SMITHSONIAN CONTRIBUTIONS TO THE EARTH SCIENCES • NUMBER 29

Climate and Moisture Variability
in a Tropical Forest: Long-term Records
from Barro Colorado Island, Panamá

Donald M. Windsor

ABSTRACT

Windsor, Donald M. Climate and Moisture Variability in a Tropical Forest: Long-term Records from Barro Colorado Island, Panamá. Smithsonian Contributions to the Earth Sciences, number 29, 145 pages, 35 figures, 11 text tables, 40 appendix tables, 1990.—Long-term environmental monitoring results are presented documenting the seasonality experienced by the lowland tropical forest on Barro Colorado Island, Panama. A dry season has developed during each of the past 62 years, typically starting in late December or early January and ending with the first heavy rains in late April or early May. Solar radiation totals climb during dry seasons to monthly levels 50% higher than normal in the wet season. Average daily maximum temperatures increase by two degrees C while average daily minimum temperatures increase by roughly one degree C. Development of the dry season is accompanied by a doubling in average daily windspeed and a drop of ten percent in average midday relative humidity.

Annual rainfall on Barro Colorado Island averages 2612 mm (1925–1989), 90% of which falls in the months of May through November. Rainfall on Barro Colorado Island and seven other sites in the middle of the isthmus has decreased significantly over time. The only long-term rainfall records without decreasing trend come from coastal sites, suggesting that convective, but not orographic, rainfall, has diminished during the last sixty years. Further, annual rainfall appears to be influenced by factors associated with El Nino events. Higher than normal rainfall tends to occur the year before and lower than normal rainfall tends to occur the year of such events. Dry-season forest and clearing temperatures on Barro Colorado Island were elevated during each of the three El Niño events occurring in the past 16 years.

Storms that drop most of their moisture in a minute or two are common during the wet season on Barro Colorado Island. The soils of Lutz catchment are steep and clay rich. Most moisture in storage resides in the upper 10 cm. Available soil moisture is largely depleted by mid- to late dry season.

Actual evapotranspiration, calculated as the difference between rainfall and runoff and changes in storage for each of 14 years averaged 64 percent of annual rainfall. Eighty-five percent (1534 mm) of annual rainfall was returned directly to the atmosphere as vapor in the dryest of the past 16 years (1976) while only 47% (1953 mm) was returned in the wettest year (1983). Comparison of average evapotranspiration and rainfall characteristics from 18 temperate and tropical hydrological studies suggests a loose positive relationship exists between these two quantities in forested ecosystems.

OFFICIAL PUBLICATION DATE is handstamped in a limited number of initial copies and is recorded in the Institution’s annual report, Smithsonian Year. SERIES COVER DESIGN: Aerial view of Ulawun Volcano, New Britain.

Library of Congress Cataloging in Publication Data

Windsor, Donald M.

Climate and moisture variability in a tropical forest: long-term records from Barro Colorado Island, Panamá / Donald M. Windsor.

(Smithsonian contributions to the earth sciences: no. 29)

Includes bibliographical references.

1.   Forest microclimatology—Panama—Barro Colorado Island. 2. Moisture—Panama—Barro Colorado Island. 3. Barro Colorado Island (Panama)—Climate. I. Title. II. Series.

QE1.S227   no. 29   [SD390.6.P2]   550 s—dc20   [551.69727′5]   89-600304

Contents

Introduction

Acknowledgments

Setting

Climate

Rainfall

Temperature

Relative Humidity

Air Movement

Solar Radiation

Moisture Availability

Geology and Soil Characteristics

Soil Moisture

Runoff

Evapotranspiration

Appendices

A: Rainfall (Tables A1–A4)

B: Temperature (Tables B1–B11)

C: Relative Humidity (Tables C1–C9)

D: Air Movement (Tables D1–D5)

E: Solar Radiation (Tables E1–E3)

F: Soil Moisture (Tables F1–F5)

G: Stream Discharge (Tables Gl, G2)

H: Evapotranspiration (Table H1)

Literature Cited

Climate and Moisture Variability
in a Tropical Forest: Long-term Records
from Barro Colorado Island, Panamá

Donald M. Windsor

Donald M. Windsor, Smithsonian Tropical Research Institute, Apartado 2072, Balboa, Republic of Panamá.

Introduction

The duration and intensity of tropical seasons vary considerably more than one might expect from the invariant yearly cycle in the midday position of the sun. Dry seasons can be short or prolonged, drier than normal or broken up by brief periods of heavy rain. Wet seasons may vary greatly in the intensity and frequency of rainstorms and in the frequency and length of periods without rain. Important elements of tropical climate are probably neither as constant nor as predictable as is widely assumed. Climatic variability impressed biologists working on Barro Colorado Island in the 1960s and eventually led to a long-term monitoring study, the “Environmental Sciences Program” (ESP) whose goal was to document long-term variability in the physical environent in parallel with long-term observations on selected plant and animal populations. Results from the early part of this program are examined in Leigh et al. (1982).

The history of environmental research on Barro Colorado Island dates from the early 1920s. W.C. Allee (1926) was the first ecologist to attempt to characterize the physical environment on Barro Colorado Island. Using a ridge-top Hura crepitans tree in the northern part of the island as an instrument platform, Allee gathered readings on wind movement, solar radiation, and evaporation from subcanopy and compared these to readings from the understory and other sites in Panamá during the dry season of 1924. He was impressed by the physical rigors of the dry season and the buffering the forest canopy provided the forest understory. The first continuous records of climate on Barro Colorado Island were begun by the Panamá Canal Company in April 1925 when a rain gauge was installed in the lab clearing. This instrument was the only monitor of the physical environment for the next 45 years. Things changed abruptly in 1971 when STRI (Smithsonian Tropical Research Institute) scientists, Stan Rand and Nicholas Smythe, instituted a program that included continuous monitoring of runoff from a 10 ha catchment, and weekly assessments of soil moisture content, as well as weekly checks of plant phenology (Leigh and Smythe, 1978; Leigh and Windsor, 1982), periodic censuses of mammal populations (Smythe, 1978; Smythe et al., 1982), and what became a continuous operation of light traps for insects (Smythe et al., 1982; Wolda, 1978a,b, 1982). Smythe selected the Lutz catchment as the focus for the physical monitoring effort and supervised the mapping of the catchment, installation of the Lutz weir and light traps for catching night-flying insects, erection of instrument towers, and the systematic collection of many other types of environmental data over the first five years of the program—some of which have been carried on for another 10 or more years and are projected to continue indefinitely.

This report attempts to consolidate and summarize the large volume of environmental data collected on Barro Colorado Island and its surroundings. For some variables, this is a 15–17 year record ending sometime after March 1988, the end of the 1987 hydrologic year. Other variables were studied for shorter periods, primarily to investigate spatial differences in the physical environment This report, then, includes the period covered earlier in four preliminary ESP reports on biotic studies and physical monitoring at Barro Colorado Island and Galeta Point Marine Lab (Rubinoff, 1973; Windsor, 1974, 1975, 1976). This report includes some data from that period that escaped inclusion in the early reports. Hourly records were not included because of their large volume. Preparation of this report necessitated the organization and printing of most hourly data. Copies of these data may be obtained by writing to the Smithsonian Tropical Research Institute, Apartado 2072, Balboa, Republic of Panamá. Finally, several tables and graphs are included presenting the long-term rainfall data gathered by the Panamá Canal Commission for a number of stations across the Isthmus of Panamá.

ACKNOWLEDGMENTS.—The terrestrial Environmental Sciences Program in Panamá was largely the inspiration of Stan Rand and Nicholas Smythe. Smythe first installed physical monitoring equipment and supervised the program for approximately seven years. Egbert Leigh has continually spoken in behalf of the monitoring program and has supervised the program at critical stages. Leigh’s scrutiny of the weir and its water level recorder is the main reason we now have a long and little-broken record of stream discharge. Full-time technicians on the project who deserve thanks include Miguel Estribé, Gary Chen, Gary Stump, Milton Clark, Douglas Rocha, and Raul Rios. Bonifacio de Leon has always helped in data collection when asked and for 15 years kept the weir basin in good order. In the beginning, Ben Morgan constructed the weir in the Lutz catchment. William Dietrich greatly expanded our understanding of the physical processes at work in Lutz. Thanks are due Michael Keller for sharing his unpublished data on soil characteristics of BCI. The Panamá Canal Commission, especially meteorologist Mike Hart and hydrologists Joe Brady and Joe Corelli, have been extremely helpful in filling our requests for environmental information on numerous occasions. Finally, I must thank my colleagues at STRI and members of my family for the time to get these numbers together.

Setting

Barro Colorado Island (lat. 9°10′N, long. 79°51′W) was formerly the highest hill (maximum elevation, 171 m) along the northward flow of the Chagres River toward the Caribbean (Figure 1). Waters of the Chagres River were first impounded by the partial completion of the Gatun dam in 1910. When the waters of Gatun Lake reached their present level several years later, the fifteen square kilometers of Barro Colorado Island formed the largest island separated by these waters from the mainland. Under the custodianship of the Smithsonian Tropical Research Institute, the flora and fauna of Barro Colorado Island continue to be subjects of intense international scientific activity.

The Lutz catchment is a 10.1 ha forested area on the northeastern slope of Barro Colorado Island, not unrepresentative of the many small catchments draining away from the center of the island. Located immediately to the east of the lab clearing and dormitory area, the Lutz catchment (Figure 2) is immediately accessible to the scientists on the island. Human traffic within the catchment, however, is discouraged to minimize damage to the drainage characteristics and vegetation of the area. The highest point in the catchment is approximately 120 m (above mean sea level), 50 m lower than the highest point on the island plateau. The lowest point in the catchment considered in this report is the site of the weir (Figure 3d) at an elevation of approximately 60 m (the greater catchment actually terminates at the shore of Gatun Lake, elevation aproximately 25 m). Straight-line distance from the weir to the upper boundary along Barbour Trail is approximately 300 m. Thus, the catchment is steep and small, a distinct advantage when attempting to quantify the entry and exit of moisture to the area. A 42 m walk-up tower 10 m upslope from the weir provides a vertical transect through one bit of the forest and gives a view over most of the catchment. The top of the tower is judged to emerge roughly 6–8 m above an irregular canopy (Figure 3b).

