater, and sub-

basin and basin streamflow over several years (Fang

et al. 2013) with acceptable predictive performance for

all variables except groundwater. Tests of CRHM in

alpine and steppe environments of the Qinghai Tibetan

Plateau show good performance for snowpack, runoff,

and streamflow simulation when blowing snow, energy

FEBRUARY 2015 KROGH ET AL . 173

balance snowmelt, and frozen soil infiltration options

are used (Zhou et al. 2014).

Atmospheric reanalyses are a scientific method for

developing a comprehensive record of weather and cli-

mate change over time and can complement the infor-

mation provided by scarce meteorological observations in

remote regions in order to obtain surface meteorology to

force land surface models (Sheffield et al. 2004). Quoting

Saha et al. (2010, p. 1015), ‘‘[t]he general purpose of

conducting reanalyses is to produce multiyear global

state-of-the-art gridded representations of atmospheric

states, generated by a constant model and a constant

data assimilation system.’’ The current generation of

reanalyses assimilates data from satellite observations;

in situ surface measurements such as 2-m temperature,

relative humidity, and wind speed; and upper-atmosphere

variables from radiosondes, wind profilers, and aircraft

(Dee et al. 2011; Saha et al. 2010). In this study, we test two

of themost recent products available: theNationalCenters

for Environmental Prediction–National Center for At-

mospheric Research (NCEP–NCAR) Climate Forecast

System Reanalysis (CFSR) and the European Centre

forMedium-RangeWeather Forecasts (ECMWF) interim

reanalysis (ERA-Interim). ERA-Interim is the latest

global atmospheric reanalysis produced by the ECMWF,

and it covers the period from 1 January 1989 to the

present. The gridded data product includes a large

variety of 3-hourly surface parameters and 6-hourly

upper-air parameters (Dee et al. 2011). This reanalysis

has a spatial resolution of 1.58 and 37 pressure levels

[increasing by 14 levels from the preceding version

40-yr ECMWF Re-Analysis (ERA-40)]. On the other

hand, CFSR spans the 31-yr period from 1979 to 2009. It

was designed to be a high-resolution coupled atmosphere–

ocean–land surface–sea ice system and to provide the

best estimation of the state of these domains over this

period (Saha et al. 2010). Its spatial resolution varies

from 0.258 at the equator to 0.58 beyond the tropics,

with 40 pressure levels. Ward et al. (2011) evaluate

several precipitation products [ERA-40, NCEP–

NCAR Global Reanalysis 1 (R-1), Precipitation Esti-

mation from Remotely Sensed Imagery Using Artificial

Neural Networks (PERSIANN), and Tropical Rainfall

Measuring Mission (TRMM)] over the Andes Cordil-

lera (Baker and Paute basins, Chile–Argentina and

Ecuador, respectively), comparing them with observed

data interpolations based on Thiessen polygons. For the

Baker River basin, precipitation products were always

above observed data, which can be explained by the low

density and low elevation of the station. Ward et al.

(2011) also highlighted the secondary importance of the

interpolation scheme used, arguing that errors in in-

terpolation are dominated by the low density of rain

gauges throughout the Baker basin. Silva et al. (2011)

compared CFSR and other NCEP–NCAR reanalyses,

that is, R-1 and the NCEP–U.S. Department of Energy

(DOE) Second Atmospheric Model Intercomparison

Project (AMIP-II; R-2), over South America. Over the

Andes Cordillera, in particular, all three reanalyses

(CFSR, R-1, and R-2) seem to overestimate total pre-

cipitation, but significant improvements are associated

with CFSR, probably because of its higher spatial reso-

lution and better representation of regional topography.

The objective of this paper is to describe the first in-

depth analysis of the hydrological function of the upper

Baker River basin (UBRB), based on a physically based

hydrological model (CRHM) driven using meteorolog-

ical station observations and global meteorological

model reanalyses. Hence, the main goals of this research

are (i) to describe a plausible combination of hydro-

logical processes giving rise to the observed streamflow

record in order to enable future global change impact

assessments and (ii) to demonstrate the value of

reanalysis data in combination with a physically based

hydrologic model in achieving the former goal. In this

paper, we also highlight the existing information gaps

that preclude a better understanding of the hydrology

of the region and suggest future avenues for improving

such knowledge.

2. Study site and observations

a. Location

TheUBRB is defined by the Bertrand Lake outlet and

has an area of 15 904 km2 (see Fig. 1). At this location,

the Baker River has a mean annual discharge of

566m3 s21 (Fig. 3), being the largest river in Chile when

it reaches the ocean. The basin is characterized by very

heterogeneous climate, geology, and land cover fea-

tures, with landscape types that include glaciers and

icefields [2787 km2 (17.5%)], rivers and lakes [2109 km2

(13.3%)], dense forest [2777 km2 (17.5%)], grassland

and shrubland [5984 km2 (37.6%)], peatlands [66 km2

(0.4%)], and bare rock [2181 km2 (13.7%)] (CONAF/

CONAMA 1999; see Fig. 7, described in greater detail

below). The regional climate is dominated by the in-

teraction of weather fronts traveling east from the Pa-

cific Ocean with the topographic barrier of the Andes

Cordillera. Here, mountains reach from 200 up to

4000m MSL over a distance of less than 100 km and

generate a steep west-to-east precipitation gradient

(Warren and Sugden 1993); as a result, precipitation in

the mountainous western part of the basin can reach

more than 2000mm annually, whereas the low-lying

eastern region has a steppe-like climate (Pampas) with

annual precipitation on the order of 400mm(seeFigs. 2, 4).


Although precipitation decreases somewhat during the

spring–summer season (September–March), rainy con-

ditions persist throughout the year. Air temperature

reaches freezing conditions during the winter (June–

August) season, whereas maximum summer tempera-

tures usually oscillate around 158C. The prevalence of

cold conditions indicates that snow and ice formation and

melt should be a dominating process in the region’s

hydrology. Figure 3 illustrates this effect, with UBRB

flows peaking during the warm season (February) with

a strong seasonal pattern.

b. Observed data

Standard streamflow and meteorological data are

available from stations operated by the DirecciónGeneral de Aguas (National Water Directorate) ofChile. Streamflow is more reliably measured than pre-cipitation in the region, mainly because only rainfall butnot snowfall is measured and because of the sparsenessof the rainfall gauge network and gaps in the rainfall datarecords. Rainfall observations are only available in val-ley bottom locations, with no measurements in themountains where most runoff occurs. Rainfall that ismeasured is subject to undercatch because of wind andfreezing effects, whereas streamflow measurements areobtained in mostly stable river sections with rating

curves that are updated periodically (B. Nazarala,Dirección General de Aguas, 2014, personal communi-cation). Tables 1 and 2 show information on the existing

stations. Data gaps in weather stations, sometimes rep-

resenting up to 83% of a station’s records within

a modeling period, prevented the use of all station re-

cords existing for the region. The density of rain gauge

stations over the basin is approximately 0.2 stations per

1000 km2. This value is compared with the recommen-

dation of the World Meteorological Organization

(WMO 1994), which set a minimum precipitation station

density of 1 station per 250km2 or 4 stations per 1000 km2

for mountainous regions; this suggests that UBRB in-

strumentation is below international standards.

c. Reanalysis of precipitation

Atmospheric model data from ERA-Interim and

CFSR were considered in an attempt to overcome the

lack of snowfall measurements and the limited number

and unrepresentative location of meteorological stations

for hydrological modeling. These data products represent

state-of-the-art global climate characterization and con-

stitute improvements with respect to previous versions.

As a first step for using these as input to the hydrological

model, they are analyzed in the context of local weather

and streamflow data.

FIG. 1. Baker River basin at the drainage of Bertrand Lake. Dashed line shows a cross section at 46.58S.


In light of the documented effect of topography on the

spatial distribution of precipitation, a first step involves

comparing how each reanalysis product represents ef-

fects of the regional topography; this is closely linked to

each product’s spatial resolution. Figure 4 shows a cross

section at 46.58S showing ERA-Interim and CFSR el-

evation compared with the Advanced Spaceborne

Thermal Emission and Reflection Radiometer–Global

Digital ElevationModel (ASTER-GDEM; NASA 2014)

with a 30-m spatial resolution; annual precipitation for

the modeling window is also shown in Fig. 4 (top), with

bars appropriately located given the spatial resolution

of each reanalysis (three centroids for ERA-Interim,

nine centroids for CFSR). Bars representing observed

precipitation are located at the approximate longitude

of the corresponding meteorological stations. Al-

though both reanalyses include realistic approxima-

tions of the regional topography, CFSR’s higher spatial

FIG. 2. Monthly mean precipitation and temperature observed at meteorological stations. Error bars correspond to

temperature standard deviations.

FIG. 3. (left) Mean monthly streamflow of the Baker River at the drainage of Bertrand Lake. Error bar

shows monthly streamflow standard error. (right) The hypsometric curve with approximate meteorological station

location.


resolution allows for a better representation than ERA-

Interim, with steeper and higher topography at the

western side of the basin. Although the absence of ob-

servations renders it impossible to assess which reanalysis

achieves better precipitation estimates at the western

edge of the region, for the central section—around

728W—CFSRmatches the observed values more closely.

