Effects of Climate Variability on Forest 

Hydrology and Carbon Sequestration 

on the Santee Experimental Forest in 

Coastal South Carolina

Zhaohua Dai, Carl C. Trettin, and Devendra M. Amatya

United States

Department of

Agriculture

Forest Service

Southern

Research Station

General Technical

Report SRS–172
February 2013

Southern Research Station

200 W.T. Weaver Blvd.

Asheville, NC 28804

www.srs.fs.usda.gov

Authors: 

Zhaohua Dai, Research Scientist, Earth System Research 

Center, Institute for the Study of Earth, Oceans, and Space, 

University of New Hampshire, Durham, NH 03824; 

Carl C. Trettin, Research Soil Scientist, and Devendra 

M. Amatya, Research Hydrologist, U.S. Department of 

Agriculture, Forest Service, Center for Forested Wetlands 

Research, Cordesville, SC 29434.

Cover photographs (left-to-right starting at the top):

Charles Harrison downloading data from the headquarters weather station on the Santee 

Experimental Forest (photo by Andrew Moreland); wet-dry deposition collector, headquarters 

weather station (photo by Julie Arnold); Pluvio rain gauge, headquarters weather station (photo by 

Charles Harrison); Santee Headquarters weather station (photo by Charles Harrison); above-canopy 

weather station, watershed WS-80 (photo by Charles Harrison); Santee Headquarters weather 

station circa 1960s (U.S. Forest Service); weir on watershed WS-79, circa 1960s (U.S. Forest 

Service); and V-notch weir on watershed WS-77 (U.S. Forest Service).
Effects of Climate Variability on Forest 

Hydrology and Carbon Sequestration 

on the Santee Experimental Forest in 

Coastal South Carolina

Z. Dai, C.C. Trettin, and D.M. Amatya

Contents

LIST OF FIGURES ....................................................................................................................................v

INTRODUCTION ......................................................................................................................................1

DATA AND METHODS..............................................................................................................................2

General Site Description................................................................................................................................................................................. 2

Environmental Monitoring Data ................................................................................................................................................................... 3

Estimates of Potential Evapotranspiration................................................................................................................................................... 5

Estimate of Water Balance ............................................................................................................................................................................. 5

RESULTS AND DISCUSSION ..................................................................................................................6

Temperature and Precipitation....................................................................................................................................................................... 6

Streamflow and Water Table Dynamics..................................................................................................................................................... 12

Potential Evapotranspiration........................................................................................................................................................................ 16

Water Balance.................................................................................................................................................................................................17

Modeling the Role of Climate Variability on Carbon Dynamics in Forests........................................................................................ 22

CONCLUSIONS AND IMPLICATIONS .................................................................................................27

ACKNOWLEDGMENTS..........................................................................................................................28

LITERATURE CITED ..............................................................................................................................28

iii

LIST OF FIGURES

Figure 1—The Santee Experimental Forest is located in the Francis Marion National Forest, about 55 km northwest of Charleston, in 

Berkeley County, in the southeast Atlantic Coastal Plain of South Carolina....................................................................................... 3

Figure 2—Long-term trends in air temperature measured at the Santee Experimental Forest Headquarters weather station, showing 

(A) temperature trend [∆T(46-08) represents the changes in temperature between 1946 and 2008; ∆T(76-08) represents the 

changes in temperature between 1976 and 2008—globally, 1976–2008 is the period of greatest recorded increase in temperature, 

with an increase of 0.2 °C per decade, the rate of increase in temperature on Santee Experimental Forest during the same period 

was 0.27 °C per decade; thin blue line represents 5-year running average; red dash line indicates changes in temperature from 

1976 to 2008; x1

 represents the year between 1946 and 2008, x2 represents the year between 1976 and 2008], (B) maximum, 

minimum, and mean temperature during 1946–2008 (maxMDT is maximum mean daily temperature; minMDT is minimum 

mean daily temperature; meanT is average daily temperature), (C) annual mean daily maximum and minimum temperatures 

relative to the mean at Santee Experimental Forest Headquarters from 1946 to 2008, (D) yearly maximum and minimum 

temperatures relative to the mean from 1946 to 2008 (ymax and ymin are the maximum and minimum temperature in a year, 

respectively), and (E) the diurnal and annual temperature range during the 63-year period (DTR is diurnal temperature range; 

ATR is annual temperature range; meanDTR is the annual mean DTR averaged from 1946 to 2008)................................................. 10

Figure 3—Long-term trends in precipitation measured at the Santee Experimental Forest Headquarters weather station, showing 

(A) deviation of precipitation from the long-term average (1370 mm) from 1946 to 2008 and (B) comparison of temperature and 

precipitation anomalies between 1946 and 2008........................................................................................................................... 13

Figure 4—(A) Average daily temperature observed at Santee Headquarters (SHQ) and Lotti weather stations (synchronized by date), 

and (B) comparison of monthly precipitation at the three field stations (Lotti, Met5, Met 25) with the SHQ weather station. ............... 13

Figure 5—Number of small (>25 mm) and large (>50 mm) storm events per year recorded at the Santee Experimental Forest from 

1946 to 2008 (dashed line and equation is the 5-year running average). .......................................................................................... 13

Figure 6—(A) Observed monthly streamflow of watersheds WS77, WS80, and WS79 on the Santee Experimental Forest for 1965–

2007, and (B) observed relationship of monthly precipitation with monthly streamflow observed on all watersheds on the Santee 

Experimental Forest for 1965–2007. ........................................................................................................................................... 14

Figure 7—Water table trends for (A) monthly average water table depth on the watershed WS77 from 1964 to 1971 and from 1992 

to 1994, (B) observed and simulated water table depth on watersheds WS77 and WS80 on the Santee Experimental Forest 

from 1992 to 1995 (-O is observed; -P is simulated), (C) daily water table depth on watersheds WS77 and WS80 from wells H 

(WS80-W1), D (WS80-W2), and J (WS77-W1) in 2005–07, and (D) comparison among deviations of annual precipitation on 

annual mean water table on the watershed WS77 with their average between 1964 and 1993 [WT is water table depth (cm); rain 

is annual precipitation (cm)]....................................................................................................................................................... 15

Figure 8—Relationship between annual precipitation and annual streamflow on watersheds WS77 and WS80 on the Santee 

Experimental Forest between 1965 and 2007; the filled diamonds represent 1990 through 2003........................................................ 15

Figure 9—Deviation in potential evapotranspiration (PET) from the long-term average measured at the Santee Headquarters from 

1946 through 2008. ................................................................................................................................................................... 19

Figure 10—(A) Predicted and observed monthly streamflow on the watershed WS79 and its sub-watersheds WS77 and WS80 for 

1965 to 2007 and (B) observed and simulated water table below the soil surface on watersheds WS77 (44 wells) and WS80 (35 

wells) for all water table observation periods between 1964 and 2007. ........................................................................................... 19

Figure 11—Estimated annual evapotranspiration (ET) calculated for the Santee Headquarters for 1946 to 2008, using the 

Thornthwaite water balance approach (Flerchinger and Cooley 2000, Ward 1972)........................................................................... 19

Figure 12— (A) Annual streamflow for the watershed WS79, 1950–2007, as measured (OB) and predicted using different models 

(WB is Thornthwaite water balance; MS is MIKE SHE; FD is Forest-DNDC); (B) relationship between observed and estimated 

annual streamflow for the watershed WS77, 1965–2007, using MIKE SHE (MS), Forest-DNDC (FD), and Thornthwaite water 

balance approach (WB).............................................................................................................................................................. 21

v
Figure 13—Soil water deficit estimated using Thornthwaite water balance approach (Flerchinger and Cooley 2000, Ward, 1972) for 

watershed WS79 from 1946 to 2008............................................................................................................................................ 21

Figure 14—Comparison of annual temperature, precipitation, and soil water storage anomalies on watershed WS79 between 1950 

and 2007................................................................................................................................................................................... 21

Figure 15—Comparison of Forest-DNDC predicted with observed parameters on (A) soil moisture on watershed WS79 in 2006–

2008, (B) soil temperature on watershed WS79 in 2006–08, (C) soil CO2

 efflux on the watershed WS79 between 2006 and 2008, 

(D) aboveground biomass for plots 21–24 on watershed WS80 in 2006 and 2007, and (E) above- and belowground biomass in 

2006 for watershed WS79. ......................................................................................................................................................... 23

Figure 16—Average annual soil CO2 efflux on watershed WS79 from 1950 to 2007............................................................................. 25

Figure 17—Average annual methane (CH4) flux on watershed WS79 from 1950 to 2007. ..................................................................... 25

Figure 18—Predicted change in annual soil CO2 flux on the watershed WS79 between 1950 and 2007 using Forest-DNDC..................... 25

Figure 19—Average annual biomass and soil carbon accumulation on watershed WS79 from 1950 to 2007 as predicted using ForestDNDC. The blank square () indicates pre- and post-Hurricane Hugo biomass reported by Hook and others (1996); the triangle 

() indicates the aboveground biomass measured in 2006 and 2007. ............................................................................................. 25

Figure 20—Methane (CH4) flux predicted using Forest-DNDC on watershed WS79 in relation to (A) annual precipitation and (B) 

annual mean temperature from 1950 to 2007................................................................................................................................ 26

Figure 21—Net ecosystem exchange (NEE) and net primary production (NPP) on watershed WS79 estimated by Forest-DNDC from 

1950 to 2007............................................................................................................................................................................. 27

Figure 22—Relationship between net ecosystem exchange (NEE) on watershed WS79 with (A) precipitation and (B) temperature 

from 1950 to 1988. ................................................................................................................................................................... 27

vi
Effects of Climate Variability on Forest 

Hydrology and Carbon Sequestration 

on the Santee Experimental Forest in 

Coastal South Carolina

Zhaohua Dai, Carl C. Trettin, and Devendra M. Amatya

Abstract 

Long-term weather and hydrology data from the Santee Experimental Forest 

were used to assess trends in air temperature, precipitation, and the water 

balance in gauged watersheds over a 63-year period. Since 1946, the mean 

annual air temperature has increased at a rate of 0.19 °C per decade, a rate 

higher than the global mean for the same period. Total annual precipitation 

has not changed significantly over the period of 1946–2008; however, 

large storm events (>50 mm precipitation) have increased 21 percent over 

the 63-year period. Annual stream discharge has varied from 5.5 percent 

of annual precipitation in dry years to 44.7 percent in wet years. In 1989, 

much of the forest was destroyed by Hurricane Hugo, a disturbance that, 

in turn, influenced streamflow. The water balance was estimated using 

the hydrologic model MIKE SHE; the long-term simulations showed that 

average annual flow was about 24 percent of annual precipitation and that 

mean annual evapotranspiration was approximately 76 percent over the 

63-year period. The carbon balance on the 500-ha watershed was evaluated 

using Forest-DNDC. The model performance efficiency was 0.67 for soil 

CO2 efflux, 0.70 for soil temperature, 0.40 for soil moisture, and 0.86 for 

wood biomass dynamics, demonstrating that this model was applicable for 

predicting carbon dynamics for this complex forest mosaic. 

Keywords: Carbon cycling, climate change, Forest-DNDC, forest 

hydrology, long-term weather data, streamflow. 

INTRODUCTION

Climatic conditions influence hydrological processes 

and carbon (C) dynamics in forested ecosystems (Trettin 

and others 2006) because changes in temperature and 

precipitation can alter the hydrologic conditions that 

regulate the C balance in forest ecosystems. These changes 

are especially important in forested wetlands where 

hydrology is one of the most important elements influencing 

C accumulation and consumption (Pacific and others 2009, 

Pietsch and others 2003, Riveros-Iregui and McGlynn 2009, 

Trettin and Jurgensen 2003). While a sink for atmospheric 

C, forested wetlands also may be a significant source of 

terrestrial greenhouse gases, e.g., CH4

 and CO2 (Trettin 

and others 2006). Understanding the effects of climate 

variability on forest hydrology and C balance of upland 

and forested wetlands is needed to assess their relative 

contributions of greenhouse gas to the atmosphere.

The forest landscape of the lower coastal plain along 

the Atlantic Ocean is typified by a mosaic of upland and 

wetland ecosystems. Streamflow and depth to water table 

within that landscape is heavily dependent on precipitation 

and evapotranspiration (Amatya and others 1996, 2006a; 

Brooks 2009; La Torre Torres and others 2011; Sun 

and others 2006). In first-order forested watersheds 

on the coastal plain, the water table level can decrease 

substantially when precipitation decreases and temperature 

increases (Amatya and others 1996, Dai and others 2010a, 

Lu and others 2009); in such watersheds, the annual 

evapotranspiration (ET) is approximately equal to potential 

evapotranspiration (PET) (Amatya and Trettin 2007a; 

Amatya and others 1996, 2006b). The decrease in the 

water table level of wetlands can lead to an increase in soil 

CO2

 release when the typically anaerobic soil environment 

is converted to aerobic conditions (Dai and others 2011a, 

Fissore and others 2008, Pietsch and others 2003, Smith 

and others 2008). It is not uncommon for first-, second-, 

and third-order watersheds within the lower coastal plain 

to have no streamflow in normal dry periods (Amatya and 

Radecki-Pawlik 2007, Amatya and others 2009, Dai and 

others 2010b). However, large storms along the coast can 

also cause flooding problems (Amatya and others 2006b, 

Callahan and others 2004, Frey and others 2010, La Torre 

Torres and others 2011, Miwa and others 2003, Young 

and Klawitter 1968). These relationships suggest that 

changes in temperature and precipitation may influence 

the hydrology of these forested watersheds on the coastal 

plain, particularly with respect to streamflow and water 

table dynamics (Amatya and others 2006a, Brooks 2009, 

Hartig and others 1997, Lu and others 2009, Scavia and 

others 2002). 

