Analysis of a changing hydrologic flood regime using the Variable

Infiltration Capacity model

Daeryong Park a, Momcilo Markus b,

aCivil and Environmental System Engineering, Konkuk University, 1 Hwayang-dong, Gwangjin-gu, Seoul 143-701, South Koreab Illinois State Water Survey, Prairie Research Institute, University of Illinois at Urbana-Champaign, 2204 Griffith Dr., Champaign, IL 61820, USA

a r t i c l e i n f o

Article history:

Received 7 June 2013

Received in revised form 3 April 2014

Accepted 2 May 2014

Available online 14 May 2014

This manuscript was handled by Laurent

Charlet, Editor-in-Chief, with the assistance

of Georgia Destouni, Associate Editor

Keywords:

VIC model

Climate change

Flood frequency

Hydrologic change

Snow hydrology

s u m m a r y

The Pecatonica River and several other streams in the Wisconsin Driftless area show a decreasing trend in

annual peak flows. Previous studies of the Pecatonica River detected a significant decreasing historical

trend in late winter snowmelt-driven floods, while the rainfall-driven spring and summer flood peaks

exhibited no significant trend during the period of record. Unlike several previous studies which attribute

the decline in flood peaks mainly to changes in land management, we hypothesize that climate change

had a significant contribution to the overall decrease in flood peaks. In particular, we hypothesize that

the increase in winter temperatures caused the decrease in snow depth, which in turn resulted in a

decreasing trend in flood peaks. In an attempt to validate this hypothesis, we used long-term daily pre-

cipitation, temperature, and river flow data observed in the watershed as inputs to the Variable Infiltra-

tion Capacity (VIC) model to generate other non-monitored climatic variables. Trends in these climatic

variables were then related to the trend in flood peaks in the Pecatonica River. Due to the complexity

of the hydrologic system and numerous data and modeling-related uncertainties, the above hypothesis

cannot be validated with certainty. Nonetheless, the results in two different modes (event and continuous

simulation) provide support to the speculation that the decreasing trend in flood peaks was a result of

decreasing snow depth. The model runs resulted in a decrease in snow depths for the period of record

(19152009), increase in sublimation and evaporation, no change in base flow, and mixed results in infil-

tration. These analyses also suggest that VIC can be used in other similar regions in snowmelt-driven

flood peak studies. It should be recognized, however, that the success of these applications can be

severely constrained by various uncertainties, including but not limited to, the poor quality or absence

of snow depth data.

2014 Elsevier B.V. All rights reserved.

1. Introduction

Many studies on the effects of climate change on water

resources have been published in the past several years. These

studies have investigated historical trends in streamflow, precipi-

tation, and various other variables. Among the studies related to

streamflow trends, most focus on the trends of seasonal and annual

streamflow (Regonda et al., 2005; Hamlet et al., 2005, 2007; Mishra

et al., 2010; Sinha et al., 2010). Relatively fewer studies have

focused on extreme events. Hirsch and Ryberg (2012) studied the

relationships between annual floods at 200 long-term streamgages

in the conterminous United States and the global mean carbon

dioxide concentration. They found no correlation between global

mean carbon dioxide concentration and flooding, except for a

negative relationship between GMCO2 and flood magnitudes in

the southwestern United States. Markus et al. (2007, 2012) and

Hejazi and Markus (2009) studied the frequency of flooding as a

result of the increasing intensity and frequency of heavy storms

and the increase in urbanization. Cunderlik and Ouarda (2009)

detected a significant number of stations with negative trends in

the magnitude of snowmelt-generated floods. Trends in peak

streamflows also have been studied by means of observed data

analysis (Lins and Slack, 1999; Novotny and Stefan, 2007). Lins

and Slack (1999) investigated trends in minimum, median, and

maximum annual flows at 395 daily streamflow stations for

19441993 in the United States. They found that a majority of sta-

tions exhibited a decreasing trend in annual maximum flow. How-

ever, for annual median and annual minimum flows, a majority of

the stations had an increasing trend. Regonda et al. (2005) ana-

lyzed observed weather data in the western United States and

found that peak flow timing had shifted to earlier months, caused

http://dx.doi.org/10.1016/j.jhydrol.2014.05.004

0022-1694/ 2014 Elsevier B.V. All rights reserved.