When comparing within a reference centroid (728S,46.58W), differences of about 3 times the mean annual

precipitation were detected (682 and 1870mmyr21 for

CFSR and ERA-Interim, respectively). As mentioned

before, CFSR has 3 times the spatial resolution of ERA-

Interim in this region, but in terms of density of centroids

within the basin, the difference rises to 6 times, with six

centroids for CFSR (about 0.4 centroids per 1000km2)

and one centroid for ERA-Interim (0.06 centroids per

1000km2).

A second analysis focused on the meteorological

characteristics of individual storm events as represented

by each data source. This analysis is considered to be

very relevant to runoff generation as the characteristics

of large storms are critical to mountain hydrology re-

gimes. Scatterplots (see Fig. 5) show important differ-

ences; for example, the observed record shows almost no

precipitation events concurrent with air temperatures

below 08C, likely because of the lack of snowfall mea-

surements, whereas reanalysis data show a significant

amount of precipitation occurring at temperatures be-

low 08C, for instance, 127 (17% of all events) and 93

(13% of all events) events for ERA-Interim and CFSR,

respectively, during the modeling period. Given that

snowstorm events are routinely noticed in the region, it

can be concluded that the current precipitation obser-

vational record does not include snowfall, and a low

bias in recorded annual precipitation results from this

lack of measurement. Another difference between re-

analysis and observed data is the fact that most of the

observed precipitation events occur at about 108C,which is 58C above the temperature at which the ma-

jority of precipitation events from the reanalyses occur.

In this context, reanalysis data tend to include colder

precipitation events, with most precipitation occurring

at temperatures below 158C. On the other hand, ob-

served temperatures show precipitation events with

daily temperatures up to 208C; the lack of snowfall

measurements clearly puts a substantial bias on ob-

servations that are expected to have a large impact on

the ability of any mountain hydrology model to simu-

late streamflow.

Also, the total number of precipitation events in the

modeling period differs significantly between reanalysis

and observational data. For example, the weather sta-

tions recorded 216 precipitation events between 1 July

2004 and 30 June 2006, while CFSR and ERA-Interim

produced 664 and 616 events, respectively. Many of

these excess events occurred during the winter season,

which was particularly problematic for weather station

observations. When inspecting observed daily streamflow

TABLE 1. Available weather stations within UBRB.Monthly precipitation and temperature are from within the modeling period (source:

Dirección General de Agua).

Weather station Start End Z (m MSL)

Available monthly

precipitation (%)

Available monthly

temperature (%) SB assignation

Puerto Guadal* Dec 1993 — 210 92 92 SB4, SB5, SB7, SB8

Bahía Murta* Aug 1993 — 240 96 96 SB2, SB7, SB8

Puerto Ibáñez* Dec 1961 — 215 100 96 SB1, SB3, SB6, SB8

Chile Chico Oct 1963 Nov 2004 215 17 0 —

Villa Cerro Castillo Oct 1992 — 345 100 0 —

*Stations used in this study.

TABLE 2. Available stream gauge stations within UBRB.Monthly streamflow is from within modeling period (source: Dirección Generalde Agua).

Stream gauge

station Start End Elev (m MSL)

Mean annual

runoff (m3 s21)

Available monthly

streamflow (%) SB assignation

Río Baker* Apr 1963 — 200 568 100 —

Río Murta* Dec 1985 — 219 93.5 100 SB2

Río Ibáñez* Aug 1970 — 220 158 100 SB1

Río Jeinimeni Dec 1995 Jan 2004 — 27 0.05 —

Río Bagno Dec 1995 — — 1.5 92 —

Río Claro Feb 1985 Nov 2002 — 9.7 0 —

*Stations used in this work.


(Río Ibáñez) and precipitation (Puerto Ibáñez) data(Fig. 6), several high-flow events are concurrent with

zero measured rainfall. Two examples of this are dis-

played and highlighted with dashed lines in Fig. 6

(bottom), this time with reanalysis data shown in col-

umns. While high-flow events can be caused by snowmelt

without precipitation, the streamflow peaks show a good

agreement with the reanalyses storm precipitation tim-

ing, in contrast with observed precipitation, where several

gaps can be seen. These observation gaps strongly affect

the potential of the observed data series to inform the

modeling exercise.

3. Methodology

a. Cold Regions Hydrological Model

The CRHM is a modular, physically based model

developed by the University of Saskatchewan for sim-

ulating the hydrological cycle across temporal and spa-

tial scales, with specific characteristics suited for

representing hydrological processes in both cold region

and temperate environments (Pomeroy et al. 2007). One

of the main features of this model is its flexible modular

structure, where different modules represent each hy-

drological process, such as snow redistribution by wind,

FIG. 4. (top) Mean annual precipitation for the modeling period (from 1 Jul 2004 to 30 Jun

2006). (bottom) Altitude cross section at 46.58S. ASTER-GDEM topography corresponds to

average altitude between 46.258 and 46.758S.

FIG. 5. Observed precipitation and temperature from Puerto Ibáñez meteorological station compared to ERA-Interim and CFSRprecipitation for centroid (468S, 72.58W) and centroid (46.58S, 728W), respectively. Dashed line at 08C is presented to approximate be-

tween snowfall and rainfall events.


snow interception, sublimation, evapotranspiration, and

infiltration. These modules may be selected and acti-

vated in order to achieve a better representation of the

hydrological system, according to the modeler’s un-

derstanding of the local processes relevant for runoff

generation. Each module is interconnected in order to

simulate the hydrological cycle. The spatial distribution of

hydrological processes over the basin is represented

through hydrologic response units (HRUs), which are

used to discretize the basin. TheHRU is the spatial unit for

mass and energy balance calculations and is defined as

being able to be represented by one set of biophysical,

pedologic, geomorphic, hydrometeorological, and hy-

draulic parameters. HRUsmay be disjointed spatially, but

they can always be linked to observable features that de-

fine hydrological behavior, such as shallow or deep soils,

steep or shallow slopes, orientation, and elevation range.

CRHM is primarily a physically based model, which

means that most hydrological processes are represented

following realistic equations, with observable parame-

ters. However, a minority of processes relevant at the

watershed scale, such as runoff routing and soil mois-

ture dynamics, can also be represented using empirical/

conceptual formulations with parameters that require

calibration. One of the advantages of physically based

modules is that parameterization can be accomplished

through direct observations in the basin or transfer of

values observed in similar basins.

b. Model setup

The UBRB catchment area is 15 904 km2 and was di-

vided into subbasins (SBs), defined by common drainage

and similar hydrometeorological or terrestrial ecologi-

cal features. The criteria for each SB delineation in-

cluded, hierarchically, (i) the existence of a stream

gauge defining an SB of the larger basin and (ii) the

existence of climatic differences that would suggest

different hydrological behavior. The second criterion is

supported by the strong climatic gradients across the

region, which result in variations in precipitation, tem-

perature, humidity, and exposure to wind. The com-

bination of these criteria resulted in the definition

of eight SBs. The first five SBs are Puerto Ibáñez (SB1,2403 km2) and Murta River (SB2, 906 km2), where

stream gauges exist, and three ungauged SBs associated

with significant streams: (.500 km2 contributing area)

Jeinimeni y los Antiguos River1 (SB3, 1985 km2), LeónRiver (SB4, 823km2), and Delta River (SB5, 640 km2).

The remaining basin area is characterized by different

meteorological conditions (most notably defined by the

west–east precipitation gradient), resulting in the

FIG. 6. (top) A comparison between observed precipitation and streamflow at Puerto Ibáñez station. (bottom) Twoprecipitation gap examples are shown, along with the precipitation data from both reanalyses.

1Río Jeinimeni y los Antiguos corresponds to two close SBs,which were combined into one because of the close distance be-tween both outlets and similarity in land cover characteristics.


windward SB7 (3385 km2) and lee SB6 (3857 km2).

General Carrera Lake is a large water body that defines

SB8 (1905 km2). Figure 7 shows SB delimitation, in-

cluding land cover over the Baker basin area, and also

SB1 and SB2 HRU designation. HRU designation cri-

teria are further discussed in section 3c(1).

The modeling strategy involved simulating each SB

separately, in order to subsequently drain the resulting

FIG. 7. Land cover map, with each SB labeled: SB1, Puerto Ibáñez; SB2, Bahía Murta; SB3, Jeinimeni y LosAntiguos; SB4, Río León; SB5, Río Delta; SB6, lee side; SB7, windward side; and SB8, General Carrera Lake.


runoff into the General Carrera Lake (SB8). Lake

processes include only open water evaporation and

routing of all incoming flows to the lake outlet. Flow

routing through the lake was carried out with an em-

pirical rating curve given by

hlake5 0:0037Rlake1 0:8683, (1)

where hlake is the daily mean lake elevation (m) and

Rlake is the lake outflow (m3 s21). This relationship was

obtained using 625 elevation and outflow estimates

during the modeling period; a value of r2 5 0.98 was

obtained.

