**Synthesis of Meteorological, Hydrology and Water Quality Data for
Trice Experimental Research Forest. Data period: 1997-2001.**

**Comparison between the Output of two Weather Stations at the same
Location. Data period: 2003-2005.**

**Submitted by: Tomaso Bisol, Ph. D. Candidate**

**Visiting Scholar, University of Padova, Italy**

**Submitted to: USDA Forest Service, Southern Research Station and
College of Charleston, Charleston, SC.**

**Date submitted: August 12, 2005**

**LIST OF CONTENTS:**

1.  **Tasks outline**

2.  **Executive summary**

3.  **Site description**

4.  **Methodology**

5.  **Data analysis**

    1.  **Precipitation**

    2.  **Weather**

    3.  **PET**

    4.  **Flow**

    5.  **Water table depth**

    6.  **Water quality**

6.  **Summary and recommendations**

7.  **Acknowledgements.**

> **APPENDIXES**
>
> A Rain and PET (1998 -- 2001)
>
> B Comparison between CR10 and CR10x weather stations data
>
> C Catchments and watershed flow
>
> D Water table depth
>
> E Water quality at catchments and watershed outlet
>
> F Files contents and location

1.  **Tasks outline as suggested by:**

Dr. Tim Callahan, College of Charleston, SC

OUTLINE

May 15 - August 10, 2005

Trice Research Forest, Sumter County, SC

Timothy Callahan, Devendra Amatya, Carl Trettin

with Tomaso Bisol, University of Padova, Italy

OBJECTIVES

1.  Data processing:

    a.  Climate

    b.  Streamflow

    c.  Soil water content and soil temperature

    d.  Water table hydrographs

    e.  Nutrient concentrations in soil water, groundwater, and
        streamflow

2.  Synthesize these data with respect to the six short-rotation woody
    crop (SRWC) catchments; the product: a publishable journal article.

3.  \[Follow-up on John Parsons\' WATRCOM model validation paper:\]
    Simulate flow and nutrient transport from the six catchments for the
    drought period of 1999-2001.

4.  Hypothesis testing paper: Is there a connection between
    afforestation of former agricultural land and increased nutrient
    export?

The ultimate goal of achieving these objectives is to determine the
baseline nutrient (nitrogen and phosphorus) concentrations in a
first-order watershed, about 100 hectares in area, which was formerly
agricultural land and now consists of catchments of soybean fields, a
plantation of short-rotation woody crops (pine and hardwood), and
natural forest stands. Of specific interest is to assess the legacy
nutrient concentration within the watershed as a result of historical
agricultural activity.

**Dr. Devendra M. Amatya, Center for forested wetlands Research, SC**

# Detailed outline of tasks suggested for Tomaso Bisol on Trice hydrology study:

### Precipitation

Process all daily rain to obtain monthly and annual for all gauges (Just
one gauge ?)

*May have already been done (check data base?)*

Compare the automatic vs manual gauge and adjust the automatic

*May have already been done (check data base?)*

Obtain long-term monthly and annual rain from Florence or Sumter, SC

Plot/tabulate monthly and annual values with long-term

Quantify seasonal rainfall (wet: Nov-Apr and dry: May-Oct)

Compare the annual rain with long term in terms of dry (drought) and wet
(tropical storms)

If more than one gauge, look for spatial variability

### Weather

Compare all weather variables (Temp, RH, Wind, Solar) for USFS and IP
stations from 2003 and 2004; Check for anomalies and inconsistencies, if
any.