Meteorological readings have been recorded in two principal areas, the lab clearing and the weir (including tower). The physical aspects of the clearing, a grass-covered area between Chapman House and the lab building (Figure 3c), have changed little since the start of the program. However, because of the proximity to buildings, the location must be regarded as less than ideal and readings should not be automatically regarded as representative of forest understory, canopy, or light gap conditions. Readings from this site are most useful as an index against which data from other parts of Barro Colorado Island can be compared. Lutz tower, located near the weir in the lower portion of the catchment (Figure 2), provides data more representative of the forest on Barro Colorado Island. Data are recorded at the base and at stations along the 40 m climb to the top.

Climate

RAINFALL

Appendix Tables A1–A4

Records on Barro Colorado Island rainfall were initiated in April of 1925 and continued until present by the Panamá Canal Commission (PCC). There are no missing monthly or annual totals over this entire period. Hourly, daily, and weekly totals (Figure 4) are available from January 1929 onward; however, several gaps (1–30 Nov 1957, 18 Mar–17 Apr 1963, and 12 Sep–18 Oct 1963) limit the usefulness of these data series for some purposes. Rainfall was initially read from chart records with 0.25 mm (0.01 in) precision until 1 Sep 1971 and with 2.5 mm (0.1 in) precision thereafter. Although the PCC rain gauge on Barro Colorado Island was relocated from time to time, all sites are thought to have been located between the current position on the roof of the dormitory (50 m above mean sea level, 25 m above the surface of Gatun Lake) and the water tank, roughly 100 m away and 10 m higher in elevation. Additionally, the ESP has maintained since 1971 a tipping-bucket gauge and one or two rainfall collectors in the lab-clearing. Rain collectors are read once per day, and because daily readings are occasionally missed, the rainfall for that day is included with that of the next. Hence, data from collectors ought to be analyzed for intervals longer than one or two days, preferably weeks, months, and years. Compared to data from these collectors, the PCC gauge and ESP tipping gauges have tended to underestimate rainfall by from 2% to 12%, depending

FIGURE 1.—Location of Barro Colorado Island on Isthmus of Panamá, distribution of rain gauges across the isthmus (A = Porto Belo, B = Colon, C = Salamanca, D = Gatun, E = Monte Lirio, F = Madden Dam (Alajuela), G = Bohio, H = Frijoles, I = Barro Colorado Island, J-O = BCI perimeter gauges, P = Gamboa, Q = Las Raices, R = Carlo, S = Summit Gardens, T = Pedro Miguel, U = Balboa) and location of the Lutz catchment and plateau on Barro Colorado Island.

FIGURE 2.—Rough map of the Lutz catchment; numbers indicate soil sampling sites utilized since 1981. Prior to 1981 samples were taken along a 20 m transect extending from station 2 on the watershed trail toward Lutz Creek.

on year. Tipping bucket gauges are particularly useful in the study of temporal characteristics of individual storms. A DP101 Datapod (Omnidata International, Logan, Utah) installed in May 1983 digitally records the number of “tips” (0.254 mm of rainfall) per minute.

ANNUAL TOTALS.—Annual rainfall on Barro Colorado Island, according to the Panamá Canal record, averages 2612 mm (1925–1986, Sdv = 446, n = 62). Barro Colorado Island registered its lowest rainfall (1679 mm) in 1976 and its highest (4133 mm) in 1981 (Figure 5). The frequency distribution of annual rainfall totals is moderately platykurtic and hence bears only a modest fit to a normal distribution. The departure from normality may be produced in part by presence of a long-term trend in the record. That is, annual totals bear a significant negative correlation with year of record. Using the slope of the least squares regression line as an indicator of trend, rainfall on Barro Colorado Island appears to be decreasing at the average rate of 8 mm per year (1925–1986). The trend is significant despite the massive rainfall in 1981. Other stations in mid-isthmus with long-term records also display a decline in rainfall (Appendix Table A4). Monte Lirio, 12 km north of Barro Colorado Island, exhibits the strongest decline of all stations with at least 45 years of data. Long-term trends appear to be strongest at sites located in mid-isthmus (Figure 6b). Records from stations along either coast do not contain significant trends.

The Monte Lirio rainfall record may provide a good indication of how annual rainfall may have varied in the two decades before records were begun on Barro Colorado Island. Monte Lirio data, as well as data from Colon, Gatun, and Gamboa, indicate 1909–1910 was the wettest two-year period of the century. More rain was registered in each of these years at Monte Lirio than fell in the recent extremely wet year, 1981. The 7.22 m of rain in 1909 is still the maximum for Colon in over 132 years of record.

The lack of long records from other regions in central Panamá obscures the eastern and western limits of this apparent rainfall anomaly. A similar rainfall anomaly appears to exist in northwestern Costa Rica (Windsor and Rand, 1985; Fleming, 1986), and Flohn (1969) presents the decreasing records from several Brazilian locations. Although these two areas are distinct in many ways, they do share the characteristic of being immediately to the south or southwest of mountainous terrain. Because prevailing winds are from the north and northeast, both areas are in “rain shadows,” areas where advected moisture is blocked and convection consequently becomes relatively a much more important source of moisture.

What could cause a decrease in rainfall over mid-isthmus? Commonly it is suggested that downward trends in Panamá are simply statistical artifacts. This could be claimed with some justification if only one or perhaps two of the 17 longest records (greater than 44 years) displayed anomalous trends. However, Spearman correlation coefficients are negative at 14 sites and positive at only three. The probability of obtaining these results by chance is the same as flipping 14 heads in 17 tosses—less than one chance in two-hundred! Further, 10 of 14 correlation coefficients negative in sign are individually significant at the ρ = 0.05 level or less. None of the stations has a significant positive trend. Therefore, it seems safe to conclude that rainfall within the catchment of the Panamá Canal has changed (diminished) significantly in this century. Although this trend could disappear or reverse before the end of the century, its presence in the rainfall record through 1989 is hard to contest.

Convection, the daily heating and subsequent rise of moist air followed by turbulence and thunderstorms, is clearly an important generator of tropical rainfall (Dickinson, 1981; Henderson-Sellers, 1981). Has convection weakened over the middle of the isthmus while the supply of advected moisture (moisture derived from adjacent areas) and its orographic delivery (air forced upward over topographic relief leading to condensation and rainfall) along the coasts remained constant? Any process that increases the reflectivity of the land produces a cooler surface, one that tends to weaken convection. A more reflective land surface is a sure consequence of converting tropical forest to pasture or degraded land as has occurred in recent decades over nearly all of the Pacific side of Panamá (Pinker et al., 1980). Additionally, the percent of rainfall rushing off the disturbed land as runoff probably increases, while soil and vegetational moisture stores decrease, thereby further diminishing the return of vapor by evaporation and transpiration (Gentry and Lopez-Parodi, 1980). If deforestation does affect climate in the way suggested here, we might predict effects will be seen first in ’reas with s)milar geographical characteristics, viz. areas where convection is the primary source of rainfall.

Could changes in rainfall in the middle of the Isthmus of Panamá be the result of creating Gatun Lake (1910) and Madden Lake (1934), both of which are near the middle of the isthmus where trends are strongest? Lake effects would include cooling, weakened convection, and possibly lower rainfall. These effects, however, would be expected to appear in a step-wise rather than progressive fashion. The behavior of annual totals in Figure 5 is not totally inconsistent with this explanation. Rainfall has continued to decrease over the past two to three decades, however, well after the formation of the second reservoir.

Climatologists have been particularly concerned about the effects of increasing concentrations of atmospheric carbon dioxide and increasing temperatures due to the “greenhouse” effect (Broecker, 1975). Computer models of general circulation project increasing rainfall for regions within five degrees of the Equator and poleward of 30 to 35 degrees and possible decreases in rainfall between these two areas. A comprehensive review of long-term rainfall records from the entire northern hemisphere by Bradley et al. (1987) found that trends in precipitation are essentially consistent with the computer models. Thus, the Panamá records are consistent with computer models of changing global circulation as the result of

FIGURE 3.—Photographs taken in 1987 of Lutz creek weir (top left); forest canopy looking south from top of Lutz tower (bottom left); and lab clearing and instrument shelter (above).

increasing carbon dioxide.

In summary, at least two rainfall anomalies occur in lower Central America, one of which appears centered within the Chagres River watershed and the Panamá Canal. A number of competing explanations based on local, regional, and global environmental change can be advanced.

SEASONAL DIFFERENCES IN RAINFALL.—The seasonal distribution of rainfall in Central America and its underlying causes have been discussed by Ellis (1962), Vivo Escoto (1964), Portig (1965, 1976), and Coen (1983). The seasonal rhythm of rainfall is thought to be driven primarily by the effects of the changing zenithal position of the sun. The most intense warming of the Earth’s surface occurs over land at the latitude where the sun is near the zenith at midday. Thus, the sun maximally warms a narrow belt of land that oscillates between 23 degrees north and south latitude during the course of a year. A loose band of low pressure and cloudiness, wider over land than ocean, is usually situated near the theoretical belt of maximal warming. This band of cloudiness and low pressure is referred to as the Intertropical Convergence Zone (ITCZ).

The northward movement of the ITCZ and the dry season-breaking rains it generates usually lag behind the sun’s crossing of the zenith by several weeks. The sun is at its zenith over Panamá on or near 20 April (week 15) and 21 August (week 33). Rainfall on Barro Colorado Island normally returns to wet season levels by week 18 (Figure 7). Weekly rainfall has three noticeable peaks on or around weeks 20, 32, and 45. The final peak occurs in November-nearly 10 weeks after the sun’s zenithal position has moved south of Panamá. Thus, factors other than the zenithal position of the sun, perhaps the strength of circulation patterns in subtropical areas, seem also to affect seasonal distribution of rainfall. Rainfall during the latter part of the wet season and early dry season may be influenced, for example, by “northers”—southward invasions of polar air—that may produce heavy and unseasonal rainfall. These events may, in turn, dramatically influence the phenological patterns of tropical forest trees (Foster, 1982).