Watersheds on Santee Experimental Forest, comprising 

wetlands and uplands, are typical of the southeastern 

Atlantic Coastal Plain. The Santee Experimental Forest was 

created in 1937 to study pine and hardwood silviculture, 

and the effects of forest management on forest hydrology 

(Amatya and Trettin 2007b). Water quality, quantity, 
runoff dynamics, and the impact of storms on downstream 

flooding have been studied since the 1960s, with results 

showing that streamflow is closely linked to precipitation 

and evapotranspiration (Amatya and Radecki-Pawlik 

2007; Amatya and others 2006a, 2006b; Harder 2004; 

Harder and others 2007; La Torre Torre and others 2011; 

Sun and others 2000; Young 1966; Young and Klaiwitter 

1968; Young and others 1972). Yet the studies also suggest 

uncertainty in the relationship, e.g., the ratio of annual 

streamflow to annual precipitation has varied between 7 

and 43 percent between 1965 and 2007 (Dai and others 

2011b, Harder and others 2007, Young 1966). Hydrological 

simulations show that the ratio could increase 2.4 percent 

with an increase in 1 percent of annual precipitation (Dai 

and others 2010a). The water table in this region is also 

influenced by precipitation (Amatya and others 2003, Dai 

and others 2010a, Harder 2004), nonlinearly increasing 

with an increase in precipitation (Dai and others 2011a) 

and streamflow (Harder and others 2007). Water table 

conditions on the coastal plain can be influenced by 

changing temperature, decreasing about 1.9 cm per °C 

temperature increase (Dai and others 2010a). The impacts 

of prescribed fires on streamflow and water quality and 

quantity have been studied since the 1970s, with results 

showing that the impact of prescribed fires on water quality 

are insignificant when only a portion of the watershed is 

burned but that prescribed fire could cause an increase in 

streamflow (Richter and others 1983a, 1983b). 

Recently, Trettin and others (2009) reported changes 

in hydrological functions and pathways with respect to 

land use change from agriculture to forest using LIDAR 

and historical land use data. That work demonstrated the 

importance of understanding the environmental history 

of an area and the associated long-term effects of land 

use change on watershed hydrology. Tropical storms are 

another factor that can shape forest hydrology and the 

functional linkages with ecosystem processes. The longterm climate and hydrological data collected on the Santee 

Experimental Forest has shown that the destruction of 

forest by Hurricane Hugo in 1989 influenced the hydrology 

on the Santee Experimental Forest for years afterwards 

(Amatya and others 2006b, Dai and others 2011b, Wilson 

and others 2006). 

Long-term environmental monitoring facilities, such 

as the Santee Experimental Forest, provide a basis to 

assess the interactions between ecosystem process and 

controlling factors such precipitation. They also provide a 

basis for assessing long-term trends in weather variables 

and in providing valuable data for the development and 

application of simulation tools that are needed to assess 

forest hydrology and the linkages between the C cycle and 

variations in climatic conditions (Amoah and others 2012; 

Dai and others 2010a, 2010b; Harder and others 2006, 2007; 

Lu and others 2006). Recently, the biogeochemical model 

Forest-DNDC (Li and others 2000) was calibrated and 

validated with data from the Santee Experimental Forest. 

Data on climate, hydrology, and carbon in biomass and 

soils was used to assess C sequestration and greenhouse 

gas emissions from watersheds consisting of a mosaic of 

upland and wetland forest types (Dai and others 2011a). The 

purpose of this study was to employ long-term climatic and 

hydrological observations on the Santee Experimental Forest 

to (1) evaluate trends in temperature and precipitation, (2) 

assess the long-term effects of potential climate variability 

on forest hydrology on the second-order watershed (WS79) 

using the hydrology model MIKE SHE (DHI 2005) and 

the Thornthwaite water balance approach (Flerchinger and 

Cooley 2000, Ward 1972), and (3) estimate the impact of 

climate variability on carbon sequestration to the forest 

ecosystems and greenhouse gas (CH4

 and CO2) emissions 

from soils using the biogeochemical model Forest-DNDC 

(Li and others 2000, Stang and others 2000). The longterm data, including climatic observations (1946–2008), 

hydrological measurements (1964–2008), and biomass 

measurements, were used to understand and assess the 

effects of precipitation and temperature on forest hydrology 

and carbon dynamics of the Santee Experimental Forest; 

the assessment includes Hurricane Hugo which destroyed 

the majority of the forest stands on the Santee Experimental 

Forest in 1989. Both the MIKE SHE and Forest-DNDC 

models have been tested and determined to be suitable for 

simulating the hydrology and C and nitrogen (N) dynamics 

in the catchment (Dai and others 2011a).

DATA AND METHODS

General Site Description

The Santee Experimental Forest was established in 1937 as a 

forest research facility representing the forested landscape of 

the lower Atlantic Coastal Plain of the Southeastern United 

States. The Santee Experimental Forest is characteristic of 

the subtropical region of the Atlantic Coast, with long, hot, 

humid summers, and short, warm, humid winters. The longterm annual average temperature (1946–2007) is 18.5 °C, 

and average annual precipitation is 1,370 mm. Over the last 

60 years, the highest temperature was 40.5 °C, which was 

recorded twice, on June 26, 1952 and July 21, 1977. The 

lowest recorded temperature was -14 °C on January 21, 

1985 (Dai and others 2011b).

Lands comprising the Santee Experimental Forest have been 

used for agriculture and forestry purposes since the early 

1700s (Hawley 1949, Smith 2012). In September 1989, the 

Santee forest was severely impacted by Hurricane Hugo, 

a category 4 tropical storm, with breakage or uprooting of 

more than 80 percent of the dominant trees in the forest 

(Hook and others 1991, Nix and others 1996, Wilson and 

others 2006). The current vegetation comprises stands that 

have regenerated since Hurricane Hugo with bottomland 

hardwoods in the riparian zone, pine-dominated stands in 

the uplands, and mixed pine-hardwoods elsewhere. The 

2
dominant trees are loblolly (Pinus taeda L.), sweetgum 

(Liquidambar styraciflua), and a variety of oak species 

(Quercus spp.) in both uplands and bottomlands, a 

combination of tree species typical of the Atlantic Coastal 

Plain (Hook and others 1991). The soils have developed 

in marine sediments and have drainage varying from very 

poorly drained in the riparian zones to moderately well 

drained in the uplands (Long 1980). 

Environmental Monitoring Data

Long-term environmental monitoring on the Santee 

Experimental Forest consists primarily of weather data and 

hydrologic data from four gauged watersheds. The secondorder watershed WS79 was the focus for this work; for a 

complete description of the hydrologic monitoring activities 

see Amatya and Trettin (2007a) or visit http://www.srs.

fs.usda.gov/charleston for climatological and hydrology data 

available for downloading.

Hydrology—Watershed WS79 was gauged in 1963, has 

a drainage area of 500 ha, and consists of three firstorder watersheds: WS77, WS80, and WS79b (which is 

between WS77 and WS80) (fig. 1). WS77 and WS80 

serve as a paired watershed system, with WS77 as a 

treatment catchment, WS80 as a control, and WS79b as a 

mixed-use area. WS80 has not been actively managed for 

over five decades. Over the last three decades, this paired 

system has been used for studies on watershed hydrology, 

biogeochemistry, and forest management, including 

prescribed fire and thinning, as well as the effects of global 

and environmental changes on forest ecosystems. After 

Hurricane Hugo, the control watershed, WS80, remained 

unmanaged, without biomass removal or salvage logging 

(Amatya and Trettin 2007a, Dai and others 2010b, Harder 

and others 2007). Most fallen trees were removed from the 

remainder of WS79. The vegetation coverage in WS77 

contains more pine than WS80 and WS79b. In recent years, 

silvicultural practices, including prescribed fire and thinning 

were conducted on WS77 and parts of WS79b. As a result, 

forest biomass in the treatment areas has been lowered, 

especially with respect to the understory layer and forest 

floor. Details of the chronological sequence of activities on 

these watersheds up to 2005 have been reported (Amatya 

and Trettin 2007a). The soils on WS79 are generally clayloams; the clay content is generally ≤30 percent in topsoil 

(within 30 cm), and 40–60 percent in subsoil (>30 cm) 

(Long 1980). The soil is acidic, with a pH between 4.5 and 

6.5. Details of the hydraulic properties of each soil have 

been reported (Harder and others 2007, Long 1980).

Figure 1—The Santee Experimental Forest is located in the Francis Marion National Forest, about 55 km northwest of Charleston, in Berkeley County, in the 

southeast Atlantic Coastal Plain of South Carolina.

3
Precipitation and temperature—Daily total precipitation 

and daily maximum and minimum temperature were 

manually measured at the weather station at the headquarters 

for the Santee Experimental Forest (hereafter referred 

to as Santee Headquarters) from 1946 to 1995. Those 

parameters and other climate parameters, including relative 

humidity, wind speed, wind direction, vapor pressure, and 

solar and net radiation, have been automatically measured 

at 30-minute intervals since 1995. There are three other 

weather stations, Met5 on WS77, Lotti, adjacent to WS79b, 

and Met25 on WS80 that have been maintained to provide 

information on the spatial variability of precipitation and 

temperature across the watershed. Daily precipitation 

was manually collected starting in 1963 at Met 5, 1964 at 

Lotti, and 1990 at Met25; daily temperature was manually 

recorded from 1996 at Met 5, 1971 at Lotti, 1997 at Met25. 

Since 2003, precipitation and temperature have been 

recorded automatically at hourly intervals at these weather 

stations (Amatya and Trettin 2007b). 

Invariably, some precipitation and temperature data were 

missing due to instrument failure and other events, such as 

Hurricane Hugo in 1989. Most of the missing temperature 

data for Santee Headquarters were estimated using 

regression equations developed from the measurements 

of daily maximum and minimum temperature between 

1972 and 2001 at Santee Headquarters and Lotti (about 

3 km away from Santee Headquarters); the remaining 

missing temperature data for Santee Headquarters were 

estimated by using the regression equations developed 

from the measurements between 1950 and 2000 at 

Santee Headquarters near Moncks Corner, SC, and 

Charleston International Airport near Charleston, SC; 

Moncks Corner and Charleston International Airport are 

about 20 and 50 km away from Santee Headquarters, 

respectively. Temperature data between 2002 and 2008 

at Santee Headquarters were compared to the data 

from the Witherbee weather station (about 2 km away, 

managed by Francis Marion National Forest) to calibrate 

the instrumental measurements. However, the missing 

temperature data for Met5 and Met25 remain missing 

because of the relatively short observation period of 

available records and a large number of data missing.

When we compared the precipitation data at the field 

weather stations with that at Charleston International Airport 

and Moncks Corner, most of the missing precipitation 

data occurred in 1991 and 1996–99. Although they are 

not far from Moncks Corner (about 20 km) or Charleston 

International Airport (50 km), the missing precipitation 

data could not be reliably estimated by using regression 

equations between stations due to the wide spatial variation 

in amount, frequency, and timing of precipitation among 

available stations. However, some of the missing data from 

Met5 and Met25 was estimated based on Lotti. For the 

simulation studies, missing temperature, and/or precipitation 

data on Lotti, Met5, or Met25 were estimated using the 

observations at Santee Headquarters.

Streamflow and water table—Stream stage has been 

measured at 10-minute intervals since 1963, 1966, and 

1968 for watersheds WS77, WS79, and WS80, respectively. 

Streamflow was calculated using standard rating curve 

methods developed for these weirs (Amatya and Trettin 

2007a), and the 10-minute values were integrated into 

daily, monthly, and annual flow in cubic meters per second 

(cms), and then normalized from cms to millimeters (mm) 

per day by dividing the watershed area to compare to daily 

precipitation (Amatya and others 2006b). The record is not 

continuous, with a hiatus in the early 1980s and periodic 

interruptions due to equipment failure.

Water table depth is measured on WS77 and WS80. Across 

watershed WS77A, 42 wells were installed; 24 wells were 

measured biweekly from 1964 to 1971, and 42 wells were 

measured between 1992 and 1995. However, data from three 

wells (11, 13, and 23) between 1964 and 1971 were missing 

and could not be included in this analysis. Across WS80, 

33 manual wells were installed, and water table depth 

was collected weekly from 1992 to 1995. After 2002, four 

automatic wells were installed, two on WS77 and two on 

WS80, to collect water table depth data at 4-hour intervals. 

The water table depth was integrated into monthly and 

annual values.