Observed rainfall was first assigned to each subbasin

by (i) adopting the value of the measurement station or

reanalysis grid centroid closest to the subbasin, when no

stations or centroids within the subbasin were present,

or (ii) adopting the average of the values observed at the

stations or centroids within the subbasin. As this first

approximation provided very poor modeling results,

these data were then adjusted based on Chile’s coun-

trywide isohyetal maps of precipitation variation with

topography, as described in detail in section 3b(3). Be-

cause of the lack of any regular observations of pre-

cipitation variation over mountain topography in the

region to inform or confirm interpolation methods, no

further interpolation methods were deployed. This is

consistent with the study objective to assess the quality

of the forcing datasets in the context of a hydrologic

modeling exercise and not to find the most accurate

representation of the real precipitation fields. Addi-

tionally, Dussaillant et al. (2012) have shown that in-

terpolation methods such as Thiessen polygon, kriging,

and cokriging do not necessarily perform well for hy-

drological calculations in this region, in part because of

the very low spatial coverage and support of the existing

stations and in part because of the very large gradients

that can be observed to depend both on elevation and on

longitude.

Air temperature was spatially distributed to the

HRUs, assuming that elevation is the only control over

this variable within an SB. The environmental tem-

perature lapse rate corresponds to that for average

adiabatic conditions (i.e., 20.00658Cm21) for all of

the HRU. Nevertheless, for the upper snow/ice HRU

for the closest SBs to the Northern Icefield (SB2, SB4,

and SB5), different environmental temperature lapse

rates (20.0058 and20.00558Cm21) were set. We found

that the latter values were necessary in order to avoid

an overly pronounced positive trend in snow/ice mass

balance, which would be unrealistic with respect to

recent work published for the area. Such work has

documented widespread retreat for major glacier

bodies. As shown by Marshall et al. (2007), lapse

rates for areas near icefield sites are commonly lower

than off-glacier sites because of katabatic drainage

flows. They also found a mean annual temperature

lapse rate of 20.0058Cm21 for the Prince of Wales

Icefield (Canada).

1) HRU DELINEATION AND MODULE SELECTION

HRUs were defined using geomorphological and land

use criteria, such as elevation, aspect, and land cover

(see Fig. 7). HRUs defined here are similar to the catena

concept described in Arnold et al. (2010), where water is

routed from one unit to another rather than being

routed directly from one unit to the outlet of the basin.

Hierarchically, the first classification used to obtain

HRUs is land cover (CONAF/CONAMA 1999), where

six dominant types were identified: (i) rock, (ii) grass

and shrubs, (iii) peat, (iv) snow and ice, (v) forest, and

(vi) open water (rivers and lakes). Subsequently, each

land cover was divided into north- and south-facing

slopes, in order to capture potential differences in melt

timing due to radiation and wind exposure, which have

been found to be important in other mountain envi-

ronments (DeBeer and Pomeroy 2010). A third classi-

fication hierarchical level was defined based on terrain

elevation, in order to capture the effect of air tem-

perature spatial variability on the occurrence of solid/

liquid precipitation. The appropriate level of vertical

discretization was assessed based on the statistical

properties of HRU elevation prior to this last hierar-

chical step. Any HRU with an elevation range greater

than 500m and with an elevation standard deviation

larger than 200m was split at the mean elevation into

upper and lower elevation bands. This discretization is

needed to distinguish climate regimes; an environmental

lapse rate of 20.00658Ckm21 over a range of 500m re-

sults in a temperature range of 3.258C between the lower

and upper boundaries of the elevation band, smaller

than the standard deviation for average daily tempera-

ture (58C). The number of HRUs for each SB varies

from 1 to 16 and is controlled by (i) the altitude range

and (ii) the variety of land cover found at each SB. Once

all HRUs are set for each basin, a drainage sequence

needs to be specified by the modeler. We set the drain-

age sequence with upper north and south faces drain-

ing to lower faces of the same exposure (see Fig. 8). A

set of physically based modules was assembled for

each SB to simulate the hydrological processes rele-

vant to the UBRB. These modules are described in the

appendix. Also, Fig. 8 shows a flowchart of CRHM

module interactions, denoting inputs required for each

module and the associated output for the following

calculations.


2) PARAMETERIZATION

(i) Parameters that were not calibrated

Most of themodel parameters lie in this category, for

which parameters were set either based on measurable

physiographic features (through remote sensing or

fieldworkobservations) or transferred fromsimilar climatic/

landscape conditions. Physiographic characteristics are

required in many modules. For example, the longwave

radiation module uses the terrain view factor to adjust

incoming longwave radiation and the routing module

uses the mean HRU slope for adjusting the parameters

of the Muskingum method. As such, model parameter-

ization was undertaken using, in the first place, terrain

characteristics such as mean elevation, aspect, slope,

view, and area obtained fromASTER-GDEM analyses.

Also, a set of parameters was assigned depending on the

land cover classification. These parameters are pre-

sented in Table 3. For routing module parameters, the

routing lengths for each HRU were estimated using

Hack’s law (Rigon et al. 1996); channel shapes adopted

were supposed as parabolic and the Manning coefficient

was set referentially from values presented by Barnes

(1967) with support from fieldwork observations. For

the soil module, parameters representing maximum soil

moisture capacity and maximum soil recharge for the

‘‘grass and shrubs’’ land use classification were trans-

ferred from western Canada, where a cool subhumid

climate in a postglacial landscape has resulted in a similar

steppe soil development as in the glacio-fluvio-volcanic

landscape of the UBRB that has an overall sandy loam

soil texture, moderately deep to deep soil profile, and

good root penetration (Casanova et al. 2013, 75–77). For

the ‘‘forest’’ land cover classification, parameters were

transferred from aspen forests in Canada (Fang et al.

2010); for ‘‘peat,’’ values measured in northern Canadian

subalpine tundra by Quinton et al. (2005) were used.

Depressional storage (DS) parameters for grass and

shrubs and forest were transferred fromFang et al. (2010)

in postglacial landscapes where high-resolution lidar

DEMs were available to estimate storage capacity. Al-

bedo values for peat, grass, and shrubswere adopted from

Armstrong (2011) and for forest and rock from Pomeroy

et al. (1997). For ice albedo, although ponding and debris

conditions can induce large variability, a value of 0.5 was

adopted as an average representative of white ice

(Perovich et al. 2002). Vegetation height, which is used

for both canopy clearing and evaporation modules, was

obtained from fieldwork measurements, estimating

a value of 0.5 and 1m for bottom and upper grass and

FIG. 8. Flowchart showing CRHM modules configuration. This setup is repeated for all SBs except SB8. The SB1

drainage sequence is also shown, where NN (SS) refers to north-facing (south facing) orientation of the HRU.


shrubs and 6–12m for bottom and upper forest. For snow

transport and blowing snow sublimation, given that no us-

able in situ wind information was available for this study,

we adopted wind direction and velocity values from ERA-

Interim for themodel forcedwith otherwisemeasureddata.

For wind direction, both reanalyses confirm that the prin-

cipal direction during the modeling period was northwest.

Precipitation phase classification is based on a mean day

temperature index. As the model is set, when temperature

rises above 28C, precipitation is set as liquid, whereas below08C it is set as snowfall; for intermediate temperatures, the

precipitation phase was linearly interpolated.

(ii) Calibrated parameters and calibrationprocedure

Only two soil moisture parameters and the storage–

discharge rating curve relationship for General Carrera

Lake were calibrated manually, with the objective of

maximizing the NS coefficient criterion applied to daily

flows. DS parameters for rock, glaciers, and peat and

daily subsurface drainage (SSD) from recharge and soil

column for grass and shrubs, forest, and peat were cali-

brated, assuming the same value for each HRU type.

Calibration was carried out by analyzing the results of

the SB1 submodel, forced with CFSR data, with the

objective of maximizing the NS coefficient value com-

puted against observed daily streamflow data. The cali-

bration procedure is as follows. First, for several

arbitrary (within a realistic range) values of SSD, the DS

value was tested over the 5–300-mm range. This exercise

showed that the model has very little sensitivity to DS,

with NS changes smaller than 0.01 for each SSD value.

With this, an arbitrary but realistic value of DS 5 5mm

was set for rock, glaciers, and peat. Second, with the DS

TABLE 3. CRHM parameterization.