Corresponding author. Tel.: +1 217 333 0237; fax: +1 217 333 2304.

E-mail address: mmarkus@illinois.edu (M. Markus).

Journal of Hydrology 515 (2014) 267280

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by increasing spring warm-spell temperatures as well as decreas-

ing precipitation in the Rocky Mountain area. In these studies,

trends in observed and simulated seasonal streamflows were

found to be highly correlated with trends in seasonal precipitation.

The Wisconsin Driftless Area, like surrounding areas in the

Midwestern United States, is a region with several streams show-

ing decreasing annual maximum streamflow. Numerous studies

attempt to explain these trends. Early studies, such as Potter

(1991) and Gebert and Krug (1996), explain the trends by the

changes in land management practices. Potter (1991) argued that

the changes in hydrology are likely due to the adoption of various

measures for soil and water conservation, and are not climate

related. Gebert and Krug (1996) suggested that the decreasing

flood peaks were caused by improving hydraulic structures such

as drainage ways and stream channels, as well as an increase in

wooded areas. Knapp (2005) studied trends in annual minimum,

average, and maximum streamflows in the Midwestern United

States and detected a decrease in annual peak flows in the Pecato-

nica River watershed, a part of the Wisconsin Driftless area. Knapp

also found no significant correlation between trends in peaks and

trends in average annual flows in this region. Kochendorfer and

Hubbart (2010) confirmed that annual peak flows in the Wisconsin

Driftless area have been decreasing and argued that the decreasing

trend was caused by widespread land-use changes associated with

soil conservation efforts in the 19392008 period.

Some of the more recent studies on the decreasing flood peaks

in this area include climate parameters as explanatory variables.

Juckem et al. (2008) discussed a climate-related step change in

precipitation and base flow around 1970, also globally observed

as a result of the Pacific Decadal Oscillation (Mantua et al., 1997).

Juckem et al. used data from the Driftless area of Wisconsin to

show that both climate and land management changes could

change the hydrologic response of the system. They state, Climatic

change appears to control the timing and direction (increase or

decrease) of the change, while land management changes amplify

the response beyond that which can be explained by climate fac-

tors alone. Markus et al. (2013) analyzed the decreasing trend of

annual peak flows in the Pecatonica River for the 19142008

period and found that the annual peak flow time has shifted from

February and March (referred to as the late winter months) to

mostly spring or summer seasons. Markus et al. also suggested that

the overall decreasing annual peak flows could be explained by the

significant decrease in late winter flows, which was possibly

related to the increasing winter temperature. Time series of

minimum, average, and maximum daily temperature in February

(Fig. 1) illustrates this increase. Fig. 2 shows three graphs: the

annual maximum streamflow time series (Fig. 2a), the maximum

FebruaryMarch flood peaks for each water year (Fig. 2b), and

the maximum flood peaks within the remaining months

(OctoberJanuary, and AprilSeptember) for each water year

(Fig. 2c). The discharges in February and March appear to dominate

the annual trend, suggesting that the decrease in annual peaks is

primarily a result of the decrease in the late winter months.

Markus et al. (2013) further speculated that this decrease is pri-

marily a result of the increasing winter season temperatures,

resulting in less snow accumulation and fewer frost days. The

changing hydrologic regime is best illustrated in Fig. 3, showing

the gradual change from predominantly snowmelt-driven annual

maximum flood peaks to predominantly rainfall-driven floods.

Fig. 3 shows times of flood peak in each year, expressed in days

after January 1st. The size of solid circles represents the magnitude

of the peak. The floods between days 0 (January 1st) and approxi-

mately 90 (March 31st) are typically snowmelt-driven, while later

peaks are typically caused by large rain events. The figure shows

that the snowmelt-driven floods became less frequent and less

dominant with time compared to the rainfall-driven floods

occurring later in the year. Three weighted trend lines in the figure

also show significant upward trends in average flood timing overall

and for rainfall-driven floods, and a statistically insignificant

downward trend for the snowmelt-driven floods.