If consistent throughout, need to remove the USFS station

Process all weather variables from hourly to daily average from the
study period (1997-2000)

*May have already been done (check data base?)*

Use Penman-Monteith method and Thornthwaite methods to estimate daily
PET

Process daily PET to obtain monthly and annual

*May have already been done (check data base?)*

Process temp data to DRAINMOD format for Thornthwaite PET

Process P-M daily PET to DRAINMOD format

### Outflows 

Process all stage data measured at flumes/weirs to compute flow rates
for all six catchments

*May have already been done (check data base?)*

Integrate all flow rates to obtain daily ditch and stream outflows for
all six catchments

*May have already been done (check data base?)*

Plot daily and cumulative ditch and stream outflows

*May have already been done (check data base?)*

Compute and tabulate monthly and annual ditch and stream outflows

### Water Table Elevations (Depths)

Plot time series of water table elevations (depths) for all catchments

Compute average (range) water table elevations for dry and wet periods
for all catchments

### Soil Moisture

Plot time series of soil moisture data for all six catchments for all
years

### Leaf Area Index

Plot time series of LAI data for all six catchments for all years

### Drainage Water Quality Concentrations

Plot time series of outlet concentrations for all nutrients and sediment
by year

### Drainage Water Quality Loadings 

Plot time series of outlet loadings for all nutrients and sediment by
year

### Soil Water Concentrations 

Plot time series of soil water nutrient concentrations in various depths
for all catchments

Average soil water nutrient concentrations by depths for each catchment

Plot time series of average soil water nutrient concentrations for all
catchments

### Ground Water Concentrations (Not sure whether it is available ??)

## WITHIN CATCHMENT ANALYSIS

### Runoff (Outflow) -- Rainfall Relationships

Use rainfall and flow rates to define storm event hydrographs

Compute event hydrograph characteristics (rainfall and outflow amount,
runoff/rainfall ratio, time to peak, peak flow rate, initial flow rate,
duration of event, base flow amount)

Compute annual runoff/rainfall ratios

Plot monthly outflow vs rainfall

### Rainfall- Water Table Relationships

Plot rainfall versus water table rise to compute drainable porosities as
per set criteria

### Water Table -- Outflow Relationships 

Plot water table depth vs outflow rates for selected large events

### Water Table -- Soil Moisture Relationships

Plot time series of water table depth vs soil moisture

### Drainage Water Concentration -- Outflow Relationships 

Plot time series of outflow vs concentration relationships for each
nutrient

Plot event outflow vs event mean concentrations

### Soil Water Concentration -- Drainage Concentration Relationships 

Plot time series of soil water vs drainage water concentration
relationships for each nutrient

Examine concentration trends between soil water and drainage water

### Water Budget Estimates

Plot daily cumulative rainfall, PET, water table, soil moisture and
outflow for each year

May use Thornthwaite water balance for monthly and annual budgets

Compare annual rainfall, PET and outflow

## BETWEEN CATCHMENTS AND SPECIES (Sweetgum, Sycamore)

### Outflows

Plot and tabulate monthly/annual outflows and R/R ratios for all six
catchments

### Water Table Elevations (Depths)

Plot and tabulate average water table elevations for all six catchments

### Leaf Area Index

Plot and tabulate average LAI for all six catchments and then by species

### Drainage Water Quality Concentrations

Plot and compare event mean or annual/seasonal mean concentrations for
all nutrients and sediment for all six catchments

### Drainage Water Quality Loadings

Plot and compare event mean or annual/seasonal mean loadings (kg ha^-1^)
for all nutrients and sediment for all six catchments

### Soil Water Quality Concentrations

Plot and compare annual/seasonal mean concentrations for all nutrients
for all six catchments by depths and also as average depth

### Evaluation of Treatment Effects

Effects of controlled drainage under the same vegetation treatment
(sycamore)-- compare outflows, concentrations and loadings of catchments
(free drainage) versus catchments (controlled drainage)

Effects of vegetation under the same drainage treatment (free) --
compare outflows, concentrations and loadings of catchment (sycamore)
versus catchment (sweeygum).