Biomass—Biomass was inventoried on WS79T using 24 

plots where total tree height (TH, m) and diameter at breast 

height (DBH, cm) were measured in 2006 and 2007. The plot 

size was 1022 m2

. The trees with DBH ≥10 cm and DBH <10 

cm and shrub were measured, respectively. Four subplots (9 

m2 each) were set within each plot to measure aboveground 

biomass for trees <10 cm DBH; the trees were harvested, 

dried, and weighed. Shrub biomass was also collected in the 

four subplots for every plot, dried and weighted. Tree volume 

for trees DBH ≥10 cm was calculated using the equation 

developed by Saucier and Clark III (1985) for tree species 

in the Southern United States based on the DBH and total 

height. The aboveground biomass was estimated by using 

the equation developed by Smith and others (2003) on the 

basis of vegetation types in the Southeastern United States. 

The total wood biomass of a plot was the sum of the biomass 

of all trees in the plot, and then normalized to unit weight, C 

Mg ha-1, by dividing the plot area (1012 m2

). There were four 

subplots in each plot for collecting litter and duff. The area of 

the basket for litter collection was 1470 cm2, and the area for 

collecting duff was 729 cm2. The litter and duff were dried 

under 60 °C and weighed. The impact of Hurricane Hugo 

on the biomass C storage in the stands on the entire Santee 

Experimental Forest was estimated by Hook and others 

(1996). The belowground biomass was estimated using the 

coefficient of roots relevant to aboveground biomass (IPCC 

2003, Schroth 1995).

4
Soil CO2

 efflux, temperature and moisture, and 

leaf area index—Soil CO2, moisture, and temperature 

were measured at eight plots on WS79 at monthly 

intervals for 2 years (2006–08) to calibrate and validate 

the biogeochemical model for simulating long-term 

atmospheric CO2 sequestration to forested ecosystems on 

the Santee Experimental Forest (Dai and others 2011a). 

The polyvinylchloride (PVC) soil collars were installed 

permanently 3 cm into the mineral soils (Sulzman and others 

2005). The measured soil CO2 efflux values using a LI-COR 

6200 were integrated into a plot average. Leaf biomass was 

collected monthly in the 24 inventory plots for 2 years to 

calculate biomass and leaf area index (LAI). LAI was also 

measured on eight plots using the LI-COR 2000. Tree height 

and DBH were measured in 2006 and 2007 at the 24 plots 

on WS79 to estimate biomass, and forest floor samples were 

collected on the same plots.

Estimates of Potential Evapotranspiration

Daily potential evapotranspiration (PET) was estimated 

using standard Penman-Monteith (P-M) equation for a 

grass reference for 2003–2008 (Monteith 1965, Xu and 

Singh 2005) and the Hargreaves equation for 1950–2008 

(Hargreaves and Samani 1985), because the observations for 

relative humidity, wind speed, vapor pressure, and solar and 

net radiation were not available for 1950–2002. The daily 

PET from Hargreaves equation, which may be somewhat 

higher than the standard P-M method, was verified and 

calibrated to an equivalent P-M value using the regression 

model developed from the daily PET estimated by P-M and 

Hargreaves for 2003 to 2008, as suggested by Amatya and 

others (1995). The Hargreaves PET (PETh

) for 1950–2008 

was estimated using the following equations (Gavilan and 

others 2006, Sepaskhah and Razzaghi 2009):

 PETh = 0.408 * 0.0023 * Ra * (Tmean+ 17.8) * (Tmax–Tmin)

0.5 (1)

where 

Tmean = daily average 

Tmax = daily maximum 

Tmin = daily minimum temperature (°C)

Ra = extraterrestrial radiation for daily period estimated as 

(Allen and others 1998):

Ra = 24(60)/π*Gsc*dr

*[ωs*sin(φ)*sin(δ)+

cos(φ)*cos(δ)*sin(ωs)] (2)

where 

Gsc = the solar constant

dr = the inverse relative distance from the Earth to the Sun

ωs = sunset hour angle

φ = the latitude of the study site

δ = the solar decimation.

The P-M-equivalent PET was used to compute the annual and 

monthly evapotranspiration (ET) for the entire study period of 

1946–2008 to evaluate the water balance for this study.

Estimate of Water Balance

Three approaches were applied to estimate water 

balance of watershed WS79: the Thornthwaite water 

balance (Flerchinger and Cooley 2000, Ward 1972), the 

hydrological model MIKE SHE (DHI 2005), and the 

biogeochemical model Forest-DNDC (Li and others 2000, 

Stang and others 2000).

The MIKE SHE hydrological model—MIKE SHE 

(Abbott and others 1986a, 1986b; DHI 2005; Graham and 

Butts 2005) is a distributed hydrological modeling system 

well designed and validated for applications in low-relief 

terrain. This model and its algorithms have been described 

in many publications (DHI 2005, Graham and Butts 2005, 

Sahoo and others 2006). For estimates of hydrological 

dynamics on the Santee Experimental Forest, MIKE SHE 

was coupled with the flow routing model MIKE 11 (Lu and 

others 2009, Sahoo and others 2006), a one-dimensional 

river/channel water movement model, to simulate the 

full hydrological cycle of the watershed, including 

evapotranspiration, infiltration, unsaturated flow, saturated 

flow, overland flow, and streamflow. Inputs for the model 

include spatial data on topography, soils, vegetation, and 

drainage network; and temporal data on daily precipitation 

and PET. The model was tested using streamflow and water 

table observations over a 2-year period (2003–04) by Lu and 

others (2006), and also tested over a 2-year period before 

Hurricane Hugo (1969–71) and a 5-year period afterwards 

(2003–08) by Dai and others (2010a).

Thornthwaite water balance—Water balance was 

estimated as (Flerchinger and Cooley 2000, Ward 1972):

E = P – (Q + ET + ΔS) (3)

where 

P = precipitation

Q = streamflow

ET = evapotranspiration

ΔS = water storage in soils and aquifers

E = the estimation error (we assume that it is negligible). 

ET is derived from PET, precipitation, soil moisture 

storage, and changes in soil moisture. The soil moisture is 

calculated using the following equation (Alley 1984) when 

precipitation is not less than PET:

SMi

 = min((SMi-1 + Pi – ETi), SM0) (4a)

where 

SMi = soil moisture at month i 

SMi-1 = soil moisture at month i-1

SM0 = soil moisture capacity, a value of 150 mm was used 

in this study (McCabe and Markstrom 2007, Wolock and 

McCabe 1999) 

Pi = ith month precipitation 

ETi = ith ET. 

5
However, if P<PET, a soil moisture deficit will occur. The 

soil moisture is estimated as:

SMi = SMi-1·EXP[-(PETi – Pi)/SM0] (4b)

The changes in soil moisture (ΔSM) can be shown as:

ΔSMi = SMi - SMi-1 (5)

We assumed that the long-term changes in the aquifer were 

small (Harder and others 2007, Heath 1975, Riekerk and 

others 1979), thus:

ΔS = ΔSM (6)

The ET was estimated by using following equations if Pi≥ETi:

ETi = PETi (7a)

and if Pi<PETi:

ETi = Pi + ΔSMi (7b)

Forest-DNDC—Forest-DNDC (Li and others 2000, Stang 

and others 2000) is a soil C and nitrogen (N) dynamics 

model (Li and others 1992a, 1992b), coupled with a forest 

growth model based on PnET (Aber and Federer 1992). 

PnET is a process-based biogeochemical model used to 

simulate forest growth and C and N dynamics in forest 

ecosystems, including soil-borne trace gas emissions 

based on the balance of water, energy, and nutrition in 

forest ecosystems (Li and others 2000, Miehle and others 

2006, Stang and others 2000). The model integrates 

photosynthesis, decomposition, nitrification-denitrification, 

carbon storage and consumption, and hydrothermal 

balance in forest ecosystems, and has been tested and used 

for estimating greenhouse gas emission from forested 

ecosystems in a wide range of climatic regions, including 

boreal, temperate, subtropical, and tropical (Kesik and 

others 2006, Kiese and others 2005, Kurbatova and others 

2008, Li and others 2004, Stang and others 2000, Zhang 

and others 2002). The original Forest-DNDC was at field 

scale and used to simulate C and N dynamics in forests 

for uplands. To estimate C and N dynamics in wetland 

ecosystems, an empirical functionality for wetlands was 

added to the field scale version of the model by Zhang and 

others (2002). Although Cui and others (2005) linked the 

physically based hydrological model MIKE SHE to ForestDNDC to further improve Forest-DNDC for simulating 

C and N dynamics in wetland ecosystems, the spatially 

hydrological characteristics supplied by MIKE SHE were 

projected to only one dimension, because they applied a 

field scale version of the model to their study. Recently 

the model has been modified to watershed scale, yielding 

a version that can utilize spatially and temporally physical 

and biogeochemical characteristics of catchments or 

regions (Dai and others 2011a), including soils, vegetation, 

hydrogeology, and climate. The modified model is a useful 

tool to assess carbon and nitrogen dynamics on watersheds 

with complex physical and biogeochemical characteristics.

Model validation—The performance of the model was 

evaluated employing widely used quantitative methods 

including coefficient of determination (R2

) (squared 

correlation coefficient) and model efficiency (E) (Moriasi 

and others 2007). The model performance efficiency is 

calculated as:

E=1- (Oi-Pi)

2

/(Oi-Omean)

2 (8)

where Oi, Omean, and Pi are the observed values, mean 

observation, and simulated values, respectively. The model 

evaluated was then used to simulate streamflow and water 

table dynamics with 58 years (1950–2007) of climate 

observations using MIKE SHE, and to estimate C dynamics 

using Forest-DNDC. 

RESULTS AND DISCUSSION

Temperature and Precipitation

Temperature variability—Data on air temperature on the 

Santee Experimental Forest for 1946–2008 are presented in 

table 1. The annual mean temperature anomaly, which is the 

difference between annual mean temperature and the mean 

for 1946–2008, showed that the air temperature significantly 

increased (p<0.01) at an average rate of 0.19 °C per decade 

in the last 63 years (fig. 2A), which is approximately 

equal to the mean rate of 0.2 °C per decade for the 30-year 

period of 1976–2005 (Hansen and others 2006, Trenberth 

and others 2007), and higher than the rate of 0.17 °C per 

decade from 1976 to 2009 (Hansen and others 2010). 

Comparing similar periods, the warming rate on the Santee 

Experimental Forest was about 0.41 °C per decade between 

1976 and 2000, considerably higher than the 0.2 rate 

reported by (Trenberth and others 2007) and 2.4 times as 

high as the global rate (0.17 °C) reported by IPCC (2001) 

for the same period. The annual mean temperature (table 

1) also shows that the warming on the Santee Experimental 

Forest is not completely synchronous with trends globally 

or in North America. Prior to 2009, the warmest year on 

the Santee Experimental Forest occurred in 1990 and 1998. 

However, the warmest year in North America and globally 

was 2006 and 2005, respectively (Hansen and others 2010, 

Trenberth and others 2007). 

Three periods are evident in the temperature trend based on 

the 5-year moving average in the 63-year period (fig. 2A). 

The temperature fluctuated but did not substantially increase 

in the first 23-year period of 1946–68; it increased rapidly 

in the second period (1969–80) and steadily increased in the 

third period (1981–2008). The warmest year in the first period 

was 1964, with a mean daily temperature (MDT) of 18.9 °C. 

The coolest year was 1958, with a MDT of 17.0 °C (fig. 2B). 

The annual mean temperature was 18 °C in the first 23-

year period. Although there was a substantial temperature 

fluctuation over this period, the average temperature 

decreased slightly. Starting in 1969, the average temperature 

6
has increased. The warmest year in the second period was 

1975; the MDT was 19.5 °C, about 0.6 °C higher than the 

MDT of the warmest year in the first period. The lowest 

MDT of the second period was recorded in 1979 (17.8 °C), 

and was about 0.8 °C higher than the MDT of the coolest 

year in the first period. The annual mean temperature in the 

12-year period from 1969 to 1980 was 18.6 °C, about 0.6 °C 

higher than the first period. The temperature increase in the 

12-year period was largely due to consecutive warm years 

between1970 and 1975. 

Due to the strongest El Niño in the last century, 1998 was 

considered to be the warmest year between the late 1800s 

and the end of last century. The first warmest year on the 

Santee Experimental Forest from 1946 to 2008 was 1990. 

However, the MDT in both 1990 and 1998 was 20 °C, about 

1.1 and 0.5 °C higher than the MDT of the warmest years 

in the first and second periods, and 2 and 1.4 °C higher 

than the annual mean temperature in these two periods, 

respectively. The coolest year in the third period occurred in 

1988 with a MDT of 17.5 °C, 0.5 °C higher than the MDT 

of the coolest year in the first period, but 0.3 °C lower than 

that in the second period. The annual average temperature 

in this period was 18.8 °C, about 0.8 and 0.2 °C higher 

than that in first and second periods, respectively. Although 

the 2 years with the highest temperatures (1990 and 1998) 

occurred in this period, the increase of the annual mean 

temperature in the 28-year period (1981–2008) was less 

than the increase in the second period (12-year, 1969–80), 

a finding affected by the assignment of the periods and the 

inherent multi-decadal fluctuations. However, the mean 

temperature was still higher in the third period than the other 

two periods (fig. 2B). 