Biologists commonly wish to know when the dry and wet seasons begin and end. Because there is no commonly accepted criterion for what constitutes either season there is no simple answer. Indeed, organisms probably have a diverse way of gauging seasonal transitions and their estimates might vary considerably. As a first start, however, we can assume that many organisms, especially plants, will respond to changes in soil moisture. A moving average that sums one-half of rainfall

FIGURE 4.—Record of weekly rainfall recorded on Barro Colorado Island January 1929 to August 1988 by the Panamá Canal Commission. Breaks in x-axis signify weeks of missing data.

FIGURE 5.—Distribution of annual rainfall totals and cumulative probabilities of differing annual rainfall totals for (a) Barro Colorado Island and (b) Monte Lirio. Cumulative probabilities were plotted on “normal probability paper” so that, had annual rainfall been normally distributed, these points would have formed a straight line. Annual rainfall totals (dots) received at the same two sites were smoothed (line) using Cleveland’s LOWESS algorithm (F = 0.2; Wilkinson, 1986).

FIGURE 6.—Average annual rainfall for (a) twelve stations distributed south to north across the Isthmus of Panamá, and (b) slope of a linear regression line fitted to annual rainfall totals over time for each of the same stations.

FIGURE 7.—Median (line) and first and third quartiles (bars) of weekly rainfall on Barro Colorado Island for years 1929–1984.

at time “t”, one-fourth of rainfall at “t-1,” one-eighth of rainfall at “t-2,” ...etc., produces a curve that closely parallels observed values of soil moisture (percent by weight). Further, when this sum is less than 20 mm, soil moisture is usually at or less than 30%—a suitable break point for determining dryness or wetness. I have used this procedure to classify all weeks in the rainfall record since 1929 as either dry or wet (Table 1). Next, I defined the start of the dry season as any run of three “dry” weeks. Similarly, the wet season was defined as any run of three “wet” weeks. Although, clearly, there are reversals in conditions, especially wet periods appearing during dry seasons, the method is reasonably objective while agreeing with most people’s subjective feel for the change of tropical seasons on Barro Colorado Island. Figure 8 was constructed from the likely starting dates employing this procedure. The modal start of the dry season is the second week of the year although the start has been as early as week 48 and as late as week 11. The distribution of wet season starting dates is narrower. Most wet seasons begin between weeks 16 and 19 (mode = week 18). The median length of dry seasons is 17 weeks (76% of dry seasons lasted 13 to 19 weeks).

The march of daily probabilities of rainfall across the year follows much the same pattern as the quantity of rainfall by date (Figure 9). The probability of measurable rainfall on a daily basis is least (0.2) during the third week of March, but climbs rapidly through the last two weeks of April and first two weeks of May. Highest daily probabilities of rainfall come in late October and early November when the chance of rainfall exceeds 0.8.

The convectional nature of rainfall on Barro Colorado Island is clearly demonstrated by the highly non-uniform distribution (Figure 10). Probability of rainfall is always highest in the four hours following midday. Highest hourly probabilities of rainfall (p>0.2) occur between 1300 and 1600 hrs during weeks 20–22 and 36–45. Dry season probabilities of rain are low, of course, at all hours of the day. Starting in week 17, however, the probability of rain increases sharply.

Afternoon rainfall may be stronger at some sites on the isthmus than others. For example, the likelihood of afternoon rainfall (calculated for the entire year based on data from 1980–1984) on Barro Colorado Island is nearly twice that at the Galeta Point Marine Laboratory located just off the Caribbean coast 6 km northeast of Colon (Figure 11). Afternoon rainfall is four to five times as likely as evening rainfall at Barro Colorado Island, only 1.5 to 2 times as likely at Galeta. The forested surface of Barro Colorado Island is quickly heated by the sun causing surrounding air to rise, cool, condense, and release moisture as rain in the unstable conditions of a tropical thunderstorm in the middle afternoon. The relatively cool ocean surface at Galeta is far less effective at heating the surrounding air. As air moves inland it is forced to rise and the resulting “orographic rainfall” may account for the more even hourly distribution of rainfall observed on the Caribbean coast.

TABLE 1.—Distribution of dry and wet weeks within and among years on Barro Colorado Island, 1929–1988; moisture status of a week is determined from rainfall according to formula given in text. (Dash = dry, x = wet, nd = total number of dry weeks, sd = numbered week when dry season starts, sw = numbered week when wet season starts, ld = total number of weeks in dry season, lw = total number of weeks in wet season, blank spaces indicate missing data.)

FIGURE 8.—Distributions of onset dates for dry and wet seasons on Barro Colorado Island (1929–1988).

FIGURE 9.—Probability for any particular day of the year of obtaining a measurable amount of rainfall (>0), an amount greater than 12.7 mm (>12), greater than 25.4 mm (>25) and greater than 50.8 mm (>50). Probabilities were calculated from 58 years of daily rainfall records for Barro Colorado Island.

FIGURE 10.—Distribution of higher probabilities (>0.1 and >0.2) of obtaining rainfall within the day and across the year on Barro Colorado Island. Data were compiled from the record beginning in 1929 and ending with 1984.

On Barro Colorado Island a high proportion of the rain associated with a convective storm may fall in a very few minutes within storms lasting an hour or longer (Figure 12). Between May 1983 and December 1985 there were 64 storms with rainfall totalling at least 25 mm for which we have detailed records (Table 2). The median duration of these storms was 150 minutes (range: 17–1269 min). Ten mm or more of rain fell in a single minute in 23 of these storms. The maximum rainfall in a single minute exceeded 30% of the total catch in 21 storms, 50% in 8 storms. The highest intensity was 92 mm, which fell in a single minute of a 116 mm storm, 2 July 1983. Relatively low-intensity rainfall (<2.5 mm/min) characterizes the remaining time in most storms. Although intense rainfall can occur at any time within a storm, there was a significant tendency for peak intensities to occur more commonly in the first half of the storm (41 of 64). The effects of high-intensity rainfall may be observed in overland flow (“surface runoff”)

TABLE 2.—Quantity (percent) of rain falling during the minutes of highest and next to highest intensity; 64 storms each totalling at least 25 mm were sampled from 1983 through 1985.

| Number of storms
Percent of storm falling in one minute | One minute interval of storm receiving greatest catch of rain | One minute interval of storm receiving next-to-greatest catch of rain
   0–10 | 32 | 60
11–20 | 11 | 3
21–30 | 0 | 0
31–40 | 6 | 1
41–50 | 7 | 0
51–60 | 4 | 0
61–70 | 1 | 0
71–80 | 2 | 0
81–90 | 0 | 0
91–100 | 1 | 0
Total | 64 | 64

FIGURE 11.—Comparison of coastal (Galeta Point Marine Laboratory) and continental (Barro Colorado Island) rainfall characteristics in Panamá: (a) probability of rainfall by hour and (b) quantity of rainfall (in mm) by hour for 1980–1984.

when rates of infiltration are exceeded.

Rain storms on Barro Colorado Island tend to be of short duration. Defining a rain storm as an uninterrupted sequence of hours with measurable rain, there were 3795 rain events between 1970 and 1984 or roughly 250 per year (Table 3). Seventy percent of these events lasted one hour or less. Forty-six percent of all rain events in this period occurred in the latter half of the wet season (Sep–Dec), 42% in the first half of the wet season (May–Aug) and the remaining 12% in the dry season. In addition to being far fewer in number, dry season rains are often shorter as 78% lasted one hour or less.

PREDICTABILITY.—Are there predictable aspects of rainfall on Barro Colorado Island? Hourly, daily, and weekly rainfall totals are all serially correlated (Table 4). Thus, the best predictor of rainfall on a particular day is knowledge of rainfall on the previous few days. Similarly, rainfall during a particular

FIGURE 12.—Intensity of rainfall by minute for 40 days (each horizontal axis represents one day). All days with total accumulation of at least 25 mm, occurring between May 1983 and December 1985, were included in graph.

TABLE 3.—Frequencies of rain storms of varying duration and intensity for dry season, early wet season, and late wet season compiled from hourly tipping bucket data (PCC) for years 1970–1984.

week is most likely to resemble that during the preceding week, and so on. Rand and Rand (1979) investigated the predictability of rainfall on Barro Colorado Island using modeling techniques. An autoregressive model was first constructed where rainfall in any week was considered a constant plus another constant times the rainfall during the previous week (lag = 1) plus another constant times the rainfall during the previous week (lag = 2), etc. Only the final three lags were useful in predicting weekly rainfall, giving an r² of 0.41. This suggested that knowledge of rainfall from three consecutive weeks permits one to reduce uncertainty about the next week’s rainfall by roughly 40%. The degree to which weekly rainfall can be predicted from preceding rainfall is interesting because this information should be available to most organisms.

TEMPORAL AND SPATIAL VARIABILITY.—One may ask if rainfall is more variable in any particular time of the year. The question can be further refined by asking whether there are times of greater absolute and relative variability. The standard deviation of cube root transformed weekly rainfall provides an estimate of the absolute variability. The coefficient of variation (standard deviation divided by the mean) computed for the same transformed values provides a relative estimate of variability. Absolute variability graphed through the year in Figure 13 is level through both wet and dry seasons with two small peaks occurring during the seasonal transitions (weeks 14–18 and 45–52). Relative rainfall variability, however, climbs throughout the dry season with highest values occurring in weeks 12–14.