We hypothesize that the flood peak decrease in the Pecatonica

River is driven in part by climate change. To test this hypothesis,

this study applied the Variable Infiltration Capacity (VIC) model,

which is a physically based land-surface model capable of simulat-

ing energy and water balance. The model simulated a number of

hydrologic and climatic variables, such as frozen soil, snow depth,

snowmelt, soil temperature, and river discharge in the Pecatonica

River watershed. A special focus was given to the late winter flood

events, as the decrease in these events was the primary reason for

the decreasing annual maximum flows in the watershed. The late

winter flood events in this watershed are typically driven by snow-

melt, or a combination of snowmelt and rainfall, due to seasonal

increases in temperature to above freezing. Snowmelt simulation

using the VIC model has been analyzed by Sinha and Cherkauer

(2010), Sinha et al. (2010), Tan et al. (2011), Andreadis et al.

(2009), and others. Particularly, Feng et al. (2008) compared the

VIC and the Snow Thermal Model (SNTHERM) by Jordan (1991)

and showed a good agreement between the two models in snow

simulation. In addition, the VIC model can simulate and evaluate

both snow and frozen soil effects unlike the Snowmelt Runoff

Model (SRM; Martinec et al., 1998; Nakayama and Watanabe,

2006). These studies indicated that the VIC model has an ability

to accurately simulate watershed processes, including snow accu-

mulation and ablation. Unlike the published applications, which

used daily step simulations to analyze monthly flows such as Dai

et al. (2004), Andreadis et al. (2005), and Sheffield and Wood

(2008), this study uses VIC to simulate daily flood peaks in two

modes, event and continuous, for late winter events to address

the decreasing flood frequency in a changing hydrologic regime.

The specific objectives of the study are to (i) Assess the applica-

bility of the VIC model to simulate flood peaks in a snowmelt-

driven hydrologic regime for a medium-sized agricultural

watershed, and (ii) Attempt to provide a climate change-related

explanation of the trend in the flood peaks in the Pecatonica River

by relating it to trends in climatic variables, both observed and

generated, using VIC for the period 19152009.

2. Study site

The Pecatonica River watershed has a drainage area of

3435 km2 and is located in southern Wisconsin and northern Illi-

nois (Fig. 4). The watershed is located approximately 100 km west

of Lake Michigan, near two large coastal metropolitan areas,

Milwaukee and Chicago. This study chose five climate stationsFig. 1. Mean maximum, mean minimum, and mean average daily temperatures

19012008 at Darlington, Wisconsin, in February (Markus et al., 2013).

268 D. Park, M. Markus / Journal of Hydrology 515 (2014) 267280

(Dodgeville, Darlington, Martintown, and Monroe in Wisconsin

and Freeport in Illinois), which will be further described in the next

section. The land use in the watershed has not changed much in

the past; recently Homer et al. (2004) found that it is about 82%

agricultural, 10% forested, and 6% developed.

3. Data

3.1. Climate data

Observed climate forcing data included daily precipitation, daily

maximum and minimum temperatures, and wind speed. Daily

observed snowfall, snow depth, rainfall, and temperature data for

19152009 based on NOAAs National Weather Service Coopera-

tive Observer Program were used (Table 1). Some of these vari-

ables, particularly snow depth, have limited data in the earlier

parts of the record, as indicated in Table 1. The daily wind speed

data were available from 1949 to the present, and were obtained

from the National Center for Environmental Prediction National

Center for Atmospheric Research 40-year Reanalysis project,

as described by Kalnay et al. (1996), and are available at

http://www.cdc.noaa.gov/cdc/reanalysis/reanalysis.shtml. The wind

speed prior to 1949 was assumed to match the daily wind clima-

tology based on the period after 1949, similar to the approach of

Hamlet and Lettenmaier (2005). All daily data were gridded for

the Pecatonica River watershed using the method described by

Hamlet and Lettenmaier (2005); spatial resolution of the grids

was 1/8, with grid cells of approximately 10 km 14 km or

140 km2.