Since 1946, there has been a significant linear increase in 

the annual average daily minimum temperature at a rate of 

0.26 °C per decade (p<0.001) on the Santee Experimental 

Forest (fig. 2C), a higher rate of annual average temperature 

(0.19 °C per decade) in the same period. Although the 

increase in the annual average daily maximum temperature 

was significant (p<0.02), the increase in magnitude (about 

0.13 °C per decade) was less than the average daily 

minimum temperature. These data demonstrate that the 

warming trend increases the daily minimum temperature 

more than daily maximum. The yearly minimum temperature 

rose linearly at a rate of 0.38 °C per decade in the last 63 

years (p<0.05) (fig. 2D) and fluctuated widely. However, 

there was a decreasing trend in the yearly maximum 

temperature of about 0.19 °C per decade (p<0.05). 

Over the last six decades on the Santee Experimental Forest, 

the annual average of diurnal temperature range (DTR) 

decreased at an average rate of 0.13 °C per decade (p<0.05) 

(fig. 2E). The DTR is the difference between the daily 

maximum and daily minimum temperatures. This value 

was larger than the global average rate in the 20th century 

(≈0.1 °C per decade, including urban and nonurban areas) 

(IPCC 2001) and lower than the DTR during 1913–98 in 

Venezuela and Colombia, as reported by Quintana-Gomez 

(1999). This decreasing trend in DTR was similar to the 

changes in northern China (at a latitude higher than the 

Southeastern United States) due to a stronger warming of 

the minimum temperature as opposed to the maximum, but 

different from the trends in southern China due to a cooling 

in maximum temperature with a slight warming in minimum 

temperature (Zhai and Ren 1999). There were larger 

decreases and fluctuation in the annual temperature range 

(ATR) among years than DTR (fig. 2E). Similar to DTR, the 

declining trend in ATR was mainly caused by a significant 

increase in yearly minimum temperature. 

The temperature on the Santee Experimental Forest 

increased linearly during the summer (June–August) 

(p<0.01), autumn (September–November) (p<0.001), and 

winter months (November–January) (p<0.05) at a rate of 

about 0.13, 0.38, and 0.26 °C per decade, respectively. 

However, the average temperature in the spring months 

(March–May) remained unchanged. These results suggest 

that the warming yields not only hot summers, but also 

warmer autumns and winters in the area. Although there was 

not a significant increasing trend (p<0.1) in the number of 

days with high temperatures (>37 °C) in a year, the annual 

average number of days with higher than 37 °C temperature 

from 1970–2008 (2.1 days) were over twice as many as the 

average (0.83 days) during the period from 1946–69. 

Precipitation variability—The year-to-year variability of 

precipitation amounts on the Santee Experimental Forest 

over the last 63 years ranged from a low of 835 mm in 1954 

to a high of 2026 mm in 1994 (table 2). The average annual 

precipitation in the 63-year period was 1370 mm. There 

were three very dry years (1951, 1954, and 1956), with the 

annual precipitation in these years about 66, 61, and 68 

percent, respectively, of the average annual precipitation in 

the 63-year period. Annual precipitation has not exhibited 

a significant increasing trend (p>0.1) since 1946 (fig. 3A). 

The three consecutive dry years in the 1950s contributed to 

the slightly increasing trend, but overall precipitation has not 

varied significantly. 

The average monthly precipitation over the last six 

decades was about 114 mm. The highest recorded monthly 

precipitation was 436.5 mm in July 1964, and the lowest 

was 0 mm in October 2000. Precipitation in the summer 

months (June–August) was much higher than other seasons, 

accounting for 39 percent of annual precipitation in the 

last 63 years; there was no difference in the proportion 

of rainfall among the other seasons (20–21 percent). The 

changes in seasonal precipitation were extremely small over 

the last six decades, exhibiting only a slight upward trend in 

fall and winter months but a downward trend in spring and 

summer. Combined with the increasing temperatures, the 

spring and summer seasons are poised to experience more 

frequent occurrences of plant water stress caused by high 

evapotranspiration levels. 

7
Table 1—Annual minimum, maximum, and mean temperature (minT, maxT, and meanT, 

respectively) at Santee Experimental Forest Headquarters (SHQ) and at Lotti (LT), an onsite

weather station adjacent to Santee Experimental Forest watershed WS79b, 1946–2008

Year SHQ-maxT SHQ-minT SHQ-meanT LT-maxT LT-minT LT-meanT

1946 24.8 12.3 18.6 N/A N/A N/A

1947 23.6 11.6 17.6 N/A N/A N/A

1948 24.2 11.9 18.0 N/A N/A N/A

1949 25.1 12.4 18.7 N/A N/A N/A

1950 24.2 11.2 17.7 N/A N/A N/A

1951 25.0 11.3 18.2 N/A N/A N/A

1952 24.4 11.7 18.0 N/A N/A N/A

1953 24.6 12.0 18.3 N/A N/A N/A

1954 24.5 11.1 17.8 N/A N/A N/A

1955 24.4 11.6 18.0 N/A N/A N/A

1956 25.2 12.0 18.6 N/A N/A N/A

1957 24.5 12.3 18.4 N/A N/A N/A

1958 23.2 10.7 17.0 N/A N/A N/A

1959 24.4 12.4 18.4 N/A N/A N/A

1960 24.0 10.8 17.4 N/A N/A N/A

1961 24.3 11.3 17.8 N/A N/A N/A

1962 24.8 11.8 18.3 N/A N/A N/A

1963 24.6 11.4 18.0 N/A N/A N/A

1964 24.9 12.9 18.9 N/A N/A N/A

1965 24.1 11.2 17.6 N/A N/A N/A

1966 23.3 11.1 17.2 N/A N/A N/A

1967 24.5 11.7 18.1 N/A N/A N/A

1968 24.3 11.3 17.8 N/A N/A N/A

1969 23.8 11.9 17.8 N/A N/A N/A

1970 24.8 12.7 18.8 N/A N/A N/A

1971 24.7 13.3 19.0 N/A N/A N/A

1972 24.9 13.5 19.2 23.1 9.0 16.1

1973 24.6 13.2 18.9 24.6 10.8 17.7

1974 25.5 13.2 19.4 25.4 10.5 17.9

1975 25.3 13.6 19.5 24.9 10.6 17.7

1976 24.4 12.2 18.3 24.7 9.3 17.0

1977 24.5 12.5 18.5 24.3 9.2 16.8

continued to next page

8
Table 1 (continued)—Annual minimum, maximum, and mean temperature (minT, maxT, 

and meanT, respectively) at Santee Experimental Forest Headquarters (SHQ) and at Lotti 

(LT), an onsite weather station adjacent to Santee Experimental Forest watershed WS79b, 

1946–2008

Year SHQ-maxT SHQ-minT SHQ-meanT LT-maxT LT-minT LT-meanT

1978 24.2 12.4 18.3 23.8 9.2 16.5

1979 23.7 11.9 17.8 23.5 9.2 16.4

1980 23.9 12.3 18.1 24.1 10.2 17.2

1981 24.1 11.6 17.9 24.1 9.3 16.7

1982 24.3 13.5 18.9 25.1 12.3 18.7

1983 23.8 12.6 18.2 25.1 11.5 18.3

1984 24.5 12.5 18.5 24.9 11.0 17.9

1985 24.5 13.0 18.8 26.0 12.6 19.3

1986 24.8 13.7 19.3 27.1 13.9 20.5

1987 23.7 12.6 18.1 25.1 12.0 18.5

1988 23.4 11.6 17.5 23.8 10.3 17.0

1989 24.4 13.1 18.7 24.2 11.7 17.9

1990 25.9 14.1 20.0 26.6 12.3 19.5

1991 24.4 15.1 19.7 26.2 13.3 19.7

1992 24.4 13.5 19.0 22.8 9.7 16.3

1993 25.4 13.0 19.2 26.8 12.3 19.5

1994 25.6 12.9 19.2 27.0 12.9 19.9

1995 25.4 12.8 19.1 26.3 12.3 19.3

1996 24.6 12.0 18.3 25.7 9.8 17.7

1997 25.1 12.3 18.7 25.6 10.5 18.0

1998 26.6 13.4 20.0 26.9 11.6 19.2

1999 26.1 12.0 19.0 27.2 10.4 18.8

2000 24.8 11.8 18.3 25.4 9.9 17.6

2001 24.9 13.1 19.0 25.4 10.7 18.1

2002 24.9 13.7 19.3 25.6 11.1 18.4

2003 24.3 12.6 18.4 24.8 10.9 17.9

2004 25.0 12.7 18.9 N/A N/A N/A

2005 24.7 12.6 18.7 N/A N/A N/A

2006 25.3 12.4 18.8 N/A N/A N/A

2007 25.4 12.7 19.1 N/A N/A N/A

2008 25.2 13.6 19.4 N/A N/A N/A

N/A = no available data.

9
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1969-­‐1980	
  

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Figure 2—Long-term trends in air temperature measured at the Santee Experimental Forest Headquarters weather station, showing (A) temperature trend [∆T(46-08) 

represents the changes in temperature between 1946 and 2008; ∆T(76-08) represents the changes in temperature between 1976 and 2008—globally, 1976–2008 is the 

period of greatest recorded increase in temperature, with an increase of 0.2 °C per decade, the rate of increase in temperature on Santee Experimental Forest during the 

same period was 0.27 °C per decade; thin blue line represents 5-year running average; red dash line indicates changes in temperature from 1976 to 2008; x1 represents 

the year between 1946 and 2008, x2 represents the year between 1976 and 2008], (B) maximum, minimum, and mean temperature during 1946–2008 (maxMDT 

is maximum mean daily temperature; minMDT is minimum mean daily temperature; meanT is average daily temperature), (C) annual mean daily maximum and 

minimum temperatures relative to the mean at Santee Experimental Forest Headquarters from 1946 to 2008, (D) yearly maximum and minimum temperatures relative 

to the mean from 1946 to 2008 (ymax and ymin are the maximum and minimum temperature in a year, respectively), and (E) the diurnal and annual temperature range 

during the 63-year period (DTR is diurnal temperature range; ATR is annual temperature range; meanDTR is the annual mean DTR averaged from 1946 to 2008).

(A) (B)

(C) (D)

(E)

10
Table 2—Annual precipitation (reported in mm) at Santee Experimental Forest Headquarters

(SHQ) and three onsite weather stations (Lotti adjacent to watershed WS79b, Met5 on 

watershed WS77, and Met25 on watershed WS80), 1946–2008a

Year SHQ Lotti Met5 Year SHQ Lotti Met5 Met25

1946 1106.9 N/A N/A 1978 1225.4 1066.4 N/A N/A

1947 1677.2 N/A N/A 1979 1546.3 1414.6 N/A N/A

1948 1632.5 N/A N/A 1980 1413.7 1197.0 N/A N/A

1949 1387.9 N/A N/A 1981 1154.5 1088.4 N/A N/A

1950 1474.7 N/A N/A 1982 1663.5 1579.3 N/A N/A

1951 900.7 N/A N/A 1983 1710.7 1286.6 N/A N/A

1952 1262.8 N/A N/A 1984 1364.5 N/A N/A N/A

1953 1442.5 N/A N/A 1985 1349.6 1141.3 N/A N/A

1954 834.6 N/A N/A 1986 1367.2 1122.1 N/A N/A

1955 1190.6 N/A N/A 1987 1660.5 1570.3 N/A N/A

1956 937.3 N/A N/A 1988 1141.8 950.2 N/A N/A

1957 1200.4 N/A N/A 1989 1595.4 1291.7 N/A N/A

1958 1459.9 N/A N/A 1990 1095.1 769.0 943.2 N/A

1959 1780.0 N/A N/A 1991 1252.7 1169.3 1252.0 1351.7

1960 1162.4 N/A N/A 1992 1472.7 1527.0 1358.1 1434.4

1961 1553.7 N/A N/A 1993 1246.9 1101.0 996.8 N/A

1962 1300.5 N/A N/A 1994 2026.4 N/A 1465.7 N/A

1963 1036.1 N/A N/A 1995 1525.1 N/A 1083.3 N/A

1964 1888.1 N/A 1895.0 1996 1208.1 N/A 1121.7 N/A

1965 1241.2 1199.5 1307.2 1997 1659.5 N/A N/A N/A

1966 1554.4 1488.9 N/A 1998 1291.6 N/A N/A N/A

1967 1238.9 1027.7 N/A 1999 1466.9 N/A N/A N/A

1968 1225.4 1143.6 N/A 2000 1167.5 N/A N/A N/A

1969 1408.0 N/A N/A 2001 1016.1 N/A N/A N/A

1970 1361.7 N/A N/A 2002 1555.7 N/A N/A N/A

1971 1694.4 N/A N/A 2003 1690.4 N/A N/A 1670.4

1972 1093.7 1156.3 N/A 2004 1118.1 938.9 934.0 961.8

1973 1428.8 1296.3 N/A 2005 1637.9 1473.9 1532.0 1509.5

1974 1412.8 1172.2 N/A 2006 1264.5 1197.4 1200.4 1258.2

1975 1439.8 1258.1 N/A 2007 1041.3 981.5 987.6 998.6

1976 1448.9 1442.0 N/A 2008 1476.2 N/A 1502.3 1561.6

1977 1273.5 1085.3 N/A N/A N/A N/A N/A N/A

N/A = no available data. a

 Data not available for Met25 before 1978.