The long term meteorological records collected at many sites across the isthmus by the Panamá Canal Company reveal that average annual rainfall increases rapidly and consistently as one moves north across the Isthmus of Panamá. Rainfall on the Pacific coast averages roughly 1700 mm per year and increases to over 4000 mm per year on the Caribbean coast near Portobelo-an increase of roughly 35 mm per year per km (Figure 5a). The long-term average from Barro Colorado Island fits close to the regression line.

From these observations, one can calculate that rainfall ought to be roughly 100 mm higher on the north side of Barro Colorado Island. The catch of rainfall on different sides of Barro Colorado Island has been examined (Rand and Rand, 1982). Rainfall in seven collectors placed around Barro Colorado Island (Figure 1) was read at two week intervals over a four-year period. The results (Table 5) suggest that annual rainfall does not vary greatly across Barro Colorado

TABLE 4.—Partial autocorrelation coefficients calculated for Barro Colorado Island rainfall record divided into intervals (lags) of differing length (“n” = number of elements in each time series).

Lag | Day | Week | Month | Year
  1 |    0.561 |    0.589 |    0.421 | −0.160
  2 |    0.270 |    0.247 | −0.086 |    0.019
  3 |    0.202 |    0.159 | −0.144 | −0.163
  4 |    0.177 |    0.069 | −0.150 |    0.017
  5 |    0.164 | −0.007 | −0.127 | −0.172
  6 |    0.104 | −0.004 | −0.089 |    0.044
  7 |    0.116 | −0.027 | −0.252 |    0.009
  8 |    0.060 | −0.075 | −0.206 | −0.141
  9 |    0.073 | −0.050 | −0.060 | −0.074
10 |    0.072 | −0.079 |    0.044 | −0.070
11 |    0.064 | −0.015 |    0.263 |    0.229
12 |    0.065 | −0.047 |    0.346 | −0.159
13 |    0.031 | −0.052 |    0.020 | −0.268
14 |    0.046 | −0.045 | −0.035 | −0.167
15 |    0.036 | −0.040 | −0.099 | −0.048
  n |    4096 |    2808 |      720 |       s61

FIGURE 13.—Average (line), standard deviation (dots) and coefficient of variation (dashes) of cube root transformed values of weekly rainfall on Barro Colorado Island.

Island. When consecutive readings from all gauges are compared by simple correlation, correlation coefficients for each pair of gauges were found to range from 0.83 to 0.96 in 1972, 0.55 to 0.98 in 1974 and 0.70 to 0.95 in 1975. However, readings from particular weeks often varied considerably between sites presumably reflecting actual differences in the intensities of particular storms at different points on the island. The greatest difference that can be seen in seasonal totals between these gauges occurred in the wet season of 1975 when a total of 2409 mm was logged for site J, while only 1986 mm was logged at station L on the opposite side of the island. Of the eight estimates of rainfall (4 dry seasons and 4 wet seasons), the northern gauges (I,N,O,J) averaged slightly higher in seven instances (2-sided binomial probability,

TABLE 5.—Rainfall accumulations (mm) in six collectors located around perimeter of Barro Colorado Island (J–O) and collector located in laboratory clearing (I). (Collector identification corresponds with those used in Figure 1.)

p = 0.07). Annual totals for the four gauges on the north side average approximately 60 mm more than the south side gauges (M,L,K). Thus, although these data do not show a significant difference between the north and south sides of the island, they vary in a pattern consistant with the north to south gradient known to exist across the isthmus. Additionally, although the north side of the island may receive slightly more rainfall, it is also the side of the island more exposed to dry season winds and thus potentially under greater stress during the dry season.

THE EL NIñO CONNECTION.—The El Niño is a warm south-flowing surface current that appears off the coast of Ecuador and Peru in December of most years. In some years, the current is far stronger than in others and is associated with a lack of ocean upwelling, a decline in the fisheries industries, and heavy rains in normally dry areas of the Peruvian and Ecuadorian coast. Only these stronger events are now referred to as El Niño. It is now established that El Niño events are not just a regional phenomenon but are part of an atmospheric circulation pattern extending across the entire Pacific basin. Atmospheric pressure conditions in the western and eastern Pacific tend to cycle in opposite directions over time (“Southern Oscillation”) (Rasmussen and Hall, 1983).

The El Niño of 1982–1983, perhaps the strongest such event of this century, brought devastating drought to the rain forests of eastern Borneo (Leighton and Wirawan, 1986) and Malaysia (Ashton et al., 1988), overheating and death to the Pacific coast of Panamá (Glynn, 1984), devastating storms and shortage of nutrients to the kelp beds of southern California (Zimmerman and Robertson, 1985; Dayton and Tegner, 1984), and uncommonly high tides to the shores of the Pacific northwest (Paine, 1986). This devastating event sparked much interest in the El Niño Southern Oscillation (Rasmussen, 1985; Rasmussen and Wallace, 1983), and previous occurrences of El Niño were found to coincide significantly with previous periods of drought in the rain forests of eastern Borneo and eastern Peninsular Malaysia (but not on the western sides) (Ashton et al., 1988).

The appearance of El Niño in the eastern Pacific near Panamá coincided with the early termination of that year’s wet season on Barro Colorado Island, and a dry season of uncommon ferocity. S.P. Hubbell (pers. comm.) found mortality rates for old-forest tree species calculated after the 1982–1983 El Niño event averaged 3% per year, considerably higher than the 1% per year Putz and Milton (1982) reported for the same forest during an earlier period. Flowering and fruit production were greatly reduced or eliminated in most tree species whose phenologies were being observed by the Environmental Sciences Program on Barro Colorado Island.

The wettest year of the century on the Isthmus of Panamá appears to have been 1981. Four and one-half meters of rain fell during the wet season on Barro Colorado Island. This period of extreme raininess came to a close only six months before the onset of the 1982–1983 El Niño. Were these independent climatic events or do years of heavier rainfall often tend to precede El Niño years in Panamá? A history of El Niño events in the Pacific along with their relative intensities has been published by Quinn et al. (1978). These values (a five point scale, 0–4) for each year since 1842 were cross correlated with annual rainfall totals (detrended) from the six Panamanian stations with longest records (Colon, Monte Lirio, BCI, Gamboa, Pedro Miguel, and Balboa). This type of analysis allows one to examine the correlation, if any, between rainfall either one, two, or more years (lag) before or after El Niño events. The results in Figure 14 indicate that none of the many correlations generated had coefficients exceeding r = 0.35. Nevertheless, each station generated at least one or two significant correlations within lags of −7 to +7 years. In particular, significant correlations occur at lags of −1 (3 occurrences), 0 (5 occurrences) and +4 (2 occurrences). Additionally, the pattern of correlations among sites is strikingly similar. Two particular correlations are noteworthy. Firstly, the strong positive correlation at a lag of −1 suggests that Panamanian rainfall in one year tends to be positively correlated with the El Niño index the following year. Thus, especially heavy rains tend to fall in Panamá the year before a strong El Niño develops. This relationship is different than that observed in Peru and Ecuador, where heavy rainfall tends to accompany strong El Niño currents. Secondly, the strong negative correlation between rainfall and the El Niño index at a lag of zero suggests that rainfall during El Niño years in Panamá is usually lower than normal. Thus, annual rainfall tends to shift from higher than normal to lower than normal before and during the expression of an El Niño in Panamá. Overall, cross correlation indicates a linkage may exist between Panamanian rainfall and the El Niño-Southern Oscillation and suggests some degree of temporal predictability may exist in rainfall among years.

Hurricanes and tropical depressions appear to have had little direct influence on rainfall in Panamá as the tracks of major storms are all well to the north and east during this century. The recent passage of Hurricane Joan (October 1988) along a path at one point only a few hundred miles north of central Panamá, suggests that major cyclonic events are not to be discounted entirely.

TEMPERATURE

Appendix Tables B1–B11

Shaded air temperature has been measured since 1971 on Barro Colorado Island in the lab clearing and 100 m away in the forest near the Lutz weir at a height of approximately 1.5 m by Taylor max-min thermometers (Taylor Instruments, Arden, North Carolina). Thermometers were checked against other thermometers of similar manufacture at irregular intervals to assure calibration. Thermometer assemblies were initially suspended from metal posts with a sheet of copper metal providing shade; in 1980 they were moved to wooden, louvered

FIGURE 14.—Correlation coefficients generated by the cross correlation of El Niño index and annual rainfall records offset (“lagged”) by −7 to +7 years. Six longest annual rainfall records from the isthmus were cross correlated against El Niño index, producing the six curves.

instrument shelters. Smaller wooden instrument shelters were used between 1981 and 1984 to house thermometers at the base (1 m), in the middle (20 m), and at the top (40 m) of the Lutz tower. Later, all but the max-min thermometer in the clearing were replaced by Omnidata DP212 datapod recorders and sensors. These recorders sampled temperature six times per hour, computed an average hourly temperature and stored this average in an EPROM memory chip. Data were removed and cleared at intervals not exceeding 42 days.

The general pattern of average monthly temperature change through the year is summarized in Figure 15. Clearing maximum temperatures reach their lowest average (30.6°C) in November and climb steadily over the dry season to their highest average (32.3°C) during April (mean of monthly means from 17 years). Clearing minimum temperatures reach their lowest average (22.8°C) in October and climb steadily over the dry season to their highest average (23.6°C) in May. Thus, the range of mean maximum temperatures through the year is roughly twice that of mean minimum temperatures. While maximum temperatures peak in the late dry season, minimum temperatures peak in the first three months of the wet season.

Temperature records from Barro Colorado Island since 1971 suggest that temperatures are trending upwards (Figure 16). Lab clearing minimum temperatures display the clearest increase while maximum temperatures are approximately level. Forest records also display an upward trend; however, the series is shorter than from the clearing (due to change to different sensors). The warming during the three El Niño events is evident and the effect of the severest of these in 1982–1983 on the series accentuates the upward trend. At the least, these data suggest that temperature records from other stations in Panamá should be examined for longer term trends.