3.2. Soil and land cover data

Soil and land cover parameters were acquired from the Land

Data Assimilation System (LDAS; Maurer et al., 2002). Soil param-

eters, including infiltration shape parameter, maximum subsurface

flow rate, fraction of the maximum soil moisture, soil layer depths,

and saturated hydrologic conductivity Ksat, commonly used in VIC

(Mishra et al., 2010), were calibrated. Vegetation parameters

(architectural resistance, albedo, minimum stomata resistance, leaf

area index, zero-plane displacement, vegetation roughness length,

fraction of root depth of each soil layer) were represented to sub-

grid variability with a factional coverage area (Mishra et al., 2010).

They were obtained from the same LDAS dataset. Both soil and veg-

etation parameters were developed at 1/8 spatial resolution.

Monthly leaf area index (LAI) data for each vegetation type

Fig. 2. Observed annual peak flows at Freeport streamgage (USGS No. 0543500) on the Pecatonica River between 1915 and 2009 for (a) complete dataset; (b) February and

March; (c) April through January.

Fig. 3. Annual flood peak timing (in days after January 1st) and magnitude

(represented by the size of circles) in the Pecatonica River watershed, showing a

gradual decrease in frequency and magnitude of snowmelt-driven annual flood

peaks (first 34 months in a calendar year).

D. Park, M. Markus / Journal of Hydrology 515 (2014) 267280 269

http://www.cdc.noaa.gov/cdc/reanalysis/reanalysis.shtml

composed of 1/8 spatial resolution were adapted from the method

developed by Myneni et al. (1997).

3.3. Daily streamflow data

Observed daily streamflow data from 1915 to 2009 were

obtained from the gage operated by the United States Geological

Survey (USGS) at Freeport, Illinois (No. 0543500), with a drainage

area of 3435 km2 (Fig. 4). This gaging station is also operated in

cooperation with the Illinois State Water Survey, University of

Illinois, and maintained by the USGS Illinois Water Science Center.

4. Variable Infiltration Capacity Model

The VIC model was originally developed jointly at the Univer-

sity of Washington and Princeton University. It is a macroscale

hydrology model used to simulate various hydrologic variables as

well as kinetic energy variables such as soil moisture, snowmelt,

data were available only at Freeport, and for 1929 at Darlington.

Data were available at most of the sites for later events. Using

snow depth data with a limited spatial extent made the calibra-

tions more uncertain. Later decades had more data and potentially

more accurate calibrations. Overall, the VIC model produced a rea-

sonable simulation accuracy of snow depths, below 2530 cm, and

underestimated the observed snow depths exceeding that level,

similar to Mishra and Cherkauer (2011). Possible explanations for

these results could be found in the calibration criteria. We cali-

brated the parameters to minimize the difference between the

observed and model-generated snow and discharge in a spatially

and temporally lumped fashion. Each observation had equal

weight. Extreme values, although critical, might not have carried

sufficient weights due to their infrequent occurrence in a time ser-

ies of the observed data, suggesting that the calibration was dom-

inated by the medium-range observations. Similarly, the

parameters were spatially averaged, possibly reducing the effects

of the highest observations.

Table 4 shows peak streamflow times of daily observed data,

the VIC model, and the corresponding NSE indexes. The VIC-based

peak times generally match the observed data. One-half of the sim-

ulated hydrographs matched the observed peak times, while the

other half were within one (1975, 1985, and 1997) or two days

(1959 and 1963). NSE for calibration ranged between 0.5 and

0.85. Fig. 7 shows the observed and simulated peaks and volumes

(represented as a sum of daily discharges for each event). The fig-

ure shows a very good agreement between these variables, partic-

ularly between the model and observed peak discharges with a

RMSE of 20.7 m3/s. In terms of volumes, except for three events,

in which the model underestimated the observed volumes by

approximately 2530%, the simulations are reasonably accurate,

within 15% of the observations. The overall accuracy for volume

simulations is RMSE = 658.7 m3/s, which is approximately 20% of

the average event volume.

The snow roughness parameter was also allowed to vary to

achieve best possible simulation accuracy. The time series of these

best-fit snow roughness parameters exhibited an increasing trend

with a 95% statistical significance (Fig. 8).

Table 4

Peak time and NSE between observed and VIC streamflows.