11
There was no relationship between the annual 

precipitation and temperature (p>0.1) (fig. 3B). While 

other researchers have suggested that the regional may 

cause increased precipitation in subtropical areas (Najjar 

and others 2000, Zhang and others 2007), the record 

from the Santee Experimental Forest does not support 

that relationship. Accordingly, the hydrology in wetlanddominated watersheds on this lower coastal plain is likely 

to change with sustained warming due to the increase in 

evapotranspiration demand. 

Spatial differences in temperature and precipitation—

Comparison of temperature and precipitation among onsite 

weather stations on the Santee Experimental Forest showed 

that there were small differences in average temperature 

among the onsite weather stations from 1996 to 2001, about 

0.1 and 0.4 °C higher at Lotti than at Met5 and Met25, 

respectively. However, there were slightly larger differences 

between Santee Headquarters and the onsite weather 

stations. The mean temperature at Santee Headquarters was 

about 0.6–0.7 °C higher than that at Lotti and Met5 from 

1971 to 2000 (fig. 4A) and about 1 °C higher than Met25.

The higher mean temperature at Santee Headquarters was 

mostly due to the lower daily minimum temperature at the 

onsite weather stations. The daily maximum temperature at 

Lotti, Met25, and Met5 was 0.5, 0.8, and 0.9 °C higher than 

that at Santee Headquarters, but the minimum was 1.8, 1.9, 

and 2.2 °C lower than at Santee Headquarters, respectively. 

These results indicate that the daytime temperature in 

woodland setting on the Santee Experimental Forest is 

warmer than Santee Headquarters and that nights are cooler. 

These results are similar to the findings of Laughlin (1982), 

Morecroft and others (1998), and Karlsson (2000), who 

reported temperatures in forested area were lower than 

the neighboring open area. However, this seemingly large 

variation in temperature within a relatively small geographic 

area merits further study to reconcile the cause(s) of the 

differences.

The spatial variability in precipitation on the Santee 

Experimental Forest is likely large, especially in summer 

months (La Torre Torres 2008). Due to incomplete 

yearly sets for the onsite stations, monthly synchronized 

observations of precipitation were used to compare spatial 

precipitation. The observed average monthly precipitation 

at Santee Headquarters was about 7, 8, and 11 mm higher 

than that at Met5 (198 months of record), Met25 (158 

months of record), and Lotti (432 months of record), 

respectively. These values indicate that the precipitation at 

Santee Headquarters might be substantially higher than that 

at the onsite weather stations; the difference translates to 

80, 90, and 130 mm per year higher than at Met5, Met25, 

and Lotti, respectively. To assess whether the difference 

between the stations may be a result of the measurement 

technique before 2000, we also compared the precipitation 

data from all weather stations recorded from 2003 to 2008 

when all of the stations were instrumented with automated 

recording rain gauges (fig. 4B). However, the results 

from automatically recorded measurements showed that 

precipitation at Santee Headquarters was indeed higher than 

the onsite stations, about 110, 100, and 80 mm per year 

higher than that at Lotti, Met5, and Met25, respectively, and 

the differences among onsite stations were small.

Effect of warming on large precipitation events—

Although air temperature on the Santee Experimental Forest 

increased significantly over the last six decades, we found 

that the warming did not bring about increased precipitation, 

contrary to the findings by Wentz and others (2007) and 

Lambert and others (2008), who suggested that global 

warming can bring more rain. However, the annual average 

storm events with >25 mm precipitation and >50 mm between 

1970 and 2008 were about 13 percent and 21 percent higher, 

respectively, than those in 1946–69 (the period without a 

significant increase in temperature). Although there has not 

been a significant upward trend in potential storm events in 

the last 63 years (fig. 5), the annual average storm events 

(>50 mm) increased from 4.4 times per year from 1946 to 

1981 (fig. 5) to 5.7 times per year from 1982 to 2008. 

Streamflow and Water Table Dynamics

The role of climate variability on streamflow and water 

table—Data on monthly streamflow as measured at the 

three gauging stations on the Santee Experimental Forest 

are presented in figure 6A. Monthly streamflow ranged from 

0 to 336 mm a month. No-flow conditions occur when low 

precipitation periods leave these headwater watersheds with 

insufficient water to satisfy evapotranspiration demand. 

There was a significant relationship (p<0.01) between 

monthly precipitation and monthly streamflow on these 

watersheds on the Santee Experimental Forest (fig. 6B). 

This relationship indicates that the streamflow on these 

catchments depends heavily on precipitation. Annual flow 

rate, which is the percentage of annual streamflow to annual 

precipitation, ranged from 5.5 to 44.7 (percent) in the 29 

years from 1965–81, 1990–94, 1996–98, and 2003–07; 

the higher the annual precipitation, the larger the flow 

rate (R2

 = 0.25, p<0.01). The low annual flow rates are 

attributed to canopy interception and evapotranspiration. 

These relationships suggest that warming will further 

reduce streamflow due to an increase in evapotranspiration 

demands, if the increased demand is not offset by additional 

precipitation. 

The changes in water table (WT) depth on the first-order 

watersheds on the Santee Experimental Forest were 

complex (figs. 7A–C), although annual mean water table 

level increases significantly with an increase in annual 

precipitation (fig. 7D) (p<0.02). Higher water table levels 

in summer and fall are mostly attributed to precipitation 

inputs. The water table depth in dry periods, especially 

in dry summers, can be more than 2 m below the ground 

12
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Figure 5—Number of small (>25 mm) and large (>50 mm) storm events per year 

recorded at the Santee Experimental Forest from 1946 to 2008 (dashed line and 

equation is the 5-year running average).

Figure 3—Long-term trends in precipitation measured at the Santee Experimental Forest Headquarters weather station, showing (A) deviation of precipitation 

from the long-term average (1370 mm) from 1946 to 2008 and (B) comparison of temperature and precipitation anomalies between 1946 and 2008.

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Figure 4—(A) Average daily temperature observed at Santee Headquarters (SHQ) and Lotti weather stations (synchronized by date), and (B) comparison of monthly 

precipitation at the three field stations (Lotti, Met5, Met 25) with the SHQ weather station.

(A) (B)

(A) (B)

13
surface. However, a large area of the watershed WS79 is 

saturated following heavy rains and wet periods (Dai and 

others 2010b). The water table level was high during most of 

the winter–spring months (December–February) (fig. 7A-C). 

However, precipitation in these seasons is much lower than 

in the summers. The high water table level in winters and/

or springs is primarily as a result of a low evapotranspiration 

(ET) demand, especially in winters, which demonstrates 

that ET is the key factor influencing the WT on these 

first-order watersheds. These relationships further indicate 

that warming should be expected to influence water table 

dynamics in the watersheds on the lower coastal plain and 

that wetlands within these first order watersheds could 

experience altered hydro-periods that may affect their status. 

Hurricane effects on forest hydrology—The lower coastal 

plain of South Carolina is within the hurricane threat area of 

the southeastern Atlantic Coastal Plain of the United States. 

While tropical storms deliver large amounts of precipitation 

in a short time period, they can also affect local hydrologic 

conditions by affecting evapotranspiration as a result of 

impacts to the vegetation. Accordingly, the relationship 

between observed streamflow and precipitation from 1965 

to 2008 (fig. 8) were used to assess the effect of Hurricane 

Hugo in 1989 on surface water hydrology. The blank 

diamonds in figure 8 represent the observed relationship 

between annual streamflow and precipitation within 14 

years (1990–2003) following the hurricane in 1989 (only 

10 observations were available due to missing data). This 

data shows that the observed streamflow within the 14-year 

period after the hurricane is above the trend line. However, 

the average annual precipitation in those years was about 

45 mm lower than the average over the entire observation 

period (1965–2007) and 70 mm lower than the long-term 

average (1370 mm). The higher observed streamflow rate 

in those years following the hurricane was because the 

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Figure 6—(A) Observed monthly streamflow of watersheds WS77, WS80, and WS79 

on the Santee Experimental Forest for 1965–2007, and (B) observed relationship of 

monthly precipitation with monthly streamflow observed on all watersheds on the 

Santee Experimental Forest for 1965–2007.

(A)

(B)

14
Date (2/1964–3/1971, 1/1992–12/1994)

Water table depth (cm)

Feb-64Feb-65Feb-66Feb-67Feb-68Feb-69Feb-70Feb-71Feb-92Feb-93Feb-94

50

0

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Figure 7—Water table trends for (A) monthly average water table depth on the watershed WS77 from 1964 to 1971 and from 1992 to 1994, (B) observed 

and simulated water table depth on watersheds WS77 and WS80 on the Santee Experimental Forest from 1992 to 1995 (-O is observed; -P is simulated), (C) 

daily water table depth on watersheds WS77 and WS80 from wells H (WS80-W1), D (WS80-W2), and J (WS77-W1) in 2005–07, and (D) comparison among 

deviations of annual precipitation on annual mean water table on the watershed WS77 with their average between 1964 and 1993 [WT is water table depth 

(cm); rain is annual precipitation (cm)].

Figure 8—Relationship between annual precipitation and annual streamflow on 

watersheds WS77 and WS80 on the Santee Experimental Forest between 1965 and 

2007; the filled diamonds represent 1990 through 2003.

(A) (B)

(C) (D)

15
hurricane destroyed over 80 percent of pre-hurricane canopy 

in this site (Hook and others 1991, 1996; Nix and others 

1996), which caused a decrease in ET. 

Potential Evapotranspiration

Data on potential evapotranspiration (PET) calculated 

using the Penman-Montieth (P-M) method are presented 

in table 3. These data indicate that PET fluctuated between 

969.9 and 1303.7 mm, with an average of 1136.5 mm and 

a standard deviation of 68.7 mm. Figure 9 shows that the 

PET increased significantly (p<0.01) at a linear rate about 

15.4 mm per decade. The mean PET from 1946 to 1969 

was 1093.1 mm, but from 1970 to 2008 the mean PET 

was 1163.2 mm, with an average increase of 6.4 percent. 

However, the precipitation has not increased significantly 

since 1946 (see the Precipitation Variability section above). 

These relationships further indicate that the flow and water 

table on the Santee Experimental Forest may decrease due to 

higher ET demands without an increase in precipitation.

Table 3—Annual rainfall, potential evapotranspiration, streamflow, and temperature on Santee 

Experimental Forest, 1946–2008

Year

Rainfall 

(mm)

PET 

(mm)

ET 

(mm)

FlowE

(mm)

MS

(mm)

FD 

(mm)

FlowO

(mm)

DTR 

(°C)

meanT 

( °C)

1946 1106.9 1161.7 1025.8 39.5 N/A N/A N/A 12.6 18.6

1947 1677.2 1070.7 1012.3 596.4 N/A N/A N/A 12.0 17.6

1948 1632.5 1103.3 1084.7 547.8 N/A N/A N/A 12.3 18.0

1949 1387.9 1162.1 1130.1 279.2 N/A N/A N/A 12.7 18.7

1950 1474.7 1102.1 994.4 458.8 308.7 305.1 N/A 13.0 17.7

1951 900.7 1089.1 962.6 0.0 61.3 22.2 N/A 13.7 18.2

1952 1262.8 1081.7 973.2 227.7 227.5 194.1 N/A 12.7 18.0

1953 1442.5 1147.9 1086.8 355.7 379.0 365.9 N/A 12.5 18.3

1954 834.6 1062.9 843.7 0.0 99.9 88.6 N/A 13.4 17.8

1955 1190.6 1082.1 981.3 200.2 243.4 218.3 N/A 12.8 18.0

1956 937.3 1141.2 996.0 25.2 97.5 76.1 N/A 13.2 18.6

1957 1200.4 1151.9 1012.3 104.3 202.7 174.3 N/A 12.2 18.4

1958 1459.9 981.6 963.6 496.3 392.8 406.2 N/A 12.5 17.0

1959 1780.0 1139.3 1073.9 706.1 636.8 747.1 N/A 12.0 18.4

1960 1162.4 1025.6 906.6 291.8 277.6 277.9 N/A 13.2 17.4

1961 1553.7 1039.8 1039.8 477.9 305.8 364.7 N/A 13.1 17.8

1962 1300.5 1126.6 1041.2 259.3 229.8 243.2 N/A 13.0 18.3

1963 1036.1 1114.2 909.3 171.7 198.3 174.9 N/A 13.2 18.0

1964 1888.1 1179.3 1136.8 706.4 771.8 800.7 N/A 12.1 18.9

1965 1241.2 1072.2 997.9 309.9 318.8 314.6 355.1 12.9 17.6

1966 1554.4 969.9 952.4 535.3 359.8 299.8 482.5 12.2 17.2

1967 1238.9 1118.8 1055.2 248.8 241.5 243.5 203.7 12.8 18.1

1968 1225.4 1058.3 963.7 196.5 236.5 263.9 186.5 13.0 17.8

1969 1408.0 1052.2 1011.2 396.8 387.6 380.8 418.1 11.9 17.8

1970 1361.7 1161.7 1021.7 340.0 354.9 343.8 336.3 12.2 18.8

1971 1694.4 1161.1 1123.7 621.3 594.8 595.4 704.9 11.4 19.0

1972 1093.7 1200.3 1023.2 42.3 209.5 228.3 217.9 11.5 19.2

1973 1428.8 1187.3 1103.7 325.1 356.2 337.9 382.7 11.4 18.9

1974 1412.8 1265.4 1160.5 252.3 331.1 283.9 287.8 12.2 19.4

1975 1439.8 1262.5 1200.8 266.9 382.6 392.9 350.9 11.7 19.5

1976 1448.9 1150.7 1053.8 367.1 371.6 363.3 399.4 12.2 18.3

1977 1273.5 1131.1 1047.2 226.3 314.5 285.8 172.4 12.0 18.5

1978 1225.4 1107.3 1041.0 184.4 214.3 206.7 179.5 11.8 18.3

continued to next page

16
Water Balance

Model validation for MIKE SHE hydrological 

simulations—MIKE SHE was calibrated and validated 

using daily and monthly flow measurements on WS79 for 

two time periods, 1969–71 and 2005–07, and water table 

measurements on WS77 and WS80 from 1992 to 1995 and 

from 2005 to 2007 (Dai and others 2011b). The results are 

given in table 4, figures 7B and C, and figures 10A and B, 

which show that MIKE SHE was applicable for predicting 

streamflow on WS79. The model performance efficiency 

(E≤1) (Moriasi and others 2007) was 0.85 and 0.80 for daily 

water table depth on WS77 and WS80, respectively, for 

1992–94, and 0.53 and 0.79, respectively, for 2005–07. The 

R2

 was 0.87 and 0.81 for WS77 and WS80, respectively, 

for 1992–94, and 0.69 and 0.82 for WS77 and WS80, 

respectively, for 2005–07; the ratio of the root mean 

squared error (RMSE) to SD (standard deviation) (good 

rating range: RSR≤0.7) was 0.42 for WS77 and 0.62 for 

Table 3 (continued)—Annual rainfall, potential evapotranspiration, streamflow, and 