### Model Testing and Application to Test Hypotheses

WATRCOM (Parsons' Works underway)

DRAINMOD

-   Testing of DRAINMOD for a typical catchment using measured weather
    data

-   Compare predicted water table and flow data with the measured for
    the study period

-   Run DRAINMOD with a long-term weather data set to determine average
    water budget for both free and control drainage treatments

-   Determine long-term average water table depths and outflows

-   H~1~: Controlled drainage would have reduced long-term outflows

-   CD would have shallower water table depths

-   Mature SRWC would have reduced water losses compared to young
    planted trees

2.  **Executive summary**

A research collaboration between the University of Padova, Italy, and
College of Charleston and US Forest Service in Charleston, SC, was
agreed upon in January, 2005, to conduct a hydrology study with data
from Trice Forest, SC.

This report presents the results of an analysis of data previously
collected at six catchments within Trice Experimental Research Forest.
The analysis was conducted between May and August 2005. Specifically, in
this period two tasks were carried out: first, an analysis of
meteorological, hydrological and water quality data available for the
period 1997 -- 2001 in order to determine the possibility of missing
data, and to compare the effect of different treatments on surface, soil
and ground water quality. The treatments are: sycamore and open drainage
(catchments 1 and 3), sycamore and controlled drainage (catchments 2 and
4), sweetgum and open drainage (catchments 5 and 6).

Second, a comparison of data was conducted between the outputs of two
different weather stations located at a distance of about 12 feet from
each other, nearby the studied catchments mentioned above, for the
period 2003-2005. The aim was to understand if the values measured by
the two stations were reasonably the same calibration, allowing to
remove one of them and to use it at a different field site.

The analysis revealed that there is a complete availability of daily
rain and PET data from the beginning of 1998 until the middle of August
2000, whereas after this date until 2001 only monthly data were
available, and the same is true for the whole 1997.

Water table depth was measured from June 1998 to July 2001, but with a
very low frequency (monthly or less). A clear treatment pattern was not
identified among the catchments.

Flow data, either directly measured for every catchment or obtained
through a regression with another catchment, are available from
4/16/1998 (catchments 1 to 4) or 5/28/1998 (catchments 5 and 6) to
8/23/2000, but with some gaps, in some periods affecting all catchments.
The results indicate an extremely high flow from at least one of the
catchments (number 3) in comparison to the rainfall. This requires
further investigation, especially considering that the catchment with an
analogue treatment (number 1) had the lowest flow, and that catchment
number 3 was chosen to develop the regression in order to extrapolate
the flow for catchments 1, 2 and 4 when it was not available.

Using all available inputs DRAINWAT (Skaggs, 1978) model was run for a
comparison, and the predicted flow was even lower than the flow measured
for catchment 1; however, more data are needed to achieve a more
accurate simulation.

Flow water samples were collected and analyzed for pH, electric
conductivity, ortho-phosphate, total nitrogen, total phosphorous,
ammonium, nitrogen, magnesium, calcium and potassium (see appendix E)
for all catchments, but in different periods; there is no single
interval of time for which samples for all catchments are available, and
a thorough comparison is therefore difficult.

As for the comparison between the two weather stations, there was a very
good correlation for air temperature, rain and solar radiation, whereas
for wind speed and relative humidity the correlation was not considered
completely acceptable and probably a calibration of the correspondent
two sensors is required.

\`

3.  **Site description**

The study site is located on the William H. Trice Research Forest, which
is situated approximately 1 mile southwest of Mayesville, in Sumter
County, South Carolina. The research forest (approx. 300 acres), is part
of a larger 3000 acre operational tract, and has been designated by
International Paper for experimental purposes.

![](media/image1.png){width="3.8958333333333335in"
height="3.1145833333333335in"}

Figure 1. Location of Trice Experimental Tract.

4.  **Methodology**

All original data were either available in Microsoft Excel format or
converted into it.

Excel was also used for all data processing and analysis including
statistical correlation and regression analysis; on three occasions
short Visual Basic for Application (Excel) subroutines were written to
make calculation faster and/or easier. Results were presented in tabular
and graphical format.

A specific Word file was written to describe these subroutines, as well
as a file with a brief description of all files content (see Appendix
F). The report was prepared using MS Word 2000 and systematically
compiled together with graphs, tables and appendixes.