Module/land cover Rock Glaciers

Grass

and

shrubs Forest Peat Water

Albedo Albedo_bare 0.15 0.5 0.17 0.091 0.11 0.1

Albedo_snow 0.9 0.9 0.9 0.9 0.9 0.1

Ayers Ground cover None None Small grains Forested Good pasture None

Texture — — Medium/fine

over fine

Medium over

medium

Coarse/medium

over coarse

—

Canopy

clearing

Leaf area index

(0.1–20)

0 0 0 3 0 0

Sbar* (kgm22) 0 0 0 6.6 0 0

Evaporation Vegetation

height (m)

0 0 0.5–1 6–12 0 0

OBS Lapse_rate

[8C (100m)21]

20.65 20.65 20.65 20.65 20.65 20.65

Tmax_allrain (8C) 2 2 2 2 2 2

Tmax_allsnow (8C) 0 0 0 0 0 0

PBSM Stalk diameter (m) 0 0 0.3 0.02 0 0

Fetch (m) 0 0 300 300 0 0

Vegetation density

(1m22)

0 0 2 3 0 0

Soil Cov_type No soil

evaporation

No soil

evaporation

Evaporation

from recharge

layer only

Evaporation

from all

soil moisture

Evaporation

from recharge

layer only

No soil

evaporation

Soil_withdrawal — — Loam Loam Organic —

Daily subsurface drainage

from soil column

(mmday21)

0 0 10 10 10 0

Daily subsurface drainage

from recharge

(mmday21)

0 0 10 10 10 0

Max DS (mm) 5 5 5 0 5 1000

Max soil moisture

capacity (mm)

0 0 600 600 340 0

Max soil

recharge (mm)

0 0 60 60 150 0

Route Manning 0.02 0.02 0.03 0.04 0.02 0.02

Channel shape Parabolic Parabolic Parabolic Parabolic Parabolic Parabolic

*Max canopy interception load.


parameter value set, the SSDparameterwas tested again in

a more selective probable range of 1–20mmday21. These

runs showed that the model has a somewhat stronger sen-

sitivity to this parameter; for small values (1–4mmday21),

the NS coefficient varied around 0.3–0.5. For higher SSD

values (5–20mmday21), the model showed a smaller sen-

sitivity, but the best NS values between 0.5 and 0.53 were

also attained. Therefore, an arbitrary 10mmday21 mid-

point value within the ‘‘insensitive’’ range was selected.

Third, the insensitivity of theDSparameterwas verified for

the selected SSD value, thus finalizing the calibration stage.

(iii) Sensitivity analysis and insights

Calibrationwas also conductedusingobserved andERA-

Interim forcing data; however, using ‘‘optimal’’ parameters

from each forcing data source revealed that CFSR always

resulted in the best performance against streamflow obser-

vations; also, the parameter selection for these forcing data

are very similar to those found for CFRS (the model is in-

sensitive to DS, and optimal SSD values of 3 and

6mmday21 were obtained for ERA-Interim and observed

data, respectively). The calibration and parameter transfer

method used here corresponds to the deduction–induction–

abduction approach recommendedbyPomeroy et al. (2013)

for designing and parameterizing models for prediction in

ungauged basins. Physically based model parameter trans-

ference across .1000km between similar ecozones was

demonstrated by Dornes et al. (2008) in northern Canada

and by Gelfan et al. (2004) between Canada and Russia.

Because of the lack of any kind of detailed (i.e., finescale)

hydrological information for this basin, we chose to transfer

previously published parameter values whenever plausible

in order to minimize calibration. We find that, after this

process, only subsurface drainage and depression storage

parameters require calibration, which is seen here as

a promising result in terms of preserving a parsimonious

model in a relatively large, complex hydrological system.

3) PRECIPITATION DATA CORRECTION

Initial test model runs revealed important discrepancies

(bias) between observed and simulated runoff when forc-

ing the model with either observed or reanalysis data. This

bias problem has been found in other studies, such as

that developed by Pan et al. (2003), where they compare

SWE from four land surface models [Noah, Mosaic,

Sacramento Soil Moisture Accounting (SAC-SMA),

and Variable Infiltration Capacity (VIC)] against 3

years of Snow Telemetry (SNOTEL)-measured data.

Experiments with the VIC model indicated that most of

the bias in SWE is removed by scaling the precipitation

by a regional factor based on the regression of the North

American Land Data Assimilation System (NLDAS)

and SNOTEL-measured precipitation.

We attempted to mitigate the bias problem by estimat-

ing a precipitation correction factor (PCF) for adjusting

daily precipitation throughout the modeling period. Cor-

rection factors for precipitation input to gauged SB1 and

SB2 were estimated using the following expression:

PCFi115Robs

Rsim,i(PCF)i, (2)

where Robs is observed flow volume (m3) for the modeling

period and Rsim,i is simulated flow volume (m3) for each

model run i, which depends on the previous PCF calculated.

A manual iterative procedure allowed us to approximate

the most adequate correction factor, and the stopping cri-

terionwas set at bias#1%over the entiremodeling period.

Although it is very likely that a similar bias problem

affects the simulation of ungauged SBs, the lack of runoff

data, combined with the high spatial variability expected

for rainfall processes in this region, preclude our ability to

extrapolate PCFs specific for each SB from those esti-

mated for gauged basins. We circumvented this problem

by adopting the mean annual precipitation estimates

contained in Chile’s National Water Budget (DirecciónGeneral de Aguas 1987). The National Water Budget

contains mean annual precipitation isohyetal maps for the

entire country at a 1:1 000000 scale, estimated based on

observed streamflow and meteorological records; a sig-

nificant amount of expert inference and knowledge was

applied in deriving this product, and to date it remains the

sole source of nationwide water budget–related data. The

estimates take into account the likely spatial distribution

of precipitation due to orographic effects, as well as

basinwide evapotranspiration losses estimated through

the Turc–Pike relation. Mean annual precipitation and

potential ET are balanced by taking into account histori-

cal runoff datawhere available.Although crude, this is the

only additional source of annual rainfall data currently

available for this region aside from the meteorological

records and reanalyses already discussed. With this, the

PCF for ungauged SBs is estimated as follows:

PCF5Pp

PpNWB

, (3)

where Pp is total precipitation input from observations

or reanalyses and PpNWB is total precipitation from the

National Water Budget (mm). PCF values obtained

through Eqs. (2) and (3) are shown in Table 4. A value

closest to 1.0 indicates that the raw precipitation product

performed best in approximating the ‘‘true’’ value in-

ferred from streamflow volumes. In this case, it is pos-

sible to infer that CFSR does the best job in estimating

areawide precipitation input to the hydrologic system,


whereas the observed record shows the largest bias, re-

quiring more than a twofold correction in order to ap-

proximate total water inputs to the basin.

4. Results and discussion

a. Model testing

We evaluate the performance of each input source by

comparing simulated streamflow at both gauged SBs

plus the outlet of General Carrera Lake. Performance

statistics include the NS coefficient (Nash and Sutcliffe

1970), root-mean-square error (RMSE) and bias:

NS5 12�(x0 2 xs)

2

�(x02 x0)2, (4)

RMSE5

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi�n

i51

(x0,i 2 xs,i)2

n

vuuut, (5)

and

BIAS5�xs

�x02 1, (6)

where x0, xs, x, and n are observed, simulated,mean of the

observed values, and total number of values, respectively.

Large differences were found for every data source

and SB (see Table 5). In general, model performance

was enhanced when forcing CRHM with reanalyses in-

stead of observed data, especially for the entire Baker

basin, where the differences are more significant. The

well-established calibrated relationship between lake

level and outlet runoff can explain, in part, the satis-

factory results associated with Baker basin model

performance. This relationship constrains the range of

possible discharge values, attenuating peak flows due to

the substantial storage in the lake. The best performance

in SB1 and SB2 discharge was when CHRM was forced

with CFSR precipitation. This can be explained through

the CFSR spatial resolution, which, as shown in Fig. 4, is

3 times higher than that of the ERA-Interim and many

times the meteorological station density. In particular,

the representation of spatially variable meteorological

conditions within SB2 is crucial for model performance,

whereas for SB1, basin average weather conditions are

well correlated with SB1 observed outflow. This can be

explained by higher meteorological variability in SB2

due to the effect of the Northern Icefield in dampening

temperature lapse rates in its vicinity (Marshall et al.

2007). Such high variability in local hydrometeorology

suggests that a higher spatial resolution reanalysis like

CFSR should provide better results. ERA-Interim has

only one centroid within the Baker basin, and so its

representativeness is lower than that of CFSR.

Figure 9 shows the simulated streamflow time series

for each SB in the model. Observed flows are included

for SB1, SB2, and SB8, and area averaged precipitation

input is shown for every SB as well. From Fig. 9 it can be

seen that SB1, SB2, SB4, and SB7 show a somewhat

synchronized response in the sense that distinct peak

flows occur on similar dates and in that daily flows are

well correlated. These SBs are located almost entirely

west of 728W, and share traits such as a steep relief,

widespread glaciation, and a wetter climate. In contrast,

SB5 has amilder hydrograph, with attenuated daily peak

flows when compared to the highest observed/modeled

events in other basins. SB3 and SB6 do exhibit a differ-

ent behavior; these are located at the lee side of the

basin (semiarid conditions) and also have shallow soil

profiles. Consequently, base flows have a relatively

TABLE 4. Correction factor used for each precipitation data source

simulation.

Representative basin Observed ERA-Interim CFSR

SB1: Puerto Ibáñez* 3.35 0.34 0.59

SB2: Bahía Murta* 4.50 0.70 1.58

SB3: Jeinimeni y Los

Antiguos**

0.80 0.07 0.36

SB4: Río León** 5.80 0.60 1.20

SB5: Río Delta** 5.50 0.60 1.50

SB6: Lee side** 0.50 0.05 0.50

SB7: Windward side** 1.00 0.11 0.90

SB8: General Carrera Lake** 0.50 0.06 0.70

Avg 2.74 0.32 0.92

Std dev 2.16 0.26 0.43

*Values estimated using Eq. (2).

**Values estimated using Eq. (3).

TABLE 5. Model performance for different input source.