Cleveland’s LOWESS algorithm was used to smooth the long series of individual maximum and minimum temperture readings (detrended) from the clearing and forest (Figure 17). This procedure produced a moving average even though data were unequally spaced along the abscissa (Wilkinson, 1986). Unequal spacing resulted from days when no readings were taken—especially on weekends. The F parameter, the approximate fraction of all points used in computation of each value, was set at 0.005 so that finer detail in the time series was preserved. The two periods of highest maximum lab clearing temperatures in the series occurred in the dry seasons of 1973 and 1983. The two periods of highest maximum forest temperatures occurred in the dry seasons of 1977 and 1983. Minimum temperatures from clearing and forest were similar over much of the series. Both curves indicate the dry seasons of 1977 and 1983 produced the warmest minimum temperatures in the series. Thus the three warmer-than-usual years on BCI in the past 16 years coincided with and were almost certainly produced by the three El Niño events: 1972–1973, 1976–1977, 1982–1983. Maximum temperatures run one to four degrees C warmer in the clearing than in the forest understory over much of the record. Clearing and forest temperature maxima are closest during the dry season of some years, perhaps during periods of canopy deciduousness. Minimum temperatures, on the other hand, do not appear to vary consistently between forest understory and lab clearing over the series, with the exception of 1980, when understory minimum temperatures averaged roughly one-half degree higher than lab clearing minimum temperatures. The deep drop in forest minimum temperatures during the last three months of 1978 and first few months of 1979 are notable. A similar but less severe cool period occurred during the 1975–1976 dry season.

Maximum and minimum temperatures were recorded at heights of 1, 20, and 40 m above the forest floor for slightly more than two years on the Lutz tower. Maximum temperatures at 40 m are roughly the same as those measured in the clearing at 1.5 m (Figure 18). The lowest maximum temperatures come from 1.5 m and intermediate temperatures from the intermediate height. Maximum temperatures at the above canopy station average roughly 2°C more than readings near the forest floor. Minimum temperatures occur in an opposite pattern. Minimum temperatures are lowest at 40 m, intermediate at 20 m and highest just above the forest floor. Thus, sometime between the times minimum and maximum temperatures normally occur (0500 and 1300 hrs) the temperature structure of the forest air reverses. A similar temperature inversion in a Thailand forest disappeared at sunrise (Pinker, 1980).

RELATIVE HUMIDITY

Appendix Tables C1–C9

Atmospheric humidity has been measured using the traditional methods of wet- and dry-bulb psychrometry. A hand-held psychrometer has been used to estimate dry- and wet-bulb temperatures at midday in the lab-clearing and at 1 m in the forest from mid-1971 until the end of 1981 and from early 1986 onward. Wet- and dry-bulb temperatures were recorded automatically from 1983 through 1986. At each station (clearing, forest 1 m, and forest 40 m) Omnidata datapod (DP 212), 2-channel recorders were used to record average hourly temperature of continuously aspirated dry and wet temperature sensors. “Muffin fans” of the type used to circulate air in microcomputers were used to draw air through a 2 cm PVC tube and past the dry and wet temperature sensors. Distilled water was delivered to a cotton wick covering the wet sensor using a modified hospital “intravenous feeder.” Relative humidity was later determined by computer using “look-up” values from a standard psychrometric table.

Strong seasonal cycles in relative humidity are evident in the monthly averages recorded in both clearing and forest (Figure 19). Wet season relative humidity readings averaged between 90% and 95% in the forest understory and 80%–85% in the lab clearing. Dry season relative humidity averaged 70%–80% in the forest understory and 62%–72% in the lab clearing. Thus, throughout the year, relative humidity in the forest understory is roughly 10% higher than in the lab clearing (Figure 20).

Vertical differences in relative humidity within the forest are evident in a study of midday meteorological conditions on the Lutz tower at heights of 1, 13, 26, and 40 m between August 1977 and July 1979 (Figure 21). Average, monthly, midday, relative humidity in this period was roughly 10% to 15% higher near the forest floor than above the canopy. Readings at the two intermediate heights were barely distinquishable from one another but were clearly intermediate to the stations above and below them. The relationship of these measurements to ones recorded in the lab clearing are shown in Figure 22. It appears that during months when relative humidity is high on Barro Colorado Island, relative humidity at 40 m on the tower is 5% to 10% lower than in the clearing. During periods when relative humdity is lower, clearing and above-canopy readings are very nearly the same. Subcanopy stations appear to give consistently higher readings than registered in the clearing, readings that increase as stations are nearer the forest floor.

AIR MOVEMENT

Appendix Tables D1–D5

Hourly wind speed and wind direction have been recorded on Barro Colorado Island using a number of different instruments. The majority of measurements used in creating a longterm record come from two mechanical, chart-recording anemometers (MRI Mechanical Weather Station model 1072, Meteorological Research Inc., Altadena, California). The first of these was installed on the roof of Chapman House in the lab clearing in 1971 where it functioned satisfactorily until June 1978. A new instrument of the same design and manufacture was installed in January 1979 atop Lutz tower. An Omnidata DP214 anemometer station was installed in 1983 when problems developed with the second MRI unit. The Omnidata instrument was less reliable than the former instrument and was discontinued in August 1987. The repaired second MRI unit was reinstalled in January 1988 and continues in service. Attempts are underway to obtain a fully automated digital

FIGURE 15.—Average (±1 standard deviation) monthly values of (a) maximum temperature in the clearing, (b) minimum temperature in the clearing, (c) midday relative humidity in the clearing and forest (dashed line), (d) daily solar radiation on Barro Colorado Island compared to Galeta Point and Chiva-chiva, (e) daily windspeed at canopy level, and (f) rainfall in the clearing.

FIGURE 16.—Smoothed daily maximum and minimum temperatures in the lab clearing (solid line) and forest understory near the Lutz weir (dashed line), 1971–1986. Each time series was detrended before plotting.

record of windspeed and direction using other sensors and data loggers.

A totalizing anemometer was placed in the lab clearing next to the evaporation pan (height of 0.2 m) in April 1983 and daily windrun was recorded from it until December 1987. Spot readings of wind velocity at midday were taken at four elevations on the Lutz tower between July 1978 and August 1979 using a Thermonetics Series 102 hot wire anemometer (Thermonetics Corp., San Diego, California) (Figure 21a). The average daily wind velocities on Barro Colorado Island and at Galeta Point Marine Laboratory (also an MRI anemometer, Cubit et al., 1988) on the Caribbean coast are compared in Figure 23. Assuming the instruments had roughly similar frictional characteristics, data indicate peak dry season winds on Barro Colorado Island are roughly a third as strong as those along the Caribbean coast. Air movement during the wet season on Barro Colorado Island is roughly ⅓ to ½ of that registered on the Caribbean coast.

Spot midday measurements of windspeed using a hotwire anemometer at four levels on the Lutz tower between August

FIGURE 17.—Comparison of daily minimum and maximum temperatures recorded (a) near forest floor, mid-level, and above canopy-level, and (b) between lab clearing and forest at 1 m.

FIGURE 18.—Departures from long-term averages of annual average minimum and maximum temperatures recorded in lab clearing and forest at height of 1.5 m.

FIGURE 19.—Average of monthly, midday, relative humidity in clearing and forest understory from late 1971 through mid-1981.

FIGURE 20.—Midday relative humidity above forest floor compared to clearing values taken at same time.

1977 and July 1978 reveal the large differences in windspeed within and above the forest (Figure 12c). Subcanopy wind-speeds increase over the entire dry season while above-canopy windspeed reaches a maximum in January. Subcanopy windspeeds would have to be monitored during other dry seasons, and at other sites on Barro Colorado Island, to know how general this pattern is.

Daily wind vectors, indicators of both direction and velocity, have been plotted in Figure 24 for the years 1981 through 1984. These data demonstrate the drop in wind velocity (length of each vector) that occurs as the wet season commences at about day 120 of each year. They also show the early start of the trade winds in November 1982 accompanying the onset of the El Niño event that persisted until mid-1983.

SOLAR RADIATION

Appendix Tables E1–E3

Global shortwave radiation has been measured on Barro Colorado Island atop a 27 m tower (Feb 1973–Jul 1975) and the 40 m Lutz tower (May 1977–Jan 1982) by the Radiation Biology Lab using Eppley pyrranometers. Because of lightning strikes and equipment failures there are many gaps in this record. Since March 1983 incoming solar radiation has been measured with a Li-Cor LI200SB pyrranometer (Li-Cor, Inc., Lincoln, Nebraska) (400–1100 nm) attached to an Omnidata DP211 datapod at the top of the Lutz tower. The datapod records hourly the average output (mv) sensed at 10-minute intervals. Sensor output was factory calibrated to give an output of 5 mv/La (= 1.0 cal/cm²/min or 11.622 Wh/m²). Sensors were replaced with new or recalibrated sensors yearly. A Li-Cor 190SB, quantum sensor (400–700 nm wave band), was installed July 1985 to provide a measure of photosynthetically active radiation (“PAR”) alongside the pyrranometer atop the Lutz tower. Alan Smith has installed additional pyrranometer and PAR sensors under “shadow bands” to estimate diffuse and direct components of global radiation. These measurements are not included in this report.

A “sunburn meter” has been maintained on the Lutz tower as a service to the Center for Photobiology, Temple University, since 1979. This meter produces an electric current proportional to intensity in short ultraviolet (UV) wavelengths, integrates the current until a preset dose is obtained, then stores the count and resets the integrator. A count of 400 represents the amount of UV producing slight reddening of untanned white skin. A count of 1000 in 30 min equals the maximum anticipated UV expected on a clear day with sun directly overhead. Shortwave radiation and UV values are often tightly correlated. Regression of shortwave values on UV values can be useful in reconstructing daily solar radiation or checking to see if values are within range.

Global radiation (400–1100 nm) on Barro Colorado Island climbs steeply during the dry season, reaching a monthly maximum of 5.7 KWh/m² in March (Table 6). Wet season

FIGURE 21.—Average monthly values of (a) midday instantaneous windspeed, (b) shaded air temperature, and (c) relative humidity measured on the Lutz tower at 1, 13, 26, and 40 m above forest floor between September 1977 and July 1979.