All works were performed at the GIS computer lab at the USFS Center for
Forested Wetland Research in Charleston, SC.

The working files are stored in the computer pcsrs3516 at the USFS
Center for Forested Wetland Research in Charleston, SC under the
directory

C:/Documents and Settings/ tbisol.PCDEDY25S71/ My Documents/ Tomaso
Bisol/ Trice.

Copies are also backed up in CDs, a copy of which is available with Dr.
Amatya (CFWR) and Dr. Callahan (CofC).

5.  **Data analysis**

    1.  **Precipitation (1998 -- 2001) -- see appendix A, figure 1.**

Note: the following paragraph describes monthly values obtained using
daily values when it was possible, i.e. in 1998 and 1999. For the year
2000 I chose to use the available monthly values, even though daily
values were present until August 15, for uniformity within the same
year. Usually the sum of the daily values for a month does not differ
much from the monthly value, but in some cases the discrepancy is big,
and therefore a further investigation on this is needed.

Following a very wet period in the end of 1997, 1998 began with 5 months
characterized by abundant rainfall: January, February and April show
values clearly higher than the long term average, whereas March and May
are comparable to the 50 years normal. As a result, 674 mm of rain fell
in this period, compared to 480 mm of the normal (+40%). The following
months of June and July had 287 mm of rain, which correspond to the
average for that period. Of the following remaining 4 months, only
September had more rain than average (+21%), whereas August, October and
November were very dry months. October (-88%) and November (-68%), in
particular, had only 30 mm of rainfall altogether, and even though
December had 10% more rain than the average, the amount of rain of the
last 3 months of the year shows a deficit of more than 100 mm compared
to the normal. The total rainfall of the whole year (1404 mm) is higher
than the 50 year normal (1215 mm), even though it has to be kept in mind
that it is the result of the alternation of wet (especially at the
beginning of the year) and dry periods (at the end).

Despite a very rainy January 1999 (151 mm), the drought continued in the
following months of the year. Beside January, only April and September
had more rain than average; on the other side, most of the other months
were relevantly dry, with a low peak in June and July (only 43 and 45%
of the average rainfall, respectively) and a minimum monthly value in
November (43 mm, while the normal is 70 mm). This leads to a total of
1034 mm in the whole year, i.e. 15% less than the normal value.

The situation is somehow similar, but worse, in 2000 and 2001. The year
2000 had only 2 months with more than 100 mm of rain (January and
September); in both cases the amount of rain is remarkably large
(respectively 172 and 173 mm, i.e. 62% and 70% more than average),
suggesting violent storms with a lot of rain in a short time. Other than
these two months, only November has a higher value than the normal
series (79 mm, +20%), while the others tend to be clearly below it.
Specifically, 4 months (February, May, October and December) had less
than 40 mm of rain, with a very rare value of 0 mm for October. In the
whole period for which rain data are available, that is since 1940, only
October 1974 had had a total absence of rain. The sum for the year 2000
is 930 mm (-24% in comparison to the 50 year average).

Almost the same value (936 mm) was measured in the year 2001, which
again had a few very wet months and several dry periods, prolonging the
drought. In particular, June 2001 had the highest monthly rainfall of
the 4 years with 203 mm (+46% compared to the average), and was preceded
by a rainy May (+48%), but on the other side each of the remaining 6
months had less than 66 mm of rain, which is the average for a November
(the driest month of the year). The rainfall in April, November and
December 2001 was below 20 mm, with monthly values going from 14 to 23%
of the average. From August to the end of the year only 221 mm of rain
fell, less than half of the normal value (457 mm).

2.  **PET (1998 -- 2001) - see appendix A, figures 2 to 6.**

PET daily data are available from 1/1/98 to 8/15/2000. Whenever
possible, Penman-Montieth PET was calculated; in the other cases,
Thornthwaite equation was used, adjusted by comparing Thornthwaite
values to those calculated by other methods (usually a Penman Comb.
method) for periods when both were available.