Data source

SB1: Puerto Ibáñez SB2: Bahía Murta UBRB

NS RMSE (m3 s21) Bias (%) NS RMSE (m3 s21) Bias (%) NS RMSE (m3 s21) Bias (%)

Observations 20.14 127 20.6 0.22 83 20.1 0.24 101 20.3

ERA-Interim 0.30 100 20.5 0.15 87 20.4 0.72 62 1.0

CFSR 0.53 82 0.4 0.32 78 20.9 0.74 60 0.4


small influence over the hydrograph. Although it is not

possible to corroborate the model results for the un-

gauged basins, Fig. 9 illustrates the potential of the

CFSR, in that it can actually predict a different climatic

regime, which in turn can be associated with a different

predicted hydrological response.

Figure 10 shows monthly average streamflow simula-

tions using CFSR input data. It can be seen that the

overall features of model performance vary across

space, with overestimation of monthly flows during fall

and winter months occurring at SB2 and UBRB. The

model overestimates winter flows (and underestimates

summer flows) for all three SBs, which, together with

a small overall bias, suggest that liquid-phase precipitation

is being overestimated as a whole over the river basin.

This effect is relativelymore important in SB2, with water

year 2005/06 showing a greater bias than year 2004/05.

Figure 10 (bottom) shows scatterplots for observed versus

simulated daily flows. For theBakerRiver (Fig. 10, bottom

left), we see a very clear trend of underestimation (over-

estimation) of flows below (above) the median value of

about 600m3 s21. The buffering effect of the lake is well

represented, and as a consequence, a systematic trend in

model fit is preserved (r2 5 0.84). For the smaller, un-

regulated basins SB1 and 2 (Fig. 10, bottom middle and

right), the scatter is much higher. The model shows better

(although noisy) fit for flow rates below 300m3 s21, but

for higher values a low bias is clearly perceptible. From

FIG. 9. Model streamflow simulations for each SB and UBRB using CFSR forcing data.


Fig. 9, it can be seen that these high daily values occur

mostly between themonths ofOctober andMarch, that is,

during spring and summer, when direct runoff from liquid

precipitation combines with melt from snow and glaciers

to give daily streamflow of up to 1000m3 s21 during the

modeling period. The fact that at the same time themodel

underestimates spring–summer monthly flows confirms

that a specific runoff-generating process or high-elevation

precipitation ismisrepresented under our currentmodeling

framework. We hypothesize that rain-on-snow events may

be more important that currently modeled, and this area

will be the focus of further research.

b. Snow component

No long-term snow accumulation data exist for this

region, but modeled SWE time series at four SBs are

shown to illustrate the differences resulting from selec-

tion of model parameters and the regionalization of

forcing data. Maximum SWE accumulation goes up to

2000mm for high-mountain SB4 and SB2 and down to

200mm for SB3 (semiarid conditions), revealing very

heterogeneous snow conditions in the basin (see Fig. 11).

Ablation curves are also very different, as well as the

duration of the snow-free period. The ablation curve

slope is steeper for semiarid SBs, whereas gradual abla-

tion curves were found for snow-dominated SBs. The

semiarid SB has longer snow-free periods, about 6–7

months long, beginning in late October. On the other

hand, snow-dominated SBs have shorter snow-free pe-

riods, about 1–2 months long, beginning in February.

Figure 11 can also be related to SB3 simulated streamflow

(see Fig. 9) in the sense that the rapid decrease in snow

cover relates well with the quick rise in streamflow during

the snowmelt period (November).

As shown in Table 6, simulated blowing snow trans-

port and sublimation have a small impact in the entire

water balance, representing 0.6mmyr21 when forcing

with CFSR. These small values, lower than expected

given the local meteorology, are attributed to our use of

daily wind speeds from reanalysis data instead of the

hourly or shorter wind speeds required to correctly

calculate these fluxes (Pomeroy and Li 2000). Table 7

shows disaggregated values for blowing snow transport

and sublimation (and other water balance components)

for each SB when forcing CRHM with CFSR data. As

expected, blowing snow transport is consistently higher

for SBs with higher wind speeds, which correspond to

SB1 with amean annual rate of 4.5 versus 3.8 for SB2 and

FIG. 10. (top) Monthly streamflow simulations and observations. (bottom) Observed vs simulated daily streamflow scatter. Simulation

results shown using CFSR forcing data.


3.2m s21 for SB4 and SB6. Although wind speed is a rel-

evant factor for blowing snow, so is vegetation height

(associated with land cover classification), as it controls

shear stress at the snow surface. The relative contribution

of blowing snow at SB6 (lee side of the basin) is the

highest (0.4 over 73mmyr21 of snowfall, about 0.7%),which

we attribute to negligible forest cover and predominance

of grassland and shrubs land cover (height of 0.5–1m).

Other studies, such as that presented by MacDonald

et al. (2009), show that in mountainous regions like the

Canadian Rockies, blowing snow transport can reach up

to 23% and down to 9% over the total snowfall. This

discrepancy can be explained by the still poor un-

derstanding of the blowing snow controls over the basin,

FIG. 11. Simulated SWE for upper-snow/ice north-facing HRU, using CFSR forcing data.

TABLE 6. UBRB water balance components. Percentage is over total precipitation.

UBRB water balance components

Observed ERA-Interim CFSR

(mmyr21) % (mmyr21) % (mmyr21) %

Rainfall 1004.6 71.5 1020.2 73.3 932.6 72.4

Snowfall 399.9 28.5 371.5 26.7 355.9 27.6

Actual evapotranspiration 84.8 6.0 100.3 7.2 61.7 4.8

Lake evaporation 80.6 5.7 80.6 5.8 30.6 2.4

Evaporation from canopy interception 5.8 0.4 11.3 0.8 7.3 0.6

Sublimation from canopy interception 10.2 0.7 13.2 0.9 4.6 0.4

Sublimation from blowing snow 0.0 0.0 0.0 0.0 0.3 0.0

Blowing snow transport* 0.0 0.0 0.0 0.0 0.6 0.0

Rainfall infiltration 713.9 50.9 773.5 55.6 698.8 54.2

Snowmelt infiltration 284.5 20.3 235.8 16.9 245.6 19.1

Rainfall runoff 211.8 13.8 149.5 10.7 173.4 13.5

Snowmelt runoff 90.9 6.4 108.9 7.8 107.3 8.3

Rainfall subsurface runoff 652.5 46.5 696.3 50.0 652.8 50.7

Snowmelt subsurface runoff 261.1 18.6 212.7 15.3 229.9 17.8

SWE storage change** 20.2 0.0 23.0 20.2 20.6 0.0

DS change** 0.0 0.0 0.1 0.0 0.0 0.0

Soil moisture change** 21.9 1.6 19.7 1.4 17.7 1.4

Soil recharge change** 2.5 0.2 2.2 0.2 2.8 0.2

*Blowing snow transport within the basin.

** Positive values indicate losses from initial conditions.


compounded with the lack of appropriate wind speed

data, which cannot be compared or adjusted with any

measurement within the basin. A site visit in October

2012 suggested substantial blowing snow transport in

alpine areas after snowfall events.

Notwithstanding the high ice depletion rates found for

the NPIF, up to 4.06 0.97myr21 (Rivera et al. 2007) for

the period between 1975 and 2001, no streamflow trend

can be seen for the period 1991–2008 at the UBRB. This

fact suggests that this depletion rate over the small

portion of the NPIF within this basin has limited in-

fluence for the runoff generation process.

c. Water balance

Mean values for modeled components over the mod-

eling period of the water balance were obtained and are

shown schematically inFig. 12.Additionally, water balance

components for each data source are displayed in Table 6.

Results with CFSR forcing data show that about 28% of

the total precipitation input corresponds to snowfall, which

is mainly controlled by the elevation discretization and the

environmental temperature lapse rate used.

Model performance in terms of monthly flows (see

Fig. 10) is characterized by a moderate overestimation of

TABLE 7. Water balance component for each SB (mmyr21). Results associated with CFSR forcing data.

Water balance component SB1 SB2 SB3 SB4 SB5 SB6 SB7

Rainfall 1550.4 3526.9 281.5 2410.6 2839.6 210.2 523.8

Snowfall 635.4 1152.1 182.1 1135.9 1075.4 60.4 237.6

Actual evapotranspiration 74.6 84.2 35.6 57.0 150.5 25.1 122.6

Lake evaporation — — — — — — —

Evaporation from canopy interception 17.9 29.3 0.0 13.0 12.7 0.0 8.0

Sublimation from canopy interception 5.3 8.9 0.0 5.0 16.8 0.0 11.3

Sublimation from blowing snow 1.0 0.0 0.0 0.1 0.0 0.5 0.0

Blowing snow transport* 2.9 0.2 0.0 0.5 0.0 0.4 0.0

Rainfall infiltration 1339.5 2653.9 278.5 1659.7 1637.9 209.5 502.6

Snowmelt infiltration 449.3 751.0 149.4 764.2 771.4 47.8 159.3

Rainfall runoff 94.2 710.0 0.6 578.9 998.0 0.0 18.5

Snowmelt runoff 136.0 486.2 55.9 546.9 388.8 9.0 80.5

Rainfall subsurface runoff 1283.7 2588.3 255.4 1620.6 1535.5 189.0 409.5

Snowmelt subsurface runoff 430.6 732.4 137.0 746.2 723.2 43.2 129.8

SWE storage change** 22.7 1.6 216.6 42.3 73.4 29.2 223.1

DS change** 20.4 20.5 1.5 1.0 20.9 20.3 0.0

Soil moisture change** 110.9 31.2 29.7 260.2 17.3 8.3 3.8

Soil recharge change** 6.3 7.2 3.9 24.8 20.4 4.7 0.5

*Blowing snow transport within the basin.