Year Peak time NSE

Observed VIC

1916 3/28/1916 3/28/1916 0.95

1929 3/16/1929 3/16/1929 0.85

1937 3/8/1937 3/8/1937 0.75

1948 3/1/1948 3/1/1948 0.69

1959 4/5/1959 4/3/1959 0.88

1963 3/21/1963 3/19/1963 0.65

1975 3/25/1975 3/24/1975 0.86

1985 2/27/1985 2/26/1985 0.65

1997 2/22/1997 2/23/1997 0.61

2005 2/16/2005 2/16/2005 0.55

Fig. 7. Event simulation: observed and VIC model simulated peak and cumulative discharges at Freeport on the Pecatonica River for 10 largest late winter events given in

Table 4.

Fig. 8. Calibrated snow roughness for event simulations.

274 D. Park, M. Markus / Journal of Hydrology 515 (2014) 267280

Fig. 9. Continuous simulation: Observed and VIC model simulated snow depth for February and March, for (a) Dodgeville, Wisconsin; (b) Darlington, Wisconsin;

(c) Martintown, Wisconsin; (d) Monroe, Wisconsin; and (e) Freeport, Illinois. Each point represents one day.

Fig. 10. Continuous simulation: observed and VIC model simulated peak and cumulative discharges at Freeport for each late winter event 19152009. Solid points show the

years also used in the event simulation approach.

D. Park, M. Markus / Journal of Hydrology 515 (2014) 267280 275

8.2. Continuous simulation

The scatter plot of observed vs. simulated snow depth (Fig. 9)

shows that the simulation accuracy varies with month (February

and March) and stations. The simulation accuracy appears reason-

able on average, with some underestimation of the highest snow

and overestimation of the lowest ones. The simulated snow depths

in March are consistently more accurate than those in February.

Also, the simulations for Darlington and Freeport stations (Fig. 9b

and e) are more accurate with lower RMSE than for the remaining

sites.

The continuous approach produced less accurate flood peak

simulations compared with the event approach. Fig. 10 (unlike

Fig. 7 for event calibration) shows a large variability in predicted

flood peaks and volumes. The solid circles show the years used in

the event calibration. The reason for larger errors is the difference

in calibrations. In the event approach, each event was calibrated

separately, while one set of parameters (except for snow roughness

parameter changing every five years) was used for the entire

record in the continuous approach. More importantly, the calibra-

tion objective in the event calibration was to match the flood

peaks, while in the continuous approach the goal was to match

snow depths, without regards to the flood peaks.

Similar to the event simulation (Fig. 8), the snow roughness

parameter for the continuous simulation (Fig. 11) exhibited an

increasing temporal trend with a 95% statistical significance based

on the Kendall Tau test. Fig. 11 shows the calibrated values for

snow roughness parameter for each five-year period in the contin-

uous simulation. Despite different datasets, objective functions,

and calibration approaches, both event and continuous calibrations

resulted in similar (increasing) temporal trends for the snow

roughness parameter.

9. Discussion

9.1. Snow monitoring and modeling uncertainty

Accurate monitoring of snow depth is of great importance in

calibration of both the continuous and the event simulations pre-

sented in this study. The estimates of snow accumulation and abla-

tion rates, however, can be biased (Varhola et al., 2010). Point

measurements may not be representative of the non-uniform spa-

tial snow coverage, particularly in larger areas. Also, the temporally

discrete, typically daily snow depth measurements do not capture

sub-daily snow variability, caused mainly by temperature and

wind, and manifested through snow melting, compacting, drifting,

or blowing.

These issues could be partially addressed through a process of

data quality control and assurance. For example, to assess the suit-

ability of climate gages for use in trend analysis, Kunkel et al.

(2007) suggest that a careful analysis of station histories and regio-

nal comparisons should be required for each gage. Such analyses

are routinely and thoroughly carried out at the Midwestern Regio-

nal Climate Center (MRCC) in Champaign, Illinois, resulting in

assessments of accuracy for each station in this study. Despite

the quality assurance and the long-term homogeneity of snowFig. 11. Calibrated snow roughness for continuous simulation.

Fig. 12. Model parameters used in event (represented by circles) and continuous (represented by lines) simulations.

276 D. Park, M. Markus / Journal of Hydrology 515 (2014) 267280

Fig. 13. Temporal trends in three observed climate variables in the Pecatonica River watershed.