temperature on Santee Experimental Forest, 1946–2008

Year

Rainfall 

(mm)

PET 

(mm)

ET 

(mm)

FlowE

(mm)

MS

(mm)

FD 

(mm)

FlowO

(mm)

DTR 

(°C)

meanT 

( °C)

1979 1546.3 1077.6 1031.5 514.8 537.5 534.5 439.9 11.8 17.8

1980 1413.7 1078.8 1049.8 363.9 412.0 417.9 360.0 11.6 18.1

1981 1154.5 1065.1 984.2 170.3 172.1 146.2 131.8 12.5 17.9

1982 1663.5 1182.5 1171.6 491.9 536.9 557.3 N/A 10.8 18.9

1983 1710.7 1072.5 1015.6 695.1 665.3 720.5 N/A 11.2 18.2

1984 1364.5 1138.5 1085.2 363.6 361.9 342.3 N/A 12.0 18.5

1985 1349.6 1167.9 1036.8 228.6 343.3 353.5 N/A 11.5 18.8

1986 1367.2 1194.2 1061.0 306.2 473.4 509.9 N/A 11.1 19.3

1987 1660.5 1075.3 1052.8 622.5 591.4 614.1 N/A 11.2 18.1

1988 1141.8 1008.4 913.1 240.9 315.1 302.3 N/A 11.8 17.5

1989 1595.4 1179.7 1127.7 440.6 513.1 574.7 N/A 11.3 18.7

1990 1095.1 1303.7 1031.2 83.4 355.9 386.5 369.5 11.7 20.0

1991 1252.7 1244.0 1100.5 248.6 485.1 513.4 304.6 9.4 19.7

1992 1472.7 1168.3 1096.9 261.9 341.4 309.7 187.7 10.9 19.0

1993 1246.9 1172.9 1080.8 180.4 301.5 292.3 272.2 12.4 19.2

1994 2026.4 1188.8 1124.6 885.6 349.9 352.9 323.3 12.7 19.2

1995 1525.1 1212.2 1052.0 473.1 461.2 464.9 N/A 12.6 19.1

1996 1208.1 1115.3 1027.5 180.6 222.5 182.0 247.2 12.6 18.3

1997 1659.5 1162.3 1122.4 537.0 477.1 515.2 524.7 12.8 18.7

1998 1291.6 1267.7 962.6 437.5 543.0 579.6 574.9 13.1 20.0

1999 1466.9 1184.5 1168.4 192.4 482.3 383.4 N/A 14.0 19.0

2000 1167.5 1138.3 974.2 193.3 218.0 212.5 N/A 13.0 18.3

2001 1016.1 1178.7 1020.0 106.5 143.6 145.2 N/A 11.8 19.0

2002 1555.7 1242.5 1084.7 360.6 444.3 448.7 N/A 11.2 19.3

2003 1690.4 1121.3 1106.8 604.9 693.5 783.8 608.0 11.7 18.4

2004 1118.1 1163.8 1018.6 144.4 130.7 126.0 60.9 12.3 18.9

2005 1637.9 1151.9 1114.0 475.7 292.5 237.3 324.1 12.1 18.7

2006 1264.5 1144.1 1052.0 212.7 207.8 191.8 148.6 12.9 18.8

2007 1041.3 1122.9 975.7 74.2 74.9 50.9 81.8 12.7 19.1

2008 1476.2 1213.0 1159.5 331.4 N/A N/A 387.3 11.6 19.4

PET = P-M equivalent; ET and FlowE are estimated by using Thornthwaite water balance approach; MS and 

FD = the flow from MIKE SHE and Forest-DNDC, respectively; FlowO = observed; DTR = annual averaged 

diurnal temperature range. 

N/A = no available data.

17
WS80; the percent bias (good rating range: -25 percent 

≤PBIAS≤25 percent) (Moriasi and others 2007) was 3.9 

and -0.08 percent for WS77 and WS80, respectively, for 

the same periods; and the difference in mean water table 

depth between the observation and simulation for the two 

validation periods was <10 cm (between -8.6 and 1.6 cm 

for WS77 and between -3.5 and 3.9 cm for WS80). These 

qualitative (figs. 7B and C, and fig. 10B) and quantitative 

assessments show that MIKE SHE is applicable to model 

water table dynamics for this site.

Evapotranspiration—Data on the estimated annual ET 

are presented in table 3 and figure 11. The average annual 

ET estimated by using the Thornthwaite water balance 

approach (WBA) (1043.4 mm) was about 90.8 percent of 

the PET (1136.5 mm) during the last 63 years. The ET 

increased significantly (p<0.02) at a linear rate of 11.9 

mm per decade in the last 63 years. Moreover, annual 

precipitation substantially influenced the annual ET, thereby 

increasing significantly with an increase in logarithmic 

annual precipitation (p<0.01). This relation between ET 

and precipitation implies that flow and water table level can 

be significantly influenced by a substantial increase in ET 

without an increase in precipitation. However, the ET may 

be overestimated using the WBA, especially for dry years, 

such as 1951, 1954, and 1956, because the streamflow might 

be underpredicted (see the discussion below) for the same 

years by using the WBA. 

The annual ET from MIKE SHE (973.6 mm) for the same 

63-year period was 70 mm (about 6.7 percent) lower than 

the values from WBA, although the PET used to simulate 

the hydrology using MIKE SHE for this watershed was 

also P-M-equivalent, the same as that applied for ET 

estimation using WBA. The difference may be because 

MIKE SHE calculates ET while taking into consideration 

the impacts of vegetation, including plant rooting depth, 

interception, evaporation on leaves, and soil moisture stress 

during dry periods. However, ET levels were likely to be 

slightly underestimated by MIKE SHE, because the TwoLayer Water Balance method was used to simulate the ET 

and the unsaturated flow in soils for this watershed. This 

method divides the unsaturated zone into a root-zone where 

ET can occur, and a below-root-zone where ET does not 

occur (Yan and Smith 1994). Therefore, MIKE SHE may 

underpredict ET for extremely dry periods during growing 

seasons, especially in summers, due to low water table 

level during low precipitation with high evapotranspiration 

demand periods. 

Evapotranspiration simulated by Forest-DNDC was 

1020.9 mm, about 2.1 percent less than the value estimated 

using the WBA in the same period, but about 4.6 percent 

higher than MIKE SHE. The annual ET from Forest-DNDC 

is between the values from MIKE SHE and from the 

WBA. However, Forest-DNDC employed the PET that was 

different from MIKE SHE and WBA to estimate ET. Both 

MIKE SHE and WBA utilize Penman-Monteith-equivalent 

PET as their inputs to calculate ET, but Forest-DNDC 

calculates PET using Thornthwaite-based equation for 

ET estimation without a site-specific Penman-Monteithequivalent correction factor. Therefore, the ET is likely to be 

slightly overestimated using Forest-DNDC. 

Although there are differences in ET estimated by using 

different methods, it can be considered that the average ET 

over the last 63 years was likely within 970-1040 mm, about 

1000 mm. This estimation is slightly higher than the values 

(950 mm and 969 mm) estimated by Amatya and Trettin 

(2007b), and Lu and others (2003), and approximate to the 

result reported by Turner (1991) from 1964 to 1976, before 

the strong air temperature rise in this area. The ET estimated 

using different approaches was lower than the P-Mequivalent PET in this forest area, which indicates that soil 

moisture deficit can occur during low precipitation periods 

although mean annual precipitation (1370 mm) is higher 

than the annual PET (1136 mm).

Table 4—Observed and simulated streamflow for the two first-order watersheds WS77 and WS80 and 

the second-order watershed WS79 consisting of WS77, WS79b, and WS80, and model efficiency in two 

validation periods, 1969–71 and 2005–07

1969–71 2005–07

Watershed O P R2 E RSR PB O P R2 E RSR PB

daily 1.35 1.35 0.82 0.80 0.44 0.69 0.51 0.52 0.61 0.52 0.63 -1.0 WS77 monthly 41.2 41.0 0.94 0.94 0.20 0.69 15.4 15.6 0.91 0.91 0.31 -1.0

daily 1.26 1.34 0.69 0.66 0.58 -6.5 0.46 0.52 0.75 0.75 0.50 -12 WS80 monthly 38.4 40.9 0.93 0.92 0.30 -6.5 14.0 15.7 0.96 0.94 0.24 -12

daily 1.18 1.37 0.78 0.77 0.48 -16 0.53 0.63 0.68 0.64 0.44 -14 WS79 monthly 36.0 41.8 0.90 0.88 0.37 -16 16.2 19.1 0.96 0.95 0.35 -14

O = mean observation; P = mean prediction; R2 = coefficient of determination; E = model efficiency; RSR = the ratio of root 

mean squared error to standard deviation; PB = percent bias between observation and simulation (PBIAS). 

18
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Figure 9—Deviation in potential evapotranspiration (PET) from the long-term 

average measured at the Santee Headquarters from 1946 through 2008.

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Figure 10—(A) Predicted and observed monthly streamflow on the watershed WS79 and its sub-watersheds WS77 and WS80 for 1965 to 2007 and (B) observed and 

simulated water table below the soil surface on watersheds WS77 (44 wells) and WS80 (35 wells) for all water table observation periods between 1964 and 2007.

Figure 11—Estimated annual evapotranspiration (ET) calculated for the 

Santee Headquarters for 1946 to 2008, using the Thornthwaite water 

balance approach (Flerchinger and Cooley 2000, Ward 1972).

(A) (B)

19
Streamflow—Data on estimated streamflow from the 

different approaches are presented in table 3 and figures 12A 

and B. The figures show some differences in flow among 

the four estimation methods. The results from MIKE SHE 

were 4.5, 9.6, and 2.4 percent higher than the observations, 

WBA, and Forest-DNDC, respectively, in 1965–2007. The 

flow predicted by MIKE SHE might be related to the artifact 

of MIKE SHE that does not allow a river/stream to dry out 

(Dai and others 2010b, Lu and others 2006). In the case of 

intermittent streams, it is typical to have no-flow periods, 

such as the dry period from the fall of 2003 to the summer 

of 2004 and the summer of 2007; however, in these cases, 

MIKE SHE maintained a very low streamflow. 

The flow estimated by WBA was 5.4 percent lower than 

the actual measured level. The lower levels are mostly due 

to an overestimation of ET, especially for the years after 

Hurricane Hugo, due to the destruction of the forest leading 

to a low ET demand within several years (Dai and others 

2011b). The underprediction showed that a simple water 

balance method did not capture the impact of hurricanes 

on the hydrology in the watershed. However, the results 

from both models (MIKE SHE and Forest-DNDC) and 

the hydrological observations exhibited the impact of 

the hurricane. The flow estimated by Forest-DNDC was 

about 2.2 percent higher than the measurements, more 

approximate to the observed than the results from MIKE 

SHE and WBA.