FIGURE 22.—Relationships obtained from regressing relative humidity values in forest at heights of 1, 13, 26, and 40 m against values obtained in lab clearing (September 1977 through July 1979).

FIGURE 23.—Comparison of average daily windrun at coastal (Galeta Point Laboratory) and continental (Barro Colorado Island) stations from 1971 through 1986.

FIGURE 24.—Daily wind mean-vectors for four years on Barro Colorado Island. North winds indicated by vectors extending straight up, south winds straight down, etc.. Length of vector indicates magnitude of daily windrun.

(June through November) radiation varies little from month to month (3.7–4.2 KWh/m²), averaging 3.9 KWh/m²—74% of average dry season radiation. Lowest average daily solar radiation values occur in June, October, and November, roughly 3.7 KWh/m². Average monthly values of global radiation measured on Barro Colorado Island (Figure 15d) agree closely with values recorded at the Galeta Point Marine Station (Cubit et al., 1988), 22 km north on the Caribbean coast and Chiva-Chiva Antenna farm, 35 km southeast near the Pacific coast (Becker, 1987).

Moisture Availability

GEOLOGY AND SOIL CHARACTERISTICS

The geology of Barro Colorado Island has been described by Woodring (1958) and that of the Lutz catchment in

TABLE 6.—Average daily solar radiation (Wh/m²) received on Barro Colorado Island by month and year.

particular by Dietrich et al. (1982). Three different types of bedrock, one igneous and two sedimentary, underlie the Lutz catchment. A stream with a wide, flat-bedrock bottom bisects the lower portion of the catchment and gives no indication of a significant outlet for water from the catchment other than through the weir. Lutz creek flows in most months of the year, but, in the middle and late dry season (March and April) of normal and dry years, flow ceases.

The Lutz catchment is entirely forested and trees greater than 60 cm dbh in 1 ha quadrats were identified and mapped in the mid-1970s (Thorington et al., 1982). Although slope in all sections of the catchment has not been sampled, slope characteristics can be drawn from the work of Putz and Milton (1982) who did estimate slopes in the quadrats mapped by Thorington. Roughly 75% of each of the three plots in the upper catchment had slopes <15 degrees and 25% were in the 15–30 degree class. Closer to the weir, a fourth plot had 46% of its area falling in the <15 degree class, 45% in the 15–30 degree class, and 9% in the 30–45 degree class. Soils on Barro Colorado Island have been classified as Frijoles Clay by Bennett (1929) and Knight (1975). In an unpublished report on the soils of Barro Colorado Island, Michael Keller has identified five different soil series: two alfisols and three oxisols. Of the two orders, oxisols are the more highly weathered. Oxisols occur on the plateau and the eastern one-quarter of the island. The Lutz catchment falls entirely within the alfisols. Soil samples were removed from ten sites within the catchment (the same sites from which soil moisture is monitored) and ten sites on the plateau in 1983 and physical characteristics were determined in laboratories of the University of Panamá. These analyses (Table 7) indicate that Lutz soils have a clay content that is considerably higher than soils on the plateau (40% vs 26%). Sand content, on the other hand, is high on the plateau (47%) and lower in Lutz (35%). Soils of the Lutz catchment and the island plateau appear to have approximately the same silt content (25%–27%). These determinations are in agreement with Keller’s soil classifications. Additionally, the high clay content of the soils on the relatively steep slopes of Lutz should translate into very low infiltration rates (0–4 mm/hr; Dunne and Leopold, 1978:171) when the soil is saturated. Periods of high rates of surface runoff or “overland flow” have been noted during the late wet season; however, they have yet to receive systematic study.

SOIL MOISTURE

Appendix Tables F1–F5

Moisture residing in the soils of the Lutz catchment has been determined gravimetrically from 11 Dec 1974 until the end of 1989 at one or two week intervals (Figure 25). Several changes in methodology instituted during this period have affected the distributions of values obtained. From the start of the study until October 1979 two soil moisture plugs were removed from the upper 5 cm of soil at each of five stations along a 20 m transect in the lower catchment—approximately 50 m from the Lutz tower along the catchment trail (see station “2” in Figure 2). These samples represented only a small range of topography within the Lutz catchment and an earlier study (Dietrich et al., 1982) used them only as an index of moisture

TABLE 7.—Comparison of chemical and physical characteristics of soils taken from the Lutz catchment and the plateau on Barro Colorado Island. Analyses were performed by Departamento de Suelos, Facultad de Agronomie, Universidad de Panamá.

conditions. Sampling depth was increased to the upper 10 cm of soil in October 1979. Additionally, the location of sampling was changed during the last week of January 1981 to ten stations spread out across the catchment (Figure 2), thereby sampling a wider range of moisture conditions. Two samples were withdrawn at each site, one from the top 10 cm, a second from 30 to 40 cm. For approximately two and one-half years, beginning in June 1981, 0–10 cm and 30–40 cm samples were additionally removed at ten stations along Wheeler and Zetek trails on the island plateau. Sampling stations were spaced at 100 m intervals starting approximately 50 m beyond the old tower clearing near the center of the island. During the first six months, sampling in Lutz and on the plateau rarely was conducted on the same day. In succeeding months samples from Lutz and the plateau were removed on the same day, thereby facilitating comparisons between the two sites. An “Oakfield punch” was used to remove the approximately 2.5 cm diameter soil cores. Late in the dry season an augur was used to remove hard soil. Samples were returned to the lab in tinned canisters, weighed, and placed in a 105°C oven to dry to constant weight (2–3 days). Dry and tare weights were then determined and gravimetric soil moisture (GSM) was calculated as the change in weight due to drying expressed as a percent of the original wet weight:

Three collections were performed concurrently at both “index sites” at 0–5 cm and the array of sites at 0–10 cm depths in mid- and late April 1983 (before and after the first heavy rains of that wet season). The equation

GSM array sites = 11.6 + (0.61 × GSM index sites).

obtained through regression of these samples (df = 2, r = 0.99), suggests that soil moisture values from 1975 through 1979 can be adjusted to better reflect catchment-wide soil moisture conditions. When this adjustment was performed, wet season soil moisture was decreased by an average of two to three percent; dry season values were little affected. Soil moisture determinations between October 1979 and 1981 were not adjusted because they were taken from deeper samples and tended to be drier than in the earlier protocol.

For the purposes of calculating the actual volume of water present in the soils of the catchment, adjusted GSM was

FIGURE 25.—Values of soil moisture (percent by weight) monitored (a) in the Lutz catchment between 1975 and August 1987, and (b) on the plateau between 1981 and early 1984 in relation to weekly rainfall (vertical bars). Where two lines occur on a graph, lower indicates soil moisture at 30–40 cm, upper indicates soil moisture at 0–10 cm.

transformed to soil moisture by volume (VSM) following Dietrich et al. (1982:31):

Bulk density appears to vary with depth according to the unpublished report on the soils of Barro Colorado Island by Michael Keller. Samples taken from two alfisols on opposite sides of Barro Colorado Island (Armour 24 and Barbour-Lathrop Trails) increased from 0.6 to 0.7 gm/cm³ in the first 20 cm to values between 0.9 and 1.2 gm/cm³ at greater depth. Accordingly, a bulk density of 0.7 gm/cm³ is assumed in the conversion of GSM to VSM for 0–10 cm samples, 1.1 gm/cm³ for samples 30 cm and deeper.

“Available Moisture” is defined as the quantity of water in the soil that is potentially extractable by plants. Available moisture is calculated as the soil depth times the proportion of the depth occupied by water (i.e., VSM). An average soil depth of 1.1 m is assumed in this study as in Dietrich et al. (1982); however, because both surface (0–10 cm) and deep soil (30–40 cm) moisture measurements were taken, the total available moisture can be adjusted to reflect actual differences

TABLE 8.—Values of parameters used in calculations of total available moisture in Lutz catchment soils, Jan 1975 through May 1988 (AWC = available water capacity).

Soil characteristic | Zone |
1 | 2* | 3 | total
Sample depth (mm) | 0–100 | 100–300 | 300–1200 | 1200
Sample thickness (mm) | 100 | 200 | 900 |
Bulk density (gm/cm) | 0.7 | 1 | 1.1 |
Min moist by wt (%) | 28 | 28 | 28 |
Max moist by wt (%) | 45 | 41 | 38 |
Min moist by vol (%) | 27.2 | 38.9 | 42.8 |
Max moist by vol (%) | 57.3 | 69.5 | 67.4 |
AWC (mm) | 30 | 61 | 222 | 313

*Zone 2 soil moisture was estimated by averaging shallow and deep samples.

with depth by computing and then summing the moisture content for 0–10, 10–30, and 40–110 cm sections. Soil moisture and bulk density for 10–30 cm is assumed to be the arithmetic average of the soil directly above and below (Table 8). Soil from 40–110 cm was assumed to have the same moisture and bulk density characteristics as soil at 30–40 cm. These assumptions seemed reasonable after viewing the depth

TABLE 9.—Average soil moisture conditions at each of ten sites in the Lutz Catchment, Jun 1981–Aug 1987, and the plateau, Jun 1981–Mar 1984.

FIGURE 26.—Average, standard error, and range of soil moisture values by sample depth for two collections during wet season in the Lutz catchment.

vs soil moisture curves from cores removed in June and July 1987 (Figure 26). Soil moisture decreased monotonically in these two samples to a depth of 30–40 cm after which it either remained constant or increased slightly. A greater number of these determinations, especially some taken during the dry season, and more soil depth measurements will considerably improve the quality of future moisture budgets for the Lutz catchment.