Fig. 14. Temporal trends in eight simulated climate variables by event simulation in the Pecatonica River watershed.

D. Park, M. Markus / Journal of Hydrology 515 (2014) 267280 277

depth data, simulation of snow depths remains to be highly uncer-

tain. Fig. 12 shows the ranges of eight parameters, calibrated in

both event (top chart) and continuous simulation (bottom chart).

Most of parameters are consistent between the two approaches,

except for Exp1 and Exp2, which appear more sensitive to the

observed datasets. Nonetheless, the ranges for the correlation

coefficient between the observed and simulated snow depth was

0.40.7 for the continuous simulation and 0.40.9 for the event

simulation, which were comparable to similar studies (e.g.,

Mishra and Cherkauer, 2011).

Additional uncertainties come frommodel selection and param-

eter estimation. Essery et al. (2009) described a comparative study

of 33 models, including VIC, for simulations of surface and energy

mass balances. They outlined numerous modeling uncertainties,

including those based on model selection. They also found that

uncertainties in parameter selection overwhelm deficiencies in

model structure when calibration data are not available. This

study, however, had somewhat incomplete datasets in the first

part of the record, but the datasets in the final decades were more

complete.

The VIC model runs in this study allowed the snow roughness

parameter to vary with time periods, every 5 years for continuous

simulation and every 10 years for event simulations. This resulted

in more accurate simulations. It is possible that smaller increments

(e.g. 1 year) for continuous calibration of roughness would have

produced more accurate simulations, but the parameter optimiza-

tions and calculations were not performed for each year due large

computational requirements. Regardless, the best-fit roughness

parameter exhibited a significant increasing temporal trend in

both event and continuous simulations. It can be speculated that

the long-term trend in the best-fit roughness parameter is related

to the long-term increase in air temperature. However, more

research, modeling and additional monitoring data would be

needed to determine if and how the increase in this parameter

relates to the changes in other climate variables.

9.2. Temporal trends in hydroclimatic variables

Figs. 1315 show the trends in climate variables in the Pecato-

nica River watershed, including the observed (Fig. 13), event simu-

lation (Fig. 14), and continuous simulation (Fig. 15) approaches.

Snowfall and precipitations are presented as cumulative values

and all other variables are presented as daily average values for

FebruaryMarch period. Fig. 13 shows three observed variables

(air temperature, snowfall, and total precipitation). Figs. 14 and

15 show eight simulated variables (bare soil temperature, infiltra-

tion, evaporation, sublimation, frost days, snow depth, snowmelt,

and SWE). These results show a high degree of consistency for

the two approaches having different data, calibration methods,

and objective functions. Table 5 shows the Kendall-Tau trend test

(Kendall, 1955; Helsel and Hirsch, 1995), the Theil-Sen trend test

(Sen, 1968; Kumar et al., 2009), the slope of the best-fit line, and

the R2 of the best-fit line for each variable in Figs. 14 and 15. Tra-

ditionally, the trends would be considered statistically significant,

if the significance levels exceed 95% or 99%. However, some of the

statistically insignificant trends may have serious environmental

consequences. Several studies show that the natural system is very

sensitive to small air temperature increases, such as 1 C per

100 years (National Research Council, 2011; Zhang et al., 2001),

which can cause significant changes in hydrologic and ecologic

Fig. 15. Temporal trends in eight simulated climate variables by continuous simulation approach in the Pecatonica River watershed.

278 D. Park, M. Markus / Journal of Hydrology 515 (2014) 267280

processes. Due to the potential importance of this statistically

insignificant but very important change observed in the Pecatonica

River watershed, the results should be interpreted in a less rigid

fashion. For that reason the table shows trends with significance

levels ranging from less than 70% to 99.9%.

Table 5 shows that the event and continuous simulations for 10

of 11 trends have identical trend signs. The only exception is the

time series for infiltration for the two approaches. The Kendall-

Tau and the Theil-Shen tests produced very consistent results.