**Positive values indicate losses from initial conditions.

FIG. 12. UBRB mass balance using CFSR forcing data over the modeling period.


winter (low) flows and underestimation of high (summer)

flows, which in turn could be related by an overestimation

of the rainfall fraction of precipitation throughout the basin

during the winter months. Four reasons could explain this

behavior: (i) a high bias in the index temperature station/

reanalysis data used for spatial interpolation, (ii) a too-

shallow lapse rate or misrepresentation of its temporal

variability, (iii) overestimation of precipitation amounts at

lower elevations due to spatial averaging at the subbasin

scale, and (iv) inadequate selection of parameter values.

No reliable field data exist in order to test the above hy-

pothesis for the modeling period, so this topic constitutes

a relevant direction for future research. A recent im-

provement to CRHM for phase determination using

a psychrometric energy balance method (Harder and

Pomeroy 2013) holds promise for future evaluations of

precipitation phase if reliable humidity measurements

become available in the region.

In terms of runoff generation, we found that sub-

surface flow is significantly more important (68.5%) that

overland runoff (21.8%). This can be explained by the

high infiltration rates and soil moisture storage capacity,

especially for forested and peat land cover. Even though

modeled liquid-phase precipitation almost triples snow-

fall, little difference exists in terms of each precipitation

overland contribution; rainfall- and snowmelt-generated

overland flows contribute 13.5% and 8.3% to total flow,

respectively. Evapotranspiration, sublimation, and lake

evaporation take up to 8.2% of total precipitation,

with negligible contributions from canopy interception

losses (,1%). These evapotranspiration losses account

for about 100mmyr21, significantly different from the

351mmyr21 estimation contained in the Chilean Na-

tionalWater Budget (DirecciónGeneral deAguas 1987),which in turn amount to 20% of the then-estimated long-

term precipitation mean for the basin of 1686mmyr21.

Because our modeling period is drier than the reference

period in the Chilean Water Budget (approximately

1400mmyr21), it is expected that ET as a percentage of

annual precipitation would be different from the clima-

tological value. However, in theory, this percentage

should be larger, not smaller, than the reference one. The

lack of intermediate state variable information precludes

us from formulating a hypothesis with respect to the na-

ture of this discrepancy, and future research should aim at

developing point-scale water balances through lysimeter

experiments in order to obtain a better understanding of

water fluxes along the atmosphere–soil column in various

climatic regimes across this region.

Low interception sublimation losses are attributed to

the largely deciduous nature of the forest canopies, and

low evaporation and sublimation capacity are derived

from existing moist and cold conditions. Simulated

blowing snow has a negligible influence on the water

balance (as discussed in section 4b), and only when

the model is forced with CFSR input data is a small

amount of drifted snow transported from each HRU

(0.7mmyr21 on average). Storage change in the basin

results from SWE, soil moisture, depression storage, and

soil recharge changes. This component has little impact

on the modeling, only 1.6%; soil moisture changes rep-

resent the most relatively important change (1.4%).

Both SWE and depression storage changes are null.

Table 7 shows spatially disaggregated SB water bal-

ance components when CRHM is forced with CFSR.

Results show that the model is capable of simulating the

west–east precipitation gradient within the basin, with

high precipitation patterns over SB1, SB2, SB4, and

SB5, which total about 72% of the entire precipitation

over the basin, to the dry eastern patterns of SB6 and

SB3, which only total about 9% of total precipitation

input over the basin. Other differences can be high-

lighted, like evapotranspiration, where SB7 contributes

42% of the total evapotranspiration, with significant

forested land (about 1029 km2) and grassland and

shrubland (699 km2).

5. Conclusions

This paper presents the first insight into the hydro-

logical cycle and water balance of a Patagonian moun-

tain and lowland basin through physically based

modeling. Like most remote and sparsely populated

regions in SouthAmerica, Patagonia has a low density of

meteorological stations, many times with incomplete

and/or unreliable records. To circumvent this problem

and evaluate the impact of data scarcity, data from ob-

served local meteorological stations and reanalyses were

analyzed and then used as forcing data to the hydrologic

model. Actual meteorological stations have poor rep-

resentativeness over the Baker basin because (i) only

rainfall gauges are available, whereas snowfall events

are frequent and significant, and (ii) meteorological

stations are all located at low altitudes (,500m MSL),

neglecting higher precipitation magnitudes at higher

elevation due to orographic effects.

The performance of a CRHM for the Baker basin was

shown to be more satisfactory when forced with re-

analyses data, obtaining values ofNS. 0.7 for daily flows,

than when forced with weather station observations.

Because bias was mostly removed from all precipitation

forcings by a precipitation correction, the higher perfor-

mance of the model when forced with reanalysis data can

be in attributed in part to a better temporal representa-

tion of precipitation events, as the observation record

does not include snowfall, and also to better spatial


averaging of precipitation when compared to point ob-

servations in valley bottoms. When evaluating the per-

formance of the hydrological model in individual

subbasins, CFSR proved to be a better estimator of local

meteorological conditions, which is reasonable since

CFSR has 3 times the spatial resolution of ERA-Interim

in this region. These results strongly indicate that re-

analysis data have great potential as an effective source of

information for hydrological understanding in ungauged

or poorly monitored basins, such as those located in Pa-

tagonia. Transferring some parameters from hydrological

studies in other cold regions ecosystems—in this case,

by abductive inference of certain parameters from

those developed in Canadian research basins—proved

to be a viable approach for this remote and poorly

gauged basin and allowed us to avoid or minimize

complex calibration schemes, obtaining satisfactory

results when no other local source of information was

available.

The model results suggest that, although snowfall was

only 28% of total precipitation, modeled snowmelt

contributed to streamflow for up to 6 months per year

and was the major source of runoff. This is not dissimilar

from the hydrology of many regions in western Canada

(Pomeroy et al. 2007). Infiltration was the principal

component in the water balance, capturing about 73%

of the total precipitation and showing the influence of

subsurface flow generation mechanisms. Some of the

infiltrated water remained in the basin until the next

hydrological year but most formed runoff. Evapotrans-

piration from soils and evaporation from lakes repre-

sented losses of only 8% of total precipitation, which is

half of the mean annual value estimated through simple

temperature-based equations in the Chilean Water

Budget. The differences are due to consideration of snow

cover suppression of evaporation and the deployment of

physically based combinationmethod evapotranspiration

and aerodynamic lake evaporation schemes in CRHM.

Evapotranspiration depends strongly on season, soil

moisture capacity, and vegetation, which are based on

land cover classification, and lake evaporation depends

on wind speed, relative humidity, andmean temperature.

Hence, the development of more accurate land cover

maps, as well as meteorological stations with wind speed

and relative humidity sensors, are crucial in order to ob-

tain more precise estimations for evaporative losses,

which should be validated against, for instance, lysimeter

experiments in representative locations.

Future research must seek finer-scale spatial repre-

sentations of mountain meteorology in the region that

better approach the HRU spatial discretization, starting

from the improved representation that the reanalyses—

CFSR, in particular—provide. Also, future research

must include refining the estimation of blowing snow

phenomena by acquiring finescale, subdaily wind speed

data, validating rain-on-snow energy exchanges through

point-based field experiments, and further refining the

contribution of the Northern Icefield to the hydrology of

the region, in view of current climate change pro-

jections, which indicate warming for the region and

a continued decrease in glacier ice storage.

Acknowledgments. The authors acknowledge the fi-

nancial support from Fondecyt Project 1090479, Grad-

uateDivision of theMathematical and Physical Sciences

Faculty, University of Chile, with their Grant ‘‘PasantíasCortas de Investigación’’; from NSERC Discovery

Grants, Canada Research Chairs; and from the Inter-

American Institute for Global Change Research (IAI)

under Grant SGP-CRA2047. The authors also thank the

Dirección General de Aguas (DGA), which provided allthe observed data used in this study. Sebastian Kroghalso thanks Xing Fang and Tom Brown for their im-portant advice on modeling strategies with CRHM.