Water storage (changes in water table depth)—Data on 

changes in estimated water storage using WBA for the last 

63-year period of 1946–2008 are presented in figure 13, and 

show that changes in annual soil water storage fluctuated 

largely in last 63-year period on these first-order watersheds 

on the Santee Experimental Forest. The storage significantly 

altered with changes in logarithmic annual precipitation 

(p<0.01). Therefore, the less precipitation an area gets, the 

larger the soil moisture deficit, and vice versa. However, 

the change in the long-term average soil water storage was 

small, only 0.4 mm yr-1, in agreement with the change in 

annual precipitation, which was 1.2 mm yr-1. This result 

indicates that the change in annual soil water storage on this 

wetland-dominated watershed with a shallow water table is 

highly influenced by precipitation, consistent to the water 

table (WT) level dynamics simulated using MIKE SHE for 

the 58-year period of 1950–2007 that varied significantly 

with logarithmic annual precipitation (p<0.02); the WT on 

this watershed increases or decreases with an increment or 

decrement in precipitation. 

Figure 14 shows the temporal changes in WT on WS79 

(higher WT, more water stored in soils), which indicates 

that the inter-annual change in soil water storage is 

complex due to interactions among temperature and 

precipitation. For example, the highest WT level occurred 

in 1998, but that year was one of the warmest (20 °C of 

mean daily temperature) since 1946. A high WT in 1998 

was likely related to an abnormal annual precipitation 

pattern, because over 78 percent of annual precipitation 

occurred in January, February, and December (winter), and 

March through May (spring) while the ET demand was 

low. This result demonstrates that the soil water storage on 

WS79 can be substantially influenced by the changes in 

seasonal precipitation.

The WT level in 1958 (with 1460 mm precipitation) was the 

second highest in the 58-year period, but the ground water 

level in 1959 (with 1780 mm precipitation) was about 29 cm 

lower than 1958. It is an example of a reversed pattern of the 

expected normal relationship between WT and precipitation 

in this area, i.e., the water table level rises usually with an 

increase in precipitation. This phenomenon was likely a 

result of two factors, storms and temperature. The coolest 

year in the 63-year period of 1946–2008 was 1958 (figs. 

2A and B), with an average daily temperature of 17 °C. The 

average daily temperature in 1959 was 1.5 °C higher than 

in 1958, most likely resulting in a higher ET demand with 

a subsequently lower water table than in 1958. Another 

potential factor could be the magnitude of summer storms, 

because there were no differences in precipitation in the 

winter, spring, and fall between these years. There were six 

storms with over 70 mm precipitation and two storms with 

over 100 mm in 1959; but there were only two storms with 

over 55 mm precipitation in 1958. High precipitation in 

1959 that was mainly caused by the larger summer storms 

might not have resulted in a substantial high water table 

for these headwater areas in that year. Therefore, storms 

with high precipitation in the summer contribute to a small 

increase in the water table on these first-order watersheds, 

although the water table generally rises with an increase in 

precipitation in this area. 

The comparison of the WT in 1988 and 1990 indicates 

that temperature substantially influences the WT dynamics 

on these first-order watersheds. The WT in 1988 was 

substantially higher than that in 1990. However, there was 

not a substantial difference in precipitation between these 

2 years, including no differences in annual and seasonal 

precipitation and storms; the main difference between these 2 

years was temperature and biomass. The average temperature 

in 1988 was 2.5 °C lower than 1990, and biomass in 1990 

was lower than 1988, due to the destruction of the forest 

canopy by Hurricane Hugo in 1989 (Hook and others 1991). 

Generally, the water table and the rate of annual streamflow 

to annual precipitation in 1990 should be higher than in 1988 

due to low demand in transpiration if climate conditions were 

similar between these 2 years, but the result was reversed 

for the water table, and in agreement for the streamflow. 

This result suggests that the WT on the Santee Experimental 

Forest was influenced by the synergy of temperature and the 

hurricane. Similarly, the difference in WT levels between 

1969 and 1970 also indicated the impact of temperature 

20
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Figure 12— (A) Annual streamflow for the watershed WS79, 1950–2007, as measured (OB) and predicted using different models (WB is Thornthwaite water 

balance; MS is MIKE SHE; FD is Forest-DNDC); (B) relationship between observed and estimated annual streamflow for the watershed WS77, 1965–2007, 

using MIKE SHE (MS), Forest-DNDC (FD), and Thornthwaite water balance approach (WB).

Figure 13—Soil water deficit estimated using Thornthwaite water balance 

approach (Flerchinger and Cooley 2000, Ward, 1972) for watershed WS79 

from 1946 to 2008.

Figure 14—Comparison of annual temperature, precipitation, and soil water 

storage anomalies on watershed WS79 between 1950 and 2007.

(A) (B)

21
on the WT. There was neither a substantial difference in 

biomass between the 2 years, nor a significant difference in 

precipitation. However, temperature in 1969 was 1 °C lower 

than 1970. The WT levels from observation and simulation 

in 1969 were substantially (over 20 cm) higher than in 

1970. These results indicate that temperature is one of the 

important factors influencing the WT on these first-order 

watersheds, and that warming without increased precipitation 

can reduce the WT level on this study catchment. 

Modeling the Role of Climate Variability 

on Carbon Dynamics in Forests

DNDC model validation for estimating carbon 

sequestration—Forest-DNDC was validated against soil 

CO2 efflux, soil temperature, soil moisture, and biomass 

observations within watershed WS79 (table 5; figs. 15A–E), 

with reasonable model performance efficiencies (0.67, 0.70, 

0.40, and 0.86, respectively) (Dai and others 2011a). For this 

study, the model was validated using biomass measurements 

on 24 plots in 2006 and the estimation of biomass loss to 

Hurricane Hugo (Hook and others 1996). The simulated 

biomass loss to the hurricane (79.3 and 12.1 C Mg ha-1 for preand post-hurricane) was in good agreement with the estimation 

(78.0 and 13.9 C Mg ha-1). The observed spatial difference 

in biomass (from observation at 24 plots) in 2006 ranged 

from 44.8 to 136.2 C Mg ha-1 (fig. 15E), and the arithmetic 

average and geometric mean were 75.6 and 73.7 C Mg ha-1, 

respectively. However, the simulated biomass for the same 

plots ranged from 59.3 to 117 C Mg ha-1 with arithmetic and 

geometric means of 74.2 and 73.1 C Mg ha-1, respectively. 

Although the difference between the measured and simulated 

mean was small (arithmetic mean: 75.6 vs. 74.2; geometric 

mean: 73.7 vs. 73.1), there were larger differences in both 

minimums and maximums. Those differences can be attributed 

to using an average canopy loss [85 percent as estimated by 

Hook and others (1991)] in response to Hurricane Hugo 

in 1989—this average was used for simulating carbon 

dynamics on this catchment due to lack of available data on 

the actual spatial canopy damage from the hurricane. In fact, 

the hurricane heterogeneously destroyed the pre-hurricane 

dominant canopy on WS79 in 1989, causing the canopy 

damage in some of the simulation cells to be higher than the 

average and some lower. However, the total error from the 

simulation that used average hurricane damage was small 

because the simulation for most observation plots was in 

agreement with the actual measurement, which was about 

3.8 percent lower than the observed. The model performance 

efficiency (E≤1) was 0.86, in the “very good” rating range 

(E>0.75) for assessing carbon storage in wood biomass in 

this catchment. The results from model validation showed 

that the Forest-DNDC is applicable for estimating carbon 

dynamics on this site with acceptable model performance 

efficiency. 

Biomass carbon—Spatial biomass distribution on WS79 

was varied. The simulated spatial difference in biomass for 

WS79 ranged from 44.8 to 137.6 C Mg ha-1, with arithmetic 

average of 74.5, geometric mean of 71.5, and median of 

65.8 C Mg ha-1 in 2006, which is near to the measured 

range and average (fig. 15E). The simulation results show 

that the spatial difference in biomass over WS79 is larger 

than on WS80—the maximum value is about 3 times the 

minimum on WS79, and twice on WS80. The difference can 

be attributed to the spatial heterogeneity in the vegetation 

type, and forest management. WS80 is a control forested 

watershed in this paired watershed system (WS79 consisted 

of three sub-watersheds, WS77 as treatment, WS80 as 

control, and WS79b as mixture). As a reference watershed, 

WS80 has not been managed, including no salvage logging, 

for nearly six decades. Current vegetation coverage was 

regenerated naturally after Hurricane Hugo in 1989. 

However, there are fewer pine stands on WS80 than on 

WS77. Prescribed fire and thinning were used on WS77 

and part of WS79b (Amatya and Trettin 2007b; Richter and 

others 1983a, 1983b). 

Soil carbon efflux—The cell-based (simulation unit) 

annual average soil CO2

 flux in watershed WS79 ranged 

from 2.34–4.65 C Mg ha-1 from 1950 to 2007 (fig. 16), 

Table 5—Observed and predicted averages for soil CO2, 

temperature and moisture, and biomass on watershed WS79

consisting of WS77, WS79b, and WS80, and model efficiency and 

the slope of regression between observations and simulations

Parameter O P E b

Soil CO2 (C kg ha-1d-1) 43.3 42.6 0.67 0.94

Soil temperature (°C) 20.4 18.6 0.70 0.91

Soil moisture (m3

m-3) 0.48 0.46 0.40 1.02

Biomass (C Mg ha-1

) 55.0 58.9 0.86 1.13

O = observed mean; P = predicted mean; E = model efficiency; b = the slope of 

regression model between observations and simulations.

Source: Nash and Sutcliffe (1970).

22
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Plot number

Figure 15—Comparison of Forest-DNDC predicted with observed parameters on (A) soil moisture on watershed WS79 in 2006–2008, (B) soil temperature 

on watershed WS79 in 2006–08, (C) soil CO2 efflux on the watershed WS79 between 2006 and 2008, (D) aboveground biomass for plots 21–24 on watershed 

WS80 in 2006 and 2007, and (E) above- and belowground biomass in 2006 for watershed WS79.

(A) (B)

(C) (D)

(E)

23
demonstrating a substantial spatial difference in the flux 

within the watershed. The main difference in soil CO2 efflux 

is likely related to soil properties including soil moisture, 

soil organic carbon content, and vegetation distribution 

(Miller and Johnson 2002, Xu and Qi 2001). The arithmetic 

mean, median, and geometric mean of the 58-year soil CO2

efflux on this catchment were the same, 3.8 C Mg ha-1, 

indicating a spatially normal distribution in this watershed. 

However, the spatial difference in the long-term average 

flux in this watershed was smaller than the variation found 

by Xu and Qi (2001) in a ponderosa pine plantation in 

California, the coefficient of variation (CV) was <10 percent 

from this study, while >30 percent was found on the pine 

plantation site. The difference in CVs between two studies 

is likely related to the length of each observation period and 

differences in site characteristics including vegetation, soils, 

and climate. However, the CV from our watershed would be 

about 20 percent if the time period in both studies was the 

same, from June 1998 to August 1999. 

There were large spatial differences in the annual average 

CH4

 flux in this forest, ranging from -4.6 to 391.8 C kg ha-1

with an arithmetic mean of 81 kg and median of 54.4 kg ha-1 

between 1950 and 2007 (fig. 17). Large differences in 

annual CH4 flux between arithmetic mean and median 

indicated that the spatial distribution of CH4 flux was 

substantially skewed, and similar to other results (Trettin 

and others 2006). However, the geometric mean was not 

applicable as a representation of the average CH4 flux level 

on WS79 because there were zero and negative fluxes in 

this catchment. The large spatial difference in CH4 flux was 

regulated by the differences in biogeochemical environment 

of methane production in the site. For example, CH4 flux 

from the cells near the stream outlet is consistently high 

because they are the lowest areas of the watershed; CH4

flux from a local depression located at the northwest of 

this catchment is also high because the bottom of the 

depression is about 50 cm lower than its surface runoff 

outlet (based on the topographic data). However, the CH4 

flux was small or negative at those places with slopes (≥1 

percent) in this watershed. These results indicate that those 

biogeochemical hotspots of CH4 production and emission 

can produce a high CH4 flux, and those hotspots are likely to 

occupy only a small fraction of the watersheds (23 percent 

in this catchment). The results indicate that the spatial 

heterogeneity in CH4 production and emission is critical to 

assess CH4 flux using a modeling approach, especially for 

those catchments with a mosaic of wetlands and uplands. 

Failure to consider the spatial heterogeneity to assess CH4

flux could induce a large error when applying a field scale 

modeling approach with averaged spatial and temporal 

conditions of study sites.

Impact of climate variability on carbon sequestration in 

forests—Climatic conditions in the 39-year period before 

Hurricane Hugo were related to forest C sequestration. A 

multivariate analysis indicates that the combination of air 

temperature and precipitation have a significant correlation 

with forest carbon sequestration (R2

=0.96, n=39, df=3, 

F=1894, p<0.001). Correspondingly, if air temperature 

increases by one degree Celsius on the basis of the current 

temperature condition without an increase in precipitation, 

carbon sequestration due to the forest product on WS79 is 

predicted to decrease by about 300 kg ha-1yr-1 based on that 

multivariate analysis. However, forest production could 

slightly rise if precipitation increased. These relationships 

indicate that warming may decrease C sequestration within 

the forest because precipitation levels have not increased in 

conjunction with the observed warming in this area.