Samples taken from the same depth but at different sample stations within the catchment give quite different soil moisture determinations (Figure 27). Stations giving the lowest overall mean soil moisture (stations 9 and 10, Table 9) are those located along the upper boundary of the catchment with little if any drainage into the sampling area (Figure 2). Stations giving the highest overall mean soil moisture (stations 3 and 8, Table 9) are those located in the lower reaches of the catchment. Soil moisture determinations differ considerably between the Lutz catchment and the island plateau. Average surface soil moisture determinations taken on the same date in the two areas when plotted against one another show a clear linear relationship (Figure 28a). When surface soil moisture is higher in the Lutz catchment soil moisture is proportionately higher on the plateau. The slope of the relationship, however, is less than one. Maximum surface soil moisture observed in Lutz was roughly 45%, on the plateau roughly 34%. Minimum values were 23% on the plateau and 27% in the catchment. Thus, at both extremes, plateau samples generally had lower soil moisture values than catchment samples.

Soil samples taken from a depth of 30–40 cm can differ markedly in soil moisture from samples taken at a depth of 0–10 cm. Unlike 0–10 cm samples, 30–40 cm samples reach maximum values (late wet season samples) at roughly 40%

FIGURE 27.—Two comparisons of monthly average soil moisture values obtained high (stations 9 and 10) and low (stations 8 and 3) in the Lutz catchment, 1981–1987.

FIGURE 28.—Scatter-plot comparisons of simultaneous soil moisture determinations for (a) different depths and (b) areas. Each data point is an average of ten sample sites within each area for a particular date between June 1981 and February 1984.

FIGURE 29.—Quantity of (a) rainfall and runoff, (b) moisture equivalents held in soil, and (c) actual evapotranspiration (AET) calculated as the difference between rainfall, runoff, and changes in storage (thicker of two lines represents three-term, equally weighted, moving average of AET) in the Lutz catchment by month between 1975 and 1987.

on the plateau but only 37% in Lutz (Figure 28b). Minimum values (dry season samples) reached by deep samples are roughly the same as surface samples. Intermediate values form a curvilinear rather than linear array. When the rainy season begins, the moisture content of soil at 30–40 cm rises more rapidly on the plateau than in the Lutz catchment. Movement of moisture is probably impeded or trapped by deep soils and an underlying andesitic cap as evidenced by the “swamp” on one part of the plateau.

RUNOFF

Appendix Tables G1–G2

“Runoff” or “stream discharge” from the Lutz catchment is determined from water level recorded behind a sharp-crested, 120 degree, V-notch weir contructed in 1971. The bottom of the V-notch is roughly 1.5 m above bedrock and impounds a 15–18 m body of water, roughly 6 m wide at the weir and 8 m long. Transported sediments accumulate behind the weir over time substantially reducing the volume of the basin. The basin is shovelled clean one to three times per year depending on the frequency of high intensity rainstorms. Height of water in the weir pond has been recorded continuously by a Stevens A-71 strip-chart, water level recorder (Leupold and Stevens Inc., Beaverton, Oregon). Water level is transcribed using a Hewlett Packard 9830 computer and digitizer, converted to flow rate, and integrated, giving daily volume leaving the catchment expressed in this report as mm rainfall equivalents. Discharge (Q in ft³/sec) is calculated from water level (H in ft) according to the following formula: Q = 4.43 x H2.449 (Hertzler, 1938) and then converted to metric units. The runoff record (Figure 29a) includes two major gaps occurring in 1972 and 1975. Runoff is used below to estimate evapotranspiration.

EVAPOTRANSPIRATION

Appendix Table H1

The recycling of moisture from tropical forests has attracted attention in recent years as large-scale deforestation and land degradation continue in the Amazonian and extra-Amazonian tropics (Salati and Vose, 1984). A central concern is whether the removal of tropical forest will lead to change in regional climate and moisture availability. Therefore, it is important to obtain estimates of the rate of return of moisture to the atmosphere from a variety of landscapes, temperate and tropical, forested and deforested.

Evapotranspiration is moisture returning to the atmosphere in the form of vapor from a given surface area. As such it is the sum of moisture evaporated from leaf and litter surfaces and moisture transpired by plants. There are a variety of methods for estimating evapotranspiration including (1) measuring evaporation directly from a standardized surface, (2) differencing rainfall, runoff, and changes in storage (“moisture budget”), (3) measuring differences in isotope concentrations in rainfall and runoff, (4) radiosonde measuring water vapor concentration and flux, and (5) using energy balance equations.

Rates of evaporation were measured on Barro Colorado Island from June 1981 until February 1984. A U.S. Weather Bureau Class-A Evaporation pan (1.22 m in diameter, 25.4 cm deep) sitting on a 10 cm high wooden scaffold permitting a free flow of air around and under the pan was located in the laboratory clearing. Evaporation was determined as the amount of water needed to bring the surface back to a predetermined depth, approximately 50 mm below the limit of the sides of the pan, after 24 hours. Evaporation readings were taken simultaneous with measurements of air and pan-water maximum and minimum temperatures, daily windrun, and precipitation, usually around 0900 hrs each morning. Because rainfall since the last reading was sufficient to cause overflow from the pan, evaporation was not calculated if the rainfall in the previous 24 hours exceeded 50 mm.

After a two-month trial period, evaporation readings were initiated in June of 1981. They were terminated in January 1984. The median daily evaporation rate for the entire study was 3.8 mm/dy (Figure 30). The median was 4.4 mm/day for dry season days and 3.0 mm/dy for rainy season days. The latter is probably an overestimate because evaporation on days of high rainfall was omitted. Average monthly evaporation rates ranged from a low of 2.5 to a high of 4.8 mm/dy. Maximum air temperature, maximum water temperature, solar radiation, and windrun were all highly correlated with evaporation rate (Figure 31). Evaporation rate correlated best with maximum daily temperature of the water in the pan. However, maximum daily water temperature only accounted for 55% of the variance in observed rates of pan evaporation.

Average monthly pan evaporation rates are plotted in Figure 32 in relation to actual evapotranspiration (rainfall minus runoff minus changes in storage), potential evapotranspiration calculated using the Penman and the Thornthwaite methods (Dunne and Leopold, 1978). Pan evaporation rates are similar to results from the Penman method. Both methods show an increase in rate during dry season months as expected. The Thornthwaite formula generates evapotranspiration values 20 mm per month higher than either the pan or Penman method for wet season months. Mid-dry season values of pan, Penman, and Thornthwaite methods are fairly similar although conditions during the 1982–1983 El Niño event seem to drive Thornthwaite values slightly higher than pan and Penman values. Actual evapotranspiration appears to reach values nearly twice that of the other methods during the wet season. Peak wet season evapotranspiration rates reach 250 to 300 mm per month; the average is roughly 200 mm per month. Dry season values routinely fall below 50 mm per month. The high evapotranspiration rates in the dry season of 1982 follow an extremely wet year and coincide with the onset of the 1982–1983 El Niño event.

FIGURE 30.—Frequency histogram of daily evaporation readings taken from a class A pan in lab clearing, Barro Colorado Island between June 1981 and January 1984.

Perhaps the most direct method of estimating evapotranspiration requires the measurement of rainfall, runoff, and changes in soil storage. Changes in soil moisture can be minimized by reconciling the moisture budget over an “hydrologic year” such that the moisture in storage in the beginning month is near that in the final month. The “hydrologic year” in the seasonal tropics can be broken in the mid- to late dry season when runoff is at its minimum and when moisture available to plants is near zero. If these conditions are met then evapotranspiration for the year is very nearly equal to precipitation minus runoff.

Estimates of annual rainfall, runoff, and moisture in storage for the Lutz catchment, 1973–1987, are summarized in Table 10. The catchment received, on average, 2587 mm of rain per year during this period (1975 has been omitted due a gap in the runoff record). Runoff averaged 929 mm per year (36% of annual rainfall). Average changes in soil moisture storage that should have been close to zero were +2 mm per year (range: −56 to +64 mm/yr). Estimated actual evapotranspiration (rainfall minus runoff minus changes in soil storage) averaged 1656 mm per year (64% of annual rainfall).

Annual estimates of evapotranspiration include both the wettest and driest of the past 62 years on Barro Colorado Island. In the dry El Niño years of 1976 and 1982 evapotranspiration was 84.2% and 84.1% of rainfall, respectively. Evapotranspiration in 1981, the wettest year since 1909,

FIGURE 31.—Pan evaporation rate versus (a) maximum air temperature, (b) maximum water temperature, (c) solar radiation, and (d) windrun.

was only 46.9% of rainfall. Other years fell within these extremes generating a positive linear relationship between annual quantities of rainfall and runoff (n = 14, p<0.01, r² = 0.95) and between rainfall and evapotranspiration (η = 14, p<0.01, r² = 0.48) (Figure 34). As above, the soil moisture term was small, however, neglecting the term altogether leads to a poorer regression of evapotranspiration on rainfall (n = 14, p<0.02, r² = 0.40). In absolute terms, years of higher rainfall are years of greater runoff; however, the relationship is less than one to one because evapotranspiration also is greater. Generally, an increase in annual rainfall of 500 mm leads to a 400 mm increase in runoff and a 100 mm increase in evapotranspiration.

Evaporation measured with a Class-A pan in the laboratory clearing produced values considerably lower than the evapotranspiration values obtained above by the moisture budget. Evapotranspiration over the 30 month pan evaporation study summed to 4576 mm, pan evaporation 3298 mm. While pan evaporation rates were routinely one-half of actual evapotranspiration values during wet season months they were considerably higher than actual evapotranspiration during dry season months in the Lutz catchment. Thus, on an annual basis pan evaporation was only 70% of actual evapotranspiration. Pan evaporation rates are probably only useful as an index of forest evapotranspiration if (1) values are summed over intervals of at least one year and (2) if a correction factor is determined. A suitable factor to correct annual evaporation on Barro Colorado Island is roughly 1.4.