As described before for the rain, when daily PET values were not
available, monthly data were used; it has to be remembered that these
are calculated with Thornthwaite method.

As expected, the only months with a PET above 100 mm are included in the
interval April -- August, whereas values for November, December and
January are always below 50 mm.

The total PET for the period is close to 2750 mm, a value comparable to
the 2778 mm of rainfall.

It has to be borne in mind, however, that from May to August the amount
of rain tends to be insufficient to compensate the PET, whereas colder
months are characterized by more abundant precipitation and lower PET
values. In the interval May -- August, in 1998, 1999 and 2000 only July
1998 had more rain than PET, while in all the other dry months PET
values are higher than rain; further, rain events during summer tend to
be violent and short, leading to big amounts of superficial run off.

The combination of these two factors (rain scarcity and violent storms)
may lead to a drought stress during summer months, even though the
yearly values of rain and PET are similar.

3.  **Comparison between weather stations (2003 -- 2005) - see appendix
    B, figures 1 to 10.**

A comparison between the outputs of two different weather stations
(International Paper CR10 and Forest Service CR10x) located at a
distance of about 12 feet from each other, nearby the 6 studied
catchments, for the period 5/16/2003-2/16/2005. The aim was to
understand if the values measured by the two stations were reasonably
the same, allowing to remove one of them and to use it in a different
location.

The best correlations were found for temperature (fig. 1), solar
radiation (fig. 2) and rainfall (fig. 7).

For temperature and solar radiation, in particular, R^2^ was always
above 0.9939, suggesting that the two station can measure these
parameters correctly.

As for rainfall, there was a problem in 2003, leading to a total lack of
correlation between the two stations. After the problem was fixed,
anyway, the measured values are very similar: R^2^ is equal to 0.9867 in
the year 2004 and to 0.9718 in the first months of 2005.

Relative humidity, both maximum (fig. 3) and minimum (fig. 4), shows a
good correlation, but even though R^2^ values are high (always above
0.96) IP CR10 weather station seems to be less precise when the humidity
gets closer to 100%; in other word the correlation appears to be very
good only when the humidity is low.

Wind speed (fig. 6) correlation does not seem to be very good; further,
IP CR10 station does not measure values below 0.2 m/s. However, it had
to be kept in mind that the two weather stations are not identical, IP's
CR10 being taller. Since the sensor measuring wind speed is on top of
the station, the parameter is very likely to be more affected by this. A
hedge located nearby (and closer to IP's CR10) may also have an impact.

During my survey at Trice Forest, in fact, the two weather stations wind
sensor were clearly under different condition: CR10x, closer to the
ground, was in a virtually total absence of wind, whereas CR10 sensor
was hit by a breeze and moving faster.

For wind direction (fig. 10), which again is probably affected by the
previous factors, the regression was calculated only in 2003, and the
correlation is not very good.

Vapor pressure (fig. 9) correlation was also calculated for the first
year, with good results.

4.  **Flow (1998 -- 2000) -- see appendix C, figures 1 to 9.**

Flow data, either directly measured for every catchment or obtained with
a regression with another catchment, are available from 4/16/1998
(catchments 1 to 4) or 5/28/1998 (catchments 5 and 6) to 8/23/2000. Data
for one or more catchments are missing for some short periods; on some
occasions, this lack of data occurs during wet periods and it is likely
to be a cause of an underestimate of the total flow.

Nevertheless, the total measured flow from some of the catchments, and
in particular catchment number 3 (fig. 3) is extremely high in
comparison to both the flow from other catchments and the actual
rainfall (fig. 7). The total rainfall in the flow monitoring period is
2116 mm, and the calculated PET is 2337 mm; a total measured flow from
catchment 3 of almost 1200 mm, i.e. more than 50% of the precipitation,
is probably not verisimilar, especially taking into account that the
flow from the catchment with the same treatment (catchment 1) was only
300 mm. Further, in the first month of surveys, the flow from catchment
3 is more than double than the rainfall (241 mm and 112 mm,
respectively); a similar thing, even though less obvious, happened in
catchment 2 (145 mm of flow occurring in the first month).