APPENDIX

CRHM Modules Description

d Global calculates the theoretical global radiation and

direct and diffuse solar radiation, as well as maximum

sunshine hours based on latitude, elevation, ground

slope, and azimuth (Garnier and Ohmura 1970).d Annandale estimates incoming shortwave radiation

from daily minimum and maximum temperatures

(Annandale et al. 2001) and theoretical global radia-

tion from the ‘‘global’’ module.d The longwave radiation module estimates incoming

longwave radiation using temperature, humidity, and

shortwave transmittance (Sicart et al. 2006).d Albedo estimates snow albedo throughout the winter

and into themelt period. Albedo is estimated following

a linear decay rate for each time period based on snow

depth, new snow, and melting (temperature and radi-

ation criteria) occurrence (Gray and Landine 1987).d Netall models net all-wave radiation to snow-free

surfaces from the Brunt equation (Brunt 1932), using

inputs from the ‘‘global’’ and ‘‘Annandale’’ radiation

modules.d Prairie Blowing SnowModel (PBSM) calculates SWE

from snowfall and blowing snow transport, redistribu-

tion, and sublimation (Pomeroy and Li 2000).d Energy-Budget Snowmelt Model (EBSM) estimates

snowmelt by calculating the energy balance of radiation,


sensible heat, latent heat, advection from rainfall, and

change in internal snowpack energy (Gray and Landine

1988).d Ayers is an empirical relationship that estimates

rainfall infiltration into unfrozen soils based on soil

texture and ground cover (Ayers 1959).d The evaporation module has two types: (i) Granger’s

evaporation expression (Granger and Gray 1989;

Granger and Pomeroy 1997) estimates actual evapo-

transpiration from unsaturated surfaces (canopy,

crops, soils) using a complementary solution to the

Penman equation, and (ii) the Priestley and Taylor

evaporation expression (Priestley and Taylor 1972)

estimates evaporation from saturated surfaces, wet-

lands, or small water bodies including advection

effects.d The canopymodule estimates the snowfall and rainfall

intercepted, sublimated, and evaporated by the forest

canopy, subcanopy snowfall, rainfall, and shortwave

and longwave radiation (Ellis et al. 2010).d The soil moisture module computes soil moisture

balance for frozen and unfrozen periods (Pomeroy

et al. 2007, 2012); moisture content exceeding field

capacity is routed away from the HRU using

‘‘netroute.’’d TheMuskingum routing module is based on a variable

discharge–storage relationship (Chow 1964) and is

used to route runoff between HRUs.d The lake evaporation module is an empirical relation-

ship that estimates monthly large lake actual evapo-

ration using monthly wind speed, relative humidity,

and temperature, following the Meyer formula with

coefficients as determined by the Prairie Provinces

Water Board in western Canada (Martin 2002).

REFERENCES

Annandale, J. G., N. Z. Jovanovic, N. Benadé, and R. G. Allen,

2001: Software for missing data error analysis of Penman–

Monteith reference evapotranspiration. Irrig. Sci., 21, 57–67,

doi:10.1007/s002710100047.

Aravena, J. C., 2007: Reconstructing climate variability using tree

rings and glaciers fluctuations in southern Chilean Andes.

Ph.D. thesis, Graduate Program in Geography, University of

Western Ontario, 220 pp.

Armstrong, R. N., 2011: Spatial variability of actual evapotrans-

piration in a prairie landscape. Ph.D. thesis, Dept. of Geog-

raphy and Planning, University of Saskatchewan, 194 pp.

Arnold, J. G., P. M. Allen, M. Volk, J. R. Williams, and D. D.

Bosch, 2010: Assessment of different representations of spa-

tial variability on SWATmodel performance. Trans. ASABE,

53, 1433–1443.Ayers, H. D., 1959: Influence of soil profile and vegetation char-

acteristics on net rainfall supply to runoff. Spillway Design

Floods: Proceedings of Hydrology SymposiumNo. 1,National

Research Council of Canada, 198–205. [Available online at

www.usask.ca/hydrology/papers/Ayers_1959.pdf.]

Barnes, H. H., 1967: Roughness characteristics of natural channels.

USGS Water Supply Paper 1849, 213 pp. [Available online at

http://pubs.usgs.gov/wsp/wsp_1849/.]

Barría, P. L., 2010: Pronóstico de Caudales Medios Mensuales enlas Cuencas de los Ríos Baker y Pascua. Civil engineeringthesis, Civil Engineering Dept., University of Chile, 167 pp.

Brunt, D., 1932: Notes on radiation in the atmosphere. Quart.

J. Roy. Meteor. Soc., 58, 389–420, doi:10.1002/qj.49705824704.Casanova, M., O. Salazar, O. Seguel, andW. Luzio, 2013: The Soils

of Chile. Springer, 185 pp.

Chow, V. T., 1964: Handbook of Applied Hydrology. McGraw-

Hill, 1468 pp.

CONAF/CONAMA, 1999: Catastro y evaluación de los recursosvegetacionales nativos de Chile. Project Rep., 89 pp. [Availableonline at www.bcn.cl/carpeta_temas_profundidad/ley-bosque-

nativo/archivos-pdf/Catastro.pdf.]

DeBeer, C. M., and J. W. Pomeroy, 2010: Simulation of the snow-

melt runoff contributing area in a small alpine basin. Hydrol.

Earth Syst. Sci., 14, 1205–1219, doi:10.5194/hess-14-1205-2010.Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis:

Configuration andperformance of the data assimilation system.

Quart. J. Roy. Meteor. Soc., 137, 553–597, doi:10.1002/qj.828.

Dirección General de Aguas, 1987: Balance hidrológico nacional.Tech. Rep. 70115, Dirección General de Aguas, 62 pp.[Available online at http://documentos.dga.cl/SUP1540.pdf.]

Dornes, P. F., A. T. Bryan, B. Davison, A. Pietroniro, J. W.

Pomeroy, and P. Marsh, 2008: Regionalization of land surface

hydrological model parameters in subarctic environments. Phys.

Chem. Earth, 33, 1081–1089, doi:10.1016/j.pce.2008.07.007.

Dussaillant, J. A., W. Buytaert, C. Meier, and F. Espinoza, 2012:

Hydrological regime of remote catchments with extreme gra-

dients under accelerated change: The Baker basin in Patagonia.

Hydrol. Sci. J., 57, 1530–1542, doi:10.1080/02626667.2012.726993.Ellis, C. R., J. W. Pomeroy, T. Brown, and J. MacDonald, 2010:

Simulation of snow accumulation and melt in needleleaf forest

environments.Hydrol. Earth Syst. Sci., 14, 925–940, doi:10.5194/

hess-14-925-2010.

Fang, X., and J. W. Pomeroy, 2007: Snowmelt runoff sensitivity

analysis to drought on the Canadian prairies. Hydrol. Pro-

cesses, 21, 2594–2609, doi:10.1002/hyp.6796.

——, and ——, 2009: Modelling blowing snow redistribution to

prairie wetlands. Hydrol. Processes, 23, 2557–2569,

doi:10.1002/hyp.7348.

——, ——, C. J. Westbrook, X. Guo, A. G. Minke, and T. Brown,

2010: Prediction of snowmelt derived streamflow in a wetland

dominated prairie basin.Hydrol. Earth Syst. Sci., 14, 991–1006,

doi:10.5194/hess-14-991-2010.

——, ——, C. R. Ellis, M. K. MacDonald, C. M. DeBeer, and

T. Brown, 2013: Multi-variable evaluation of hydrological

model predictions for a headwater basin in the Canadian

Rocky Mountains. Hydrol. Earth Syst. Sci., 17, 1635–1659,

doi:10.5194/hess-17-1635-2013.

Garnier, B. J., and A. Ohmura, 1970: The evaluation of surface

variations in solar radiation income. Sol. Energy, 13, 21–34,

doi:10.1016/0038-092X(70)90004-6.

Garreaud, R., M. Vuille, R. Compagnucci, and J. Marengo, 2009:

Present-daySouthAmerican climate.Palaeogeogr.Palaeoclimatol.

Palaeoecol., 281, 180–195, doi:10.1016/j.palaeo.2007.10.032.

Gelfan, A. N., J. W. Pomeroy, and L. S. Kuchment, 2004:Modeling

forest cover influences on snow accumulation, sublimation


http://dx.doi.org/10.1007/s002710100047

http://www.usask.ca/hydrology/papers/Ayers_1959.pdf

http://pubs.usgs.gov/wsp/wsp_1849/

http://dx.doi.org/10.1002/qj.49705824704

http://www.bcn.cl/carpeta_temas_profundidad/ley-bosque-nativo/archivos-pdf/Catastro.pdf

http://www.bcn.cl/carpeta_temas_profundidad/ley-bosque-nativo/archivos-pdf/Catastro.pdf

http://dx.doi.org/10.5194/hess-14-1205-2010

http://dx.doi.org/10.1002/qj.828

http://documentos.dga.cl/SUP1540.pdf

http://dx.doi.org/10.1016/j.pce.2008.07.007

http://dx.doi.org/10.1080/02626667.2012.726993



http://dx.doi.org/10.1002/hyp.6796




http://dx.doi.org/10.1016/0038-092X(70)90004-6

http://dx.doi.org/10.1016/j.palaeo.2007.10.032

and melt. J. Hydrometeor., 5, 785–803, doi:10.1175/

1525-7541(2004)005,0785:MFCIOS.2.0.CO;2.

Gonthier, C., 2011: Influencia de la escala espacial y representaciónhidrológica de una Cuenca de montaña sobre la capacidadpredictiva de un modelo hidrológico. M.S. thesis, Civil Engi-neering Dept., University of Chile, 165 pp.

Granger, R. J., and D. M. Gray, 1989: Evaporation from natural

non-saturated surfaces. J. Hydrol., 111, 21–29, doi:10.1016/0022-1694(89)90249-7.

——, and J. W. Pomeroy, 1997: Sustainability of the western Ca-

nadian boreal forest under changing hydrological conditions.