How do the estimates of evapotranspiration from Barro Colorado Island compare to those from other studies? Table 11 summarizes evapotranspiration estimates from catchments in both the north-temperate and low latitudes. Evapotranspiration is highest for the Maryland and Florida catchments with low slope and high rainfall, lowest for the forested mountainous catchments at Hubbard Brook, Walker Branch, and Coweeta. Both rainfall and evapotranspiration are generally higher in the tropics. However, estimates of evapotranspiration rates in the Amazon basin vary considerably between studies. Two separate Brazilian studies (Leopoldo et al., 1982; Franken and Leopoldo, 1984) report evapotranspiration as 65% and 81% of annual precipitation (roughly 2000 mm). Other methods of estimating evapotranspiration (vapor flux, isotope ratios, energy budgets) reviewed by Salati and Marques (1984) suggest evapotranspiration over the entire Amazon basin may be closer to 50% of average annual precipitation. Shuttleworth (1988), using an energy budget approach to calculate transpiration and measurements of interception, estimated that 50% of rainfall was returned as vapor from Brazilian forest during two years (two year rainfall was 5454 mm). Jordan and Heuveldop (1981), working on the northern edge of the Amazon basin at San Carlos de Río Negro, Venezuela, estimated evapotranspiration by tritium dilution, pan evaporation, and an energy budget model as 59%, 48%, and 44% of average annual precipitation (3664 mm). The middle estimate was accepted and has been the one usually cited. Thus, there is still a relatively broad range of estimates of evapotranspiration (45%–81% of annual rainfall) for the Amazon basin.

Evapotranspiration has been calculated in several short studies on hydrological conditions in Panamanian forests. The earliest (McGinnis et al., 1968), conducted during wet season months in the Darien Province, reported that roughly 67% of rainfall (1768 mm in 221 days) returned as either evaporation or transpiration. Read (1977, 1979) constructed a moisture budget for the forest on Isla de Majé based on a two year record of rainfall and canopy interception. Evapotranspiration was indirectly estimated as 70% of annual rainfall. Estimates of evapotranspiration considerably lower than those for Barro Colorado Island were obtained during a 4-year study conducted by the Panamá Canal Commission on the Río Agua Salud, a mainland catchment roughly 9 km east of Barro Colorado Island (Table 11). Results from a collection of hydrological studies in both temperate and tropical latitudes indicate a loose (r² = 0.56) among-site relationship between average annual evapotranspiration and precipitation (Figure 35). The average value from Barro Colorado Island appears to lie comfortably close to the principal axis through the data. Clearly, there are many other variables affecting this relationship that are ignored. Nevertheless, from a diverse collection of studies, some much more thorough than others, evapotranspiration appears to fall between 50 and 60 percent of rainfall in average years.

FIGURE 32.—Monthly values of (a) solar radiation and windspeed, and (b) relative humidity and temperature used to calculate (c) potential evapotranspiration using the Penman method. Observed rates of pan evaporation and Thomthwaite estimates of PET are also included in latter panel.

FIGURE 33.—Comparison of observed measurements of pan evaporation and actual evapotranspiration rates by month with calculated rates of potential evapotranspiration using Penman and Thornthwaite methods.

TABLE 10.—Moisture budget for the Lutz catchment (units are mm rainfall equivalents; soil storage for first two years assumed to be zero).

FIGURE 34.—Annual quantities of moisture leaving the Lutz catchment as evapotranspiration and runoff in relation to quantities of moisture entering the catchment as rainfall.

TABLE 11.—Partial listing of annual water balance relationships for catchments in North and South America and Asia (P = precipitation; R = runoff in mm equivalents).

FIGURE 35.—Correlation existing between precipitation and evapotranspiration obtained from eighteen hydrological studies.

Appendix A: Rainfall

TABLE A1.—Daily rainfall (mm) on Barro Colorado Island recorded at 900 hrs by the ESP, 1971–1989.

TABLE A2.—Weekly rainfall on Barro Colorado Island as recorded by the PCC, 1929–1986.

TABLE A3.—Monthly rainfall on Barro Colorado Island as recorded by the PCC, 1925–1986.

TABLE A4.—Annual rainfall totals for 17 PCC gauges located across central Panamá, 1863–1986.

Appendix B: Temperature

TABLE B1.—Daily maximum-minimum temperatures (tenths of degree C) in the laboratory clearing, Barro Colorado Island, Panamá, 1971–1989.

TABLE B2.—Monthly average maximum (above) and minimum temperatures (tenths of degree C) in the laboratory clearing, Barro Colorado Island, Panamá, 1971–1989.

TABLE B3.—Daily maximum-minimum temperatures (tenths of degree C), 1 m elevation, Lutz catchment, Barro Colorado Island, Panamá, 1971–1989.

TABLE B4.—Monthly average maximum (above) and minimum temperatures (tenths of degree C), 1 m elevation, in the Lutz catchment, Barro Colorado Island, Panamá, 1971–1989.

TABLE B5.—Daily maximum-minimum temperatures (tenths of degree C), 20 m elevation, Lutz tower, Barro Colorado Island, Panamá, 1981–1983.

TABLE B6.—Monthly average maximum (above) and minimum temperatures (tenths of degree C), 20 m elevation, Lutz tower, Barro Colorado Island, Panamá, 1981–1983.

TABLE B7.—Daily maximum-minimum temperatures (tenths of degree C), 40 m elevation, Lutz tower, Barro Colorado Island, Panamá, 1981–1983.

TABLE B8.—Monthly average maximum (above) and minimum temperatures (tenths of degree C), 40 m elevation, Lutz tower, Barro Colorado Island, Panamá, 1981–1987.

TABLE B9.—Hourly average shade temperatures (tenths of degree C), laboratory clearing, Barro Colorado Island, Panamá, 1983–1987.

TABLE B10.—Hourly average shade temperatures (tenths of degree C), 1 m elevation, Lutz catchment, Barro Colorado Island, Panamá, 1984–1987.

TABLE B11.—Hourly average shade temperatures (tenths of degree C), 40 m elevation, Lutz catchment, Barro Colorado Island, Panamá, 1983–1987.

Appendix C: Relative Humidity

TABLE C1.—Midday relative humidity, laboratory clearing, Barro Colorado Island, Panamá, 1971–1989.

TABLE C2.—Midday relative humidity, 1 m elevation, Lutz catchment, Barro Colorado Island, Panamá, 1971–1981.

TABLE C3.—Midday relative humidity, 13 m elevation, Lutz catchment, Barro Colorado Island, Panamá, 1977–1980.

TABLE C4.—Midday relative humidity, 26 m elevation, Lutz catchment, Barro Colorado Island, Panamá, 1977–1980.

TABLE C5.—Midday relative humidity, 40 m elevation, Lutz catchment, Barro Colorado Island, Panamá, 1977–1981.

TABLE C6.—Monthly average midday relative humidity at different sites on Barro Colorado Island, Panamá, 1971–1981, 1986–1989.

TABLE C7.—Hourly average relative humidity, laboratory clearing, Barro Colorado Island, Panamá, 1983–1987.

TABLE C8.—Hourly average relative humidity, 1 m elevation, Lutz catchment, Barro Colorado Island, Panamá, 1984, 1986–1987.

TABLE C9.—Hourly averge relative humidity, 40 m elevation, Lutz catchment, Barro Colorado Island, Panamá, 1984–1987.

Appendix D: Air Movement

TABLE D1.—Daily average wind direction at canopy level, Barro Colorado Island, Panamá, 1971–1987.

TABLE D2.—Number of hours wind was recorded in each octant by month and year at canopy level, Barro Colorado Island, Pamamá, 1971–1987.

TABLE D3.—Circular average (above) and standard deviation of monthly wind direction by octant (N = 1, NE = 2, etc.) at canopy level on Barro Colorado Island, Panamá, 1971–1986.

TABLE D4.—Average daily wind speed (km/hr) at canopy level, Barro Colorado Island, Panamá, 1971–1987.

TABLE D5.—Average hourly wind speed (km/hr) at canopy level, Barro Colorado Island, Panamá, 1971–1987.

Appendix E: Solar Radiation

TABLE E1.—Daily solar radiation (Wh/m²) measured by Eppley Pyrranometers on Barro Colorado Island, Panamá, 1973–1975, 1977, 1979–1989.

TABLE E2.—Daily values of photosynthetically active radiation (PAR) measured by Li-190S quantum sensors at 40 m elevation on the Lutz tower (Einsteins/m²/dy), 1985–1989.

TABLE E3.—Daily counts recorded by the Temple University UVB sunburn meter atop Lutz tower, Barro Colorado Island, Panamá, 1978–1979, 1981–1987.

Appendix F: Soil Moisture

TABLE F1.—Moisture content of soils (percent wet weight, average of ten sites) at 0–5, 0–10, and 30–40 cm, in Lutz catchment, 1975–1989.

TABLE F2.—Monthly average moisture content of soils (percent wet weight) at 0–10 cm and 30–40 cm in the Lutz catchment by hydrologic year, 1975–1989.

TABLE F3.—Estimated monthly average moisture content of shallow (0–10 cm), medium (10–30 cm) and deep (30–120 cm) catchment soils is expressed as percent by weight, percent of volume, and mm equivalents, 1975–1989. Total available water content (t-awc) of average soil column calculated in mm equivalents.

TABLE F4.—Monthly average moisture content of soils (percent wet weight) at 0–10 cm and 30–40 cm on the plateau by date, 1981–1984.

TABLE F5.—Monthly average moisture content of soils (percent wet weight) at 0–10 cm and 30–40 cm on the plateau by date, 1981–1984.

Appendix G: Stream Discharge

TABLE G1.—Total daily runoff in cubic meters from the 10 ha Lutz catchment, 1972–1988.

TABLE G2.—Monthly quantities of moisture (mm equivalents) leaving the Lutz catchment as runoff, entering as rainfall, remaining as soil moisture, and the sum of entry (+), exit (−) and changes in storage (+ or −). A yearly reconciliation of these quantities is presented at the bottom, 1972–1987 (ra = rainfall, ro = runoff, sm = soil moisture).

Appendix H: Evaporation

TABLE H1.—Daily evaporation (mm/dy) from Class-A evaporation pan in laboratory clearing, Barro Colorado Island, Panamá, 1981–1983.

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