Climate variability can influence C sequestration in other 

components of the forest. The annual soil CO2 efflux from 

the study catchment between 1950 and 2007 indicated 

that the annual flux fluctuated and linearly increased 

at an average rate of about 28 C kg ha-1yr-1 before the 

hurricane (fig. 18). The slow and linear increase in soil 

CO2

 efflux before the hurricane likely resulted from two 

factors, an increase in biomass (fig. 19) and the increase 

in air temperature. The increase in biomass, including 

root and leaf material, can increase soil CO2 efflux; the 

temperature increased at an average rate of 0.21 °C per 

decade between 1950 and 1988 (the pre-hurricane period) 

(Dai and others 2011b), which can lead to an increase 

in soil organic matter decomposition (Xu and Qi 2001). 

However, the inter-annual soil CO2 efflux fluctuation in 

the same period was principally related to the changes in 

soil moisture regime. Annual soil CO2 efflux decreased 

insignificantly at an exponential rate with an increase in 

annual precipitation [4.24*exp(-0.199*AP), 0.1≥p>0.05, 

where AP is annual precipitation (m)]. For example, soil 

CO2 efflux in 2007 on WS79 was 1.1 C Mg ha-1 higher than 

the flux in 2006 because 2007 was a dry year, with 333 mm 

less precipitation than in 2006; the flux in 1997 was 

2.2 C Mg ha-1 lower than that in 1998 as air temperature 

was 1.3 °C higher and precipitation was 360 mm lower in 

1998 than 1997, respectively. These results show that an 

increase in air temperature and decrease in precipitation can 

cause an increase in soil CO2 efflux on WS79, which are 

similar to findings of Pietsch and others (2003), who found 

that the WT level decrease in their sites led to an increase in 

soil carbon loss. These results suggests that warming could 

induce more soil CO2 release from this type of forested 

watershed because the general warming trend does not 

correspond with additional rain in this subtropical area (Dai 

and others 2011b, Zhang and others 2007).

Temporal changes in annual CH4 flux from this watershed 

were large, watershed-based arithmetic mean annual flux 

ranged from -2.7 to 103.5 C kg ha-1 from 1950 to 2007. 

The large difference in the flux year-to-year over the 

58-year period was mainly influenced by temperature 

and precipitation regulating biogeochemical conditions 

of CH4 production and emission. Although there is not 

a correlation between daily precipitation and daily CH4

24
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±

Methane flux (kg C/ha/yr)

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" 101 - 200

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1–100

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201–300

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Soil C

Measured C

BAH-C

Linear (Soil C)

Figure 16—Average annual soil CO2 efflux on watershed WS79 

from 1950 to 2007.

Figure 17—Average annual methane (CH4) flux on watershed 

WS79 from 1950 to 2007.

Figure 18—Predicted change in annual soil CO2

 flux on the 

watershed WS79 between 1950 and 2007 using Forest-DNDC.

Figure 19—Average annual biomass and soil carbon accumulation on watershed WS79 

from 1950 to 2007 as predicted using Forest-DNDC. The blank square () indicates 

pre- and post-Hurricane Hugo biomass reported by Hook and others (1996); the triangle 

() indicates the aboveground biomass measured in 2006 and 2007.

25
flux from this watershed (p>0.1), annual CH4

 flux was 

significantly correlated to annual precipitation (p<0.01) 

(fig. 20A). This result is similar to the findings of Inubushi 

and others (2003), who found that the CH4 flux was 

correlated to monthly precipitation in the coastal area of 

South Kalimantan. Methane production and emission from 

this watershed highly depends on the WT level regulating 

biogeochemical soil conditions, aerobic or anaerobic. 

However, the WT level in these first-order watersheds is in 

turn regulated by precipitation (Amatya and others 2003, 

Dai and others 2010a). Therefore, precipitation becomes 

one of the important factors influencing CH4 production and 

emission in the catchment. This watershed can act as a CH4

sink in extreme dry years, as was the case in 1951, when 

there was only 901 mm of precipitation, about 470 mm less 

than the long-term average, such that the annual CH4 flux 

was -2.7 C kg ha-1.

The impact of temperature variability on CH4 flux was 

linear (fig. 20B). An increase in temperature might 

raise the flux due to an increase in soil organic carbon 

decomposition (Xu and Qi 2001). However, the increase in 

methane flux with temperature rise is influenced by changes 

in precipitation. If precipitation does not significantly 

increase with an increase in temperature, the WT level will 

obviously decrease due to an increase in ET caused by a 

large increase in temperature, which leads to a decrease 

in CH4

 flux in the watershed with a shallow water table. 

For instance, CH4 flux was lower in the two warmest years 

(1990 and 1998) in the last 58-year period, with an average 

temperature of 20 °C in both years, causing a decrease 

in the water table. These results suggest that prolonged 

warming might convert this catchment from a CH4 resource 

to a sink, because wetlands within the catchment may be 

diminished due to a decrease in the water table.

Impact of Hurricane Hugo on CO2 sequestration—The 

simulation results show that the carbon sequestration within 

the forests on watershed WS79 did not monotonously 

change because there was a large disturbance brought by the 

hurricane (figs. 18 and 19). Carbon sequestration across the 

watershed recovered quickly after a significant proportion of 

the overstory canopy was destroyed. The hurricane did not 

only destroy the forest in this catchment, but it also added 

a large amount of coarse woody debris (CWD) to forest 

floor and dead roots to mineral soil. Therefore, organic C 

in mineral soils (SOC), excluding CWD, litter, and duff, 

increased after the hurricane (fig. 19), which was primarily 

a result from the dead roots. However, figure 19 also shows 

that the impact of dead roots added by the hurricane has 

likely been small to the present time. Accordingly, the 

hurricane significantly influenced soil CO2

 flux due to the 

large amount of litter left by the storm, and those residues 

decomposed quickly under the subtropical climate and 

produced a high soil CO2 flux within a short period after the 

hurricane (fig. 18).

Unlike C sequestration to wood product and soil CO2

 flux, 

annual CH4 flux from this catchment was not substantially 

influenced by Hurricane Hugo. The cause is likely a 

reflection that precipitation regimes are independent of 

single storm events. Although most of the overstory was 

destroyed, the water table did not exhibit a sustained 

effect because of transpiration from the understory and 

regenerating trees. Hurricanes cause a large increase in 

detrital organic matter to the forest floor, which could be 

a potential C resource for CH4 production. However, the 

potentiality will interact with biogeochemical conditions in 

this watershed.

The spatial variation of net ecosystem exchange (NEE) in 

the catchment was large. The cell-based annual average NEE 

ranged from -0.99 to -3.49 C Mg ha-1 with an arithmetic 

mean of -1.6 and geometric value of -1.47 from 1950 to 

2007. Although there are large differences in NEE on this 

catchment, the spatial distribution was normal. However, 

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Figure 20—Methane (CH4) flux predicted using Forest-DNDC on watershed WS79 in relation to (A) annual precipitation and (B) annual mean 

temperature from 1950 to 2007.

(A) (B)

26
net primary productivity (NPP) was not low (fig. 21) and 

spatially ranged from 2.1 to 9.6 with an arithmetic mean 

of 4.86 C Mg ha-1. The high NEE likely resulted from the 

decomposition of the large amount of litter left by Hurricane 

Hugo in 1989. This is because CO2 released from the 

decomposition of the litter left by the hurricane was much 

higher than the CO2 amount consumed by the regenerated 

forest within a few years after the storm (fig. 21). 

The NEE increased significantly with a decrease in 

precipitation before Hurricane Hugo in this forested 

watershed (p<0.02) (fig. 22A), but insignificantly with an 

increase or decrease in temperature (R2=0.01) (fig. 22B), 

indicating that NEE can be substantially influenced by the 

changes in soil moisture regime regulated by precipitation 

in this watershed. Soil CO2 flux was lower in the wet years 

than dry years in this site (Dai and others 2011a) because the 

soil was saturated for longer periods in wet years (Dai and 

others 2010b). These results imply that warming can reduce 

carbon sequestration to this forest because the warming does 

not bring more rain to this subtropical area (Dai and others 

2011b, Zhang and others 2007).

CONCLUSIONS AND IMPLICATIONS

The daily mean temperature increased on the Santee 

Experimental Forest at a rate of 0.19 °C per decade over 

the last 63-year period, substantially higher than the global 

average of 0.07 °C per decade in 20th century (IPCC 2001). 

The temperature data from the Santee Experimental Forest 

suggest that the increase in warming in this area began in 

1970, 6 years earlier than reported elsewhere (Hansen and 

others 2006). The warming conditions affect the surface 

hydrology on the Santee Experimental Forest, specifically 

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Changes in annual precipitation (mm)

Changes in annual net ecosystem exchange (C Mg ha-1)

y = -112.4x + 10.2

R2

 = 0.16 !"#"$%$&'(")"$%$*+"

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!"#$%&'()$(#$$.#2(344(/5%(!("#67 Changes in annual net ecosystem exchange (C Mg ha 1( -1)

y = 0.046x - 0.012

R2

 = 0.01

Changes in temperature (°C)

Figure 22—Relationship between net ecosystem exchange (NEE) on watershed WS79 with (A) precipitation and (B) temperature from 1950 to 1988. 

Figure 21—Net ecosystem exchange (NEE) and net primary 

production (NPP) on watershed WS79 estimated by Forest-DNDC 

from 1950 to 2007.

(A) (B)

27
streamflow and water table level. A continued warming 

trend will further affect forest hydrology by decreasing 

streamflow and water table in the summer and fall, 

especially if precipitation remains constant.

Although there is not enough observed data to demonstrate 

that hurricanes can substantially affect the water table in this 

watershed, streamflow did change and remained affected 

for several years following Hurricane Hugo. Large impacts 

to forest cover from destructive winds led to decreased 

transpiration and large water surplus conditions, in turn 

sustaining larger streamflow volumes for 5 to 8 years 

following the hurricane. From data collected from other 

large, yet non-destructive storm events, both streamflow 

and water table on those coastal forest landscapes on the 

southeast Atlantic Coastal Plain can increase in a short 

period in response to the large storm water volume.

The results from water balance budgets using different 

methods are similar. The mean streamflow was about 24 

percent of water from precipitation, and ET occupied 

about 76 percent from 1946 to 2008, indicating that the 

eco-hydrology in this watershed is highly dependent on 

precipitation and ET. Based on the projections of the 

hydrologic changes of this forested watershed, using the 

synthesis of the observations and simulations, the extent 

of areas with wetland hydrology, dependent on shallow 

water table regulated by precipitation, will either shrink or 

disappear in the future; this is because the trend of increasing 

air temperature will cause an increase in ET demand.

The C dynamics simulated for this catchment using ForestDNDC show that the model performs well for estimating 

biomass and soil C, and can be employed to assess the 

impacts of destructive winds on C sequestration to the 

forest ecosystems on the Santee Experimental Forest and 

similar landscape settings. The spatial differences in C 

dynamics, including soil CO2

, CH4, and biomass in this 

watershed indicate that those biogeochemical “hot spots” 

influence C dynamics. Those spots cannot be negligible for 

evaluating C sequestration to and greenhouse emissions 

from the landscape mosaic with wetlands and uplands using 

biogeochemical models. These results also show that longterm climatic and hydrologic observations are of high value 

for multiple areas of study, and are specifically useful for the 

calibrations and validations of models, including hydrologic 

and biogeochemical models.

ACKNOWLEDGMENTS

We acknowledge our predecessors on the Santee Experimental 

Forest who were responsible for initiating the long-term 

monitoring. We thank Charles Harrison, who has sustained 

the hydrologic and climatological monitoring stations since 

the late 1990s, and we thank Julie Arnold for maintaining the 

forest inventory studies. We also thank Tim Callahan, of the 

College of Charleston, and Bo Song, of Clemson University, 

for their helpful review of our manuscript.

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32


Zhaohua Dai; Trettin, Carl C.; Amatya, Devendra M. 2013. Effects of climate 

variability on forest hydrology and carbon sequestration on the Santee Experimental 

Forest in coastal South Carolina. Gen. Tech. Rep. SRS–172. Asheville, NC: U.S. 

Department of Agriculture Forest Service, Southern Research Station. 32 p.

Long-term weather and hydrology data from the Santee Experimental Forest were used to 

assess trends in air temperature, precipitation, and the water balance in gauged watersheds 

over a 63-year period. Since 1946, the mean annual air temperature has increased at a rate 

of 0.19 °C per decade, a rate higher than the global mean for the same period. Total annual 

precipitation has not changed significantly over the period of 1946–2008; however, large 

storm events (>50 mm precipitation) have increased 21 percent over the 63-year period. 

Annual stream discharge has varied from 5.5 percent of annual precipitation in dry years to 

44.7 percent in wet years. In 1989, much of the forest was destroyed by Hurricane Hugo, 

a disturbance that, in turn, influenced streamflow. The water balance was estimated using 

the hydrologic model MIKE SHE; the long-term simulations showed that average annual 

flow was about 24 percent of annual precipitation and that mean annual evapotranspiration 

was approximately 76 percent over the 63-year period. The carbon balance on the 500-ha 

watershed was evaluated using Forest-DNDC. The model performance efficiency was 0.67 

for soil CO2

 efflux, 0.70 for soil temperature, 0.40 for soil moisture, and 0.86 for wood 

biomass dynamics, demonstrating that this model was applicable for predicting carbon 

dynamics for this complex forest mosaic. 

Keywords: Carbon cycling, climate change, Forest-DNDC, forest hydrology, long-term 

weather data, streamflow. 

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