This requires further investigation, especially considering that
catchment number 3 was chosen to calculate the regression in order to
obtain the flow for catchments 1, 2 and 4 when it was not available. Two
possible explanations for this are: the response of the catchment to the
rain is slow, and the high flow may be an effect of previous abundant
rainfalls; during major rain events, the flume is submerged and
overestimates the flow. Even though the beginning of 1998 was very wet
(550 mm of rain fallen from the beginning of January till April 15, i.e.
when flow measurements began), anyway, the first explanation does not
seem to be sufficient.

DRAINMOD hydrology simulation model was also used to understand the
possible reason for this clear difference between rainfall and outflow
(appendix C, figures 8 and 9). Some simulations were run for the period
1/1/98 -- 9/30/99 and for catchment 1, using either measured or
literature data, but the predicted flow was always very low. The
situation changed after available daily PET was used as an input, but
the predicted flow (80 mm) was always much lower than the measured (196
mm).

Considering that catchment 1 is that with the lowest measured flow, that
this flow is considered verisimilar, and that the model on some
occasions is not predicting any flow even after significant rain events
(e.g.: no flow predicted after 25 mm of rain fell on November 7, 1998),
we decided that the available input data (probably, mainly soil
characteristics) were not sufficient to run a correct simulation. Since
DRAINMOD calibration was not one of the main tasks of the project, the
beginning of a new set of simulations with more data was postponed.

5.  **Water table depth (1998 -- 2001) -- see appendix D, figures 1 to
    6.**

Water table depth was measured monthly in every catchment. A common
pattern could not be identified for catchments with the same treatment:
catchment number 1 (SYO) and 2 (SYC) have similar water table depths,
and the same can be said for catchment 3 (SYC) and 4 (SYO). Therefore
different treatments appear to have a similar behavior in the case of
the sycamore. More specifically, in catchment number 4 the water table
was never above 120 cm, even in the shallow wells; the trend is
analogous to catchment 3, but the minimum depth observed was 150 cm.
Consequently, because the shallow wells were constructed at a depth of
180 cm, they were found to be dry in many occasions (24 times in
catchment 3 and 19 in number 4). More frequent data were available for
the deep wells; the patterns were again similar, with values on average
30 cm deeper for number 3 (average depth 275 cm). The maximum depths
measured are 383 and 337 cm for catchment 3 and 4, respectively.

Water table was usually closer to the surface in catchments 1 and 2, and
shallow wells were dry during 7 (catchment number 1) and 9 (number 2)
surveys. When they were not, water table was usually closer to the
surface in catchment number 2 (average depth is 82 cm, compared to 120
mm in number 1), as expected because it was in controlled drainage.

Deep wells had a very similar trend in catchments number 1 and 2;
absolute values tend to be higher in catchment 2 (average depth is close
to 200 cm), even though the minimum value was measured in the same
catchment, too (11 cm on 1/20/2000). However, this was the only time
when the water table in the deep well was within one meter, whereas four
observations in the range of 80-90 cm were found in catchment 1; the
average deep well depth was 181 cm. Maximum depths for the catchments1
and 2 were 316 and 324 cm, respectively.

Catchments 5 (SWO) and 6 (SWO), i.e. those where sweet gum trees were
grown and the drainage was open, showed a pattern similar to the
catchments 1 and 2. Maximum depths were not much deeper than 300 cm, and
the average values in the deep wells were respectively 144 and 160 cm in
catchment number 5 and 6. Shallow wells were dry only for a few times (5
times in catchment 5 and 6 in number 6), as in catchments 1 and 2, and
measured absolute values were similar to those of catchment number 2.

6.  **Stream and ditches water quality (1997 -- 2001) -- see appendix E,
    tables 1 to 10.**

Note: the following description is about periods shorter than the whole
monitoring period, in order to compare data as uniform as possible.
Tables with statistical parameters for the whole monitoring period are
available in Appendix E.