None of the changes in infiltration exceeded statistical significance

of 70%. While precipitation has been increasing, all four snow-

related variables (snowfall, snow depth, snowmelt, and SWE) have

been decreasing for both event and continuous simulations. Also,

for both the event and continuous simulations, the increasing

trends in sublimation appear to be partly responsible for the

decreasing snow depth, which in turn could result in decreasing

flood peaks. Variability in most of these processes, and thus their

trends, could be attributed to an increasing air temperature. In

conclusion, these results indicate that the VIC model is capable of

simulating snowmelt-runoff hydrologic processes in a changing

hydrologic regime and serving as a tool which can be used to

detect trends in hydroclimatic variables. These trends, such as

the decreasing trends in snow depth, and the increasing trends in

sublimation, for both event or continuous simulations, were con-

sistent with the decreasing trend in flood peaks, potentially sug-

gesting that the contribution of climate change to the decreasing

trend in flood peaks in the Pecatonica River is significant.

10. Conclusions

This research tested the applicability of the Variable Infiltration

Capacity (VIC) model in a flood study in a changing hydrologic

regime. For this task, two modes of model applications, event

and continuous simulations, were designed and implemented.

The event simulation mode tested the models ability to simulate

flood events accurately and also to provide an insight in the

changes in various hydroclimatic variables during the flood event,

while the continuous simulation was designed to provide overall

watershed conditions during the flood season.

The model was applied to the Pecatonica River at Freeport in

Illinois. This river has been known for its decreasing annual flood

peaks based on 95 years of monitoring data. The results indicate

that climate change may have contributed to this trend, and that

the key change in the watershed occurred in the late winter during

the snowmelt. The climate variables produced by both event and

continuous simulations exhibited trends in hydroclimatic variables

from more favorable to snowmelt flooding early in the record, to

less favorable to snowmelt flooding in the past several decades.

In particular, the model results suggest that the observed increas-

ing air temperature resulted in decreasing trends in snow depth

and snowmelt, and also in the increasing trends in sublimation

and evaporation during the late winter months. This shift in flood

peaks from predominantly a snowmelt-driven regime to primarily

a rainfall-driven flood regime could be partly explained by the

temperature increase, and could characterize watersheds in other

geographically similar regions. Nonetheless, to provide a compre-

hensive analysis of trends in floods, similar studies should be

accompanied by those relating land-use change and other vari-

ables to these trends.

Acknowledgments

This research was partially supported by the National Center for

Supercomputing Applications at the University of Illinois in

Urbana-Champaign. The authors would like to acknowledge the

contribution of Dr. Gi-Hyeon Park from the University of Wyoming,

Dr. Tom Over from USGS, and Drs. Laura Bowling and Keith Cher-

kauer from Purdue University for valuable advice in various stages

of this research. Dr. Edward J. Hopkins, Assistant Wisconsin State

Climatologist, provided valuable information on snow data moni-

toring, history of the gages, and quality of the observed data. The

authors would also like to acknowledge the help from the follow-

ing colleagues at ISWS/UIUC: Nancy Westcott and Alena Bartosova

for providing very useful review comments, Lisa Sheppard for edi-

torial review, and Sara Olson for reviewing and modifying the

figures.

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Table 5

Trend analysis results for three observed (in italics) and eight model-generated hydroclimatic variables.

Variable Event simulation Continuous simulation

Kendall

trend

coeff.

Statistical

significance

of trend (%)

Theil-Sen

estimator

Statistical

significance

of trend (%)

Slope of

the

best-fit

line

R2 of

the

best-fit

line

Kendall

trend

coeff.

Statistical

significance

of trend (%)

Theil-Sen

estimator

Statistical

significance

of trend (%)

Slope of

the

best-fit

line

R2 of

the

best-fit

line

Air temperature (C) +0.511 95.0 0.031 92.6 +0.027 0.480 +0.070

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of a changing hydrologic flood regime using the Variable Infiltration Capacity model1 Introduction2 Study site3 Data3.1 Climate data3.2 Soil and land cover data3.3 Daily streamflow data4 Variable Infiltration Capacity Model5 Model application design5.1 Event simulation5.2 Continuous simulation6 Model performance evaluation7 Model calibration7.1 Event simulation7.2 Continuous simulation8 Results8.1 Event simulation8.2 Continuous simulation9 Discussion9.1 Snow monitoring and modeling uncertainty9.2 Temporal trends in hydroclimatic variables10 ConclusionsAcknowledgmentsReferences