II. Summer energy and water use. IAHS Publ., 240, 243–249.[Available online at http://iahs.info/uploads/dms/iahs_240_

0243.pdf.]

Gray, D. M., and P. G. Landine, 1987: Albedo model for shallow

prairie snow covers.Can. J. Earth Sci., 24, 1760–1768, doi:10.1139/

e87-168.

——, and ——, 1988: An energy-budget snowmelt model for the

Canadian Prairies. Can. J. Earth Sci., 25, 1292–1303, doi:10.1139/e88-124.

Harder, P., and J. Pomeroy, 2013: Estimating precipitation phase

using a phycrometric energy balance method. Hydrol. Pro-

cesses, 27, 1901–1914, doi:10.1002/hyp.9799.Lopez, P., P. Sirguey, Y. Arnaud, B. Pouyaud, and P. Chevalier,

2008: Snow cover monitoring in the Northern Patagonia Ice-

field usingMODIS satellite images (2000–2006).Global Planet.

Change, 61, 103–116, doi:10.1016/j.gloplacha.2007.07.005.

MacDonald, M., J. Pomeroy, and A. Pietroniro, 2009: Parameter-

izing redistribution and sublimation of blowing snow for hy-

drological models: Test in a mountainous subartic catchment.

Hydrol. Processes, 23, 2570–2583, doi:10.1002/hyp.7356.

Marshall, S. J., M. Sharp, D. Burgess, and F. Anslow, 2007: Near-

surface-temperature lapse rates on the Prince of Wales Ice-

field, Ellesmere Island, Canada: Implications for regional

downscaling of temperature. Int. J. Climatol., 27, 385–398,

doi:10.1002/joc.1396.

Martin, F. R., 2002: Gross evaporation for the 30-year period

1971–2000 in the Canadian Prairies. Hydrology Rep. 143,

Agriculture and Agri-Food Canada Prairie Farm Reha-

bilitation Administration Technical Service, 35 pp.

NASA, cited 2014: ASTER Global Digital Elevation Model.

[Available online at http://gdem.ersdac.jspacesystems.or.jp/.]

Nash, J. E., and J. V. Sutcliffe, 1970: River flow forecasting through

conceptual models. Part I—A discussion of principles. J. Hy-

drol., 10, 282–290, doi:10.1016/0022-1694(70)90255-6.Pan, M., and Coauthors, 2003: Snow process modeling in the North

American Land Data Assimilation System (NLDAS):

2. Evaluation of model simulated snow water equivalent.

J. Geophys. Res., 108, 8850, doi:10.1029/2003JD003994.

Perovich, D. K., T. C. Grenfell, B. Light, and P. V. Hobbs, 2002:

Seasonal evolution of the albedo of multiyear Arctic sea ice.

J. Geophys. Res., 107, 8044, doi:10.1029/2000JC000438.Pomeroy, J.W., andL. Li, 2000: Prairie andArctic areal snow cover

mass balance using a blowing snow model. J. Geophys. Res.,

105, 26 619–26 634, doi:10.1029/2000JD900149.

——, R. J. Granger, A. Pietroniro, J. E. Elliott, B. Toth, and

N. Hedstrom, 1997: Hydrological pathways in the PrinceAlbert

Model Forest. Final Rep. to Prince Albert Model Forest Asso-

ciation, 220 pp. [Available online at www.pamodelforest.sk.ca/

pubs/PAMF3400.pdf.]

——, D. M. Gray, T. Brown, N. R. Hedstrom, W. L. Quinton, R. J.

Granger, and S. K. Carey, 2007: The cold regions hydrological

model: A platform for basing process representation and

model structure on physical evidence. Hydrol. Processes, 21,

2650–2667, doi:10.1002/hyp.6787.

——,X. Fang, and C. Ellis, 2012: Sensitivity of snowmelt hydrology

in Marmot Creek, Alberta, to forest cover disturbance. Hy-

drol. Processes, 26, 1891–1904, doi:10.1002/hyp.9248.

——, P. H. Whitfield, and C. Spence, Eds., 2013: Putting Prediction

in Ungauged Basins into Practice. IAHS, 362 pp.

Priestley, C.H.B., andR. J. Taylor, 1972:On the assessment of surface

heat flux and evaporation using large-scale parameters. Mon.

Wea. Rev., 100, 81–92, doi:10.1175/1520-0493(1972)100,0081:

OTAOSH.2.3.CO;2.

Quinton, W. L., T. Shirazi, S. K. Carey, and J. W. Pomeroy, 2005:

Soil water storage and active-layer development in a sub-alpine

tundra hillslope, southern Yukon Territory, Canada. Perma-

frost Periglacial Processes, 16, 369–382, doi:10.1002/ppp.543.

Rigon, R., I. Rodriguez-Iturbe, andA.Maritan, 1996: OnHack’s law.

Water Resour. Res., 32, 3367–3374, doi:10.1029/96WR02397.

Rivera, A., T. Benham,G. Casassa, J. Mabmer, and J. Dowdeswell,

2007: Ice elevation and areal changes of glaciers from the

Northern Patagonia Icefield, Chile.Global Planet. Change, 59,

126–137, doi:10.1016/j.gloplacha.2006.11.037.

Saha, S., and Coauthors, 2010: TheNCEPClimate Forecast System

Reanalysis. Bull. Amer. Meteor. Soc., 91, 1015–1057,

doi:10.1175/2010BAMS3001.1.

Sheffield, J., A. Ziegler, and E.Wood, 2004: Correction of the high-

latitude rain day anomaly in the NCEP–NCAR reanalysis for

land surface hydrological modeling. J. Climate, 17, 3814–3828,

doi:10.1175/1520-0442(2004)017,3814:COTHRD.2.0.CO;2.

Sicart, J. E., J. W. Pomeroy, R. L. Essery, and D. Bewley, 2006:

Incoming longwave radiation to melting snow: Observations,

sensitivity and estimations in northern environments. Hydrol.

Processes, 20, 3697–3708, doi:10.1002/hyp.6383.Silva, V., V. Kousky, and R. Higgins, 2011: Daily precipitation

statistics for South America: An intercomparison between

NCEP reanalyses and observations. J. Hydrometeor., 12, 101–

117, doi:10.1175/2010JHM1303.1.

Ward, E., W. Buytaert, L. Peaver, and H. Wheather, 2011: Eval-

uation of precipitation products over complex mountainous

terrain: A water resources perspective. Adv. Water Resour.,

34, 1222–1231, doi:10.1016/j.advwatres.2011.05.007.Warren, R., and D. Sugden, 1993: The Patagonia Icefield: A gla-

ciological review. Arct. Alp. Res., 25, 316–331, doi:10.2307/

1551915.

WMO, 1994: Guide to hydrological practices. 5th ed. WMO Publ.

164, 735 pp.

Zhou, J., J. W. Pomeroy, W. Zhang, G. Cheng, G. Wang, and

C. Chen, 2014: Simulating cold regions hydrological processes

using a modular model in the west of China. J. Hydrol., 509,

13–24, doi:10.1016/j.jhydrol.2013.11.013.


http://dx.doi.org/10.1175/1525-7541(2004)005<0785:MFCIOS>2.0.CO;2

http://dx.doi.org/10.1175/1525-7541(2004)005<0785:MFCIOS>2.0.CO;2

http://dx.doi.org/10.1016/0022-1694(89)90249-7

http://dx.doi.org/10.1016/0022-1694(89)90249-7

http://iahs.info/uploads/dms/iahs_240_0243.pdf

http://iahs.info/uploads/dms/iahs_240_0243.pdf

http://dx.doi.org/10.1139/e87-168

http://dx.doi.org/10.1139/e87-168

http://dx.doi.org/10.1139/e88-124

http://dx.doi.org/10.1139/e88-124


http://dx.doi.org/10.1016/j.gloplacha.2007.07.005


http://dx.doi.org/10.1002/joc.1396

http://gdem.ersdac.jspacesystems.or.jp/

http://dx.doi.org/10.1016/0022-1694(70)90255-6

http://dx.doi.org/10.1029/2003JD003994

http://dx.doi.org/10.1029/2000JC000438

http://dx.doi.org/10.1029/2000JD900149

http://www.pamodelforest.sk.ca/pubs/PAMF3400.pdf

http://www.pamodelforest.sk.ca/pubs/PAMF3400.pdf



http://dx.doi.org/10.1175/1520-0493(1972)100<0081:OTAOSH>2.3.CO;2

http://dx.doi.org/10.1175/1520-0493(1972)100<0081:OTAOSH>2.3.CO;2

http://dx.doi.org/10.1002/ppp.543

http://dx.doi.org/10.1029/96WR02397

http://dx.doi.org/10.1016/j.gloplacha.2006.11.037

http://dx.doi.org/10.1175/2010BAMS3001.1

http://dx.doi.org/10.1175/1520-0442(2004)017<3814:COTHRD>2.0.CO;2


http://dx.doi.org/10.1175/2010JHM1303.1

http://dx.doi.org/10.1016/j.advwatres.2011.05.007

http://dx.doi.org/10.2307/1551915

http://dx.doi.org/10.2307/1551915

http://dx.doi.org/10.1016/j.jhydrol.2013.11.013

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