During the whole monitoring period 134 samples from 5 of the 6
catchments were analyzed for pH. The values were in a range between 4.4
and 7.3, showing that the water in the studied area tends to be slightly
acid. Considering the samples collected from December 1997 to May 1998,
catchment 3 has a median value of 4.8, quite low if compared to the 6.2
of catchment 1. The span of the ranges is similar (4.7-7.0 for catchment
1 and 4.4-6.6 for catchment 3), but most of the samples in catchment 3
are below 5.0 and only one is above 6.0, while the exact opposite is
true for catchment 1.

There were only a smaller number of pH measurements for catchment 4, and
their results are comparable to those of catchment 1. In the same period
pH was analyzed for 23 samples from both catchment 5 and 6. The results
are similar: the median values are respectively 5.9 and 6.0, with a
standard deviation equal to 4.0 (catchment 5) and 3.8 (catchment 6).

No data were available for catchment number 2.

From December 1997 to March 1999 water samples from 5 of the 6
catchments were analyzed for NO3_N; analysis for catchment number 2 were
not available. The lowest median values are found for catchments 4 (4.7
mg/L) and 6 (4.6 mg/L), whereas in the other three catchments they are
included in the interval 6.3 -- 6.8 mg/L.

NO3_N data were again available for five catchments from January till
April 2000 (January and April for catchment 4, and January for catchment
5); in this interval there were no data for catchment number 1.

In this case, too, the higher median values are found in catchment 3 and
5 (8.7 and 7.8 mg/L), followed by 4 (7.4 mg/L), that has a higher
variability (standard deviation = 2.5). The lowest medians belong to
catchments 6 and 2 (5.4 and 5.2 mg/L); catchment 2 has also the lowest
minimum and maximum values.

Comparing these data to those collected until March 1999, it looks like
nitrate nitrogen concentrations are higher in the second selected
period, even though it must be kept in mind that the number of samples
analyzed differ much from one period to another.

Given the lack of data for catchment 2 in the first period and catchment
1 in the second, it is difficult to compare the different treatments;
further, catchments with the same treatment seem to have different
behaviors in term of nutrient concentrations, so a common pattern could
not be identified.

During the period December 1997 -- March 1999 the median concentration
of ammonium nitrogen in all catchments is low and varies from 0.2
(catchments 5 and 6) to 0.4 mg/L (1 and 4). It is, therefore, very close
to the minimum value in each case. The maximum values were highest in
catchments 1 and 6 (respectively 5.4 and 5.9 mg/L), while in catchments
3, 4 and 5 they were below 3.0 mg/L.

Since a significant number of analyzed samples gave a result of a non
numeric value (e.g.: \<0.1 mg/L) it was not possible to correctly
calculate statistical parameters for the second period (January -- April
2000). What the data suggest, anyway, is that the concentration of
ammonium tends to be low in every catchment, being below 0.2 mg/L in
most of the cases, and often below 0.1 mg/L.

In the first selected period (Dec 97 -- Mar 99) Mg concentration was
higher in catchments 1 and 3, with median values above 4.0 mg/L in both
cases; catchments 4, 5 and 6 have a median concentration equal to 2.7,
3.2 and 3.4 respectively. Catchments 1 and 3 had also a clearly higher
range: 2.5-6.9 and 3.0-6.4 mg/L, respectively.

The second period, for which the number of analysis available is lower
for all catchments, is generally characterized by higher values: the
medians for these data fall in the interval 4.0-5.6 mg/L (catchments 4
and 3), and the minimum values are similar to, if not higher than the
medians of the first period. Again, catchment 3 has higher
concentrations: even though in this case there are no data for catchment
number 1, that has the same treatment (SYO), there seems to be a
treatment effect on Mg concentration.

As for calcium concentration, from December 1997 to March 1999 the
higher values are found in catchments 1 and 5: the median is equal to
(catchment 5) or higher than (catchment 1) 9.8 mg/L, which is equal to
the maximum value reached in catchment number 4; catchments 3 and 6 have
lower maximum concentrations. The medians in catchments 3, 4 and 6 fall
in the range 7.0 to 8.0 mg/L.

In the second period (January -- April 2000) the values are higher than
in the first, with the minimum values comparable to the medians of
December 1997-May 1999, if not clearly higher. The medians vary from 8.2
(catchment number 6) to 13.7 mg/L (number 4), and they are above 10.0
mg/L in three catchments (2, 4 and 5). The highest maximum value (18.6
mg/L) is found in catchment 4.

Catchments with the same treatment do not have similar calcium
concentrations, and therefore a treatment effect could not be
identified.

It is difficult to describe K concentration: it was almost always below
the detection threshold, and, consequently, the values were non numeric
(e.g.: \<2).

6.  **Summary and recommendations**

Under the sponsorship of US Forest Service, Center for Forested Wetlands
Research and College of Charleston, a three month (May -- August 2005)
long study was carried out to investigate and synthesize meteorological,
hydrologic and water quality data collected during 1997 -- 2001 study
period at Trice Experimental Forest near Sumter, SC.

The analysis revealed that there is a complete availability of daily
rain and PET data from the beginning of 1998 until the middle of August
2000, whereas after this date until 2001 only monthly data are
available, and the same is true for the whole 1997.

Water table depth was measured almost constantly from June 1998 to July
2001, but with a very low frequency (monthly or less). A clear treatment
pattern was not identified in water table among catchments.

Flow data, either directly measured for every catchment or obtained with
a regression with another catchment, are available from 4/16/1998
(catchments 1 to 4) or 5/28/1998 (catchments 5 and 6) to 8/23/2000, but
with missing gaps, in some occasions affecting all catchments. The
results indicate an extremely high flow from at least one of the
catchments (number 3) in comparison to the rainfall. This requires
further investigation, especially considering that the catchment with an
analogue treatment (number 1) had the lowest flow, and that catchment
number 3 was chosen to calculate the regression in order to obtain the
flow for catchments 1, 2 and 4 when it was not available.

Using available data, DRAINMOD model was run for a comparison, and the
predicted flow was even lower than flow measured for catchment 1;
however, more data are needed to achieve a more accurate simulation.

Flow water samples were collected and analyzed for all catchments, but
in different periods; there is no interval of time for which samples for
all catchments are available, and a thorough comparison is, therefore,
difficult.

As for the comparison between the two weather stations, there was a very
good correlation for air temperature, rain and solar radiation, whereas
for wind speed and relative humidity the correlation was not considered
completely acceptable.

Although it is hoped that the findings in this data synthesis report may
help as a basis for future developments of the project, further
investigation is needed to understand if measured flow is correct for
every catchment, and if so why discharges differ so much in catchments
with the same treatment. Two factors that were not considered for
monitoring were lateral seepage between artificial catchment boundaries
and vertical deep seepage, so future monitoring for quantification of
both of these processes is highly recommended to accurately determine
the water budget.

A more constant way of sampling is required for all catchments, in order
to identify common patterns for analogous treatments and to compare
different treatments.

Both weather stations, CR10 and CR10x, seemed to be working efficiently,
but wind speed and relative humidity sensors of CR10 station may require
a calibration.

7.  **Acknowledgements**

I would like to thank Dr. Devendra **Amatya** for his constant and very
precious help and Dr. Tim **Callahan** for all his support and his
advice during my whole stay in Charleston; Dr. Carl **Trettin** and
Prof. John **Parsons** for the information they kindly gave me on the
existent data whenever I needed them; Mr. Scott **Harder** for being so
helpful and for his help with evapo-transpiration; Miss Julie **Arnold**
for being such a good guide at Trice experimental forest.

Also, I would like to thank all the people at **Charleston Southern
Research Station** for welcoming me and making me feel at home and
surrounded by friends, which, I believe, gave a priceless contribution
to improve the quality of my work.